text
stringlengths
254
1.16M
--- title: Mixed-reality-based human-animal interaction can relieve mental stress authors: - Heewon Na - Suh-Yeon Dong journal: Frontiers in Veterinary Science year: 2023 pmcid: PMC10060814 doi: 10.3389/fvets.2023.1102937 license: CC BY 4.0 --- # Mixed-reality-based human-animal interaction can relieve mental stress ## Abstract ### Introduction Interacting with animals has been demonstrated to possess the healing benefits to humans. However, there are limitations in physical interaction due to COVID-19 and safety issues. Therefore, as an alternative, we created mixed-reality (MR)-based human-animal interaction (HAI) content and experimentally verified its effect on mental stress reduction. ### Methods We created three types of interactive content: observing the movement of a non-reactive virtual cat, interacting with a virtual cat whose responses can be seen, and interacting with a virtual cat whose responses can be both seen and heard. The experiment was performed by 30 healthy young women, and a mental arithmetic task was used to induce mild mental stress before experiencing each content. During the experiment, the subject's electrocardiogram was continuously recorded, and the psychological state was evaluated through a questionnaire. ### Results The results showed that MR-based virtual cat content significantly reduces mental stress and induces positive emotions after stressful situations. In particular, when the virtual cat provided audiovisual feedback, the activation amount of the parasympathetic nervous system and the increase of positive emotions were the greatest. ### Discussion Based on this encouraging research result, this method should be further investigated to see if it can replace real HAI for human mental health management. ## 1. Introduction Human-animal interactions (HAI) have been studied over the past two decades to assess their therapeutic value. Since there is a strong bond between humans and animals, interactions with animals are widely believed to reduce human anxiety, loneliness, and depression, thereby reduce severity of diseases and relieve stress [1]. The Animal Visitation Program (AVP) is a program on nearly 1,000 U.S. university campuses that aims to reduce university students' stress caused by other factors providing them with the opportunity to interact with real animals such as cats and dogs [2]. In the previous studies, the AVP has demonstrated its effect of relieving the perceived stress momentarily [3, 4], increasing the level of positive emotions, and decreasing the level of negative emotions [5]. However, one study highlighted that real HAIs can cause animal welfare problems. Some dogs participating in AVP exhibited stress behaviors such as lip licking and yawning [6]. So while HAIs may provide healing effects in humans, they may also increase stress in animals. In addition, there are several limitations to supporting HAI in universities, such as allergies to animals, safety issues, and a lack of space. In addition, face-to-face activity has been greatly reduced due to COVID-19, and HAI has become more difficult due to the risk of contagiousness from contact with the same animal. To overcome these limitations, interactions with virtual animals can be considered an alternative to interacting with real ones. Our previous study first investigated whether a stress-relieving effect could be observed even by replacing a real animal with a virtual one. To this end, the physiological and psychological responses of participants were compared after interaction with a virtual cat and after viewing photos of real cats. Surveys and electrocardiograms (ECGs) showed that interactions with virtual cats can reduce negative emotions and induce positive emotions in users in stressful situations [7]. However, it was not clear whether the resulting effect was directly due to the interaction. Since many participants were unfamiliar with the experience in a mixed environment, proposed MR content may have had a greater effect compared to viewing static images. This study aims to understand how the stress-relieving effects of virtual cats are caused by diversifying the interaction types. MR content without interaction was set as a virtual cat moving alone, while MR content with interaction was set as a virtual cat interacting with the user and reacting in various ways according to the user's commands. Moreover, unlike the previous study, the virtual cat's auditory feedback was added for a more realistic interaction, and the virtual cat's reaction in the interaction condition was divided into visual-only and audiovisual. The stress-relieving effect was observed by the survey and heart-rate variability (HRV) from ECGs. HRV analysis specifically focused on the mean HR (Heart Rate; average heart rate). We hypothesize that this approach will lead to a more detailed understanding of which aspects of interactions with virtual animals cause stress-reducing effects. ## 2. Related works Lin et al. reported that pet games can provide emotional support through interaction with virtual pets, without involving animal-related issues such as allergies [8]. It was also noted that interactions with virtual pets can have the effect of promoting collaboration, and empathy for users. Norouzi's study found that walking with an AR dog affects a user's walking speed, passing distance, and head rotation. In particular, it was found that if an AR dog reacted to a collision with another person, it increased the user's co-presence and had a positive effect on the user's behavior [9]. Furthermore, some other studies have shown that interactions with virtual animals can improve the academic performance of students and have positive effects on their physical activities. One study designed and built a mixed reality system for children to train and play with virtual pets [10]. Interactions with virtual pets have succeeded in motivating the treatment groups for physical activity, demonstrating the real potential of mixed reality to influence motor behavior in children. Another study linked a user's daily steps to the growth of a virtual fish in a fishbowl to encourage more physical activity [11]. They also succeeded in improving their physical activity through collaboration and competition with other users. Finally, there was a study that showed that animals play various educational roles in a digital environment, helping children to motivate, reflect, and interact with members [12]. Unlike the existing entertainment platforms, augmented reality (AR) and mixed reality (MR) techniques will allow virtual animals to coexist with humans in the real world. MR glasses allow users to interact with virtual animals in the real world. Additionally, the graphical representation of virtual animals can provide users with more advanced graphics and an appearance that resembles real animals compared to robots and desktops used so far [13]. The presence or absence of an animal reaction to the user's actions may also have an effect. In one study, to solve a mathematical fraction problem, an interactive virtual reality learning activity was compared with learning by observing the learning of a robot without interaction. The average number of correct answers was slightly higher for interactive learning activities [14]. Also, a user's emotions may vary depending on whether an auditory sound is included. One study demonstrated that the effects of sounds from nature and footsteps induced by user movement have statistically significant effects on presence and the feeling of “being there” [15]. ## 3.1. Platform overview Microsoft HoloLens1 is a pair of 3D holographic glasses that can recognize and utilize the surrounding environment through spatial feature point extraction methods with a central processing unit (CPU), graphics processing unit (GPU), and holographic processing unit (HPU) [16]. Its superior features over traditional AR devices include a stereoscopic 3D display, gaze control, gesture control, spatial sound development, and spatial mapping [17]. In order to create content for HoloLens, it is necessary to use the Universal Windows Platform (UWP). Unity is a platform that supports the UWP and integrates and links component assets used within a project, such as 3D objects, and audio [18]. Furthermore, Microsoft recently restructured the Mixed Reality Toolkit (MRTK), a powerful SDK useful for developing applications for HoloLens [19]. In this study, MRTK v2.4.0 was used to develop MR content. Virtual animals were animated and controlled within Unity through the use of C# using Visual Studio 2019. ## 3.2. Animations Schwind et al. noted that if the virtual cat model was more natural, of higher quality, and had a less intimidating appearance, users could feel more comfortable with the virtual cat model and interact better with it [20]. Considering this aspect, this study used the “Kitten (short)” asset2 from the Unity Asset Store. Developers could manipulate the cat's behavior by animating its actions such as sitting, running, and jumping, based on various conditions. In this study, nine behaviors were constructed: idle, eating, walking, jumping, sitting, sleeping, running, lying down, and showing affection. The idle is a default state as shown in Figure 1. For each animation, a bool-type parameter was connected with the idle state that will control the transition. The virtual cat slept when the content started, went idle as soon as the user woke it up, and remained idle whenever the user isn't interacting with it. The parameter value was initialized to false which means to maintain idle. An animation was activated when its parameter becomes true under the user's control. After the animation was finished, its parameter returned to false and the cat's state returned to idle. To ensure that the movement of the virtual cat was not interrupted, transitions connected in the direction of entering an idle state had a fixed end time. **Figure 1:** *The idle state of virtual cat.* ## 3.3. Gestures An MRTK profile for the HoloLens 1 version was created and the input action handler was used to recognize user gestures. In HoloLens, the air-tap is a gesture of gently bending the index finger as if tapping the air while clenching a fist with the back of the user's right hand facing upward. The MRTK input action handler recognizes this gesture as “Select.” Interaction through gestures consisted of catching, feeding, and stroking the ball as shown in Figure 2. Additionally, in the audiovisual condition, cat sounds were added for a more realistic interaction. The sounds of the cat were not added for all gestures or voice commands, but sounds were added only in situations where the cat mainly made sounds by referring to actual cat videos. The cat sounds were selected from Free Cat Sound Effects.3 **Figure 2:** *Examples of interaction with gestures. (A) Catching a ball, (B) feeding, and (C) petting.* First, as shown in Figure 2A, when the user air-taps the ball, the cat follows the ball, bites it, and brings it back to the user. At this time, the sound of a cat excited while chasing the ball is played. Afterward, when the virtual cat bites the ball, the sound stops and the virtual cat runs to the user. For this animation, we implemented the code in C# to control the position, movement speed, and rotation of the ball and set it to be activated when the air tap is performed. The virtual cat rotates in the direction of the ball as it rolls and a “run” animation is activated to move along the ball's position. When the cat's position reaches the ball, the cat bites the ball and approaches the user's position. Also, when the cat returns to its original position, the ball is deactivated. Next, when the user air-taps the fish or ham, the virtual cat is set to eat it gradually (Figure 2B). The fish and ham consist of four parts, time is allocated through the IEnumerator, and each part is sequentially deactivated to express the cat's gradual eating of ham and fish. When food is placed in front of the virtual cat, the virtual cat makes a hungry sound and starts eating, and returns to idle again when only the bones of the ham and fish are left. Also, the star-shaped particle effect spreads after the cat has finished eating, indicating that the cat likes the food. Finally, the user can pet the cat. When the virtual cat is idle, users can air-tap the cat to make it feel good and cute. At this time, to express the cat's satisfaction, a heart-shaped particle effect is added and a cute meow sound is added (Figure 2C). ## 3.4. Voice commands The game voice control plugin 4 is used to recognize the user's voice. In this study, four types of voice commands and interactions are constructed as shown in Figure 3. GameVoiceControl is loaded into the Hierarchy, *English is* chosen as a command language, and Commands are registered with the Textlog so the four words “ump,” “Lie down,” “Come here,” and “Sit down” are recognized. A voice command is displayed and recognized in the Text object, and an animation is triggered when it matches one of the four commands. In the case of “Come here,” the cat makes a happy sound as it approaches the user. When the motion is complete, the animation is disabled again and goes back to idle. **Figure 3:** *Examples of interaction with voice commands. (A) Sit down, (B) lie down, (C) jump, and (D) come here.* ## 4.1. Subjects The experiment involved 30 healthy female adults in their twenties (mean (M) ± standard deviation (SD) age 22.07 ± 2.29). Subjects with a history of heart disease and neuropsychiatric disorders, those currently taking medications, pregnant women, or women about to become pregnant were excluded from the study. Additionally, subjects who normally wear glasses were asked to wear contact lenses in order to use HoloLens during the experiment. All experimental procedures involving human subjects were approved by the affiliated institutional review board (SMWU-2008-HR-073). The entire experimental procedure was introduced orally to the participants and written consent was obtained. All participants who took part in the experiment were given a monetary reward after the experiment was over. In addition, the experiment was conducted 1:1 with the experimenter and the subject in an independent room. ## 4.2. Experimental design The experiment was conducted in the order of “Preparation—Tutorial—Baseline— Experiment 1—Rest—Experiment 2—Rest—Experiment 3,” and the total time required was approximately 60 min. For convenience, subjects wore the HoloLens only when experiencing Tutorial and MR contents, and took it off for the rest of the experiment. Each phase of the experiments will be explained in detail. ## 4.2.1. Preparation At the beginning of the experiment, a preparatory phase gave the subject the details of attaching the ECG sensor to their chest on their own. Also, the first survey was conducted to measure the baseline psychological states of the subjects. The surveys were conducted 7 times in total to measure psychological changes, first time after the Baseline and two times per Experiment phase. ## 4.2.2. Tutorial After wearing the HoloLens, a tutorial was conducted so that the user can become familiar with the MR environment, gestures, and voice commands. As shown in Figure 4, the subject went through an acclimatization process by speaking into a microphone and rolling a ball using the air tap gesture to confirm the operation of the speech recognition system. The tutorial was repeated until the subject determined that they were sufficiently familiar with speech recognition and gestures. **Figure 4:** *Scene examples of the tutorial phase. (A) Gestures and (B) voice command.* ## 4.2.3. Baseline The subject was then asked to sit still for 3 min to measure the baseline states by recording ECGs and responding the surveys. The survey was conducted after 3-min ECG recording is completed. ## 4.2.4. Experiment and rest Three Experiment phases consist of “Mental arithmetic (MA) task—Survey—Interaction task—Survey.” MA task is well-known to induce mild stress [21]. In this study, subjects were asked to guess the correct answer by looking at a randomly generated 4-digit number in the center of a screen and subtracting a constant 2-digit number appearing above the 4-digit number as shown in the Figure 5. After subjects said aloud their answer, they could see whether their answer was correct or not by looking at the 4-digit number that followed. The four- and two-digit numbers used for subtraction were renewed every 30 mental arithmetic problems. **Figure 5:** *Examples of mental arithmetic task. Background color changes when the initial 4-digit number changed.* As per the hypothesis, Interaction task presents one among three types of MR content: observing the movement of a non-reactive virtual cat (referred to as “Observation”), interacting with a virtual cat whose responses can be seen (“Visual”), and both seen and heard (“Audiovisual”). The duration of each Interaction task was set to 3 min, the average time for all defined commands to be repeated up to two times through the pilot test. To compare the effect of adding or reducing the virtual cat's response, the Interaction task presents three MR contents in two orders: “Observation—Visual—Audiovisual” and “Audiovisual—Visual— Observation.” After each Experiment phase is completed, the 5-min *Rest is* followed. As a result of several pilot tests, the overall HRV values returned to the baseline when a 5-min period was given. In order to remove the influence of the previous tasks, subjects were allowed to take a rest and sit comfortably for 5 min. ## 4.3. Electrocardiogram recording At the Preparation phase, a wireless ECG sensor was attached to record ECG continuously until the end of the experiment. All subjects had to wear a patch-type electrocardiogram sensor (T-REX, Taewoong Medical, Korea) on the proper location to measure lead II ECG. The sensor is rectangular-shaped with removable patch that can non-invasively assess the electrical activity of the heart. After the recording, mean HR was calculated. In this study, We specifically focused on the HR, which can intuitively understand that the value changes according to the state of tension and rest. An increase in HR suggests a state of tension, i.e., being under stress, whereas HR decreases during recovery [22]. ## 4.4. Survey The PANAS (Positive Affect and Negative Affect Schedule) is a clinically used questionnaire that evaluates positive and negative emotions in humans. Using a total of 20 items, including 10 items of positive emotion and 10 items of negative emotion, it has been widely used to diagnose one's emotion and mood [23]. Subjects were asked to respond on a 5-point Likert scale. The higher the score, the higher the corresponding emotion. In order to prevent the subjects from knowing the purpose of this study and answering intentionally while doing MR content after the MA task, the order of negative and positive questions in the PANAS questionnaire used in the experiment was jumbled, and the survey was quick to respond as soon as each task was completed. In addition, a question was added to the subjects to choose their current stress level from a number between 0 and 4. The higher the number, the higher the stress level. ## 5.1. Average heart rate To quantitatively evaluate the physiological response of the subject to stress during this experiment, the mean HR was analyzed. In this experiment, 30 samples were used, and the Friedman Test was performed on seven task scores, along with a post-test, since the normality was not satisfied. For comparisons between many groups over multiple data sets, Friedman test with post-hoc Nemenyi test was recommended in a previous study [24]. Therefore, in this study, the Nemenyi post-hoc test was performed if Friedman's test was significant. Firstly, as shown in Figure 6, the mean HR of the three types of MR content was significantly lower compared to the mean HR of the previous MA. Also, the mean HR of MR content was significantly lower than baseline. In particular, the mean HR of “Audiovisual” was the lowest, and the statistical significance of “Audiovisual” compared to MA and baseline were also the greatest compared to those of the rest Interaction tasks, i.e., “Observation” and “Visual.” **Figure 6:** *Comparison of mean HR between baseline, MA, and interaction tasks (observation, visual, and audiovisual). N = 30, mean±standard error, **p < 0.01.* ## 5.2. Survey The mood status was evaluated by scoring two sub-areas of the PANAS questionnaire immediately after each task. Friedman and Nemenyi post-tests were included in the psychological evaluation for each of seven scores. In the case of negative emotions, after the three MR contents, the negative emotions scores were significantly reduced compared to those of MA. Also, they were all significantly lower than the baseline negative scores (Figure 7A). In particular, “Audiovisual” had the lowest negative emotional score, and the greatest statistical significance with baseline and MA. Next, in the case of positive emotions, the scores of the three MR contents were significantly higher than the MA scores performed immediately before, and were higher in the order of “Audiovisual,” “Visual,” and “Observation” (Figure 7B). Lastly, in the case of stress index, similar to the negative emotion score of PANAS, the three MR contents had significantly lower stress index than baseline and MA (Figure 7C). Also, “Audiovisual” had the greatest statistical significance with baseline and MA. There were no significant differences in the correction rates among three MA tasks. **Figure 7:** *Comparison of survey results between baseline, MA, and interaction tasks (observation, visual, and audiovisual). N = 30, mean±standard error, *p < 0.05 and **p < 0.01. (A) Negative affect, (B) positive affect, and (C) stress index.* ## 5.3. Comparison by personal experience of MR and companion animals To investigate the effect of personal experience with companion animals and a virtual environment, prior questions were asked to each subject before the experiment. Based on their answers, we divided subjects into two groups for each experience. The experiment was conducted by adjusting the number of assignments to each group evenly. A significant difference in mean HR was not found between groups, but it was confirmed that the mean HR of the group with prior experience of MR was lower. The subject group that had companion animals had a lower mean HR than the group that did not. This means that the subjects who have experienced MR content or have had companion animals experienced a more relaxed state on average. In the case of PANAS, the positive affect score was higher and the negative affect score was lower in the group without prior experience of MR and the group with experience raising companion animals. This result may imply that positive emotions have arisen under the influence of the novelty of experiencing MR content and familiarity with animals. Finally, in the case of stress index, the group that had companion animals was significantly lower than the group that did not. To sum up, the more you have experience raising companion animals, the greater the effect of relieving mental stress by MR content. In addition, tension was relieved more when having prior experience of the virtual environment, and positive emotions increased more when there was no prior experience of the virtual environment. ## 6. Discussion This study aims to investigate the effect of stress relief using the MR-based human and virtual cat interaction. A total of 30 healthy female college students participated in the experiment. Experimentally, subjects were asked to do a mental arithmetic task to induce their mental stress. Subsequently, three types of MR content were compared corresponding to stress reduction: observation of the movement of the virtual cat (Observation), visual interaction with the virtual cat (Visual), and audiovisual interaction with the virtual cat (Audiovisual). For quantitative evaluation of the stress-reduction effect, a single-lead ECG and a questionnaire survey were used. Firstly, mean HR showed significant changes due to stress and recovery. Mean HR was significantly decreased in all three types of MR when compared to MA. These results are consistent with the general findings of low HR during recovery in the ECG study [25]. Moreover, mean HRs of the three types of MR contents were significantly decreased even when compared to baseline, indicating effective stress relief. In particular, “Audiovisual” had the lowest mean HR compared to two other MR contents, “Observation,” and “Visual,” and had the greatest statistical significance with baseline and MA. In addition, it was investigated whether there was an effect on the order of the MR content. The mean HR was compared by dividing the group into a sequential group (Observation—Visual—Audiovisual) and a reverse group (Audiovisual—Visual—Observation), but the order of the MR contents did not significantly affect the effect of stress relief. Consequently, MR content employing a virtual cat can effectively relieve tension by reducing heart rate in stressful situations, and audiovisual interactions have been observed to further decrease heart rate. Next, positive and negative affect values were compared through PANAS. Negative affect and stress index, which indicate negative emotions, were significantly decreased in all three types of MR contents compared to MA, which is the baseline and stressful situation. This suggests that MR-based HAI can help to relieve negative emotions. The positive affect was significantly higher in MR contents than in the MA, suggesting that MR-based HAI can help to induce positive emotions to the subjects. In addition, in the case of “Audiovisual,” which had an audiovisual interaction with a virtual cat, it was significantly higher than that of MA. Also, MR contents with interactions (Visual and Audiovisual) had lower negative emotional scores and higher positive emotional scores than just observing the virtual cat (Observation). Consequently, MR-based animal contents can be used to reduce mental stress and induce positive emotions, and the effect can be even greater if interaction is included. Mental stress and relief can be influenced by a number of factors, including gender, age, occupation, personal experiences with pets, and personal experiences in virtual environments. This study has limitations in that it only targeted female university students in their 20s to control for the effects of gender, age, and occupation. Also, the order of Interaction tasks has been investigated only two sequences, ascending and descending of interactions. In order to further emphasize the effect of visual and auditory interaction, future research will need to further develop content for various age groups and genders and prove it in a more specific way. In this study, we tried to find the most natural and high-quality model to make our users more comfortable and more interactive, so we decided that the cat model we used was the best fit. However, we plan to add another animal model such as a dog, which is the most familiar and friendly companion animal to many people, in future studies. In addition, compared to robots or mobile apps, AR and MR clearly have the advantage of sharing real physical space with virtual animals, giving a greater sense of reality, but the critical problem is that people do not generally own AR headsets. In order to be widely applied to regular treatment of mental healthcare or AVP activities in the future, further research will be essential. ## 7. Conclusion In this study, as an alternative to interaction with real animals, interaction content was developed between humans and virtual animals using MR, a core technology of the 4th industrial revolution. Through gestures or voice commands, users can easily interact with virtual animals. In order to intensively verify the effect of interaction with virtual animals, three types of MR contents were created: content without interaction, content with interaction using visual feedback, and content with interaction using audiovisual feedback. The effect of relieving mental stress was evaluated using physiological indicators and psychological questionnaires. As a result, all three types of MR contents had the effect of reducing mental stress regardless of the presence or absence of interaction with the virtual cat and the type of interaction. However, when both visual and auditory feedback were provided, the effect on physiological response and psychological state was the greatest. The results of this study show that interaction with virtual animals can reduce the stress of the younger generation, who are most familiar with virtual environments than other generations. We expect this study can contribute to the wider application of MR in the field of mental healthcare. ## Data availability statement The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author. ## Ethics statement The studies involving human participants were reviewed and approved by the Institutional Review Board of Sookmyung Women's University. The patients/participants provided their written informed consent to participate in this study. ## Author contributions HN designed the methods, performed the experiments, analyzed the results, and wrote the manuscript. S-YD designed the methods, discussed the results, and extensive revisions to the paper. Both authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## References 1. Wilson CC, Barker SB. **Challenges in designing human-animal interaction research**. *Am Behav Sci* (2003) **47** 16-28. DOI: 10.1177/0002764203255208 2. Crossman MK, Kazdin AE. **Animal visitation programs in colleges and universities: an efficient model for reducing student stress**. *Handbook on Animal-Assisted Therapy* (2015) 333-37 3. Pendry P, Vandagriff JL. **Animal visitation program (AVP) reduces cortisol levels of university students: a randomized controlled trial**. *Aera Open* (2019) **5** 2332858419852592. DOI: 10.1177/2332858419852592 4. Barker SB, Barker RT, McCain NL, Schubert CM. **A randomized cross-over exploratory study of the effect of visiting therapy dogs on college student stress before final exams**. *Anthrozoös* (2016) **29** 35-46. DOI: 10.1080/08927936.2015.1069988 5. Pendry P, Carr AM, Roeter SM, Vandagriff JL. **Experimental trial demonstrates effects of animal-assisted stress prevention program on college students' positive and negative emotion**. *Hum Anim Interact Bull* (2018) **6** 81-97. DOI: 10.1079/hai.2018.0004 6. Pendry P, Kuzara S, Gee NR. **Characteristics of student-dog interaction during a meet-and-greet activity in a university-based animal visitation program**. *Anthrozoös* (2020) **33** 53-69. DOI: 10.1080/08927936.2020.1694311 7. Na H, Park S, Dong SY. **Mixed reality-based interaction between human and virtual cat for mental stress management**. *Sensors* (2022) **22** 1159. DOI: 10.3390/s22031159 8. Lin C, Faas T, Dombrowski L, Brady E. **Beyond cute: exploring user types and design opportunities of virtual reality pet games**. *Proceedings of the 23rd ACM Symposium on Virtual Reality Software and Technology* (2017) 1-10 9. Norouzi N, Kim K, Lee M, Schubert R, Erickson A, Bailenson J. **Walking your virtual dog: analysis of awareness and proxemics with simulated support animals in augmented reality**. *2019 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)* (2019) 157-68 10. Johnsen K, Ahn SJ, Moore J, Brown S, Robertson TP, Marable A. **Mixed reality virtual pets to reduce childhood obesity**. *IEEE Trans Vis Comput Graph* (2014) **20** 523-30. DOI: 10.1109/TVCG.2014.33 11. Lin JJ, Mamykina L, Lindtner S, Delajoux G, Strub HB. **Fish'n'Steps: encouraging physical activity with an interactive computer game**. *International Conference on Ubiquitous Computing* (2006) 261-78 12. Chen ZH, Chou CY, Deng YC, Chan TW. **Active open learner models as animal companions: motivating children to learn through interacting with My-Pet and Our-Pet**. *Int J Artif Intell Educ* (2007) **17** 145-67. DOI: 10.5555/1435369.1435373 13. Norouzi N, Bruder G, Bailenson J, Welch G. **Investigating augmented reality animals as companions**. *2019 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)* (2019) 400-3 14. Roussou M, Slater M. **Comparison of the effect of interactive versus passive virtual reality learning activities in evoking and sustaining conceptual change**. *IEEE Trans Emerg Top Comput* (2017) **8** 233-44. DOI: 10.1109/TETC.2017.2737983 15. Kern AC, Ellermeier W. **Audio in VR: effects of a soundscape and movement-triggered step sounds on presence**. *Front Robot AI* (2020) **7** 20. DOI: 10.3389/frobt.2020.00020 16. Taylor AG. **HoloLens hardware**. *Develop Microsoft HoloLens Apps Now* (2016) 153-9 17. Wang X, Besançon Besançon L, Rousseau D, Sereno M, Ammi M, Isenberg T. **Towards an understanding of augmented reality extensions for existing 3D data analysis tools**. *Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems* (2020) 1-13 18. Beitzel S, Dykstra J, Toliver P, Youzwak J. **Exploring 3d cybersecurity visualization with the microsoft hololens**. *International Conference on Applied Human Factors and Ergonomics* (2017) 197-207 19. Wilson N.. *Augmented Virtual Reality Telecommunication Tool.* (2020) 20. Schwind V, Leicht K, Jäger S, Wolf K, Henze N. **Is there an uncanny valley of virtual animals? A quantitative and qualitative investigation**. *Int J Hum Comput Stud* (2018) **111** 49-61. DOI: 10.1016/j.ijhcs.2017.11.003 21. Kirschbaum C, Pirke KM, Hellhammer DH. **The ‘Trier social stress test'-a tool for investigating psychobiological stress responses in a laboratory setting**. *Neuropsychobiology* (1993) **28** 76-81. DOI: 10.1159/000119004 22. Berntson GG, Thomas Bigger J, Eckberg DL, Grossman P, Kaufmann PG, Malik M. **Heart rate variability: origins, methods, and interpretive caveats**. *Psychophysiology* (1997) **34** 623-48. DOI: 10.1111/j.1469-8986.1997.tb02140.x 23. Watson D, Clark LA, Tellegen A. **Development and validation of brief measures of positive and negative affect: the PANAS scales**. *J Pers Soc Psychol* (1988) **54** 1063. DOI: 10.1037/0022-3514.54.6.1063 24. Demšar J. **Statistical comparisons of classifiers over multiple data sets**. *J Mach Learn Res* (2006) **7** 1-30. DOI: 10.5555/1248547.1248548 25. Nishime EO, Cole CR, Blackstone EH, Pashkow FJ, Lauer MS. **Heart rate recovery and treadmill exercise score as predictors of mortality in patients referred for exercise ECG**. *JAMA* (2000) **284** 1392-8. DOI: 10.1001/jama.284.11.1392
--- title: Serum creatinine levels, traditional cardiovascular risk factors and 10-year cardiovascular risk in Chinese patients with hypertension authors: - Xin Chen - Hang Jin - Dan Wang - Jiali Liu - Yu Qin - Yongqing Zhang - Yuqing Zhang - Quanyong Xiang journal: Frontiers in Endocrinology year: 2023 pmcid: PMC10060819 doi: 10.3389/fendo.2023.1140093 license: CC BY 4.0 --- # Serum creatinine levels, traditional cardiovascular risk factors and 10-year cardiovascular risk in Chinese patients with hypertension ## Abstract ### Background Serum creatinine is associated with cardiovascular risk and cardiovascular events, however, the relationship between serum creatinine levels and cardiovascular risk is not well established in hypertensive population in Jiangsu Province. We aimed to evaluate the association of serum creatinine levels with traditional cardiovascular risk factors and 10-year cardiovascular risk in a Chinese hypertensive population. ### Methods Participants were patients with hypertension registered and enrolled in health service centers in 5 counties or districts from January 2019 to May 2020 in Jiangsu Province of China followed strict inclusion and exclusion criteria, demographics as well as clinical indicators and disease history and lifestyle were collected. Participants were divided into four groups according to quartiles of serum creatinine levels, then the China-PAR model was used to calculate 10-year cardiovascular risk for each individual. ### Results A total of 9978 participants were enrolled in this study, 4173($41.82\%$) were males. The blood pressure level and prevalence of dyslipidemia, elderly, current smokers and drinking as well as obesity were higher in the Q4 group than the Q1 group (all $P \leq 0.05$). Multivariable logistic regression showed that serum creatinine in the Q4 group compared with that in the Q1 group was positively associated with overweight and obesity (OR=1.432, $95\%$ CI 1.237-1.658, $P \leq 0.001$), while negatively associated with physical activity (OR=0.189, $95\%$CI 0.165-0.217, $P \leq 0.001$), and so on. Multiple linear regression showed 10-year cardiovascular risk is positively associated with serum creatinine levels after adjusting for multiple risk factors (β=0.432, $P \leq 0.001$). ### Conclusion Serum creatinine was associated with several traditional cardiovascular risk factors and the 10-year cardiovascular risk in hypertensive patients. Creatinine-reduction and kidney-sparing therapy are essential for patients with hypertension to optimize control of cardiovascular risk. ## Introduction Serum creatinine (Scr) is the anhydride form of creatine and serves a marker of renal function, which mainly comes from muscle metabolism [1]. In clinical studies, elevated serum creatinine levels are generally considered as an adverse events or outcomes, often indicating renal impairment [2, 3], meanwhile, studies have shown that impaired renal function is often accompanied by increased cardiovascular risk [4]. Serum creatinine levels are not only a contributor to the development cardiovascular events, but also strongly associated with longitudinal risk for cardiovascular disease (CVD) and mortality [5, 6]. Previous studies have shown that slight changes in serum creatinine incrementally associated with increased risk for CVD such as coronary heart disease and heart failure [6, 7], but their association with 10-year cardiovascular risk has not been evaluated in patients with essential hypertension in China. It has been reported that serum creatinine is significantly correlated with pre-inflammatory markers such as Lipoprotein (a)(Lp(a)) and high sensitive C Reactive Protein (hs-CRP) [8, 9], however, the relationship between serum creatinine levels and traditional cardiovascular risk factors such as diabetes and dyslipidemia remains controversial. The prediction model for atherosclerotic cardiovascular disease (ASCVD) risk in China (China-PAR) has been validated in several Chinese population cohort, which is considered to be a suitable cardiovascular risk prediction model for Asians (10–12). Based on the risk score calculated by the China-PAR risk prediction model, participants were classified into different risk levels and then subsequently treated with corresponding intervention measures. Because of the interaction between serum creatinine and cardiovascular events, we suspected that serum creatinine might be associated with the predicted 10-year cardiovascular risk. However, the data on the association between serum creatinine and predicted 10-year cardiovascular risk is fairly limited. Therefore, the aim of our study is to estimate the association between serum creatinine levels and traditional cardiovascular risk factors and 10-year cardiovascular risk based on data from a hypertensive population in Jiangsu Province of China, in order to provide references for the prevention of CVD in hypertensive patients with higher creatinine levels. ## Data source and participants Five representative counties or districts in Jiangsu province were selected by multistage stratified random sampling method according to the characteristics of regional economic development (north, midland and south Jiangsu Province), population distribution and lifestyle to ensure the representativeness of research participants. Participants with essential hypertension were registered and enrolled from 50 towns or communities (10 towns or communities were randomly selected according the Random number table method from each counties or districts) in health service centers from January 2019 to May 2020 in Jiangsu Province of China followed strict inclusion and exclusion criteria. Participants aged 40-70 years old, continued residence in registration location for more than half a year, and voluntarily signed informed consent were included. The exclusion criteria were lacking of basic information, diagnosed with secondary hypertension or coronary heart disease, stroke and heart failure and other CVD, used serum creatinine-lowering agents or other drugs that may affect creatinine levels and inability or unwillingness to participate in the survey. A total of 9978 participants with essential hypertension were included in the final analysis (Figure 1). Traditional cardiovascular risk factors such as height and weight, blood pressure and waist circumference (WC), as well as disease history such as diabetes and dyslipidemia, and lifestyle factors such as smoking, drinking, levels of vegetables and fruits intake were collected. Participants were divided into four groups according to quartiles of serum creatinine levels: quartile1 (Q1) group (Scr ≤ 68.00 µmol/l, $$n = 2414$$), quartile 2 (Q2) group (Scr 68.01-78.00 µmol/l, $$n = 2470$$), quartile 3 (Q3) group (Scr 78.01-89.99µmol/l, $$n = 2540$$), and quartile 4 (Q4) group (Scr ≥ 90.00µmol/l, $$n = 2554$$). The procedures followed in this study were approved by the Ethics Review Board of Jiangsu Center for Disease Control and Prevention (SL2015-B004-01). Informed consent was obtained from all participants, and all study procedures were conducted in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology guidelines, and complied with the principles of the Declaration of Helsinki (1975, revised 2013). **Figure 1:** *Flow chart of participants. CVD, cardiovascular disease.* ## Serum creatinine measurement The participants are required to control diet and not to over eat meat or do strenuous exercise within three days prior to the measurement. Blood samples were obtained from an antecubital vein after fasting for at least 8 hours and then aliquoted within 2 hours and frozen at −80°C, then transported in dry ice to the central laboratory in Jiangsu Province Center for Disease Control and Prevention, which was certificated by The National Laboratory Certification of China. The serum creatinine was measured by automatic biochemical analyzer (Abbott Laboratories, USA) within one week, and the whole process of laboratory testing was strictly controlled by professionals. ## Definition of traditional cardiovascular risk factors In our study, hypertension was defined as systolic blood pressure (SBP) ≥140 mmHg and/or diastolic blood pressure (DBP) ≥90 mmHg or with anti-hypertensive treatment or participants had a reported history of hypertension [13]. Hypertension treatment was defined as regularly taking antihypertensive drugs within two weeks before investigation. Diabetes was defined as fasting plasma glucose (FPG)≥7.0 mmol/L or participants are taking hypoglycemic drugs or have a history of diabetes [14]. Dyslipidemia was defined as total cholesterol (TC)≥6.22 mmol/L and/or triglyceride (TG)≥2.26 mmol/L and/or high density lipoprotein-cholesterol (HDL-C) <1.04 mmol/L and/or low density lipoprotein-cholesterol (LDL-C) ≥4.14 mmol/L, or participants reported a history of dyslipidemia or were taking lipid-lowering drugs [15]. Body mass index (BMI) is calculated as weight divided by the square of height and then be divided into four grades: <18.5 kg/m2 as the underweight group; 18.5–23.9 as the normal group; 24.0–27.9 as the overweight group; ≥28.0 kg/m2 as the obesity group [16, 17]. Smoking was defined as participants smoked one or more cigarettes per day during the 30 days prior to the survey [18]. Drinking was defined as having consumed alcohol in the 30 days before the survey and at least once a week [19]. ## Risk estimation model The China-PAR model was adopted to calculate the 10-year cardiovascular risk for each individual, which is available online (https://www.cvdrisk.com.cn/ASCVD/ Articles/Index/52). The China-PAR risk score included sex, age, SBP, WC, smoking, diabetes, geographic region, urbanization, family history of CVD, and treatment for hypertension. Based on the calculated risk score, the participants were divided into three risk categories: <$5\%$ as low risk group, 5-$9.9\%$ as moderate risk group, and ≥$10\%$ as high risk group [10]. ## Statistical analysis Continuous variables meeting the normal distribution were expressed using the mean ± standard deviation (SD) and use F-test. Categorical variables are expressed as counts and percentages and were compared using x2-test. Multivariate logistic regression analysis was used to determine the association of serum creatinine with cardiovascular risk factors. Sensitivity analysis excluded the participants receiving hypertension treatment and diabetes, and multiple linear regression analysis was used to study the relationship between serum creatinine level and 10-year cardiovascular risk. Statistical analyses were performed using SPSS version 27.0(IBM, Armonk, NY, United States), and two-sided P values of <0.05 were considered statistically significant. ## Characteristics of participants *The* general characteristics of the 9978 participants enrolled in this study by serum creatinine levels are presented in Table 1. Among all participants, the average age was 58.58 years, with male gender distribution of $41.82\%$. Results of the study grouped by serum creatinine quartiles were indicated that the blood pressure level and prevalence of dyslipidemia, elderly, current smokers and drinking as well as obesity were higher in the Q4 group than the Q1 group (all $P \leq 0.05$). **Table 1** | Variables | All the participants(n=9978) | Quartiles of Serum creatinine (Scr) | Quartiles of Serum creatinine (Scr).1 | Quartiles of Serum creatinine (Scr).2 | Quartiles of Serum creatinine (Scr).3 | P value | | --- | --- | --- | --- | --- | --- | --- | | Variables | All the participants(n=9978) | Q1 group(Scr ≤ 68.00 µmol/l,n = 2414) | Q2 group(Scr68.01-78.00 µmol/l,n = 2470) | Q3 group(Scr78.01-89.99µmol/l,n = 2540) | Q4 group(Scr≥90.00µmol/l,n = 2554) | P value | | Demographics characteristics | Demographics characteristics | Demographics characteristics | Demographics characteristics | Demographics characteristics | Demographics characteristics | Demographics characteristics | | Age, years | 58.58 ± 7.30 | 57.89 ± 7.30 | 58.68 ± 7.24 | 58.46 ± 7.41 | 59.26 ± 7.17 | <0.001 | | Age, % | Age, % | Age, % | Age, % | Age, % | Age, % | Age, % | | <60 | 4935(49.46) | 1311(54.31) | 1223(49.51) | 1270(50.00) | 1131(44.28) | <0.001 | | ≥60 | 5043(50.54) | 1103(45.45.69) | 1247(50.49) | 1270(50.00) | 1423(55.72) | | | Male, % | 4173(41.82) | 512(21.21) | 825(33.40) | 1098(43.23) | 1738(68.05) | <0.001 | | Rural, % | 8678(86.97) | 1984(82.19) | 2213(89.60) | 2294(90.31) | 2187(85.63) | <0.001 | | Clinical characteristics | Clinical characteristics | Clinical characteristics | Clinical characteristics | Clinical characteristics | Clinical characteristics | Clinical characteristics | | SBP, mm Hg | 150.79 ± 11.95 | 149.45 ± 10.82 | 150.73 ± 11.25 | 151.33 ± 12.27 | 151.57 ± 13.13 | <0.001 | | DBP, mm Hg | 94.16 ± 7.37 | 92.71 ± 6.59 | 93.80 ± 7.22 | 94.75 ± 7.36 | 95.30 ± 7.93 | <0.001 | | WC, cm | 89.40 ± 10.14 | 87.53 ± 10.05 | 89.08 ± 10.39 | 90.02 ± 10.13 | 90.87 ± 9.70 | <0.001 | | BMI, % | BMI, % | BMI, % | BMI, % | BMI, % | BMI, % | BMI, % | | Underweight <18.5 kg/m2 | 107(1.07) | 37(1.53) | 36(1.46) | 21(0.83) | 13(0.51) | <0.001 | | Normal 18.5–23.9 kg/m2 | 2407(24.12) | 659(27.30) | 654(26.48) | 572(22.52) | 522(20.44) | | | Overweight 24.0–27.9 kg/m2 | 4287(42.96) | 1009(41.80) | 1042(42.19) | 1099(43.27) | 1137(44.52) | | | Obesity ≥28.0 kg/m2 | 3177(31.84) | 709(29.37) | 738(29.88) | 848(33.39) | 882(34.53) | | | Heart rate, bpm | 73.52 ± 8.41 | 74.12 ± 6.90 | 73.37 ± 7.97 | 73.37 ± 9.21 | 73.26 ± 9.24 | 0.001 | | Diabetes, % | 614(6.15) | 138(5.72) | 150(6.07) | 162(6.38) | 164(6.42) | 0.714 | | Dyslipidemia, % | 3511(35.18) | 787(32.60) | 821(33.24) | 910(35.83) | 993(38.88) | <0.001 | | TC, mmol/L | 4.86 ± 1.92 | 4.66 ± 1.98 | 4.75 ± 0.93 | 4.89 ± 1.05 | 5.10 ± 2.94 | <0.001 | | TG, mmol/L | 1.83 ± 1.33 | 1.80 ± 1.35 | 1.81 ± 1.32 | 1.83 ± 1.25 | 1.88 ± 1.42 | 0.239 | | HDL-C, mmol/L | 1.64 ± 0.54 | 1.70 ± 0.54 | 1.63 ± 0.46 | 1.63 ± 0.59 | 1.61 ± 0.54 | <0.001 | | LDL-C, mmol/L | 2.70 ± 0.89 | 2.71 ± 0.92 | 2.69 ± 0.87 | 2.69 ± 0.88 | 2.71 ± 0.89 | 0.794 | | Course of hypertension, % | Course of hypertension, % | Course of hypertension, % | Course of hypertension, % | Course of hypertension, % | Course of hypertension, % | Course of hypertension, % | | < 5 years | 6040(60.53) | 1593 (65.99) | 1563(63.28) | 1498(59.98) | 1386(54.27) | <0.001 | | ≥5 years | 3938(39.47) | 821(34.01) | 907(36.72) | 1042(40.02) | 1168(45.73) | | | Hypertension treatment, % | 8156(81.74) | 1953(80.90) | 1953(78.07) | 2063(81.22) | 2187(85.63) | <0.001 | | Lifestyle characteristics | Lifestyle characteristics | Lifestyle characteristics | Lifestyle characteristics | Lifestyle characteristics | Lifestyle characteristics | Lifestyle characteristics | | Smoking, % | 1899(19.03) | 237(9.73) | 395(15.99) | 506(19.92) | 761(29.80) | <0.001 | | Drinking, % | 1632(16.36) | 179(7.42) | 314(12.71) | 433(17.05) | 706(27.64) | <0.001 | | Intake of vegetables and fruits, % | Intake of vegetables and fruits, % | Intake of vegetables and fruits, % | Intake of vegetables and fruits, % | Intake of vegetables and fruits, % | Intake of vegetables and fruits, % | Intake of vegetables and fruits, % | | ≤400g/d | 3320(33.27) | 769(31.86) | 794(32.15) | 826(32.52) | 931(36.45) | 0.001 | | >400g/d | 6658(66.73) | 1645(68.14) | 1676(67.85) | 1714(67.48) | 1623(63.55) | | | Limiting intake of high fatcholesterol foods, % | 5198(52.09) | 1243(51.49) | 1291(52.27) | 1302(51.26) | 1362(53.33) | 0.449 | | Physical exercise, % | Physical exercise, % | Physical exercise, % | Physical exercise, % | Physical exercise, % | Physical exercise, % | Physical exercise, % | | <3 times/week | 3950(39.59) | 473(20.42) | 954(39.62) | 1198(47.17) | 1325(51.88) | <0.001 | | ≥3 times/week | 6028(60.41) | 1941(80.41) | 1516(61.38) | 1342(52.83) | 1229(48.12) | | ## Association between serum creatinine levels and cardiovascular risk factors Multivariable logistic regression was performed to study the relationship between serum creatinine and cardiovascular risk factors, as shown in Table 2, in total participants, the odds ratio (OR) for age, overweight and obesity, drinking, physical exercise, and intake of vegetables and fruits and so on, were 1.403($95\%$ CI 1.237-1.592, $P \leq 0.001$), 1.432($95\%$CI 1.237-1.658, $P \leq 0.001$), 1.336($95\%$ CI 1.080-1.653, $$P \leq 0.008$$), 0.189($95\%$CI 0.165-0.217, $P \leq 0.001$) and 0.727($95\%$CI 0.637-0.829, $P \leq 0.001$) in the Q4 group compared with that in the Q1 group respectively, which suggests that serum creatinine levels are positively correlated with age, obesity and alcohol consumption, but negatively correlated with fruits and vegetables intake. **Table 2** | Variables | Variables.1 | OR (95%CI) | P value | | --- | --- | --- | --- | | Age | <60 | 1(Reference) | | | Age | ≥60 | 1.403(1.237,1.592) | <0.001 | | Male | Male | 9.775(8.316,11.490) | <0.001 | | Rural | Rural | 1.517(1.265,1.819) | <0.001 | | SBP | SBP | 0.997(0.991,1.003) | 0.280 | | DBP | DBP | 1.035(1.025,1.046) | <0.001 | | BMI | <24 | 1(Reference) | | | BMI | ≥24 | 1.432(1.237,1.658) | <0.001 | | Heart rate | Heart rate | 0.991(0.983,0.998) | 0.013 | | Dyslipidemia | Dyslipidemia | 1.311(1.150,1.494) | <0.001 | | Course of hypertension≥5 years | Course of hypertension≥5 years | 1.530(1.341,1.746) | <0.001 | | Hypertension treatment | Hypertension treatment | 1.320(1.115,1.564) | 0.001 | | Smoking | Smoking | 1.053(0.863,1.284) | 0.610 | | Drinking | Drinking | 1.336(1.080,1.653) | 0.008 | | Intake of vegetables and fruits>400g/d | Intake of vegetables and fruits>400g/d | 0.727(0.637,0.829) | <0.001 | | Physical exercise≥3 times/week | Physical exercise≥3 times/week | 0.189(0.165,0.217) | <0.001 | ## Association between serum creatinine and cardiovascular risk factors according gender stratification Results further analyzed according to gender stratification was shown in Figure 2, the OR for age, drinking and physical exercise was 1.389 ($95\%$ CI 1.126-1.714, $P \leq 0.05$), 1.385($95\%$ CI 1.098-1.747, $P \leq 0.05$), 0.194($95\%$ CI 0.149-0.251, $P \leq 0.05$) in the Q4 group compared with that in the Q1 group in men, respectively. **Figure 2:** *Association between serum creatinine and cardiovascular risk factors according to gender stratification. CI, confidence interval. *The difference in males or females was statistically significant (the Q4 group vs. the Q1 group, P < 0.05). **The difference both in males and females were statistically significant (the Q4 group vs. the Q1 group, P<0.05).* As for women, the OR for age, hypertension treatment, physical exercise and intake of vegetables and fruits was 1.492($95\%$ CI 1.255-1.773, $P \leq 0.05$), 1.310($95\%$ CI 1.024-1.677, $P \leq 0.05$), 0.258($95\%$CI 0.216-0.308, $P \leq 0.05$), 0.704($95\%$CI 0.590-0.839, $P \leq 0.05$) in the Q4 group compared with that in the Q1 group, respectively. The results showed that serum creatinine was positively associated with age, while negatively associated with physical activity in both genders. ## 10-year cardiovascular risk according to quartiles of serum creatinine The 10-year predicted cardiovascular risk according to quartiles of serum creatinine is shown in Figure 3. In the total participants, the average risk of CVD in Q1 and Q4 groups were $10.57\%$ and $14.10\%$, respectively, and the difference was statistically significant ($P \leq 0.001$). The mean risk of CVD in Q1 and Q4 groups were $13.56\%$ and $15.53\%$ in men, and $9.76\%$ and $11.07\%$ in women, respectively (all $P \leq 0.001$). **Figure 3:** *Cardiovascular risk according to quartiles of serum creatinine. **P < 0.001 (the Q4 group vs. the Q1 group).* ## Distribution of 10-year cardiovascular risk classes according to quartiles of serum creatinine The distribution of cardiovascular risk classes according to quartiles of serum creatinine is shown in Figure 4. In the total participants, the proportions of participants with high predicted risk in Q1 and Q4 groups were $54.18\%$ and $77.56\%$, respectively, and the difference was statistically significant ($P \leq 0.001$). After grouping by gender, the proportions of individuals at high risk in Q1 and Q4 groups were $80.07\%$ and $86.42\%$ in males, and $47.21\%$ and $58.70\%$ in females, respectively (all $P \leq 0.05$). **Figure 4:** *Distribution of cardiovascular risk classes according to quartiles of serum creatinine. *P < 0.05 (the Q4 group vs. the Q1 group). **P < 0.001 (the Q4 group vs. the Q1 group).* ## Association between serum creatinine level and 10-year cardiovascular risk The results of the multiple linear regression are shown in Table 3. In the total participants, 10-year cardiovascular risk derived from the China-PAR model is positively associated with serum creatinine levels after adjusting for multiple risk factors in Model 3 (β = 0.432, $P \leq 0.001$). Comparing by gender, cardiovascular risk was positively correlated with serum creatinine level in men (β = 0.504, $$P \leq 0.001$$), although no statistical significance has been found in women. **Table 3** | Unnamed: 0 | Model 1 | Model 1.1 | Model 2 | Model 2.1 | Model 3 | Model 3.1 | | --- | --- | --- | --- | --- | --- | --- | | | Beta coefficients | P value | Beta coefficients | P value | Beta coefficients | P value | | Total participants | 2.502 | <0.001 | 0.920 | <0.001 | 0.432 | <0.001 | | Male | 1.337 | <0.001 | 1.268 | <0.001 | 0.504 | 0.001 | | Female | 0.733 | <0.001 | 0.409 | <0.001 | 0.165 | 0.131 | ## Sensitivity analysis In order to exclude the confounding effects of hypertension treatment and diabetes on serum creatinine, we conducted sensitivity analysis and excluded hypertension patients with hypertension treatment or diabetes, as shown in Table 4. After adjusting various factors, the serum creatinine level was positively correlated with 10-year cardiovascular risk in participants without receive antihypertensive drugs (β=1.337, $P \leq 0.001$), especially in men (β=0.602, $P \leq 0.001$). In addition, serum creatinine is positively correlated with cardiovascular risk in hypertensive patients without diabetes (β=0.490, $P \leq 0.001$) in Model 3, comparing by gender, serum creatinine and 10-year cardiovascular risk are positively correlated in both men (β=0.569, $P \leq 0.001$) and women (β=0.209, $$P \leq 0.046$$), respectively. **Table 4** | Sensitivity analysis | Model 1 | Model 1.1 | Model 2 | Model 2.1 | Model 3 | Model 3.1 | | --- | --- | --- | --- | --- | --- | --- | | | Beta coefficients | P value | Beta coefficients | P value | Beta coefficients | P value | | Participants without hypertension treatment | 2.522 | <0.001 | 0.810 | <0.001 | 1.337 | <0.001 | | Male | 1.243 | <0.001 | 1.194 | <0.001 | 0.602 | <0.001 | | Female | 0.560 | 0.004 | 0.269 | 0.084 | 0.114 | 0.352 | | Participants without diabetes | 2.574 | <0.001 | 0.933 | <0.001 | 0.490 | <0.001 | | Male | 1.360 | <0.001 | 0.346 | <0.001 | 0.569 | <0.001 | | Female | 0.696 | <0.001 | 0.421 | <0.001 | 0.209 | 0.046 | ## Discussion The morbidity and mortality of CVD have been increasing gradually in China, partly attributed to the increased exposure and aggregation of multiple cardiovascular risk factors [20]. Previous studies have shown that prevention and control measures based on risk factors can effectively reduce cardiovascular risk [21]. Elevated creatinine levels have been reported to play a role in the increased risk of a variety of CVD, in addition, some clinical studies have found that increased serum creatinine levels, which often indicate a decrease in glomerular filtration rate, may be used as a predictive marker for CVD [22, 23], similarly, our study showed that the average risk in serum creatinine levels Q1 and Q4 groups were $10.57\%$ and $14.10\%$, and the proportions of participants with high predicted risk of CVD were $54.18\%$ and $77.56\%$ in the total population. Hypertension is an important risk factor in the development of CVD. The relationship between creatinine and multiple cardiovascular risk factors and cardiovascular risk in patients with hypertension needs to be fully elucidated. In our study, serum creatinine levels were found to be strongly associated with cardiovascular risk factors in hypertensive patients. The results have important clinical significance. Firstly, we used a relatively novel risk prediction model to evaluate individual cardiovascular risk and provided reference for the control of hypertensive patients with high creatinine level to reduce cardiovascular risk. Secondly, serum creatinine is a relatively convenient biochemical index, and serum creatinine in addition to traditional CVD risk factors should be taken into account when evaluating risk for development of CVD in hypertensive patients, especially in male. Based on the China-PAR project, the China-PAR risk score has good internal consistency and has been proved to be a suitable method for predicting 10-year cardiovascular risk in Chinese population [10]. The 10-year cardiovascular risk for each individual was calculated using the China-PAR risk calculation equation. Hypertension patients are at high risk of CVD, at the same time, the pathogenesis of hypertension is closely related to the kidney, which is not only an important organ for blood pressure regulation, but also one of the target organs of hypertension, studies have shown that hypertensive patients with renal impairment have an increased risk of overall CVD, and even mild renal dysfunction can lead to increased mortality and morbidity of CVD [24]. In our study, it was also shown that hypertensive individuals with higher creatinine levels had a higher cardiovascular risk. The rationale for the positive association between serum creatinine and cardiovascular risk remains to be fully clarified. Serum creatinine refers to endogenous serum creatinine, which is the product of human muscle metabolism and the surrogate of renal function [23]. Its level is relatively constant in normal people, when the majority of the human kidney is suffering from pathological damage and the proportion of glomerular filtration rate is decreased (more than $50\%$), the situation of increased serum creatinine may be clinically apparent, and its concentration depends on many factors such as creatinine production rate, distribution volume, extrarenal metabolism and renal injury [23, 25, 26]. Serum creatinine behaves as a marker of pro-inflammatory state, and inflammation-mediated endothelial dysfunction has been shown to be associated with the occurrence of cardiovascular events in women with reduced renal function, in addition, high serum creatinine is often accompanied by a decrease in glomerular filtration rate, which is prone to water-sodium retention and increases the burden on the heart as well as cardiovascular risk [9, 27]. Although this study highlights the association between serum creatinine levels and cardiovascular risk factors and 10-year risk of CVD in patients with hypertension in Jiangsu province of China, there are several limitations. The participants with hypertension were recruited from a single province in China, so extrapolation to other populations should be cautious. In addition, our study is a cross-sectional study, and prospective studies are needed for further verification. Finally, other factors that may affect creatinine levels and cardiovascular risk factors such as urea nitrogen and uric acid were missing in this study, and the number and type of antihypertensive drugs were not considered in the study, but we conducted a sensitivity analysis to reduce part of the confounding effect. ## Conclusions In conclusion, serum creatinine was associated with several cardiovascular risk factors in a hypertensive population in Jiangsu province. The 10-year cardiovascular risk was higher in hypertensive patients with higher serum creatinine levels, especially in men. Creatinine-reduction and kidney-sparing therapy is essential for patients with hypertension to optimize control of cardiovascular risk factors and reduce cardiovascular risk. ## Data availability statement The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s. ## Ethics statement The studies involving human participants were reviewed and approved by the Ethics Review Board of Jiangsu Center for Disease Control and Prevention (SL2015-B004-01). The patients/participants provided their written informed consent to participate in this study. ## Author contributions Study concept and design: QX, YuZ, YoZ, YQ, XC; Acquisition of data: XC, DW, HJ, JL, YQ, QX; Analysis and interpretation of data: XC, DW, HJ, JL, YQ, YoZ, QX; Drafting of the manuscript: XC, DW, JL, YQ, QX; Critical revision of the manuscript for important intellectual content: QX, YQ, YuZ; Statistical analysis: XC, DW, JL, YQ, QX; Obtained funding: QX; Technical, or material support: YQ, YoZ, YuZ, QX; Study supervision: QX. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## References 1. Hsieh MC, Hsiao JY, Tien KJ, Chang SJ, Hsu SC, Liang HT. **Chronic kidney disease as a risk factor for coronary artery disease in Chinese with type 2 diabetes**. *Am J Nephrol.* (2008) **28**. DOI: 10.1159/000111388 2. Lewis EJ, Hunsicker LG, Clarke WR, Berl T, Pohl MA, Lewis JB. **Renoprotective effect of the angiotensin-receptor antagonist irbesartan in patients with nephropathy due to type 2 diabetes**. *N Engl J Med* (2001) **345**. DOI: 10.1056/NEJMoa011303 3. Brenner BM, Cooper ME, de Zeeuw D, Keane WF, Mitch WE, Parving HH. **Effects of losartan on renal and cardiovascular outcomes in patients with type 2 diabetes and nephropathy**. *N Engl J Med* (2001) **345**. DOI: 10.1056/NEJMoa011161 4. Hall WD. **Abnormalities of kidney function as a cause and a consequence of cardiovascular disease**. *Am J Med Sci* (1999) **317**. DOI: 10.1097/00000441-199903000-00007 5. Mann JF, Gerstein HC, Pogue J, Bosch J, Yusuf S. **Renal insufficiency as a predictor of cardiovascular outcomes and the impact of ramipril: The HOPE randomized trial**. *Ann Intern Med* (2001) **134**. DOI: 10.7326/0003-4819-134-8-200104170-00007 6. Fried LF, Shlipak MG, Crump C, Bleyer AJ, Gottdiener JS, Kronmal RA. **Renal insufficiency as a predictor of cardiovascular outcomes and mortality in elderly individuals**. *J Am Coll Cardiol* (2003) **41**. DOI: 10.1016/s0735-1097(03)00163-3 7. Shlipak MG, Stehman-Breen C, Vittinghoff E, Lin F, Varosy PD, Wenger NK. **Creatinine levels and cardiovascular events in women with heart disease: Do small changes matter**. *Am J Kidney Dis* (2004) **43** 37-44. DOI: 10.1053/j.ajkd.2003.08.044 8. Onat A, Can G, Ademoğlu E, Çelik E, Karagöz A, Örnek E. **Coronary disease risk curve of serum creatinine is linear in Turkish men, U-shaped in women**. *J Invest Med* (2013) **61** 27-33. DOI: 10.2310/JIM.0b013e318276de59 9. Rye K-R, Barter P. **Function and metabolism of pre-beta migrating, lipid-poor apolipoprotein a-I**. *Artrioscler Thromb Vasc Biol* (2004) **24**. DOI: 10.1161/01.ATV.0000104029.74961.f5 10. Yang X, Li J, Hu D, Chen J, Li Y, Huang J. **Predicting the 10-year risks of atherosclerotic cardiovascular disease in Chinese population: The China-PAR project (Prediction for ASCVD risk in China)**. *Circulation* (2016) **134**. DOI: 10.1161/circulationaha.116.022367 11. Yang XL, Chen JC, Li JX, Cao J, Lu XF, Liu FC. **Risk stratification of atherosclerotic cardiovascular disease in Chinese adults**. *Chronic Dis Transl Med* (2016) **2**. DOI: 10.1016/j.cdtm.2016.10.001 12. Tang X, Zhang DD, Liu XF, Liu QP, Cao Y, Li N. **Application of the China-PAR stroke risk equations in a rural northern Chinese population**. *Beijing Da Xue Xue Bao Yi Xue Ban.* (2020) **52**. DOI: 10.19723/j.issn.1671-167X.2020.03.008 13. Wang Z, Chen Z, Zhang L, Wang X, Hao G, Zhang Z. **Status of hypertension in China: Results from the China hypertension survey, 2012-2015**. *Circulation* (2018) **137**. DOI: 10.1161/CIRCULATIONAHA.117.032380 14. Fan GQ, Hao HB, Yang Y, Zhou Y. **Interpretation of 2013 Chinese Guidelines for prevention and treatment of Type 2 diabetes mellitus**. *Chinese Journal of Clinicians* (2015) **43** 92-94. DOI: 10.3969/j.issn.2095-8552.2015.10.037 15. Chen Y, Chen YB, Tao RF. **2016 Guidelines for the prevention and treatment of dyslipidemia in Chinese adults**. *J Chinese journal of practical internal medicine* (2017) **37** 38-42. DOI: 10.19538/j.nk2017SI0113 16. Zhou BF. **Cooperative meta-analysis group of the working group on obesity in china. predictive values of body mass index and waist circumference for risk factors of certain related diseases in Chinese adults–study on optimal cut-off points of body mass index and waist circumference in Chinese adults**. *BioMed Environ Sci* (2002) **15** 83-96. DOI: 10.1046/j.1440-6047.11.s8.9.x 17. He Y, Jiang B, Wang J, Feng K, Chang Q, Zhu S. **BMI versus the metabolic syndrome in relation to cardiovascular risk in elderly Chinese individuals**. *Diabetes Care* (2007) **30**. DOI: 10.2337/dc06-2402 18. McClave AK, McKnight-Eily LR, Davis SP, Dube SR. **Smoking characteristics of adults with selected lifetime mental illnesses: results from the 2007 national health interview survey**. *Am J Public Health* (2010) **100**. DOI: 10.2105/AJPH.2009.188136 19. Xu WC, Qin Y, Su J, Cui L, Du W, Zhou JY. **Interaction of alcohol consumption and obesity among 35-year-old community residents in jiangsu province**. *China Public Health* (2020) **36**. DOI: 10.11847/zgggws1123881 20. Weiwei C, Runlin G, Lisheng L, Manlu Z, Wen W, Yongjun W. **Outline of the report on cardiovascular diseases in China, 2014**. *Eur Heart J Suppl* (2016) **18** F2-F11. DOI: 10.1093/eurheartj/suw030 21. Weiner SD, Rabbani LE. **Secondary prevention strategies for coronary heart disease**. *J Thromb Thrombolysis.* (2010) **29** 8-24. DOI: 10.1007/s11239-009-0381-8 22. Bagheri B, Radmard N, Faghani-Makrani A, Rasouli M. **Serum creatinine and occurrence and severity of coronary artery disease**. *Med Arch* (2019) **73**. DOI: 10.5455/medarh.2019.73.154-156 23. Onat A, Yüksel H, Can G, Köroğlu B, Kaya A, Altay S. **Serum creatinine is associated with coronary disease risk even in the absence of metabolic disorders**. *Scand J Clin Lab Invest.* (2013) **73**. DOI: 10.5455/medarh.2019.73.154-156 24. Zeng XH, Du Y, Li XZ. **Relationship between serum creatinine, blood urea nitrogen and complications of hypertension research**. *J chronic Dis Prev control China* (1994) **03)** 130-131+145. DOI: 10.16386/j.carolcarrollJPCCD. 25. Lan YY, Li HB, Zhang ZY, Qin XH, Xing HX, Tang GF. **Serum creatinine level and its influencing factors in patients with mild to moderate hypertension**. *Chin J Dis Control* (2014) **18** 26. Li JL. **What is the clinical significance of elevated creatinine**. *Health Life* (2022) **07)** 27 27. Rasouli M, Trischuk TC, Lehner R. **Calmodulin antagonist W-7 inhibits de novo synthesis of cholesterol and suppresses secretion of**. *Biochim Biophys Acta* (2004) **1682** 92-101. DOI: 10.1016/j.bbalip.2004.02.002
--- title: Bacillus subtilis DSM29784 attenuates Clostridium perfringens-induced intestinal damage of broilers by modulating intestinal microbiota and the metabolome authors: - Yuanyuan Wang - Yibin Xu - Guangtian Cao - Xihong Zhou - Qian Wang - Aikun Fu - Xiuan Zhan journal: Frontiers in Microbiology year: 2023 pmcid: PMC10060821 doi: 10.3389/fmicb.2023.1138903 license: CC BY 4.0 --- # Bacillus subtilis DSM29784 attenuates Clostridium perfringens-induced intestinal damage of broilers by modulating intestinal microbiota and the metabolome ## Abstract Necrotic enteritis (NE), especially subclinical NE (SNE), without clinical symptoms, in chicks has become one of the most threatening problems to the poultry industry. Therefore, increasing attention has been focused on the research and application of effective probiotic strains as an alternative to antibiotics to prevent SNE in broilers. In the present study, we evaluated the effects of *Bacillus subtilis* DSM29784 (BS) on the prevention of subclinical necrotic enteritis (SNE) in broilers. A total of 480 1-day-old broiler chickens were randomly assigned to four dietary treatments, each with six replicates pens of twenty birds for 63 d. The negative (Ctr group) and positive (SNE group) groups were only fed a basal diet, while the two treatment groups received basal diets supplemented with BS (1 × 109 colony-forming units BS/kg) (BS group) and 10mg/kg enramycin (ER group), respectively. On days 15, birds except those in the Ctr group were challenged with 20-fold dose coccidiosis vaccine, and then with 1 ml of C. perfringens (2 × 108) at days 18 to 21 for SNE induction. BS, similar to ER, effectively attenuated CP-induced poor growth performance. Moreover, BS pretreatment increased villi height, claudin-1 expression, maltase activity, and immunoglobulin abundance, while decreasing lesional scores, as well as mucosal IFN-γ and TNF-α concentrations. In addition, BS pretreatment increased the relative abundance of beneficial bacteria and decreased that of pathogenic species; many lipid metabolites were enriched in the cecum of treated chickens. These results suggest that BS potentially provides active ingredients that may serve as an antibiotic substitute, effectively preventing SNE-induced growth decline by enhancing intestinal health in broilers. ## 1. Introduction Necrotic enteritis (NE), a ubiquitous poultry disease, is a severe intestinal disease caused by *Clostridium perfringens* (CP). Birds with acute NE may experience sudden death, with up to $50\%$ mortality (Caly et al., 2015). However, the more common form of NE is subclinical as it may persist in broiler flocks without overt clinical manifestation; hence, there is a general consensus that the subclinical form of NE (SNE) is more harmful than the clinical form (Olkowski et al., 2008). In addition, due to the large potential economic costs associated with SNE and the high risk of pathogen transfer to the food chain and public health concerns, industry experts perceive this problem as a major issue (Van Immerseel et al., 2004). Antibiotics have been used to prevent coccidiosis and NE for many decades (Diarra and Malouin, 2014), however, growing concerns about drug residues and antibiotic resistance, as well as their potential harmful effects on the homeostasis of gut microbiota, restrict their usage (Silva et al., 2009). Meanwhile, halting the administration of antibiotics causes the animals to be more susceptible to infections, such as NE, which has a significant negative impact on production yields (Gadde et al., 2017). Therefore, identifying an approach that may gradually replace antibiotics as an effective method to control disease infection in poultry, while maintaining good production yields and the health of the birds, is of high importance. Probiotic application for SNE prevention is becoming a common method in the post-antibiotic era (Eeckhaut et al., 2016). Numerous studies have shown that live probiotic bacteria can support the host’s physiological and immunological development, improve disease resistance, compete with pathogens for nutrients and adhesion, produce metabolites that can directly inhibit bacterial diseases, and support the growth of potentially beneficial microbial organisms in the intestinal tract (Ducatelle et al., 2015; Elshaghabee et al., 2017). In the poultry industry, *Bacillus has* exhibited the greatest potential among feed probiotics due to its ability to produce spores that are resistant to the high temperatures used in modern production of pelleted poultry feed, as well as to the low pH, bile, and enzymes present in the upper digestive tract of chickens (Elshaghabee et al., 2017). Bacillus subtilis (BS) is a gram-positive aerobic bacterium that is widely used in the production of heterologous proteins (Earl et al., 2008). It secretes a variety of enzymes to degrade various substrates, enabling bacteria to survive in the changing environment. In addition, BS is an ideal multi-functional probiotic that can potentially prevent pathogen growth and promote nutrient absorption (Olmos et al., 2020). BS DSM29784 (referred to here as BS) not only improves the growth performance of turkeys, but also improves their intestinal health (Mohammadigheisar et al., 2019) as well as that of chickens (Rhayat et al., 2017; Neijat et al., 2019b). Specifically, a 1 × 109 cfu /kg BS diet may optimize growth performance compared to other doses under farm conditions (Mohammadigheisar et al., 2019). Our team has previously reported that supplementing with probiotic BS can serve as an effective substitute for broiler antibiotics to reduce the feed conversion rate and improve gut health (Verdes et al., 2020). Owing to the ability of BS to inhibit CP growth in vitro (unpublished results), it is further speculated to be an effective feed additive to control SNE in broilers. The animal intestinal microbiota plays a key role in the collection, storage, and consumption of energy obtained from the diet (Krajmalnik-Brown et al., 2012). These functions not only improve the health but can also increase the weight of the animal (Krajmalnik-Brown et al., 2012). Interestingly, FAO [2013] reported that probiotic application for animal nutrition might function as a gut ecosystem enhancer (Organization F. A, 2013). Moreover, the interaction between the gut microbiota and the immune system mediates long-term microbial colonization in the gut (Wandro et al., 2018). The microbiota can then interact directly with the immune system, or indirectly via release of metabolites that can be directly absorbed by immune cells and epithelial cells (Wikoff et al., 2009; Dodd et al., 2017). Therefore, metabolic activity is an important feature of the intestinal flora and a potential mechanism of host flora interaction (Zarrinpar et al., 2018). For instance, short chain fatty acids (SCFAs) produced by bacteria can affect the health and integrity of intestinal epithelia and immune cells (Willemsen et al., 2003; Chang et al., 2014; Kelly et al., 2015). Moreover, early exposure to microorganisms and their metabolites is a normal part of the development process, which has a significant, yet underexplored, impact on the immune system (Wandro et al., 2018). Although few studies have used metabolites alongside bacterial community profiling to explore the effect of probiotics on preventing SNE development, the current study aimed to evaluate the effect of dietary supplementation of BS on SNE prevention in broilers. To this end, we systematically studied the protective role of BS in the gut immune response during SNE infection caused by the major pathogen CP, by combining broiler models and multiomics analyses. Moreover, we investigated whether oral supplementation with BS effectively prevents SNE-related pathogenesis, and performance damage, as has been demonstrated for antibacterial agents. Furthermore, we analyzed the cecal metabolome and microbiome profiles in SNE broilers and controls. Specifically, we investigated whether changes in metabolites and the composition of gut microbiota are associated with SNE infection. ## 2. Materials and methods All procedures were carried out in accordance with the Chinese Animal Welfare Guidelines and approved by the Institutional Animal Care and Use Committee of Zhejiang University (Permission number: ZJU2019-480-12). ## 2.1. Bacterial strain preparation and experimental diets The probiotic bacteria used in the present study was BS, which was provided by the Chinese Academy of Sciences. This strain was cultured in Luria-Bertani broth (Fisher Scientific, Ottawa, ON, Canada) and incubated at 37°C overnight in a shaking incubator at 180 rpm. CP type-A strain (China Veterinary Culture Collection Center, Being, China) was used for infection in this present study. CP was cultured in a Reinforced Clostridial Medium (Huankai, Guangdong, China) in an anaerobic environment at 37°C for 24 h, and subsequently used for challenge. The two bacterial pellets were collected after incubation at 5,000 ×g for 10 min at 4°C, respectively. After washing twice with sterile phosphate buffer saline (pH 7.3), the prepared *Bacillus powder* (2 × 109 cfu/g) was diluted with starch and added to the basic feed to a final concentration of 109 cfu/kg. The same amount of starch was added to compensate for the differences in dietary nutrients for each group. The coccidiosis quadrivalent live vaccine for chickens was purchased from Foshan Zhengdian Biotechnology Co., Ltd. (Guangdong, China) (Wang Y. et al., 2021). ## 2.2. BS extract antimicrobial activity BS has a unique potential to secrete highly active bactericidal compounds. Therefore, the inhibitory effect of BS cell-free extract on CP was tested. Briefly, approximately 100 ml of liquid seed medium was inoculated with $1\%$ freshly grown BS suspension. The inoculated seed medium was cultured in a shaking incubator at 180 rpm at 37°C for up to 24 h. After incubating for 24 h, the fermentation broth was centrifuged at 4000 ×g for 15 min, and the cell-free supernatant was further filtered through a 0.45-μm polysulfonate membrane filter. The filtered cell-free supernatant was considered to be the crude bactericidal extract, and the agar well diffusion assay was used to test against selected strains. Cultures were incubated overnight with CP (1 × 108 cfu) on 150 ml tryptose-sulfite-cycloserine agar medium (Huankai, Guangdong, China). A sterile cork borer with a diameter of 10 mm was used to cut the agar well. Next, the cell-free supernatants (200 μl) were added to the wells in the plate and incubated overnight at 37°C (Xu et al., 2018). ER (100 μg/ml) and sterile Reinforced Clostridial Medium were used as positive and negative controls, respectively. ## 2.3. Experimental design and bird husbandry A total of 480 Lingnan Yellow feathered-broilers with similar initial weights were randomly allotted to four groups with six replicates per group and 20 chicks per replicate (10 males and 10 females). All chicks were housed in 24 floor pens (2 m × 4 m) covered with fresh wood shavings. Fresh water and diet were provided ad libitum. The chicks were kept under a 2 l-1D light–dark cycle every day. Broilers in the negative (Ctr) and positive (SNE) control groups were fed the basal diet. Broilers in the BS group were fed a basal diet containing *Bacillus concentration* of 109 cfu/kg. Broilers in the ER group were fed a basal diet containing 10 g/t of enramycin (ER; Schering-Plough, Shanghai, China). The temperature of the room was maintained at 33–35°C for the first 3 d and then reduced by 2–3°C per week to a final temperature of 25°C and 60–$65\%$ humidity. The experimental diet was designed according to the requirements of the National Research Council. The composition and nutritional level of the basic diet are shown in Table 1. **Table 1** | Ingredients, % | Starter (1–21d) | Grower (22–42d) | Finisher (43–63d) | | --- | --- | --- | --- | | Corn | 62.5 | 67.5 | 75 | | Soybean meal | 31 | 23.5 | 14.5 | | CPM c | 2 | 4 | 5 | | Soybean oil | 0.5 | 1 | 1.5 | | NaCl | 0.3 | 0.3 | 0.3 | | CaHPO4 | 1.2 | 1 | 0.8 | | Limestone | 1.5 | 1.3 | 1.2 | | Zeolite | - | 0.4 | 0.7 | | Premixa | 1 | 1 | 1 | | Total | 100 | 100 | 100 | | Nutrient levels b (%) | Nutrient levels b (%) | Nutrient levels b (%) | Nutrient levels b (%) | | ME (MJ/kg) | 12.22 | 12.59 | 12.97 | | CP | 21.09 | 19.16 | 16.07 | | Lys | 1.09 | 0.99 | 0.87 | | Met | 0.49 | 0.38 | 0.35 | | Met+Cys | 0.87 | 0.73 | 0.65 | | Calcium | 0.9 | 0.85 | 0.69 | | Total phosphorus | 0.58 | 0.52 | 0.45 | ## 2.4. SNE broiler model The SNE broiler model was established as previously described but with a small modification (Wang Y. et al., 2021). On day 15, the SNE-challenged groups, in addition to the negative control group, received a 20-fold dose coccidiosis vaccine per bird by oral gavage. Each bird in each group was then gavaged with 1 ml of CP (2 × 108 cfu/ml) per day on days 18–21 (also, no food will be provided in the night before previous night during the period of 18–21 days). Meanwhile, the birds in the negative control group instead received equivalent sterile phosphate-buffered saline (PBS) on day 15, and days 18–21. All samples were collected on day 35. ## 2.5. Measurement of growth performance and sample collection On days 1, 21, 42, and 63 of the experiment, birds were weighed per whole replicate. The variables of growth performance [final body weight (BW), average daily feed intake (ADFI), average daily gain (ADG), and feed:gain ratio (F:G)] were measured (Wang Y. et al., 2021). In detail, dead birds were recorded and weighed to adjust the estimates of gain, intake, and feed conversion ratios as appropriate. The average daily gain, average daily feed intake (ADFI), and feed:gain ratio (F:G) were calculated (Wang Y. et al., 2021). Sample collection was performed in accordance with our previously described methods (Wang Y. et al., 2021). Before sample collection, all broilers were given sufficient water while no diet was provided for 12 h before analysis. On day 35, two birds (close to average BW) per replicate were selected and weighed. The right vein was punctured, and 10 ml blood was collected into a procoagulant vacuum tube and centrifuged (3,500 ×g, 10 min at 4°C). Pure serum samples were pipetted and transferred into 1.5-mL sterilized Eppendorf tubes, and stored at −80°C for further analysis. The chicks were then euthanized by a well-trained team. First, the small intestine from each bird was removed, opened, and subjected to lesion scoring, by the same trained personnel, according to previously described methods (Johnson and Reid, 1970). Next, a 0.5 cm sample of the jejunum wall was fixed in $2.5\%$ glutaraldehyde (pH 7.4) and $4\%$ paraformaldehyde, respectively. Additionally, the mucosa of 10 cm sections of the jejunum and duodenum, were gently scraped off and collected. The cecal contents were also collected and snap-frozen (Wang Y. et al., 2021). ## 2.6. DNA extraction, 16S rRNA sequencing, and microbial composition analysis According to our previously described methods (Wang Y. et al., 2021), the microbial genome DNA was extracted from cecal content samples (TIANamp Stool DNA Kit DP328, TIANGEN, JP). The DNA extract was stored at 20°C until further analysis. The extracted DNA was quantified using a NanoDrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, United States) and agarose gel electrophoresis. Bacterial 16S rRNA gene sequences (V3–V4 region) were amplified using the Premix Ex Taq™ Hot Start Version (Takara, Dalian, China) and the following universal primers: 319F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′). Each polymerase chain reaction (PCR) mixture was prepared in a final volume of 50 μl containing 12.5 μl of the master mix, 1 μM of each primer, 50 ng of template DNA, and PCR-grade water. PCR reactions were performed using a gradient PCR instrument (L96G; LongGene, Hangzhou, China). MiSeq Illumina sequencing was further performed using the sequencing reaction (Illumina Inc., San Diego, CA, United States) for paired-end reads. The paired-end reads were then assembled and merged using FLASH and then assigned to each sample according to the unique barcodes. High-quality tags were clustered into operational taxonomic units (OTUs) using Usearch in QIIME software based on $97\%$ sequence similarity, and these OTUs were further subjected to analysis using the Greengene database with the RDP algorithm. Alpha and beta diversity was assessed, and partial least squares discriminant analysis (PLS-DA), as well as the unweighted pair-group method with arithmetic mean (UPGMA) analysis were conducted using QIIME. Linear discriminant analysis (LDA) effect size (LEfSe) analyses were performed using the LEfSe tool (Wang K. et al., 2017). The associations between biomarker genera in the two groups and selected predictive functions were determined by Spearman’s correlation analysis (SPSS 23.0). The raw data from the high-throughput sequencing were deposited in the NCBI database1 with the BioProject ID PRJNA714475. ## 2.7. Untargeted metabolome profiling using gas chromatography–mass spectroscopy According to our previously described methods (Wang Y. et al., 2021), frozen cecal digest (0.5 g) were lyophilized for 24 h and then transferred into 1 ml of polyethylene tubes. The digest was then mixed with 100 μl of methoxyamine hydrochloride in pyridine (20 mg/ml) and vortexed vigorously for 30 s. The sample was heated at 37°C for 90 min, after which 200 μl of a $1\%$ trimethylchlorosilane solution of bis(trimethylsilyl)-trifluoroacetamide was added. The samples were heated at 70°C for 60 min and then kept at room temperature for 30 min. Subsequently, the samples were centrifuged at 10,000 ×g for 10 min at 4°C, and 100 μl of the supernatant of each sample was transferred into a GC vial. After adding 400–500 μl n-hexane, the samples were used for gas chromatography–mass spectroscopy (GC–MS) in the automatic sampling mode. Each 1 μl sample was injected into the Agilent 6890A/5973C system equipped with a fused silica capillary column (30.0 m × 0.25 mm i.d.) packed with 0.25 μm HP-5MS. Helium was used as the carrier at a constant flow rate of 1.0 ml/min. Each 1 ml sample was injected into the device. The column temperature was maintained at 70°C for 2 min, increased to 200°C at a rate of 10°C/min, increased to 280°C at a rate of 5°C/min, and then maintained for 6 min. Mass detection was performed in the full scan mode, with a detection range of 50–650 (m/Z). GC–MS raw data files were converted into mzXML format and analyzed using the XCMS toolbox with the R statistical language (v3.4.1); post editing was performed using Excel 2010 software. The results were organized into a two-dimensional data matrix, including retention time (RT), mass charge ratio (MZ), sample amount, and peak intensity (Yuan et al., 2019). The processed data were first subject to principal component analysis (PCA) using SIMCA 14.1 (Umetrics, Malmo, Sweden) after unit variance scaling to evaluate the similarities and differences between each sample. PLS-DA was then performed using the SIMCA-P software (version 12.0; Umetrics AB, Umeå, Sweden) for group classification and discrimination analysis. The heat map was generated using HemI (Heatmap Illustrator)2 (Deng et al., 2014), and metabolite classification was performed using ClassyFire3 (Djoumbou Feunang et al., 2016). The metabolite list for each comparison was separately subject to pathway analysis, which was performed using MetaboAnalyst4 (Chong et al., 2018) according to the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database.5 Significantly varied pathways were identified with a cut-off $p \leq 0.05.$ ## 2.8. Jejunum morphology and histomorphological measurements The paraffin sections were subjected to hematoxylin and eosin (H&E) staining for histopathology analysis. Transmission electron microscopy and scanning electron microscopy was performed for the jejunal tissue according to our previous described protocols (Wang Y. et al., 2021). Morphometric measurements of jejunum villi were performed according to a previous described method (Awad et al., 2009). ## 2.9. Total RNA extraction and quantitative real-time PCR According to previously described methods (Wang Y. Y. et al., 2021), total RNA was extracted from powdered frozen intestinal mucosa (RNAiso Plus reagent, TAKARA, Tokyo, Japan) and reverse-transcribed using M-MLV reverse transcriptase (Takara Bio). Real-Time PCR was performed using SYBR® Green Premix Ex Taq™ (Takara) and the ABI 7500 Fast Real-Time PCR system (Applied Biosystems, Carlsbad, CA, United States). The primers used are shown in Table 2. Results were normalized to the abundance of β-actin transcripts and relative quantification was calculated using the 2−ΔΔCT method. **Table 2** | Gene name | Primers (5′-3′) | Products | GenBank | | --- | --- | --- | --- | | Claudin-1 | F: TGGCCACGTCATGGTATGG | 62 | NM_001013611 | | Claudin-1 | R: AACGGGTGTGAAAGGGTCATAG | 62 | NM_001013611 | | Occluding | F: GAGCCCAGACTACCAAAGCAA | 68 | NM_205128 | | Occluding | R: GCTTGATGTGGAAGAGCTTGTTG | 68 | NM_205128 | | Muc-2 | F: GCCTGCCCAGGAAATCAAG | 59 | NM_001318434 | | Muc-2 | R: CGACAAGTTTGCTGGCACAT | 59 | NM_001318434 | | β -actin | F: GAGAAATTGTGCGTGACATCA | 152 | NM_205518 | | β -actin | R: CCTGAACCTCTCATTGCCA | 152 | NM_205518 | ## 2.10. Biochemical determinations The activities of sucrase, amylase, and maltase in the duodenal mucosa were measured through colorimetric methods with a spectrophotometer. The assays were conducted using assay kits according to the manufacturer’s instructions (Nanjing Jiancheng Bioengineering, Nanjing, China). The absorbance was measured using an Infinite M200 Pro NanoQuant. ## 2.11. Enzyme linked immunosorbent assay The levels of interleukin (IL)-1β (No. H002), IL-6 (No. H007), secretory immunoglobulin A (sIgA), interferon-gamma (IFN-γ; H052), tumor necrosis factor α (TNF-α; No. H052-1), and immunoglobulin G (IgG) were determined colorimetrically using ELISA kits (Nanjing Jiancheng Institute of Bioengineering), according to the manufacturer’s instructions. ## 2.12. Immunofluorescence staining Staining was performed in three independent replicates to confirm the results. Tissue sections were deparaffinized in xylene, rehydrated with a series of graded ethanol, and washed in distilled water and PBS. The tissue sections were subsequently placed in a repair box filled with EDTA antigen repair buffer (ph8.0) and then repaired in a microwave oven. After natural cooling and washing with PBS, $3\%$ bovine serum albumin (Solarbio, Beijing, China) was added to evenly immerse the tissue, which was then at 37°C for 30 min. After removal of the sealing liquid, tissue sections were incubated with IgA (goat polyclonal, working dilution 1:500; ab112814; Abcam, Cambridge, United Kingdom) antibodies at 4°C overnight. After being washed in PBS, sections were exposed to secondary antibody goat anti-rabbit IgG [H + L] (Jackson ImmunoResearch, West Grove, PA, United States; 111-545-003) at 37°C for 1 h. Finally, the sections were stained with 4′,6-diamidino-2-phenylindole (DAPI) solution (Servicebio, Wuhan, China; G1012) for 10 min under dark conditions at room temperature, and a Nikon Eclipse TI-SR fluorescence microscope and Nikon DS-U3 imaging system were used to analyze the samples. ## 2.13. Statistical analyses The metabolic profile data were processed using the SIMCA software (version 13.0; Umetrics ab). PCA, projections to PLS-DA, and orthogonal PLS-DA were used to process the cecum metabolomic data. The effect of variables was assessed with the projection (VIP > 1) and Welch’s t-test ($p \leq 0.05$) values to obtain the profile of each metabolite. In addition, other data were subjected to one-way analysis of variance in SPSS (version 22.0; IBM Corp., Armonk, NY, United States) and expressed as the mean ± standard error of the mean (SEM). Analyses of 16S rRNA gene sequencing data were conducted using the Benjamini & Hochberg -based algorithm to correct the p value to reduce the false positive rate. Differences between treatment means were examined using Tukey’s multiple range test. Statistical significance was set at $p \leq 0.05.$ ## 3.1. BS supplementation prevents SNE-induced growth decline in broilers As shown in Table 3, higher F:G and mortality were observed in SNE on days 1–21 ($p \leq 0.05$) compared to those of the Ctr group, whereas no significant differences were observed between BS and ER treatments. On days 22–63, the SNE group presented lower BW and ADG than those of the other three groups ($p \leq 0.05$). Lower ADFI and higher mortality were found in the SNE group ($p \leq 0.05$) compared to those of the Ctr group, while no significant difference was observed between the BS and ER groups. The Ctr and ER groups showed significantly lower F:G compared with that of the SNE treatment group ($p \leq 0.05$), whereas no significant differences were found between SNE and BS groups. No significant differences were observed in overall F:G among all groups, whereas significantly lower ADG, ADFI, and higher mortality were observed in the SNE group compared with those in the other three groups on days 1–63 ($p \leq 0.05$). **Table 3** | Items | Ctr | SNE | BS | ER | SEM | p-Value | | --- | --- | --- | --- | --- | --- | --- | | 1 ~ 21d | 1 ~ 21d | 1 ~ 21d | 1 ~ 21d | 1 ~ 21d | 1 ~ 21d | 1 ~ 21d | | BW(g) | 551.97 | 526.47 | 535.92 | 547.04 | 12.664 | 0.214 | | ADG(g/d) | 24.14 | 22.95 | 23.38 | 23.92 | 0.607 | 0.225 | | ADFI(g/d) | 41.99 | 42.34 | 42.06 | 42.22 | 1.146 | 0.99 | | F:G | 1.74b | 1.85a | 1.80ab | 1.77ab | 0.037 | 0.057 | | Mortality (%) | 1.67b | 7.50a | 5.00ab | 2.50b | 0.013 | 0.001 | | 22 ~ 63d | 22 ~ 63d | 22 ~ 63d | 22 ~ 63d | 22 ~ 63d | 22 ~ 63d | 22 ~ 63d | | BW (g) | 2676.99a | 2215.68c | 2508.00b | 2600.14a | 32.366 | <0.001 | | ADG (g/d) | 50.60a | 40.22c | 46.95b | 48.88ab | 0.725 | <0.001 | | ADFI (g/d) | 140.53a | 124.18b | 136.29ab | 135.49ab | 4.843 | 0.019 | | F:G | 2.78b | 3.09a | 2.90ab | 2.77b | 0.096 | 0.012 | | Mortality (%) | 3.38b | 8.97a | 6.14ab | 4.21ab | 0.019 | 0.039 | | 1 ~ 63d | 1 ~ 63d | 1 ~ 63d | 1 ~ 63d | 1 ~ 63d | 1 ~ 63d | 1 ~ 63d | | ADG (g/d) | 41.78a | 34.46c | 39.10b | 40.56a | 0.515 | <0.001 | | ADFI (g/d) | 102.09a | 89.92b | 98.88a | 100.27a | 2.822 | 0.002 | | F:G | 2.44 | 2.61 | 2.53 | 2.47 | 0.064 | 0.082 | | Mortality (%) | 5.00c | 15.83a | 10.83b | 6.67bc | 0.017 | <0.001 | ## 3.2. The BS fermentation supernatant directly inhibits Clostridium perfringens growth An in vitro bacterial inhibition assay was performed to assess the direct effect of BS fermentation supernatant on CP growth. BS had a significant inhibitory effect; however, the antibacterial effect, compared to that of the Ctr group, was not as strong as that elicited by 100 μg/ml ER (Figures 1A,B). The results show that BS fermentation supernatant has a direct effect on CP growth and proliferation in vitro. **Figure 1:** *(A) In vitro antibacterial activity of BS fermentation supernatant (BSF) against Clostridium perfringen (CP). CP (1 × 108 CFU/ml) were cultured in Trptose-sulfite-Cycloserine agar medium and treated with BSF, ER (100 μg/ml) and sterile Reinforced Clostridial Medium was used as a positive control and negative control, respectively, at 37°C. The size of the inhibition zone was observed (B) and analyzed statistically with a line chart. The OD600 kinetics were determined to analyze the effect of BSF against CP. (C) Schematic outline of the experimental design. SNE, subclinical necrotic enteritis. (D) Lesion scores of broilers. (a, b) Mean values with unlike letters between different groups are significantly different (p < 0·05). SEM, standard error of mean. Each value represents the mean ± SEM of 12 replicates (n = 12). The abbreviation of Ctr, SNE, BS and ER have the same meaning as Table 3. BSF, BS cell-free extract.* ## 3.3. BS supplementation attenuates intestinal lesions in broilers We replicated the SNE model induced by the coccidiosis vaccine plus CP (Figure 1C). Intestinal lesions scored in the small intestine on day 35 are presented in Figure 1D. Birds in all treatments had low lesion scores, indicating that the challenge was subclinical. The SNE group had a higher lesion score than that of the other three groups ($p \leq 0.05$), and no significant difference was observed among the Ctr, BS, and ER groups ($p \leq 0.05$). ## 3.4. BS supplementation ameliorates SNE-induced intestinal mucosal injury H&E staining showed that the jejunum mucosa structure in the Ctr group was integrated, the intestinal villi were ordered, and the gland structure was clear and complete. However, the SNE treatment group showed an incomplete jejunum mucosa, with sparsely distributed villi of a relatively short length. With BS and ER pretreatments, the intestinal mucosal structure was significantly improved, and intestinal villi were higher ($p \leq 0.05$) with a denser arrangement (Figure 2A). Moreover, the Ctr group exhibited a higher villus height/crypt depth ratio than that of the SNE treatment group, as was also observed in the BS and ER groups (Table 4). We performed scanning electron microscopy (Figure 2B) and transmission electron microscopy (Figure 2C) to further examine the intestinal structure after the different treatments. The results showed that the Ctr group had complete jejunum villi, which formed full and closely arranged structures. The jejunum villi in the SNE group were severely damaged, whereas those in both the BS and ER groups showed greater improvement. These observations suggest that BS effectively prevented jejunal mucosal injury caused by SNE infection ($p \leq 0.05$). **Figure 2:** *(A) Histopathology of the intestinal mucosa analyzed by H&E staining (top, scale bars = 80 μm). (B) Scanning electron micrograph (bottom, scale bars = 200 μm) and (C) transmission electron micrographs (bottom, scale bars = 0.5 μm) of jejunal brush border in broilers. TJ, tight junction; AJ, adherens junction; DS, desmosomes. Asterisk shows the pathology. Blue arrows show the features of necrotic cell death. The abbreviation of Ctr, SNE, BS and ER have the same meaning as Table 3.* TABLE_PLACEHOLDER:Table 4 ## 3.5. BS supplementation increases the expression of genes related to intestinal tight junctions As shown in Figure 3, compared with the Ctr group, the mRNA expression of CLDN1 (claudin-1) and OCLN (occludin) in the jejunum of the SNE group were significantly reduced ($p \leq 0.05$). Meanwhile, BS pretreatment markedly upregulated the relative expression of CLDN1 ($70.95\%$, $p \leq 0.05$) and OCLN ($44.47\%$, 0.05 < $p \leq 0.1$) in comparison with that in the SNE group. Notably, no significant differences in CLDN1 or OCLN expression were observed among the Ctr, BS, and ER groups ($p \leq 0.05$). In addition, no significant difference was observed in the expression of MUC2 among the treatment groups ($p \leq 0.05$). **Figure 3:** *Changes on relative gene expression in the jejunum. (a, b) Mean values with unlike letters between different groups are significantly different (p < 0·05). ns, not significant; SEM, standard error of mean. Each value represents the mean ± SEM of 12 replicates (n = 12). The abbreviation of Ctr, SNE, BS and ER have the same meaning as Table 3.* ## 3.6. BS supplementation alters the number of IgA+ B cells and immunoglobulins in the jejunum of broilers As shown in Figure 4, compared with the SNE group, the BS group had a higher number of IgA+ B cells in the lamina propria of the jejunum ($p \leq 0.05$), while no significant differences were observed among the Ctr, BS, and ER groups ($p \leq 0.05$). In addition, similar results were observed in the levels of sIgA in the jejunum. No difference was detected in IgG levels in the jejunum of the four experimental groups ($p \leq 0.05$). **Figure 4:** *(A) IgA+ B cell abundance within the lamina propria in the jejunum under original magnification (×10 and ×40). (B) Relative quantification of the immunofluorescence results. (C) Changes in sIgA (D) and IgG levels in the jejunum. (a, b) Mean values with unlike letters between different groups are significantly different (p < 0·05). ns, not significant. SEM, standard error of mean. Each value represents the mean ± SEM of 12 replicates (n = 12). The abbreviation of Ctr, SNE, BS and ER have the same meaning as Table 3.* ## 3.7. BS supplementation alters digestive enzyme activity and immune response in broilers SNE significantly decreased maltase activity in the duodenum ($p \leq 0.05$) compared with that of the Ctr group. Furthermore, BS and ER markedly increased maltase activity compared to that in the SNE group ($p \leq 0.05$), whereas no difference was observed in the activities of sucrase and amylase (Figure 5). Cytokine secretion in the serum and jejunum mucosa is shown in Table 5. Proinflammatory cytokine secretion in the jejunum mucosa results revealed that the TNF-α level in SNE was markedly increased by 129.69 and $50.72\%$ compared to that in Ctr and ER, respectively, while no significant differences were observed when compared to that of the BS group. In addition, the SNE group had higher serum levels of IFN-γ and TNF-α than the other three groups ($p \leq 0.05$), and no significant differences were observed between the BS and ER groups. No significant changes were observed in IL-1β or IL-6 levels in either the jejunum or serum. **Figure 5:** **Duodenal mucosa* biochemistry parameters of broiler chickens. Values are means ($$n = 10$$), with standard error of mean represented by vertical bars. (a, b, c) Mean values with unlike letters between different groups are significantly different ($p \leq 0$·05). SEM, standard error of mean. Each value represents the mean ± SEM of 12 replicates ($$n = 12$$). The abbreviation of Ctr, SNE, BS and ER have the same meaning as Table 3.* TABLE_PLACEHOLDER:Table 5 ## 3.8. BS supplementation induces a shift in the gut microbiota composition Rarefaction curve analysis of OTUs in all samples approached the plateau (Figure 6A), indicating that the sampling depths were sufficient to capture the overall microbial diversity. Next, alpha diversity analysis was conducted using diversity indices (Shannon and Simpson) and richness estimates (Chao 1 and ACE). As can be seen in Figure 6B, the richness estimate increased significantly in the SNE group compared to that in the BS group ($p \leq 0.05$), whereas the diversity indices were similar among the three groups, except for the higher Shannon index in the SNE group than that in the ER group ($p \leq 0.05$). To examine the alteration in the composition of the gut microbiota, analysis of PCoA scatter plots was conducted and indicated a significant difference in the composition of the gut microbiota among the three groups (Figure 6C). The dissimilarity of the cecal microbiome presented was also confirmed by the separately clustered gut microbiota of the three groups shown in PLS-DA (Figure 6D) and UPGMA analysis (Figure 6E). In addition, the BS group was separated from the SNE group, and BS exhibited a tendency to cluster toward the ER group (Figures 6D,E), suggesting that BS administration attenuated the SNE-induced gut microbiota dysbiosis. **Figure 6:** *The cecal bacterial community of broilers among SNE, BS and ER treatments. (A) Rarefaction curve for total OTUs. (B) α-Diversity of gut microbiota was analyzed among SNE, BS and ER treatment groups by determination of principal dimension Simpson indices. (C) Three dimensional figures at the operational taxonomic unit (OTU) level obtained from PCoA based on the Bray–Curtis phylogenetic distance metric. (D) Partial least squares discriminant analysis of gut microbiota at the OTU level. (E) Unweighted pair-group method with arithmetic means (UPGMA) analysis based on the unweighted UniFrac. SEM, standard error of mean. Each value represents the mean ± SEM of 8 replicates (n = 8). The abbreviation of Ctr, SNE, BS and ER have the same meaning as Table 3.* Next, the relative microbial taxa abundances were compared among the three groups using analysis of variance. The top 20 most abundant microbial taxa at the phylum, family, and genus levels are shown in Figure 7A. At the phylum level, the abundance of Bacteroidetes, Proteobacteria, Actinobacteria, and Epsilonbacteraeota was increased, while that of Firmicutes and Tenericutes was decreased ($p \leq 0.05$) in the BS group compared with the SNE group. The ratio of Bacteroidetes to Firmicutes was higher in the BS (0.585) and ER (0.589) groups than in the SNE (0.324) group, although the difference was not significant, indicating that BS, similar to ER, profoundly benefited gut microbiota. At the family level, Lactobacillaceae ($p \leq 0.05$), Enterococcaceae ($p \leq 0.05$), and Bifidobacteriaceae ($p \leq 0.05$) were more abundant in the BS group than in the SNE group, whereas Ruminococcaceae, uncultured_bacterium_o_Mollicutes_RF39 ($p \leq 0.05$), and Christensenellaceae ($p \leq 0.05$) were more abundant in the SNE group. At the genus level, the BS group had a higher abundance of Lactobacillus ($p \leq 0.05$) and a lower abundance of Ruminococcaceae_UCG014 ($p \leq 0.05$) than that in the SNE group. **Figure 7:** *Structural changes in intestinal microbiota following dietary Bacillus subtilis DSM 29784 supplementation. (A) Relative abundance of microbial community in the cecum at the phylum, family, and genus levels. (B) LEfSe score plot of the discriminative microbial taxa (LDA score >4) that are more enriched in the BS (red), ER (green) and SNE (blue) groups. (C) Resulting bar plots display relative abundances of the phyla that are significantly altered, obtained from LEfSe analysis [o: order, f: family level, g: genus, s, species]. SEM, standard error of mean. Each value represents the mean ± SEM of 8 replicates (n = 8). The abbreviation of Ctr, SNE, BS and ER have the same meaning as Table 3.* LEfSe was performed to explore the differences in bacterial content among the three groups (Figures 7B,C, LDA score >4). Lactobacillales were enriched in BS, but not in the other groups, suggesting that BS increased the number of beneficial bacteria. In addition, ER increased the abundance of Bacteroidaceae and Bacteroides, while the relative abundance of Ruminococcaceae UCG 014 and Alistipes sp. N15 MGS 157 increased in the SNE group. ## 3.9. BS supplementation alters cecal metabolic composition To explore the effect of BS supplementation on cecal microbiota, the cecal metabolite concentrations in the three groups were analyzed. Multivariate analysis between different groups was performed using PCA and PLS-DA. The results of the unsupervised PCA analysis indicated that the metabolome profiles of the three groups were separated from one another (R2X [1] = 0.446, R2X [2] = 0.169). However, the score plot also showed that the BS group clustered between the SNE and ER groups, with a tendency toward the ER group (Figure 8A). In addition, PLS-DA analysis was performed between the groups (BS vs. SNE, BS vs. ER, ER vs. SNE) (Figure 8B). Broilers in the SNE group compared to those in BS or ER groups were separated into distinct clusters according to their metabolic differences (BS vs. SNE: R2X = 0.663, R2Y = 0.949, Q2 = 0.894; ER vs. SNE: R2X = 0.795, R2Y = 0.991, Q2 = 0.898), while the BS group exhibited a tendency to cluster toward the ER group. Additionally, the PLS-DA permutation test demonstrated that the PLS-DA model was valid for the present study (Figure 8C). Overall, the results showed that the SNE model group had a metabolic composition distinct from that in the ER positive control group and the SNE model pretreated with BS. **Figure 8:** *(A) Principal component analysis (PCA) score plots. (B) Projections to latent structure-discriminant analysis (PLS-DA) score plots of metabolic profiles obtained by BS vs. SNE [R2X = 0.663, R2Y = 0.949, Q2 = 0.894], ER vs. BS [R2X = 0.44, R2Y = 0.88, Q2 = 0.22] and ER vs. SNE [R2X = 0.795, R2Y = 0.991, Q2 = 0.898]; (C) permutation test of PLS-DA obtained by BS vs. SNE [left], ER vs. BS [middle] and ER vs. SNE [right]. SEM, standard error of mean. Each value represents the mean ± SEM of 7 replicates (n = 7). The abbreviation of Ctr, SNE, BS and ER have the same meaning as Table 3.* One of the primary aims of this study was to investigate the role of BS in the development of SNE. Subsequently, the metabolites that contributed to the change in the metabolic composition among the groups were selected based on the thresholds of VIP score >1 and $p \leq 0.05.$ As depicted in Figure 9A, 97 differentially-abundant metabolites were identified from the comparison between the BS and SNE groups. In the BS group, 44 compounds (malic, lactic, pyruvic, and glyceric acids, among other compounds) were increased, and 53 compounds (2-hydroxyglutaric, linoleic, tetracosanoic, and nonadecanoic acids and tyrosine, among other compounds) were decreased in the BS group compared to those in SNE. Further metabolic pathway enrichment analysis demonstrated that broilers fed BS significantly altered their ABC transporters; carbon metabolism; and biosynthesis of aminoacyl-tRNA, amino acids, and unsaturated fatty acids, alanine, aspartate, glutamate, valine, leucine, and isoleucine; glyoxylate and dicarboxylate metabolism; pantothenate and CoA biosynthesis; and fatty acid biosynthesis ($p \leq 0.00001$, rich factor >0.15, Figures 9B,C). **Figure 9:** *(A) Heat map of the significantly differentially-abundant metabolites between the BS group and the SNE group (VIP >1 and p < 0.05). A row presents the data obtained from a metabolite, and a column represents one sample. Red and green correspond to the increased and decreased levels of the metabolites, respectively; Bubble (B) and bar (C) charts of top 20 most enriched KEGG pathways. p-values are determined using two-tailed Student’s t-tests. SEM, standard error of mean. Each value represents the mean ± SEM of 7 replicates (n = 7). The abbreviation of SNE and BS have the same meaning as Table 3.* ## 3.10. Correlation between the cecal metabolome and gut microbiome To investigate the relationships between cecal metabolites and gut microbiota, Spearman correlation analysis was performed between cecal metabolites and cecal microbiota in SNE vs. BS (Figure 10). Beneficial bacteria such as Lactobacillales and Bifidobacterium were primarily correlated with metabolites that were higher in the cecum of the BS group, while they were negatively correlated with metabolites that were higher in the cecum of the SNE group. Specifically, the relative abundance of Lactobacillales was positively correlated with methylsuccinic, lactic, pyruvic, and malic acids, D-glucitol, glycerol, 2-hydroxyisocaproic, fumaric, and alpha-hydroxyisobutyric acids, uracil, glycerol 3-phosphate, L-hydroxyproline, L-proline, and glyceric acid. Meanwhile, Lactobacillales was negatively correlated with sedoheptulose, 2-hydroxyglutaric, linoleic, pyroglutamic, tetracosanoic, and nonadecanoic acids, tyrosine, and D-arabinose. The relative abundance of Bifidobacterium was positively correlated with malic and pyruvic acids, and 3-pyridinol, and negatively correlated with 2-hydroxyglutaric acid. Moreover, the relative abundance of Alistipes was negatively correlated with 3-methyladipic, cis-11-eicosenoic, stearic, eicosanoic, and heptadecanoic acids, 2-deoxy-pentitol, palmitic, docosanoic, and heneicosanoic acids, 5-alpha-cholestanol, and tricosanoic acid, and negatively correlated with fumaric acid. The relative abundance of Christensenellaceae and Christensenellaceae_R-7_group was positively correlated with methylsuccinic, lactic, and malic acids, D-glucitol, glycerol, 2-hydroxyisocaproic, fumaric, and alpha-hydroxyisobutyric acids, and uracil; meanwhile, they were negatively correlated with 2-hydroxyglutaric, linoleic, pyroglutamic, and tetracosanoic acids, tyrosine, nonadecanoic, tricosanoic, and eicosanoic acids, D-arabinose, and 2-deoxy-pentitol. **Figure 10:** *Correlations between significantly altered cecal metabolites and the bacterial strains between the BS-treated and SNE group. *p > 0.01 and p < 0.05, ***p < 0.01. SEM, standard error of mean. Each value represents the mean ± SEM of 7 replicates (n = 7).* ## 4. Discussion Bacteriocins are ribosomally synthesized and are potent antimicrobial peptides mainly produced by Bacillus spp. ( Khochamit et al., 2015). Historically, some *Bacillus species* have been considered safe for use in food and industry and as important vectors of ecological balance in animal models (Pedersen et al., 2002). Specifically, BS natto can inhibit pathogens such as *Salmonella typhimurium* and dysentery bacteria, which may be due to the production of bacitracin, polymyxin, 2,6-pyridinedicarboxylic acid, and other antibiotics (Zhang et al., 2020). Compared with other drugs, the antibacterial compound derived from BS natto has a broad antibacterial spectrum and is safe to the human body. Furthermore, previous studies have reported that BS inhibits the growth and virulence of Staphylococcal (Gonzalez et al., 2011; Piewngam et al., 2018). Similar results were obtained in the present study, and the antibacterial activity assay revealed that the BS fermentation supernatant directly inhibited CP growth. The findings of this study provide a theoretical basis for future in vivo animal experiments; however, the antimicrobial compounds require further investigation and characterization. SNE is associated with huge economic losses owing to significantly worsened performance and intestinal necrosis, however, has a low associated mortality (Skinner et al., 2010). Previous statistical analyses showed that SNE is strongly correlated with feed conversion rate increase and growth retardation in broilers (Kaldhusdal and Hofshagen, 1992). Therefore, the use of feed additives to prevent SNE has been explored to reduce economic losses (Keerqin et al., 2021). In the animal industry, probiotics have been used to improve animal health and prevent various intestinal diseases, mostly since the prohibition of antibiotic growth promoters in animal husbandry (Mingmongkolchai and Panbangred, 2018). In this study, we found that dietary supplementation with BS led to higher BW and ADG in SNE-infected broilers. The growth-promoting effect of probiotics reported here is consistent with the results of other studies reporting the use of probiotics in broilers (Neijat et al., 2019a,b; Wang Y. Y. et al., 2021). Nutrient absorption occurs primarily in the small intestine, particularly along the length of villi. Thus, the villi height directly determines the surface area absorbed by the intestine, which in turn affects the growth and development of animals (Salim et al., 2013). In contrast, shorter villi and deeper crypts may lead to decreased disease resistance and growth performance, malabsorption of nutrients, and increased gastrointestinal secretion (Mohammadagheri et al., 2016). As such, the villi length/crypt depth ratio is considered an important parameter for evaluating gut health and also implies that the epithelium is sufficiently mature and functionally active (Jayaraman et al., 2013). In the present study, BS effectively improved the morphology of the jejunum, including villi length and the villi length/crypt depth ratio, in SNE birds. Zhao et al. [ 2020] found that the addition of *Bacillus licheniformis* H2 increases the villi height: crypt depth ratio and villi height in the ileum (Zhao et al., 2020). We also showed that BS improves intestinal development and digestion primarily by increasing the villi height: crypt depth ratio in the jejunum (Wang Y. Y. et al., 2021). This evidence suggests that the well-developed small intestine may be related to the preventive effect of SNE. Many luminal and systemic factors can independently influence barrier function and cause leakage of plasma proteins and watery diarrhea (Camilleri, 2019). For instance, tight junctions can facilitate paracellular permeability, which is an essential component of intestinal mucosal barriers (Kucharzik et al., 2001). These factors also comprise several unique proteins, including claudin-1, occludin, and Muc-2 (Wang et al., 2018). In this study, the relative abundance of jejunal transcripts of CLDN1 and OCLN increased in BS and ER pretreatments compared with that in the SNE group. This result indicates that BS, similar to enramycin, could improve the intestinal physical barrier of SNE broilers. Avian IgA exists in most intestinal cells, similar to mammalian IgA in mammals, and releases sIgA into the intestinal cavity through epithelial transport (Lindner et al., 2012). IgA protects the mucosal surface from viruses, toxins and bacteria by neutralizing or preventing the binding of pathogens to the mucosal surface (Lammers et al., 2010). Hence, sIgA determines the composition of the intestinal microbiota and affects the development of systemic immunity, thus, making it critical for the maintenance of mucosal homeostasis (Lammers et al., 2010). In the present study, SNE infection significantly reduced the number of IgA+ B cells and sIgA content, while BS pretreatment significantly improved, indicating that BS can enhance Igs to prevent SNE in broilers. Inflammation is the main immune response of the body; however, an excessive immune response leads to a sharp rise in cytokines and production of the inflammatory cytokine storm, leading to immune system disorders and causing irreversible damage to host organs (Liu et al., 2020). In the present study, the levels of proinflammatory cytokines (IFN-γ and TNF-α) and chemokines (IL-1β and IL-6), which are key mediators in regulating the immune response, were measured. BS supplementation attenuated the concentration of IFN-γ in the jejunum and TNF-α in both the jejunum and serum. These results are in agreement with previous results of Wang H. S. et al. [ 2017] and Wang K. et al. [ 2017], who showed that feeding *Lactobacillus johnsonii* BS15 reduces the concentration of proinflammatory cytokines induced by SNE in the intestines of birds (Wang H. S. et al., 2017). The results indicated that dietary BS supplementation might reduce the adverse effects of inflammation on organs and maintain animal health. In addition, the improvement of the intestinal epithelial integrity and mucosal immune function may further affect the intestinal microenvironment and improve nutrient digestibility (Jiang et al., 2009). Sucrase, maltase, and amylase play an important role in the fermentation process of related nutrients and ultimately affect the production performance and health of animals. In this study, BS was identified as a potential replacement for antibiotics in promoting maltase activity. Similar results were observed by Wang and Gu [2010], who reported that *Bacillus coagulans* may promote the growth of chickens and improve the digestibility of feed by secreting protease, α-amylase, xylanase, lipase, and other enzymes (Wang and Gu, 2010). Compared with SNE treatment, the increased maltase activities induced by BS and ER treatment could contribute to the higher ADG and BW in these treatments. Maintenance of the host-microbiota balance is the key for homeostasis in animals (Wu et al., 2020). Changes in the microbiome and metabolome, as well as their interactions with the immune, endocrine, and mucosal systems, are related to a variety of diseases and vice versa. Diseases and pathological conditions often lead to an imbalance in intestinal microbiota and changes in microbial metabolites, leading to imbalances in metabolism and the immune system (Belkaid and Hand, 2014; Schmidt et al., 2018; Blacher et al., 2019). Nurmi and Rantala [1973] proposed that supplementation with probiotics might restore the protective gut microbiota and facilitate competitive exclusion (Nurmi and Rantala, 1973). To date, many studies have demonstrated the competitive exclusion of *Bacillus in* reducing the colonization of avian pathogens, such as reducing the counts of *Salmonella enteritidis* (La Ragione and Woodward, 2003), CP (Jayaraman et al., 2013; Jeong and Kim, 2014), Enterobacteriaceae (Jeong and Kim, 2014), Campylobacter (Guyard-Nicodème et al., 2016), and Salmonella (Vila et al., 2009; Knap et al., 2011) in the intestine. Moreover, in poultry nutrition, Lactobacillus and Bifidobacterium are generally considered beneficial as they promote intestinal health, improve the immune response of broiler systems, and enhance the health and performance of chickens (Salim et al., 2013). Alternatively, Alistipes spp. was recently identified as one of the top ten most abundant genera associated with human colorectal carcinoma (Feng et al., 2015). Moreover, Moschen et al. [ 2016] reported that facultative pathogenic Alistipes spp. induces colitis and site-specific tumors in IL10−/− mice (Moschen et al., 2016). Kang et al. [ 2019] further reported that the abundance of Alistipes positively correlates with inflammatory genes, such as those coding for IL-6 (Kang et al., 2019). Ruminococcaceae are primairly responsible for fermenting dietary fiber and other plant components, such as inulin and cellulose, to produce SCFAs, which can be used as energy by the host (Scheppach and Weiler, 2004) and elicit anti-inflammatory effects in the intestine (Kles and Chang, 2006). In the present study, BS supplementation increased the abundance of Lactobacillus and Bifidobacteriaceae, while the abundance of Ruminococcaceae and Alistipes spp. was enhanced in the SNE group. The increased abundance of Ruminococcaceae found in the SNE group may be due to excessive inflammation in the intestine. Meanwhile, the increased abundance of beneficial bacteria in the BS group may have inhibited the growth of pathogens, such as Erysipelotrichaceae and Escherichia-Shigella. In summary, our results indicate that the addition of BS promotes the growth of beneficial bacteria while inhibiting the colonization of harmful bacteria to prevent the imbalance of intestinal flora and inflammatory injury caused by SNE. A few studies have been performed on the metabolomic patterns in NE animal models. However, bacterial metabolites are essential elements in the interaction between the microbiota and the host (Russo et al., 2019). Metabolomics can identify different patterns of small molecules produced in the metabolic process of host and microbial cells, which may help to identify biomarkers of microbial patterns and disorders (Tuohy et al., 2009; Cevallos-Cevallos et al., 2011; Ponnusamy et al., 2011). The systemic effects of the gut microbiota are attributed to the less studied SCFAs, which are produced in the gut as the final products of fiber fermentation (Cook and Sellin, 1998). Volatile fatty acids, together with branched chain fatty acids, lactic acid, and other acids, play an important role in the gastrointestinal tract of birds by inhibiting the growth of various pathogenic bacteria (Svihus et al., 2013). This is achieved through the pH reduction caused by the acids, leading to inhibition of metabolic reactions, thereby reducing bacterial growth (Cherrington et al., 1991; Youssef et al., 2020). Therefore, high volatile fatty acids in the gut are generally considered healthy for the intestines (Timbermont et al., 2011). The current study showed that BS supplementation markedly increased the concentrations of lactic acid; hence, the production of acids may alleviate the intestinal mucosal damage caused by CP. Increasing evidence shows that release of microbiota metabolites may affect the health of the host. In fact, recent studies have shown that the contribution of intestinal microbiota to host immune regulation is primarily due to microbial metabolism (Huttenhower et al., 2012; Li et al., 2014). Our results revealed that intestinal metabolites fluctuated with the structure of the intestinal microbiota. Pretreatment with BS regulated the levels of 97 metabolites, including benzenoids, lipids, nucleotides, organic acids, and organic nitrogen/oxygen compounds. These metabolites are primarily involved in several important metabolic pathways, including ABC transporters, carbon metabolism, as well as amino acid, fatty acid, and unsaturated fatty acid biosynthesis. Butyrate is an anti-inflammatory microbial metabolite that is important for intestinal homeostasis (Riviere et al., 2016). Moreover, butyric acid-producing bacteria coexist with Bifidobacteria (Riviere et al., 2016). This was also shown in our finding that malic acid, which is positively correlated with Bifidobacteria, was more abundant in the BS group. The results of the present study imply that many microorganisms may be involved in the alteration of intestinal metabolomics, thus affecting intestinal health. These findings indicate that the observed alternations in cecal metabolites upon coccidiosis vaccine plus CP coinfection were likely derived from the gut microbiota. Collectively, the strong correlations observed between the gut microbial changes and shifted metabolic levels indicated that SNE may have resulted in significant changes in the gut microbiota, leading to marked shifts in host metabolite abundance. These shifts result in the dysregulation of the host immune response leading to poorer growth properties in broilers. In summary, we found that BS pretreatment significantly prevented the SNE-induced decrease in broiler growth performance. This may be because BS pretreatment increased villi height and maltase activity while decreasing the mucosal inflammatory response. In addition, BS pretreatment might modulate intestinal microbial composition and the gut metabolic profile as part of the microbial function. These findings provide a better understanding of the mechanism by which BS or ER promotes the prevention of SNE, which could provide useful insights for the development of an effective and safe alternative to antibiotics in the poultry industry. ## Data availability statement The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material. ## Ethics statement The animal study was reviewed and approved by all procedures were carried out in accordance with the Chinese Animal Welfare Guidelines and approved by the Institutional Animal Care and Use Committee of Zhejiang University (permission number: ZJU2019-480-12). ## Author contributions XZha and YW conceived and designed the experiments. YW, QW, YX, XZho and AF performed the experiments. YW analyzed the data, made the figures, and wrote the paper. YW, XZha and GC revised the manuscript. All authors contributed to the article and approved the submitted version. ## Funding This study was supported by the Key R&D program of Zhejiang Province (project: 2023C02026), China Agriculture Research System of MOF and MARA (project: CARS-41, Beijing, China), and the project of Hangzhou Agricultural and social development key research and development (project: 2022ZDSJ0157). ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## References 1. Awad W. A., Ghareeb K., Abdel-Raheem S., Bohm J.. **Effects of dietary inclusion of probiotic and synbiotic on growth performance, organ weights, and intestinal histomorphology of broiler chickens**. *Poultry Sci.* (2009) **88** 49-56. DOI: 10.3382/ps.2008-00244 2. Belkaid Y., Hand T. W.. **Role of the microbiota in immunity and inflammation**. *Cells* (2014) **157** 121-141. DOI: 10.1016/j.cell.2014.03.011 3. Blacher E., Bashiardes S., Shapiro H., Rothschild D., Mor U., Dori-Bachash M.. **Potential roles of gut microbiome and metabolites in modulating ALS in mice**. *Nature* (2019) **572** 474. DOI: 10.1038/s41586-019-1443-5 4. Caly D. L., D'Inca R., Auclair E., Drider D.. **Alternatives to antibiotics to prevent necrotic enteritis in broiler chickens: a Microbiologist's perspective**. *Front. Microbiol.* (2015) **6** 1336. DOI: 10.3389/fmicb.2015.01336 5. Camilleri M.. **Leaky gut: mechanisms, measurement and clinical implications in humans**. *Gut* (2019) **68** 1516-1526. DOI: 10.1136/gutjnl-2019-318427 6. Cevallos-Cevallos J. M., Danyluk M. D., Reyes-De-Corcuera J. I.. **GC-MS based metabolomics for rapid simultaneous detection of**. *J. Food Sci.* (2011) **76** M238-M246. DOI: 10.1111/j.1750-3841.2011.02132.x 7. Chang P. V., Hao L. M., Offermanns S., Medzhitov R.. **The microbial metabolite butyrate regulates intestinal macrophage function via histone deacetylase inhibition**. *Proce. Natl Acad Sci U. S. A.* (2014) **111** 2247-2252. DOI: 10.1073/pnas.1322269111 8. Cherrington C. A., Hinton M., Mead G. C., Chopra I.. **Organic-acids - chemistry, antibacterial activity and practical applications**. *Adv. Microb. Physiol.* (1991) **32** 87-108. DOI: 10.1016/S0065-2911(08)60006-5 9. Chong J., Soufan O., Li C., Caraus I., Li S., Bourque G.. **MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis**. *Nucleic Acids Res.* (2018) **46** W486-W494. DOI: 10.1093/nar/gky310 10. Cook S. I., Sellin J. H.. **Review article: short chain fatty acids in health and disease**. *Aliment. Pharm. Ther.* (1998) **12** 499-507. DOI: 10.1046/j.1365-2036.1998.00337.x 11. Deng W. K., Wang Y. B., Liu Z. X., Cheng H., Xue Y.. **HemI: a toolkit for illustrating Heatmaps**. *PLoS One* (2014) **9** e111988. DOI: 10.1371/journal.pone.0111988 12. Diarra M. S., Malouin F.. **Antibiotics in Canadian poultry productions and anticipated alternatives**. *Front. Microbiol.* (2014) **5** 282. DOI: 10.3389/fmicb.2014.00282 13. Djoumbou Feunang Y., Eisner R., Knox C., Chepelev L., Hastings J., Owen G.. **ClassyFire: automated chemical classification with a comprehensive, computable taxonomy**. *J. Cheminformatics* (2016) **8** 61. DOI: 10.1186/s13321-016-0174-y 14. Dodd D., Spitzer M. H., van Treuren W., Merrill B. D., Hryckowian A. J., Higginbottom S. K.. **A gut bacterial pathway metabolizes aromatic amino acids into nine circulating metabolites**. *Nature* (2017) **551** 648+. DOI: 10.1038/nature24661 15. Ducatelle R., Eeckhaut V., Haesebrouck F., Van Immerseel F.. **A review on prebiotics and probiotics for the control of dysbiosis: present status and future perspectives**. *Animal* (2015) **9** 43-48. DOI: 10.1017/S1751731114002584 16. Earl A. M., Losick R., Kolter R.. **Ecology and genomics of**. *Trends Microbiol.* (2008) **16** 269-275. DOI: 10.1016/j.tim.2008.03.004 17. Eeckhaut V., Wang J., Van Parys A., Haesebrouck F., Joossens M., Falony G.. **The probiotic Butyricicoccus pullicaecorum reduces feed conversion and protects from potentially harmful intestinal microorganisms and necrotic enteritis in broilers**. *Front. Microbiol.* (2016) **7** 1416. DOI: 10.3389/fmicb.2016.01416 18. Elshaghabee F. M. F., Rokana N., Gulhane R. D., Sharma C., Panwar H.. *Front. Microbiol.* (2017) **8** 1490. DOI: 10.3389/fmicb.2017.01490 19. Feng Q., Liang S. S., Jia H. J., Stadlmayr A., Tang L. Q., Lan Z.. **Gut microbiome development along the colorectal adenoma-carcinoma sequence**. *Nat. Commun.* (2015) **6** 6528. DOI: 10.1038/ncomms7528 20. Gadde U., Oh S. T., Lee Y. S., Davis E., Zimmerman N., Rehberger T.. **The effects of direct-fed microbial supplementation, as an alternative to antibiotics, on growth performance, intestinal immune status, and epithelial barrier gene expression in broiler chickens**. *Probiotics Antimicro.* (2017) **9** 397-405. DOI: 10.1007/s12602-017-9275-9 21. Gonzalez D. J., Haste N. M., Hollands A., Fleming T. C., Hamby M., Pogliano K.. **Microbial competition between**. *Microbiology-Sgm.* (2011) **157** 2485-2492. DOI: 10.1099/mic.0.048736-0 22. Guyard-Nicodème M., Keita A., Quesne S., Amelot M., Poezevara T., le Berre B.. **Efficacy of feed additives against campylobacter in live broilers during the entire rearing period**. *Poultry Sci.* (2016) **95** 298-305. DOI: 10.3382/ps/pev303 23. Huttenhower C., Gevers D., Knight R., Abubucker S., Badger J. H., Chinwalla A. T.. **Structure, function and diversity of the healthy human microbiome**. *Nature* (2012) **486** 207-214. DOI: 10.1038/nature11234 24. Jayaraman S., Thangavel G., Kurian H., Mani R., Mukkalil R., Chirakkal H.. *Poultry Sci.* (2013) **92** 370-374. DOI: 10.3382/ps.2012-02528 25. Jeong J. S., Kim I. H.. **Effect of**. *Poultry Sci.* (2014) **93** 3097-3103. DOI: 10.3382/ps.2014-04086 26. Jiang Z. Y., Sun L. H., Lin Y. C., Ma X. Y., Zheng C. T., Zhou G. L.. **Effects of dietary glycyl-glutamine on growth performance, small intestinal integrity, and immune responses of weaning piglets challenged with lipopolysaccharide**. *J. Anim. Sci.* (2009) **87** 4050-4056. DOI: 10.2527/jas.2008-1120 27. Johnson J., Reid W. M.. **Anticoccidial drugs: lesion scoring techniques in battery and floor-pen experiments with chickens**. *Exp. Parasitol.* (1970) **28** 30-36. DOI: 10.1016/0014-4894(70)90063-9 28. Kaldhusdal M., Hofshagen M.. **Barley inclusion and Avoparcin supplementation in broiler diets. 2. Clinical, pathological, and bacteriological findings in a mild form of necrotic enteritis**. *Poultry Sci.* (1992) **71** 1145-1153. DOI: 10.3382/ps.0711145 29. Kang Y. B., Li Y., Du Y. H., Guo L. O., Chen M. H., Huang X. W.. **Konjaku flour reduces obesity in mice by modulating the composition of the gut microbiota**. *Int. J. Obesity.* (2019) **43** 1631-1643. DOI: 10.1038/s41366-018-0187-x 30. Keerqin C., Rhayat L., Zhang Z. H., Gharib-Naseri K., Kheravii S. K., Devillard E.. **Probiotic**. *Poultry Sci.* (2021) **100** 100981. DOI: 10.1016/j.psj.2021.01.004 31. Kelly C. J., Zheng L., Campbell E. L., Saeedi B., Scholz C. C., Bayless A. J.. **Crosstalk between microbiota-derived short-chain fatty acids and intestinal epithelial HIF augments tissue barrier function**. *Cell Host Microbe* (2015) **17** 662-671. DOI: 10.1016/j.chom.2015.03.005 32. Khochamit N., Siripornadulsil S., Sukon P., Siripornadulsil W.. **Antibacterial activity and genotypic-phenotypic characteristics of bacteriocin-producing**. *Microbiol. Res.* (2015) **170** 36-50. DOI: 10.1016/j.micres.2014.09.004 33. Kles K. A., Chang E. B.. **Short-chain fatty acids impact on intestinal adaptation inflammation, carcinoma, and failure**. *Gastroenterology* (2006) **130** S100-S105. DOI: 10.1053/j.gastro.2005.11.048 34. Knap I., Kehlet A. B., Bennedsen M., Mathis G. F., Hofacre C. L., Lumpkins B. S.. *Poultry Sci.* (2011) **90** 1690-1694. DOI: 10.3382/ps.2010-01056 35. Krajmalnik-Brown R., Ilhan Z. E., Kang D. W., DiBaise J. K.. **Effects of gut microbes on nutrient absorption and energy regulation**. *Nutr. Clin. Pract.* (2012) **27** 201-214. DOI: 10.1177/0884533611436116 36. Kucharzik T., Walsh S. V., Chen J., Parkos C. A., Nusrat A.. **Neutrophil transmigration in inflammatory bowel disease is associated with differential expression of epithelial intercellular junction proteins**. *Am. J. Pathol.* (2001) **159** 2001-2009. DOI: 10.1016/S0002-9440(10)63051-9 37. La Ragione R. M., Woodward M. J.. **Competitive exclusion by**. *Vet. Microbiol.* (2003) **94** 245-256. DOI: 10.1016/S0378-1135(03)00077-4 38. Lammers A., Wieland W. H., Kruijt L., Jansma A., Straetemans T., Schots A.. **Successive immunoglobulin and cytokine expression in the small intestine of juvenile chicken**. *Dev. Comp. Immunol.* (2010) **34** 1254-1262. DOI: 10.1016/j.dci.2010.07.001 39. Li J. H., Jia H. J., Cai X. H., Zhong H. Z., Feng Q., Sunagawa S.. **An integrated catalog of reference genes in the human gut microbiome**. *Nat. Biotechnol.* (2014) **32** 834-841. DOI: 10.1038/nbt.2942 40. Lindner C., Wahl B., Fohse L., Suerbaum S., Macpherson A. J., Prinz I.. **Age, microbiota, and T cells shape diverse individual IgA repertoires in the intestine**. *J. Exp. Med.* (2012) **209** 365-377. DOI: 10.1084/jem.20111980 41. Liu J., Liu G. H., Chen Z. M., Zheng A. J., Cai H. Y., Chang W. H.. **Effects of glucose oxidase on growth performance, immune function, and intestinal barrier of ducks infected with**. *Poultry Sci.* (2020) **99** 6549-6558. DOI: 10.1016/j.psj.2020.09.038 42. Mingmongkolchai S., Panbangred W.. *J. Appl. Microbiol.* (2018) **124** 1334-1346. DOI: 10.1111/jam.13690 43. Mohammadagheri N., Najafi R., Najafi G.. **Effects of dietary supplementation of organic acids and phytase on performance and intestinal histomorphology of broilers**. *Vet. Res. Forum.* (2016) **2010** 189-195. DOI: 10.4061/2010/479485 44. Mohammadigheisar M., Shirley R. B., Barton J., Welsher A., Thiery P., Kiarie E.. **Growth performance and gastrointestinal responses in heavy tom turkeys fed antibiotic free corn-soybean meal diets supplemented with multiple doses of a single strain**. *Poultry Sci.* (2019) **98** 5541-5550. DOI: 10.3382/ps/pez305 45. Moschen A. R., Gerner R. R., Wang J., Klepsch V., Adolph T. E., Reider S. J.. **Lipocalin 2 protects from inflammation and tumorigenesis associated with gut microbiota alterations**. *Cell Host Microbe* (2016) **19** 455-469. DOI: 10.1016/j.chom.2016.03.007 46. Neijat M., Habtewold J., Shirley R. B., Welsher A., Barton J., Thiery P.. *Appl. Environ. Microb.* (2019a) **85** 104-108. DOI: 10.1128/AEM.00402-19 47. Neijat M., Shirley R. B., Welsher A., Barton J., Thiery P., Kiarie E.. **Growth performance, apparent retention of components, and excreta dry matter content in shaver white pullets (5 to 16 week of age) in response to dietary supplementation of graded levels of a single strain**. *Poultry Sci.* (2019b) **98** 3777-3786. DOI: 10.3382/ps/pez080 48. Nurmi E., Rantala M.. **New aspects of salmonella infection in broiler production**. *Nature* (1973) **241** 210-211. DOI: 10.1038/241210a0 49. Olkowski A. A., Wojnarowicz C., Chirino-Trejo M., Laarveld B., Sawicki G.. **Sub-clinical necrotic enteritis in broiler chickens: novel etiological consideration based on ultra-structural and molecular changes in the intestinal tissue**. *Res. Vet. Sci.* (2008) **85** 543-553. DOI: 10.1016/j.rvsc.2008.02.007 50. Olmos J., Acosta M., Mendoza G., Pitones V.. *Arch. Microbiol.* (2020) **202** 427-435. DOI: 10.1007/s00203-019-01757-2 51. Organization F.A (2013). Poultry Development Review. Role of Poultry in Human Nutrition. Food and agriculture organization.. *Poultry Development Review. Role of Poultry in Human Nutrition* (2013) 52. Pedersen F. A., Bjornvad M. E., Rasmussen M. D., Petersen J. N.. **Cytotoxic potential of industrial strains of**. *Regul. Toxicol. Pharmacol.* (2002) **36** 155-161. DOI: 10.1006/rtph.2002.1574 53. Piewngam P., Zheng Y., Nguyen T. H., Dickey S. W., Joo H. S., Villaruz A. E.. **Pathogen elimination by probiotic**. *Nature* (2018) **562** 532+. DOI: 10.1038/s41586-018-0616-y 54. Ponnusamy K., Choi J. N., Kim J., Lee S. Y., Lee C. H.. **Microbial community and metabolomic comparison of irritable bowel syndrome faeces**. *J. Med. Microbiol.* (2011) **60** 817-827. DOI: 10.1099/jmm.0.028126-0 55. Rhayat L., Jacquier V., Brinch K. S., Nielsen P., Nelson A., Geraert P. A.. *Poultry Sci.* (2017) **96** 2274-2280. DOI: 10.3382/ps/pex018 56. Riviere A., Selak M., Lantin D., Leroy F., De Vuyst L.. **Bifidobacteria and butyrate-producing colon bacteria: importance and strategies for their stimulation in the human gut**. *Front. Microbiol.* (2016) **7** 979. DOI: 10.3389/fmicb.2016.00979 57. Russo E., Giudici F., Fiorindi C., Ficari F., Scaringi S., Amedei A.. **Immunomodulating activity and therapeutic effects of short chain fatty acids and tryptophan post-biotics in inflammatory bowel disease**. *Front. Immunol.* (2019) **10** 2754. DOI: 10.3389/fimmu.2019.02754 58. Salim H. M., Kang H. K., Akter N., Kim D. W., Kim J. H., Kim M. J.. **Supplementation of direct-fed microbials as an alternative to antibiotic on growth performance, immune response, cecal microbial population, and ileal morphology of broiler chickens**. *Poultry Sci.* (2013) **92** 2084-2090. DOI: 10.3382/ps.2012-02947 59. Scheppach W., Weiler F.. **The butyrate story: old wine in new bottles? current opinion in clin**. *Nutr. Metabol. Care.* (2004) **7** 563-567. DOI: 10.1097/00075197-200409000-00009 60. Schmidt T. S. B., Raes J., Bork P.. **The human gut microbiome: from association to modulation**. *Cells* (2018) **172** 1198-1215. DOI: 10.1016/j.cell.2018.02.044 61. Silva R. O. S., Salvarani F. M., Assis R. A., Martins N. R. S., Pires P. S., Lobato F. C. F.. **Antimicrobial susceptibility of**. *Braz. J. Microbiol.* (2009) **40** 262-264. DOI: 10.1590/S1517-83822009000200010 62. Skinner J. T., Bauer S., Young V., Pauling G., Wilson J.. **An economic analysis of the impact of subclinical (mild) necrotic enteritis in broiler chickens**. *Avian Dis.* (2010) **54** 1237-1240. DOI: 10.1637/9399-052110-Reg.1 63. Svihus B., Choct M., Classen H. L.. **Function and nutritional roles of the avian caeca: a review**. *World. Poultry Sci. J.* (2013) **69** 249-264. DOI: 10.1017/S0043933913000287 64. Timbermont L., Haesebrouck F., Ducatelle R., Van Immerseel F.. **Necrotic enteritis in broilers: an updated review on the pathogenesis**. *Avian Pathol.* (2011) **40** 341-347. DOI: 10.1080/03079457.2011.590967 65. Tuohy K. M., Gougoulias C., Shen Q., Walton G., Fava F., Ramnani P.. **Studying the human gut microbiota in the trans-Omics era - focus on metagenomics and metabonomics**. *Curr. Pharm. Design.* (2009) **15** 1415-1427. DOI: 10.2174/138161209788168182 66. Van Immerseel F., De Buck J., Pasmans F., Huyghebaert G., Haesebrouck F., Ducatelle R.. *Avian Pathol.* (2004) **33** 537-549. DOI: 10.1080/03079450400013162 67. Verdes S., Trillo Y., Pena A. I., Herradon P. G., Becerra J. J., Quintela L. A.. **Relationship between quality of facilities, animal-based welfare indicators and measures of reproductive and productive performances on dairy farms in the northwest of Spain**. *Ital. J. Anim. Sci.* (2020) **19** 319-329. DOI: 10.1080/1828051x.2020.1743784 68. Vila B., Fontgibell A., Badiola I., Esteve-Garcia E., Jimenez G., Castillo M.. **Reduction of salmonella enterica var. Enteritidis colonization and invasion by**. *Poultry Sci.* (2009) **88** 975-979. DOI: 10.3382/ps.2008-00483 69. Wandro S., Osborne S., Enriquez C., Bixby C., Arrieta A., Whiteson K.. **The microbiome and metabolome of preterm infant stool are personalized and not driven by health outcomes, including necrotizing Enterocolitis and late-onset**. *Sepsis* (2018) **3** 1335-1339. DOI: 10.1128/mSphere.00104-18 70. Wang Y. B., Gu Q.. **Effect of probiotic on growth performance and digestive enzyme activity of arbor acres broilers**. *Res. Vet. Sci.* (2010) **89** 163-167. DOI: 10.1016/j.rvsc.2010.03.009 71. Wang Y. Y., Heng C. A. N., Zhou X. H., Cao G. T., Jiang L., Wang J. S.. **Supplemental**. *Br. J. Nutr.* (2021) **125** 494-507. DOI: 10.1017/S0007114520002755 72. Wang K., Jin X., You M., Tian W., Le Leu R. K., Topping D. L.. **Dietary Propolis ameliorates dextran sulfate sodium-induced colitis and modulates the gut microbiota in rats fed a Western diet**. *Nutrients* (2017) **9** 875. DOI: 10.3390/nu9080875 73. Wang H. S., Ni X. Q., Qing X. D., Liu L., Lai J., Khalique A.. **Probiotic enhanced intestinal immunity in broilers against subclinical necrotic enteritis**. *Front. Immunol.* (2017) **8** 1592. DOI: 10.3389/fimmu.2017.01592 74. Wang Y., Wang Y., Xu H., Mei X., Gong L., Wang B.. **Direct-fed glucose oxidase and its combination with B. amyloliquefaciens SC06 on growth performance, meat quality, intestinal barrier, antioxidative status, and immunity of yellow-feathered broilers**. *Poultry Sci.* (2018) **97** 3540-3549. DOI: 10.3382/ps/pey216 75. Wang Y., Xu Y., Xu S., Yang J., Wang K., Zhan X.. *Front. Microbiol.* (2021) **12** 2470. DOI: 10.3389/fmicb.2021.723187 76. Wikoff W. R., Anfora A. T., Liu J., Schultz P. G., Lesley S. A., Peters E. C.. **Metabolomics analysis reveals large effects of gut microflora on mammalian blood metabolites**. *Prcoe. Natl. Acad. Sci. U. S. A.* (2009) **106** 3698-3703. DOI: 10.1073/pnas.0812874106 77. Willemsen L. E. M., Koetsier M. A., van Deventer S. J. H., van Tol E. A. F.. **Short chain fatty acids stimulate epithelial mucin 2 expression through differential effects on prostaglandin E-1 and E-2 production by intestinal myofibroblasts**. *Gut* (2003) **52** 1442-1447. DOI: 10.1136/gut.52.10.1442 78. Wu T., Xu F. M., Su C., Li H. R., Lv N., Liu Y. Y.. **Alterations in the gut microbiome and Cecal metabolome DuringKlebsiella pneumoniae-induced Pneumosepsis**. *Front. Immunol.* (2020) **11** 1331. DOI: 10.3389/fimmu.2020.01331 79. Xu X. G., Gong L., Wang B. K., Wu Y. P., Wang Y., Mei X. Q.. **Glycyrrhizin attenuates salmonella enterica Serovar Typhimurium infection: new insights into its protective mechanism**. *Front. Immunol.* (2018) **9** 2321. DOI: 10.3389/fimmu.2018.02321 80. Youssef F. S., Eid S. Y., Alshammari E., Ashour M. L., Wink M., El-Readi M. Z.. **Chrysanthemum indicum and Chrysanthemum morifolium: chemical composition of their essential oils and their potential use as natural preservatives with antimicrobial and antioxidant activities**. *Foods.* (2020) **9** 1460. DOI: 10.3390/foods9101460 81. Yuan H. L., Xu Y., Chen Y. Z., Zhan Y. Y., Wei X. T., Li L.. **Metabolomics analysis reveals global acetoin stress response of**. *Metabolomics* (2019) **15** 25. DOI: 10.1007/s11306-019-1492-7 82. Zarrinpar A., Chaix A., Xu Z. J. Z., Chang M. W., Marotz C. A., Saghatelian A.. **Antibiotic-induced microbiome depletion alters metabolic homeostasis by affecting gut signaling and colonic metabolism**. *Nat. Commun.* (2018) **9** 2872. DOI: 10.1038/s41467-018-05336-9 83. Zhang J., Bilal M., Liu S., Zhang J. H., Lu H. D., Luo H. Z.. **Isolation, identification and antimicrobial evaluation of bactericides secreting**. *PRO* (2020) **8** 259. DOI: 10.3390/pr8030259 84. Zhao Y., Zeng D., Wang H. S., Qing X. D., Sun N., Xin J. G.. **Dietary probiotic**. *Probiotics Antimicrob* (2020) **12** 883-895. DOI: 10.1007/s12602-019-09597-8
--- title: Analysis of eight types of RNA modification regulators and their correlation with the prognosis in hepatocellular carcinoma authors: - Wan Qin - Chen Jin - Jun Zou journal: Frontiers in Genetics year: 2023 pmcid: PMC10060831 doi: 10.3389/fgene.2023.1127301 license: CC BY 4.0 --- # Analysis of eight types of RNA modification regulators and their correlation with the prognosis in hepatocellular carcinoma ## Abstract RNA modification plays important role in the occurrence and development of hepatocellular carcinoma. The best characterized RNA modification is m6A, while other kinds of RNA modifications have not been fully investigated in hepatocellular carcinoma (HCC). In the current study, we investigated the roles of one hundred RNA modification regulators belonging to eight different types of cancer-related RNA modifications in HCC. Expression analysis revealed that nearly $90\%$ RNA regulators exhibited significantly higher expression in tumors than normal tissues. By consensus clustering, we identified two clusters with distinct biological characteristics, immune microenvironment, and prognostic pattern. An RNA modification score (RMScore) was constructed and stratified patients into high- and low-risk group, which showed significantly different prognosis. Moreover, a nomogram including clinicopathologic features and the RMScore could well predict the survival in HCC patients. This study indicated the important role of eight types of RNA modification in HCC and develop a RMScore, which will be a new method to forecast the prognosis of HCC patients. ## Introduction Hepatocellular carcinoma (HCC) is one of the leading cause of cancer-related deaths worldwide, with few effective therapeutic options (Sung et al., 2021). Most patients have already progressed to the middle and late stages when they are diagnosed, leading to a poor prognosis of HCC patients (Sia et al., 2017). The complicated etiologies and complex pathogenesis lead to the high heterogeneity of HCC, making the efficiency of treatment very low (Park et al., 2010; Seehawer et al., 2018). Identifying novel treatment and prognostic targets for HCC is of urgent need. RNA modification is a critical posttranscriptional regulators of gene expression program (Nombela et al., 2021). With the rapid development of molecular and sequencing technologies, RNA modification has become a hot research topic in recent years. Emerging reports confirm that dysregulation of RNA modification gives rise to a variety of human diseases, particularly hepatocellular carcinoma (Xu et al., 2021). Various types of cancer-related RNA modifications can be found in eukaryotes, including N6-methyladenosine (m6A), N1-methyladenosine (m1A), and 5-methylcytosine (m5C), 2′-O-methylation (Nm), m7G N7-methylguanosine (m7G), pseudouridylation (Ψ), adenosine-to-inosine (A-to-I), 5-methoxycarbonylmethyl-2-thiouridine (mcm5s2U) (Boccaletto et al., 2018; Frye et al., 2018). Among them, m6A is the most investigated and best characterized one (Shi et al., 2019; Wei and He, 2021). For example, KIAA1429 was demonstrated to promote HCC invasion and migration by altering the methylation of m6A in ID2 and GATA3 mRNA (Cheng et al., 2019; Lan et al., 2019). ALKBH5 suppresses proliferation and invasion of HCC via m6A-guided epigenetic inhibition of LYPD1 (Chen et al., 2020). METTL3 promotes HCC progression via post-transcriptional silencing of SOCS2 (Chen et al., 2018). Other methylations are also reported to be related to HCC. For example, aberrant NSUN2-mediated m5C modification of H19 lncRNA is proved to be associated with poor differentiation of HCC (Sun et al., 2020). TRMT6/TRMT61A-mediated m1A methylation is required for self-renewal of liver cancer stem cells and tumorigenesis (Wang et al., 2021a). However, these studies mostly focused on the functions of only one RNA modification regulator. The crosstalk between different regulators remains unknown. Recently, the interactions between different regulators within one type of RNA modification have been investigated. For example, Fang et al. reported a two m6A gene-based signature (HNRNPA2B1 and RBM15) could predict the prognosis of HBV-related HCC (Fang and Chen, 2020). A risk signature consisted of four m1A regulators (TRMT6, TRMT61A, TRMT10C, and YTHDF1) was remarkably associated with HCC patient prognosis (Shi et al., 2020). A risk signature comprised of six m6A-related genes was reported to be able to predict the prognosis of HCC[19]. However, these studies ignored the relationship and crosstalk between different types of RNA regulators. It is very necessary to systematically characterize the relationship and crosstalk of other kinds of RNA modification regulators in HCC. In the current research, we profiled one hundred RNA regulators from eight types of RNA modifications in HCC for the first time. We clustered HCC patients into two RNA modification-related subtypes with different prognosis and cancer hallmarks. An RNA modification score was constructed to serve as an independent prognosis predictor for HCC patients. These results would provide insight into the prognosis prediction and therapeutic management of HCC in the future. ## Data collection *The* gene mutation, expression and clinical information of HCC patients was downloaded from the TCGA and ICGC database. There were huge number of RNA regulators identified in eukaryotes. However, we only choose RNA regulators which were proved to play critical roles in HCC and other cancers (Delaunay and Frye, 2019; Barbieri and Kouzarides, 2020; Han et al., 2021). As a result, a total of one hundred RNA regulators which belong to eight types of RNA modifications (m6A, m1A, m5C, Nm, m7G, Ψ, A-to-I, and mcm5s2U) were included in our study. They were listed in Supplementary Table S1. ## Expression and mutation analysis of RNA modification regulators Unpaired t-test were used to compare the expression of 100 RNA regulators between tumor and adjacent normal tissues. The “Maftools” R package was used to analyze the mutation and interaction of 100 RNA modification genes. The protein-protein interaction between 100 RNA modification regulators were analyzed by the STRING database and visualized by using Cytoscape software (Szklarczyk et al., 2019). ## Identification of RNA modification regulator-related subgroups To figure out the relationship between the expression of 100 RNA modification regulator and HCC subtypes, consensus clustering analysis was conducted in TCGA-LIHC patients by using the “ConsensusClusterPlus” R package. ## Pathway enrichment analysis To reveal the biological function enriched in different group of patients, the Gene Set Variation Analysis (GSVA) was performed by utilizing the R package “GSVA”. The annotated gene set “h.all.v7.4.symbols.gmt” was used for GSVA analysis. The enrichment scores of immune cells in sample from different groups were determined with single-sample gene set enrichment analysis (ssGSEA). ## Construction and validation of the RNA modification score (RMScore) To construct the RMScore, we first identified 1,427 differentially expressed genes (DEGs) in cluster 1 and cluster 2 groups. We divided TCGA-LIHC into training set and validation set. In the training set, univariate cox regression analysis revealed that 266 DEGs were identified to be significantly related to the prognosis of HCC patients ($p \leq 0.001$). Then LASSO regression analysis was conducted to select survival-associated DEGs. Multivariate cox regression analysis was conducted to construct the final prognostic model. The RMScore was calculated by the following equation: RMScore = Exp1* Coe 1 + Exp2* Coe 2 + … Exp N* Coe N. The validation dataset of TCGA-LIHC, whole TCGA-LIHC dataset, and ICGC dataset were utilized as external validation dataset. The RMScore of each sample was calculated by the formula presented above. Patients were divided into low RMScore and high RMScore groups by the median value of the RMScore. The “Survival” R package was conducted to analysis the survival of two groups. The “SurvivalROC” R package was used to plot the ROC curves. ## Chemotherapy response prediction To predict the sensitivity to chemotherapeutic drugs, we use the R package “pRRophetic” to estimate the half-maximal inhibitory concentration (IC50) in individual patients. The R package “ggplot2” was utilized to generate the plots. ## Construction of a nomogram The stage and RMScore, both of which were identified to be an independent prognostic value by multivariate Cox proportional hazards analysis, were included to construct the nomogram. The calibration curve was plotted to observe the prediction probabilities of the nomogram against the observed rates. The prognostic ability of the nomogram was evaluated by receiver operating (ROC) curves and decision curve analysis (DCA) curves. ## Dysregulation and mutation of eight types of RNA regulators in HCC We choose a total of one hundred RNA modification regulators which were reported to play roles in at least one kind of cancer to perform following analysis (Supplementary Table S1). In order to characterize the expression profile of 100 RNA regulators, unpaired t-test was conducted to compare the gene expression between tumor and normal tissues. Notably, nearly $90\%$ RNA regulators exhibited significantly higher expression in tumors than normal tissues (Figure 1A). Among the eight types of RNA modification regulators, all regulators in mcm5s2U, Ψ and A-to-I showed elevated expression in tumor tissues. The dysregulation of these RNA regulators may play key roles in the development of HCC. **FIGURE 1:** *The dysregulation and mutation landscape of 100 RNA modification regulators included in our study in HCC. (A) The gene expression level of 100 RNA modification regulators in normal and tumor tissues in TCGA-LIHC. (B) Genomic mutational landscape of RNA modification regulators with mutation rates ≥1% in the TCGA-LIHC cohort. (C) Correlation of the mutation between genes with mutation rates ≥1%. (D) The protein-protein interactions between one hundred RNA regulators analyzed by the STRING database. (p-value < 0.05 and correlation coefficient ≥0.6). (ns, not significant; *, p < 0.05: **, p < 0.01; ***, p < 0.001).* The mutation profiles of 100 hundred regulators in HCC were depicted together. As a results, 94 of 371 samples (about $25.34\%$) showed RNA modification regulator mutations. M5C eraser TET1 and A-to-I writer ADARB2 had a mutation rate of $2\%$. About $40\%$ RNA regulators had a mutation rate of $1\%$ (Figure 1B). Genetic interaction analysis showed that mutation of m5C eraser TET3 was positively correlated with mutations of TRMT10B, ADAT2, PRRC2A, YTHDC1 and TET2. Mutation of m6A reader HNRNPA2B1 was positively correlated with mutations of WTAP, ELAVL1, MRM2, HNRNPC, HNRNPA1, YTHDC1, PRRC2A. Mutation of m6A writer ZC3H13 was positively correlated with mutations of TRMT10B, TET3, TRMT13, PRRC2A, YTHDC1, ADAR, YTHDC2 and ZCCHC4, while negatively correlated with mutations of METTL1 and CTU2 (Figure 1C). To reveal the interactive relationships between proteins encoded by 100 RNA regulators, we constructed a PPI network using STRING and Cytoscape software. Correlations with adjusted p-value <0.05 and |r| ≥ 0.6 were shown in Figure 1D. We can see that these RNA regulators, especially regulators of m6A and m5C, showed close correlations with each other. Results above collectively demonstrated that there were huge interaction and crosstalk between these 100 RNA regulators. They may form a complicated network to synergistically mediate the development and progression of HCC. ## Identification of different RNA modification patterns in HCC In order to excavate the RNA modification patterns of HCC, consensus clustering analysis were conducted in 374 patients in TCGA-LIHC dataset. Patients in TCGA-LIHC dataset were separated into diverse clusters ($K = 2$–9) according to the expression of 100 RNA modification genes via an unsupervised consensus clustering analysis. The cumulative distribution function (CDF) curve and the area under the CDF curve was plotted, which indicated that the cohort was well distributed when $K = 2$ (Figures 2A–C). Kaplan-Meier survival analysis showed that patients in cluster 1 had a significantly better prognosis than patients in cluster 2 ($p \leq 00.001$) (Figure 2D). GSVA and ssGSEA enrichment analysis were performed to analyze biological process in two clusters. As shown in Figure 2E, most signaling pathways including DNA repair, MYC targets, G2M checkpoint, E2F targets were significantly enriched in Cluster 2. These may lead to the high degree of malignancy and poor prognosis of tumors in Cluster 2. Signaling pathways such as oxidative phosphorylation, reactive oxygen species pathway, cholesterol homeostasis and fatty acid metabolism were enriched in Cluster 1. There were more NK cells, eosinophils, neutrophils, effector memory CD8 T cells in Cluster 1, while more activated CD4 T cells, T follicular helper cells, type 2 T helper cell and effector memory CD4 T cells were accumulated in Cluster 2. This result implied two clusters had different immune infiltrating patterns. **FIGURE 2:** *Identification of consensus clusters by eight types of RNA modification regulators in LIHC. (A) Cumulative distribution function (CDF) for k = 2–9. (B) The area under the CDF curve k = 2–9. (C) Consensus clustering matrix for k = 2. (D) Kaplan–Meier survival curve of two clusters. (E) The heatmap of the different cancer hallmarks enriched in two clusters. (F) The difference of the immune cells infiltrated in tumor microenvironments between two clusters.* Results above collectively suggested that 100 RNA modification regulators defined two RNA modification patterns in HCC, which exhibited different malignancy, immune microenvironment, and prognosis. ## Establishment and verification of RNA modification score in HCC To further investigate the potential genetic alterations between two RNA modification patterns, differential expression analyses were carried out between two clusters (Figure 3A). A total of 1,472 differentially expressed genes (DEGs) were identified (Supplementary Table S2). Next, we divided the TCGA-LIHC queue into a training cohort and a verification cohort. Among the 1,427 DEGs, 266 genes were associated with the prognosis of HCC patients in training cohort by univariate COX regression analysis (Supplementary Table S3). Then, the LASSO regression analysis was conducted to further narrow the survival-related DEGs (Supplementary Table S4). Finally, six genes were identified by stepwise multivariate regression analysis and subsequently used to construct the RNA modification score (RMScore). The six genes identified were CYP2C9, CBX2, NDRG1, EPS8L3, FAM83D and MYCN. The RMScore = 0.1034*Exp of CBX2 + 0.0087*Exp of NDRG1 + 0.0392*Exp of EPS8L3 + 0.0517*Exp of FAM83D + 0.0596*Exp of MYCN−0.0031*Exp of CYP2C9. **FIGURE 3:** *Development and validation of RNA modification related signature for HCC. (A) The workflow of the construction of the RMScore. (B) Survival curves stratified by the RMScore in the TCGA training cohort. (C) Receiver operating characteristic (ROC) curves of RMScore for predicting survival in the TGGA training cohort. (D) Survival curves stratified by the RMScore in the TCGA validation cohort. (E) ROC curves of RMScore for predicting survival in the TGGA validation cohort. (F) Survival curves stratified by the RMScore in the ICGC cohort. (G) ROC curves of RMScore for predicting survival in the ICGC cohort.* By Kaplan-Meier analysis, we found that patients in low-risk group had a clear survival advantage over the high-risk group in training set ($p \leq 0.001$) (Figure 3B). The same survival advantage was also observed in the validation cohort ($$p \leq 0.033$$) and the ICGC cohort ($p \leq 0.001$) (Figures 3D, F). The time-dependent ROC curves were utilized in assessing the performance of RMScore, which showed the RMScore had good sensitivity and specificity in prognosis prediction (Figures 3C, E, G). These results suggested that the RMScore had good prediction ability for the survival of HCC patients. ## Independent prognostic value of RMScore The RMScore together with clinicopathological factors, such as age, gender, grade, and stage were included for univariate and multivariate Cox regression analysis. In the univariate analysis, the RMScore was significantly related to the prognosis of HCC patients (hazard ratio [HR], 1.053; $95\%$ confidence interval [CI], 1.037–1.069; $p \leq 0.001$) (Figure 4A). Multivariate analysis also demonstrated that the RMScore was an independent risk of the prognosis of HCC patients (hazard ratio [HR], 1.045; $95\%$ confidence interval [CI], 1.028–1.062; $p \leq 0.001$) (Figure 4B). In the heatmap incorporating gene expression and clinicopathological traits, the majority of patients in Cluster 1 were gathered in low RMScore group, which demonstrated that the RMScore well represented the traits of patients in Cluster 1. Among the six gene which constructing the RMScore, only CYP2C9 was upregulated in the Cluster 1 as well as low RMScore group. The other five genes (CBX2, NDRG1, EPSBL3, FAM83D and MYCN) were upregulated in Cluster 2 as well as high RMScore group (Figure 4C). We explored the pathway enrichment associated with the RMScore to identify the internal mechanisms involved. Similar to the pathway enrichment results between Cluster 1 and Cluster 2, the high RMScore group was mainly enriched in signaling pathways related to DNA repair, MYC targets, G2M checkpoint, and E2F targets, while the low RMScore group was enriched in oxidative phosphorylation, reactive oxygen species pathway, cholesterol homeostasis and fatty acid metabolism (Figure 4D). **FIGURE 4:** *(A) Univariate Cox regression analysis and (B) multivariate Cox regression analysis verifying independent prognostic value of RMScore for the prognosis of HCC patients. (C) Heat map for the association of gene and clinicopathologic features with prognosis of HCC patients. (D) Visualization of biological processes analyzed by GSVA in two RMScore subgroups.* ## Potential predictive biomarker for chemotherapy and target therapy We investigated the association between the RMScore and the sensitivity to chemotherapeutics in HCC patients. As a result, significant differences in the estimated IC50 between the two risk groups were observed for common chemotherapy or targeted drugs including paclitaxel (Figure 5A), sorafenib (Figure 5B), 5-flurouracil (Figure 5C), doxorubicin (Figure 5D), gemcitabine (Figure 5E) and erlotinib (Figure 5F). Lower RMScore was related to higher IC50 among paclitaxel, sorafenib, 5-flurouracil, gemcitabine, suggesting that patients with low RMScore were less sensitive to these treatments. However, patients with low RMScore had a lower IC50 to erlotinib, suggesting that they may be more sensitive to erlotinib than high RMScore patients. **FIGURE 5:** *Estimated IC50 for paclitaxel (A), sorafenib (B), 5-flurouracil (C), doxorubicin (D), gemcitabine (E) and erlotinib (F) between high- and low-RMScore group.* ## Development of a predictive nomogram To facilitate the clinical applicability and availability of the RMScore, a predictive nomogram for 1-, 3-, 5-year OS combined with tumor stage and RMScore was developed (Figure 6A). The calibration curves showed that actual overall survival of patients was almost consistent with the predicted overall survival of patients (Figure 6B). The DCA plot suggested that the nomogram had a superior efficiency than RMScore in predicting patient survival outcomes (Figure 6C). The AUCs of the nomogram in the 1-, 3-, and 5-year ROC curves were 0.750, 0.743 and 0.735, respectively (Figure 6D). **FIGURE 6:** *Construction of a nomogram by clinicopathological factors and RMScore. (A) Prognostic nomogram based on RMScore and tumor stage. (B) The 1-, 3-, and 5-year calibration plots of the nomogram. (C) DCA curves of the nomogram and other clinicopathological parameters. (D) The 1-, 3-, and 5-year ROC curves of the nomogram.* ## Discussion The current interventions for HCC are not satisfactory, and more precise prognostic indicator and promising strategies need to be explored. Recent studies point out that RNA modifiers play vital roles in HCC cell proliferation, exacerbation, and metastasis (Zhu et al., 2021). However, these studies are mainly limited to several RNA modification regulators. The function of other kinds of RNA modification regulators and the interaction with each other remain to be elucidated. Here, we systematically analyzed the expression, mutation, and the prognostic value of a total of one hundred RNA modifiers from eight types of RNA modifications in HCC. We found that most of the RNA modifiers were highly expressed in the tumor tissues compared to normal tissues. At the genetic level, nearly half of genes showed a least one kind of mutation. Most of the mutations were positively interacted with each other. These results suggested that eight types of RNA modifications might play important role in HCC. Two RNA modification patterns with distinct prognosis were identified in HCC patients. Then, we constructed RMScore to calculate the modification status of HCC patient. Patients with low RMScore showed better prognosis than patients with high RMScore. Similar to the pathway enrichment results in Cluster 1, pathway related to the cell cycle and proliferation, such as “DNA repair,” “G2M checkpoint,” “E2F targets” were all significantly enriched in the low RMScore group. All these results implied that the RMScore could well represent the modification status of the HCC patients. There were some studies which documented different RNA modification patterns in various type of tumors, including HCC. For example, Qi et al. identified two distinct m6A modification patterns, one of which showed higher expression of m6A modification regulators and poor prognosis (Qi et al., 2020). Similarly, Li et al. reported two distinct clusters with different prognosis and clinical features based on the expression of 45 m6A/m5C/m1A regulated genes (Li et al., 2022). In addition, RNA modification patterns, especially m6A modification-related patterns, are also reported to have prognostic predictive value in lung cancer (Liu et al., 2020; Zhang et al., 2021), gastric cancer (Zhang et al., 2020), prostate cancer (Liu et al., 2021), kidney renal clear cell carcinoma (Li et al., 2021) and pancreatic adenocarcinoma (Wang et al., 2021b). In the current study, a total of one hundred cancer-related RNA regulators from eight-types of RNA modifications were included. We identified two subgroups with distinct cancer hallmarks, tumor microenvironments, and overall survival. These results showed the importance of other types of RNA modifications in HCC. Finding optimal strategy to select patients most likely to benefit from available chemotherapeutic regimens can facilitate therapeutic decision-making. Recently, RNA modification was proved to be associated with sensitivity to anticancer therapies (Lan et al., 2021). For example, m6A writer METTL3 was documented to regulate sorafenib resistance though FOXO3-mediated autophagy in HCC (Lin et al., 2020). M7G writer WDR4 was demonstrated to play a role in sorafenib resistance by inducing CCNB1 translation in HCC. Herein, we used GDSC database to assess the differences in the sensitivity to various chemotherapeutics between the two RMScore groups. In our study, patients with high RMScore had a lower IC50, implying to be more sensitive to chemotherapy drugs including paclitaxel, sorafenib, 5-flurouracil, doxorubicin, mitomycin C and gemcitabine. The RMScore could be served as a good measure to predict the drug efficacy for clinicians. Several limitations for our study should be recognized. Firstly, there were several cancer-related RNA modification regulators, such as m6Am regulators PCIF1 and METTL4, were not included in our analysis. This may lead to subtle deviations in our results, but does not affect the main conclusions. Secondly, due to the limited sample size, we still need more patients to verify the reliability of our conclusions. Thirdly, the potential mechanism of the role of genes contained in the RMScore was not based on experimental evidence, and required further in vivo and in vitro experiment. Last but not the least, as most of HCC patients were Caucasian (most were Hepatitis C virus-related HCC), whether the RMScore is also applicable to non-*Caucasian is* unknown. However, our study was the first study to give an insight into the mutation and expression of eight types of RNA modifications in HCC. This study paves the way for further investigations of the roles of eight types of RNA modifications in the HCC in the future. ## Data availability statement Publicly available datasets were analyzed in this study. This data can be found here: https://portal.gdc.cancer.gov; https://dcc.icgc.org. ## Author contributions Conception and design: WQ and JZ; Acquisition of data: CJ; Analysis of data: WQ and CJ; Writing the manuscript: WQ and JZ; All authors read and approved the final manuscript. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgene.2023.1127301/full#supplementary-material ## References 1. Barbieri I., Kouzarides T.. **Role of RNA modifications in cancer**. *Nat. Rev. Cancer* (2020) **20** 303-322. DOI: 10.1038/s41568-020-0253-2 2. Boccaletto P., Machnicka M. A., Purta E., Piatkowski P., Baginski B., Wirecki T. K.. **Modomics: A database of RNA modification pathways. 2017 update**. *Nucleic Acids Res.* (2018) **46** D303-D307. DOI: 10.1093/nar/gkx1030 3. Chen M., Wei L., Law C. T., Tsang F. H., Shen J., Cheng C. L.. **RNA N6-methyladenosine methyltransferase-like 3 promotes liver cancer progression through YTHDF2-dependent posttranscriptional silencing of SOCS2**. *Hepatology* (2018) **67** 2254-2270. DOI: 10.1002/hep.29683 4. Chen Y., Zhao Y., Chen J., Peng C., Zhang Y., Tong R.. **ALKBH5 suppresses malignancy of hepatocellular carcinoma via m(6)A-guided epigenetic inhibition of LYPD1**. *Mol. Cancer* (2020) **19** 123. DOI: 10.1186/s12943-020-01239-w 5. Cheng X., Li M., Rao X., Zhang W., Li X., Wang L.. **KIAA1429 regulates the migration and invasion of hepatocellular carcinoma by altering m6A modification of ID2 mRNA**. *Onco Targets Ther.* (2019) **12** 3421-3428. DOI: 10.2147/OTT.S180954 6. Delaunay S., Frye M.. **RNA modifications regulating cell fate in cancer**. *Nat. Cell Biol.* (2019) **21** 552-559. DOI: 10.1038/s41556-019-0319-0 7. Fang Q., Chen H.. **The significance of m6A RNA methylation regulators in predicting the prognosis and clinical course of HBV-related hepatocellular carcinoma**. *Mol. Med.* (2020) **26** 60. DOI: 10.1186/s10020-020-00185-z 8. Frye M., Harada B. T., Behm M., He C.. **RNA modifications modulate gene expression during development**. *Science* (2018) **361** 1346-1349. DOI: 10.1126/science.aau1646 9. Han X., Wang M., Zhao Y. L., Yang Y., Yang Y. G.. **RNA methylations in human cancers**. *Semin. Cancer Biol.* (2021) **75** 97-115. DOI: 10.1016/j.semcancer.2020.11.007 10. Lan Q., Liu P. Y., Bell J. L., Wang J. Y., Huttelmaier S., Zhang X. D.. **The emerging roles of RNA m(6)A methylation and demethylation as critical regulators of tumorigenesis, drug sensitivity, and resistance**. *Cancer Res.* (2021) **81** 3431. DOI: 10.1158/0008-5472.CAN-20-4107 11. Lan T., Li H., Zhang D., Xu L., Liu H., Hao X.. **KIAA1429 contributes to liver cancer progression through N6-methyladenosine-dependent post-transcriptional modification of GATA3**. *Mol. Cancer* (2019) **18** 186. DOI: 10.1186/s12943-019-1106-z 12. Li D., Li K., Zhang W., Yang K. W., Mu D. A., Jiang G. J.. **The m6A/m5C/m1A regulated gene signature predicts the prognosis and correlates with the immune status of hepatocellular carcinoma**. *Front. Immunol.* (2022) **13** 918140. DOI: 10.3389/fimmu.2022.918140 13. Li H., Hu J., Yu A., Othmane B., Guo T., Liu J.. **RNA modification of N6-methyladenosine predicts immune phenotypes and therapeutic opportunities in kidney renal clear cell carcinoma**. *Front. Oncol.* (2021) **11** 642159. DOI: 10.3389/fonc.2021.642159 14. Lin Z., Niu Y., Wan A., Chen D., Liang H., Chen X.. **RNA m(6) A methylation regulates sorafenib resistance in liver cancer through FOXO3-mediated autophagy**. *EMBO J.* (2020) **39** e103181. DOI: 10.15252/embj.2019103181 15. Liu Y., Guo X., Zhao M., Ao H., Leng X., Liu M.. **Contributions and prognostic values of m(6) A RNA methylation regulators in non-small-cell lung cancer**. *J. Cell Physiol.* (2020) **235** 6043-6057. DOI: 10.1002/jcp.29531 16. Liu Z., Zhong J., Zeng J., Duan X., Lu J., Sun X.. **Characterization of the m6A-associated tumor immune microenvironment in prostate cancer to aid immunotherapy**. *Front. Immunol.* (2021) **12** 735170. DOI: 10.3389/fimmu.2021.735170 17. Nombela P., Miguel-Lopez B., Blanco S.. **The role of m(6)A, m(5)C and Psi RNA modifications in cancer: Novel therapeutic opportunities**. *Mol. Cancer* (2021) **20** 18. DOI: 10.1186/s12943-020-01263-w 18. Park E. J., Lee J. H., Yu G. Y., He G., Ali S. R., Holzer R. G.. **Dietary and genetic obesity promote liver inflammation and tumorigenesis by enhancing IL-6 and TNF expression**. *Cell* (2010) **140** 197-208. DOI: 10.1016/j.cell.2009.12.052 19. Qi L. W., Jia J. H., Jiang C. H., Hu J. M.. **Contributions and prognostic values of N6-methyladenosine RNA methylation regulators in hepatocellular carcinoma**. *Front. Genet.* (2020) **11** 614566. DOI: 10.3389/fgene.2020.614566 20. Seehawer M., Heinzmann F., D'Artista L., Harbig J., Roux P. F., Hoenicke L.. **Necroptosis microenvironment directs lineage commitment in liver cancer**. *Nature* (2018) **562** 69-75. DOI: 10.1038/s41586-018-0519-y 21. Shi H., Wei J., He C.. **Where, when, and how: Context-dependent functions of RNA methylation writers, readers, and erasers**. *Mol. Cell* (2019) **74** 640-650. DOI: 10.1016/j.molcel.2019.04.025 22. Shi Q., Xue C., Yuan X., He Y., Yu Z.. **Gene signatures and prognostic values of m1A-related regulatory genes in hepatocellular carcinoma**. *Sci. Rep.* (2020) **10** 15083. DOI: 10.1038/s41598-020-72178-1 23. Sia D., Villanueva A., Friedman S. L., Llovet J. M.. **Liver cancer cell of origin, molecular class, and effects on patient prognosis**. *Gastroenterology* (2017) **152** 745-761. DOI: 10.1053/j.gastro.2016.11.048 24. Sun Z., Xue S., Zhang M., Xu H., Hu X., Chen S.. **Aberrant NSUN2-mediated m(5)C modification of H19 lncRNA is associated with poor differentiation of hepatocellular carcinoma**. *Oncogene* (2020) **39** 6906-6919. DOI: 10.1038/s41388-020-01475-w 25. Sung H., Ferlay J., Siegel R. L., Laversanne M., Soerjomataram I., Jemal A.. **Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries**. *CA Cancer J. Clin.* (2021) **71** 209. DOI: 10.3322/caac.21660 26. Szklarczyk D., Gable A. L., Lyon D., Junge A., Wyder S., Huerta-Cepas J.. **STRING v11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets**. *Nucleic Acids Res.* (2019) **47** D607-D613. DOI: 10.1093/nar/gky1131 27. Wang L., Zhang S., Li H., Xu Y., Wu Q., Shen J.. **Quantification of m6A RNA methylation modulators pattern was a potential biomarker for prognosis and associated with tumor immune microenvironment of pancreatic adenocarcinoma**. *BMC Cancer* (2021) **21** 876. DOI: 10.1186/s12885-021-08550-9 28. Wang Y., Wang J., Li X., Xiong X., Wang J., Zhou Z.. **N(1)-methyladenosine methylation in tRNA drives liver tumourigenesis by regulating cholesterol metabolism**. *Nat. Commun.* (2021) **12** 6314. DOI: 10.1038/s41467-021-26718-6 29. Wei J., He C.. **Chromatin and transcriptional regulation by reversible RNA methylation**. *Curr. Opin. Cell Biol.* (2021) **70** 109-115. DOI: 10.1016/j.ceb.2020.11.005 30. Xu Y., Zhang M., Zhang Q., Yu X., Sun Z., He Y.. **Role of main RNA methylation in hepatocellular carcinoma: N6-Methyladenosine, 5-methylcytosine, and N1-methyladenosine**. *Front. Cell Dev. Biol.* (2021) **9** 767668. DOI: 10.3389/fcell.2021.767668 31. Zhang B., Wu Q., Li B., Wang D., Wang L., Zhou Y. L.. **m(6 A regulator-mediated methylation modification patterns and tumor microenvironment infiltration characterization in gastric cancer**. *Mol. Cancer* (2020) **19** 53. DOI: 10.1186/s12943-020-01170-0 32. Zhang H., Hu J., Liu A., Qu H., Jiang F., Wang C.. **An N6-methyladenosine-related gene set variation score as a prognostic tool for lung adenocarcinoma**. *Front. Cell Dev. Biol.* (2021) **9** 651575. DOI: 10.3389/fcell.2021.651575 33. Zhu L. R., Ni W. J., Cai M., Dai W. T., Zhou H.. **Advances in RNA epigenetic modifications in hepatocellular carcinoma and potential targeted intervention strategies**. *Front. Cell Dev. Biol.* (2021) **9** 777007. DOI: 10.3389/fcell.2021.777007
--- title: The association between neutrophil counts and neutrophil-to-lymphocyte ratio and stress hyperglycemia in patients with acute ischemic stroke according to stroke etiology authors: - Xianjing Feng - Fang Yu - Minping Wei - Yunfang Luo - Tingting Zhao - Zeyu Liu - Qin Huang - Ruxin Tu - Jiaxin Li - Boxin Zhang - Liuyang Cheng - Jian Xia journal: Frontiers in Endocrinology year: 2023 pmcid: PMC10060840 doi: 10.3389/fendo.2023.1117408 license: CC BY 4.0 --- # The association between neutrophil counts and neutrophil-to-lymphocyte ratio and stress hyperglycemia in patients with acute ischemic stroke according to stroke etiology ## Abstract ### Background and purpose Stress hyperglycemia ratio (SHR), which is used to assess stress hyperglycemia, is associated with the functional outcome of ischemic stroke (IS). IS can induce the inflammatory response. Neutrophil counts and neutrophil-to-lymphocyte ratio (NLR) as good and easily available inflammatory biomarkers, the relationship between neutrophil counts and NLR and SHR were poorly explored in IS. We aimed to systemically and comprehensively explore the correlation between various blood inflammation markers (mainly neutrophil counts and NLR) and SHR. ### Methods Data from 487 patients with acute IS(AIS) in Xiangya Hospital were retrospectively reviewed. High/low SHR groups according to the median of SHR (≤1.02 versus >1.02). Binary logistic regression analysis was used to evaluate the correlation between neutrophil counts and NLR and high SHR group. Subgroup analyses were performed in the TOAST classification and functional prognosis. ### Results The neutrophil counts and NLR were all clearly associated with SHR levels in different logistic analysis models. In the subgroup analysis of TOAST classification, the higher neutrophil counts and NLR were the independent risk factors for high SHR patients with large-artery atherosclerosis (LAA) (neutrophil: adjusted OR:2.047, $95\%$ CI: 1.355-3.093, $$P \leq 0.001$$; NLR: adjusted OR:1.315, $95\%$ CI: 1.129-1.530, $P \leq 0.001$). The higher neutrophil counts were the independent risk factor for high SHR patients with cardioembolism (CE) (adjusted OR:2.413, $95\%$ CI: 1.081-5.383, $$P \leq 0.031$$). ROC analysis showed that neutrophil counts was helpful for differentiating high SHR group with CE and low SHR group with CE (neutrophil: AUC =0.776, $$P \leq 0.002$$). However, there were no difference in levels of neutrophil counts and NLR between patients with SVO and without SVO. The higher neutrophil counts and NLR independently associated with high SHR patients with mRS ≤2 at 90 days from symptom onset, (neutrophil: adjusted OR:2.284, $95\%$ CI: 1.525-3.420, $P \leq 0.001$; NLR: adjusted OR:1.377, $95\%$ CI: 1.164-1.629, $P \leq 0.001$), but not in patients with mRS >2. ### Conclusions This study found that the neutrophil counts and NLR are positively associated with SHR levels in AIS patients. In addition, the correlation between neutrophil counts and NLR and different SHR levels are diverse according to TOAST classification and functional prognosis. ## Introduction In recent decades, stroke has been the first leading cause of mortality and disability in China and imposes a substantial burden on family, society, and economy [1]. Ischemic stroke (IS) accounts for ~$81.9\%$ of hospitalizations in all strokes in China [2]. With the acceleration of China’s life expectancy and aging process, incidence of IS shows an increasing trend. Accordingly, how to improve prevention and treatment of IS has been a great concern. Numerous studies have demonstrated that the immunity and inflammation play key roles in stroke [3, 4]. Not only immune cells, but also cytokines and biochemical blood markers are involved in the mechanisms of IS progression. However, in daily practice, the assays of cytokines and immune cells are expensive and not widely available in hospitals. In turn, the assays of whole blood counts, such as white blood cells (WBC), neutrophil counts, and lymphocyte counts, have the advantages of speed, simplicity, and lower cost. In addition, whole blood counts as systemic inflammatory markers, which can provide valuable assessment for inflammatory response. Current studies have shown the important significance of whole blood counts, such as WBC, neutrophil counts, lymphocyte counts, and their combination ratios (neutrophil-lymphocyte ratio [NLR]), as markers of inflammation in AIS (5–7). Moreover, recent evidence also showed higher NLR was a predictor of stroke-associated pneumonia [8]. To our knowledge, almost half of IS patients may have stress hyperglycemia [9]. Previous study found that acute hyperglycemia is an independent risk factor for in-hospital mortality and poor functional outcome after IS [10], regardless of the type of stroke treatment [11, 12], but with significance difference among diabetic and non-diabetic patients [13]. In addition, hyperglycemia promoted the release of proinflammatory factors such as tumor necrosis factor-α(TNF-α) and interleukin-6 (IL-6) in vitro [14]. A number of studies used the stress hyperglycemia ratio (SHR) as a tool to evaluate stress hyperglycemia [15, 16]. Moreover, many studies have been found that SHR is associated with poor prognosis in AIS patients (17–19). Recent evidence revealed that elevated SHR was a clinical predictor of stroke-associated pneumonia [9]. However, up to now, the relationship between SHR and blood routine inflammatory indicators were poorly explored in AIS. This research aimed to systemically and comprehensively explore the correlation between various blood inflammation markers (mainly neutrophil counts and NLR) and SHR. ## Study participants This study included consecutive AIS patients seen between July 2020 and September 2022 in Changsha Xiangya Hospital. AIS was defined by diffusion-weighted imaging (DWI) images. Inclusion criteria: [1] age ≥18 years old, [2] disease onset ≤ 14 days. Exclusion criteria: [1] Patients who no fasting blood glucose (FBG) and glycated hemoglobin (HbA1c), [2] Patients who no WBC, neutrophil counts and lymphocyte counts at admission, [3] Patients with other neurological diseases, [4] liver or renal failure. ## Clinical assessments We collected demographic variables (including age and sex), and vascular risk factors, including systolic blood pressure (SBP), diastolic blood pressure (DBP), history of stroke, history of hypertension, history of diabetes mellitus (DM), history of coronary heart disease (CAD), history of Hyperlipidemia, history of smoking and drinking. The definition of vascular risk factors was the same as the previous studies [20]. Laboratorial findings included WBC, neutrophil, lymphocyte, triglycerides (TG), total cholesterol (TC), low-density lipoprotein (LDL), high-density lipoprotein (HDL), FBG, HbA1c, and homocysteine (Hcy). Stroke severity at admission was assessed with the National Institutes of Health Stroke Scale (NIHSS) score, mild stroke: NIHSS score <6, moderate to severe stroke: NIHSS score of ≥6 [21]. Discharge functional outcome was assessed with a modified Rankin Scale (mRS) score, good functional outcome: mRS score ≤ 2, poor functional outcome: mRS score of >2 [22]. Etiology of ischemic stroke was assessed with Trial of Org 10172 in Acute Stroke Treatment (TOAST) [23]. ## Definition of stress hyperglycemia ratio Fasting venous blood samples were collected within 24 hours after admission, and SHR was calculated from the following formula: FBG (mmol/L)/HbA1c (%) [24]. ## Statistical methods SPSS 26.0, GraphPad Prism 8 and R software version R 4.2.1 were used to analyze statistical data and plot of the data. Categorical and continuous data were showed as counts and percentage (%) and medians [interquartile range (IQR), respectively. The Mann-Whitney U test was used to analyze continuous variables, and chi-squared test was used to analyze for categorical variables. high/low SHR groups according to the median of SHR (≤1.02 versus >1.02). Binary logistic regression models were used to evaluate the differences among WBC, neutrophil counts, lymphocyte counts and NLR with different SHR groups. Three models were used by logistic regression. Spearman rank correlation test was used for correlation analyses among WBC, neutrophil counts, lymphocyte counts, NLR, and SHR. We performed subgroup analysis according to function prognosis at discharge and TOAST classification. Receiver operating characteristic (ROC) curve analysis was used to evaluate the values of neutrophil counts for differentiating high SHR group with CE and low SHR group with CE. Statistical significance was set at a 2-tailed P value <0.05. ## Baseline characteristics A total of 487 AIS patients (male=320($65.7\%$); female=167($34.3\%$); median age=61 years) were enrolled in this study. The median of SHR was 1.02. High SHR group was associated with high frequency of DM, Hyperlipidemia, and higher SBP and DBP values. Simultaneously, High SHR group had higher levels of blood WBC, neutrophil counts, NLR, TG, TC, HDL, LDL, FBG, HbA1c, and lower levels of lymphocyte counts and Hcy levels. High SHR group had higher mRS score (Table 1). **Table 1** | Variables | Total | SHR | SHR.1 | P-value | | --- | --- | --- | --- | --- | | Variables | Total | Low SHR group (n=243) (≤1.02) | High SHR group(n=244) (>1.02) | P-value | | Age, years | 61(53-69) | 61 (53-70) | 60 (54-69) | 0.825 | | Sex (male, N, %) | 320(65.7%) | 163 (67.1%) | 157 (64.3%) | 0.525 | | Stroke, (N, %) | 81(16.6%) | 37 (15.2%) | 44 (18.0%) | 0.406 | | HBP, (N, %) | 375(77.7%) | 181 (74.5%) | 194 (79.5%) | 0.188 | | DM, (N, %) | 168(34.5%) | 63 (25.9%) | 105(43.0%) | P<0.001 | | Hyperlipidemia, (N, %) | 184(37.8%) | 76 (31.3%) | 108 (44.3%) | 0.003 | | CAD, (N, %) | 90(18.5%) | 44 (18.1%) | 46(18.9%) | 0.832 | | Smoking, (N, %) | 235(48.5%) | 120 (49.4%) | 116 (47.5%) | 0.684 | | Drinking, (N, %) | 167(34.3%) | 81 (33.3%) | 86 (35.21%) | 0.657 | | SBP, mmHg | 146.00(133.00-160.00) | 142.00(130.00-154.75) | 150.00 (137.75-166.00) | P<0.001 | | DBP, mmHg | 85.00(76.00-94.00) | 84.00(76.00-93.00) | 86.00(77.00-95.25) | 0.045 | | WBC, ×109/L | 7.00(5.80-8.40) | 6.80 (5.70-8.00) | 7.30 (5.80-9.10) | 0.003 | | Platelet, ×109/L | 204.00(164.00-244.00) | 203.50 (164.25-242.75) | 204.00(126.75-246.00) | 0.909 | | Neutrophil, ×109/L | 4.50(3.60-5.90) | 4.25 (3.50-5.20) | 5.10(3.70-6.70) | P<0.001 | | lymphocyte, ×109/L | 1.60(1.20-2.00) | 1.60(1.30-2.10) | 1.50(1.10-1.90) | 0.001 | | NLR | 2.93(2.00-4.41) | 2.57(1.92-3.59) | 3.32(2.14-5.19) | P<0.001 | | TG, mmol/L | 1.60(1.15-2.24) | 1.47 (1.11-2.05) | 1.75(1.24-2.56) | P<0.001 | | TC, mmol/L | 4.51(3.66-5.39) | 4.24(3.55-5.07) | 4.69 (3.82-5.60) | 0.003 | | HDL, mmol/L | 1.04(0.87-1.23) | 1.00 (0.84-1.17) | 1.07 (0.90-1.28) | 0.001 | | LDL, mmol/L | 2.82(2.25-3.42) | 2.63(2.16-3.25) | 2.96(2371-3.46) | 0.001 | | FBG, mg/dl | 6.22(5.18-8.28) | 5.20(4.75-5.78) | 7.86(6.50-11.39) | P<0.001 | | Homocysteine, µmol/L | 13.57(11.12-16.74) | 13.87 (11.56-16.90) | 12.99 (10.98-16.60) | 0.047 | | HbA1c (%), median (IQR) | 5.90(5.50-7.20) | 5.90(5.50-6.40) | 6.10 (5.60-7.95) | 0.019 | | SHR,median (IQR) | 1.02(0.88-1.26) | 0.88(0.82-0.94) | 1.26(1.10-1.47) | P<0.001 | | NIHSS at admission, median (IQR) | 4(2-7) | 4(2-7) | 4(2-7) | 0.115 | | mRS at discharge, median (IQR) | 2(1-3) | 2(2-3) | 2(2-4) | 0.045 | | TOAST Etiology | | | | 0.565 | | LAA | 287(58.9%) | 137(56.3%) | 150(61.4%) | | | CE | 46(9.4%) | 24(9.8%) | 22(9.0%) | | | SVO | 109(22.4%) | 56(23.0%) | 53(21.7%) | | | SOE | 15(3.1%) | 4(2.8%) | 8(3.2%) | | | SUE | 30(6.2%) | 19(7.8%) | 11(4.5%) | | ## Differences of WBC, neutrophil counts, lymphocyte counts, and NLR between low SHR group and high SHR group There were 243 AIS patients in low SHR group, and 244 AIS patients in high SHR group. WBC, neutrophil counts, lymphocyte counts, and NLR as continuous variables, all the four parameters were independently associated with high SHR group in different logistic analysis models. When the first quartile was regarded as reference, the fourth quartiles of WBC, neutrophil counts, lymphocyte counts, and NLR were significantly associated with high SHR group in different logistic analysis models. In addition, the second and third quartiles of lymphocyte levels were independently associated with high SHR group in different logistic analysis. The third quartiles of NLR independently associated with high SHR group in model1 (Table 2 and Figure 1). To assess the linear association among the WBC, neutrophil counts, lymphocyte counts, and NLR with SHR, we constructed Spearman rank correlation analysis (Supplementary figure 1). Linear positive correlations among the WBC ($r = 0.18$, $p \leq 0.001$), neutrophil counts ($r = 0.230$, $p \leq 0.001$), and NLR ($r = 0.210$, $p \leq 0.001$) with SHR, respectively. The correlation between lymphocyte counts and SHR was not statistically significant (r=-0.087, $$p \leq 0.056$$). The result suggested that the strongest association was observed between neutrophil counts and SHR. ## Subgroup analyses were conducted between neutrophil counts and NLR with different SHR levels Subgroup analyses were performed according to TOAST classification (LAA vs. SVO vs. CE) of ischemic stroke etiology and function prognosis at discharge and 90 days from stroke outset (mRS≤ 2 vs. mRS >2). In the subgroup analysis of TOAST classification, high SHR patients with large-artery atherosclerosis (LAA) had clearly higher levels of neutrophil counts and NLR than low SHR patients with LAA; Multivariable logistic regression analysis showed the higher levels of neutrophil counts and NLR were the independent risk factors for high SHR patients with LAA (neutrophil:adjusted OR:2.047, $95\%$ CI: 1.355-3.093, $$P \leq 0.001$$; NLR: adjusted OR:1.315, $95\%$ CI: 1.129-1.530, $P \leq 0.001$) (Table 3 and Figures 2A, B). Multivariable logistic regression analysis also showed high SHR patients with cardioembolism (CE) had clearly higher neutrophil counts than low SHR patients with CE (adjusted OR:2.413, $95\%$ CI: 1.081-5.383, $$P \leq 0.031$$) (Table 3 and Figure 2C). However, there’s no difference in neutrophil counts and NLR between high SHR patients with small-vessel occlusion (SVO) than low SHR patients with SVO (Table 3). ROC analysis showed that neutrophil counts was helpful for differentiating high SHR group with CE and low SHR group with CE (neutrophil: AUC =0.776, $95\%$CI 0.633-0.919; $$P \leq 0.002$$, specificity 0.750, sensitivity 0.857; optimal cut-off: 4.850, Figure 2D). In the subgroup analysis of discharge function prognosis, there’s no difference in the subgroup analysis of function prognosis. Independently of mRS status, high SHR correlates with higher neutrophil counts and higher NLR (Table 4). **Table 4** | Variables | Unadjusted model | Unadjusted model.1 | Multivariable- Model | Multivariable- Model.1 | | --- | --- | --- | --- | --- | | Variables | OR (95% CI) | p value | OR (95% CI) | p value | | Neutrophil, ×109/L | | | | | | mRS≤2 | 1.172(1.035-1.326) | 0.012 | 1.176(1.029-1.344) | 0.018 | | mRS>2 | 1.413(1.196-1.670) | P<0.001 | 2.096(1.308-3.358) | 0.002 | | NLR | | | | | | mRS≤2 | 1.218(1.07-1.385) | 0.003 | 1.220(1.069-1.323) | 0.003 | | mRS>2 | 1.319(1.148-1.515) | P<0.001 | 1.344(1.132-1.596) | 0.001 | In the subgroup analysis of 90 days function prognosis, the correlation of the high SHR correlates with higher neutrophil counts and higher NLR were dependent on different prognosis. Multivariable logistic regression analysis showed the higher levels of neutrophil counts and NLR as the independent risk factors for High SHR patients with mRS ≤2, (neutrophil: adjusted OR:2.284, $95\%$ CI: 1.525-3.420, $P \leq 0.001$; NLR: adjusted OR:1.377, $95\%$ CI: 1.164-1.629, $P \leq 0.001$), but not in high SHR patients with mRS >2. ( Table 5). **Table 5** | Variables | Unadjusted model | Unadjusted model.1 | Multivariable- Model | Multivariable- Model.1 | | --- | --- | --- | --- | --- | | Variables | OR (95% CI) | p value | OR (95% CI) | p value | | Neutrophil, ×109/L | | | | | | mRS≤2 | 1.313(1.156-1.492) | P<0.001. | 2.284(1.525-3.420) | P<0.001 | | mRS>2 | 1.397(1.097-1.1778) | 0.007 | 1.867 (0.900-3.873) | 0.093 | | NLR | | | | | | mRS≤2 | 1.367(1.181-1.583) | P<0.001 | 1.377(1.164-1.629) | P<0.001 | | mRS>2 | 1.246(1.032-1.504)) | 0.022 | 1.204(0.935-1.552) | 0.150 | ## Discussion The current study is the first study that systemically and comprehensively investigated the correlation between various systemic blood inflammatory factors and stress hyperglycemia. First, our study found that all four parameters of WBC, neutrophil counts, lymphocyte counts, and NLR were independently associated with high SHR group; second, among different stroke etiology patients, in accordance with the TOAST classification, there’s not always a correlation between SHR levels, neutrophil counts and NLR, and neutrophil counts was helpful for differentiating high SHR group with CE and low SHR group with CE; last, the correlation of the high SHR correlates with higher neutrophil counts and higher NLR were dependent on different functional prognosis. In patients with IS, elevated neutrophil counts and NLR predicted poor outcome and stroke recurrence [25]. Consistent with previous studies, higher neutrophil counts and NLR were predictors for worse functional outcome in AIS patients in our study (Supplementary Figure 2). Acute cerebral ischemia triggers the rapid inflammatory reaction. After IS, the integrity of the blood-brain barrier (BBB) is disrupted. Destruction of the BBB promotes the migration of peripheral immune cells to the brain. Neutrophils are rapidly recruited into cerebral tissue. The study found neutrophil recruitment in leptomeninges from 6 h in an animal model of IS, in the cortical basal lamina from 15 h, and in the cerebral parenchyma at 24 h by confocal microscopy in mice and human IS [26]. Neutrophil extracellular traps (NETs) were released by neutrophils, which can promote thrombus formation, exacerbate injury of neurons, foster inflammation, and impair vascular remodeling after IS (27–29). Low lymphocyte counts were demonstrated to have a neuroprotective effect in AIS [30, 31]. NLR is defined by neutrophil counts divided by lymphocyte counts. Previous studies showed that NLR could predict the clinical prognosis, hemorrhagic transformation (HT), and stroke-associated pneumonia in IS patients [8, 32, 33]. The catecholamines, inflammatory cytokines, and IR act synergistically to promote stress hyperglycemia in different diseases [34, 35]. SHR, defined as FBG/HbA1c ratio, was used to represent the state of stress hyperglycemia. SHR was associated with functional outcome, complications, HT, and stroke recurrence in AIS patients [9, 18, 36]. Although the pathogenic mechanisms are not so clear, it was proposed that hyperactivated stress could trigger BBB breakdown, oxidative stress response, inflammation and cytokine release in stroke [19]. Zhao et al. found that high glucose could promote inflammation of endothelial cell by hypoxia-inducible factor-1 alpha signaling pathway [37]. Chronic hyperglycemia can promote oxidative stress and the chronic accumulation of advanced glycation end products (AGEs) [38]. AGEs have been proved that could induce inflammatory activation in different diseases [39], such as AGEs increase interleukin (IL)-6 expression via NF-κB pathways [40]. Although stress hyperglycemia has been shown to correlate with inflammatory cytokines, the assays of cytokines and immune cells are expensive and not widely available in hospitals. In turn, WBC, neutrophil counts, lymphocyte counts, and NLR are available and inexpensive biomarkers from routine laboratory data. Therefore, we explore the correlation between various blood inflammatory markers and different SHR levels. In our study, we found that high SHR levels (SHR>1.02) were clearly associated with higher levels of WBC, neutrophil counts, and NLR. TOAST classification is the widest tool to determine IS etiology, which categorizes ischemic stroke into five etiological subtypes: LAA, CE, SVO, other determined etiology (SOE); and undetermined etiology (SUE), respectively [41]. LAA, which is the most common subtype of IS, is primarily caused by atherosclerosis. Previous studies have found atherosclerosis is a chronic inflammatory disease, which can promote the formation, progression, and rupture of atherosclerotic plaque [42]. Hyperglycemia promotes of formation AGEs, which could induce progression of atherosclerosis via inflammation and oxidative stress response [43]. In our study, we found the higher levels of neutrophil counts and NLR were associated with high SHR levels in the IS patients with LAA. We speculated that stress hyperglycemia may promote the progression of atherosclerosis by activating peripheral blood lymphocytes and neutrophils and disrupting BBB in IS patients with LAA. Previous studies found that CE promoted more inflammatory cytokines release, is the most serious strokes, and has the worst prognosis compared to other IS etiologies [44, 45]. Previous study indicated that inflammation might induce stress hyperglycemia by promoting hepatic gluconeogenesis [35]. In our study, we found the higher levels of neutrophil counts were associated with high SHR levels in the IS patients with CE. We speculated that CE promoted more severe inflammation, and inflammation induced stress hyperglycemia. However, there were no difference in levels of neutrophil counts and NLR between patients with SVO and without SVO. The present study indicated that the association between neutrophil counts and NLR and stress hyperglycemia may be more likely to occur in IS patients with LAA and CE subtypes. This study has several limitations. First, this study was performed in single time point, the association between dynamic changes of blood inflammatory factors and different SHR levels were expected in future. Second, the study was designed to collect clinical data only from Xiangya Hospital, which may result in the selection bias. Third, this study was observational, and the causal relationship cannot be clarified. Thus, prospective, multi-center studies were expected to clarify this relationship. ## Conclusions This study found that the high levels of neutrophil counts and NLR are positively associated with SHR levels in AIS patients. In addition, the correlation between neutrophil counts and NLR and different SHR levels are diverse according to TOAST classification of IS etiology and functional prognosis. ## Data availability statement The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author. ## Ethics statement The studies involving human participants were reviewed and approved by Xiangya Hospital Ethics Committee. The patients/participants provided their written informed consent to participate in this study. ## Author contributions Concept and design: XF. Clinical data: XF, FY, MW, YL, TZ, ZL, QH, RT, JL, BZ, LC. Statistical analyses: XF and FY. Draft manuscript: XF and FY. JX reviewed the manuscript, and contributed to discussions. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2023.1117408/full#supplementary-material ## References 1. Zhou M, Wang H, Zeng X, Yin P, Zhu J, Chen W. **Mortality, morbidity, and risk factors in China and its provinces, 1990-2017: a systematic analysis for the global burden of disease study 2017**. *Lancet (London England)* (2019) **394**. DOI: 10.1016/S0140-6736(19)30427-1 2. Wang Y-J, Li Z-X, Gu H-Q, Zhai Y, Jiang Y, Zhao X-Q. **China Stroke statistics 2019: A report from the national center for healthcare quality management in neurological diseases, China national clinical research center for neurological diseases, the Chinese stroke association, national center for chronic and non-communicable disease control and prevention, Chinese center for disease control and prevention and institute for global neuroscience and stroke collaborations**. *Stroke Vasc Neurol* (2020) **5**. DOI: 10.1136/svn-2021-001374 3. Henein MY, Vancheri S, Longo G, Vancheri F. **The role of inflammation in cardiovascular disease**. *Int J Mol Sci* (2022) **23** 12906. DOI: 10.3390/ijms232112906 4. Pawluk H, Kołodziejska R, Grześk G, Kozakiewicz M, Woźniak A, Pawluk M. **Selected mediators of inflammation in patients with acute ischemic stroke**. *Int J Mol Sci* (2022) **23** 10614. DOI: 10.3390/ijms231810614 5. Shi J, Peng H, You S, Liu Y, Xu J, Xu Y. **Increase in neutrophils after recombinant tissue plasminogen activator thrombolysis predicts poor functional outcome of ischaemic stroke: a longitudinal study**. *Eur J Neurol* (2018) **25** 687-e45. DOI: 10.1111/ene.13575 6. Herz J, Sabellek P, Lane TE, Gunzer M, Hermann DM, Doeppner TR. **Role of neutrophils in exacerbation of brain injury after focal cerebral ischemia in hyperlipidemic mice**. *Stroke* (2015) **46**. DOI: 10.1161/STROKEAHA.115.010620 7. Maestrini I, Strbian D, Gautier S, Haapaniemi E, Moulin S, Sairanen T. **Higher neutrophil counts before thrombolysis for cerebral ischemia predict worse outcomes**. *Neurology* (2015) **85**. DOI: 10.1212/WNL.0000000000002029 8. Nam K-W, Kim TJ, Lee JS, Kwon H-M, Lee Y-S, Ko S-B. **High neutrophil-to-Lymphocyte ratio predicts stroke-associated pneumonia**. *Stroke* (2018) **49**. DOI: 10.1161/STROKEAHA.118.021228 9. Tao J, Hu Z, Lou F, Wu J, Wu Z, Yang S. **Higher stress hyperglycemia ratio is associated with a higher risk of stroke-associated pneumonia**. *Front In Nutr* (2022) **9**. DOI: 10.3389/fnut.2022.784114 10. Capes SE, Hunt D, Malmberg K, Pathak P, Gerstein HC. **Stress hyperglycemia and prognosis of stroke in nondiabetic and diabetic patients: a systematic overview**. *Stroke* (2001) **32**. DOI: 10.1161/hs1001.096194 11. Merlino G, Smeralda C, Gigli GL, Lorenzut S, Pez S, Surcinelli A. **Stress hyperglycemia is predictive of worse outcome in patients with acute ischemic stroke undergoing intravenous thrombolysis**. *J Thromb Thrombolysis* (2021) **51**. DOI: 10.1007/s11239-020-02252-y 12. Merlino G, Pez S, Gigli GL, Sponza M, Lorenzut S, Surcinelli A. **Stress hyperglycemia in patients with acute ischemic stroke due to Large vessel occlusion undergoing mechanical thrombectomy**. *Front In Neurol* (2021) **12**. DOI: 10.3389/fneur.2021.725002 13. Merlino G, Pez S, Tereshko Y, Gigli GL, Lorenzut S, Surcinelli A. **Stress hyperglycemia does not affect clinical outcome of diabetic patients receiving intravenous thrombolysis for acute ischemic stroke**. *Front In Neurol* (2022) **13**. DOI: 10.3389/fneur.2022.903987 14. Morohoshi M, Fujisawa K, Uchimura I, Numano F. **Glucose-dependent interleukin 6 and tumor necrosis factor production by human peripheral blood monocytes vitro**. *Diabetes* (1996) **45**. DOI: 10.2337/diab.45.7.954 15. Yang Y, Kim T-H, Yoon K-H, Chung WS, Ahn Y, Jeong M-H. **The stress hyperglycemia ratio, an index of relative hyperglycemia, as a predictor of clinical outcomes after percutaneous coronary intervention**. *Int J Cardiol* (2017) **241** 57-63. DOI: 10.1016/j.ijcard.2017.02.065 16. Yang C-J, Liao W-I, Wang J-C, Tsai C-L, Lee J-T, Peng G-S. **Usefulness of glycated hemoglobin A1c-based adjusted glycemic variables in diabetic patients presenting with acute ischemic stroke**. *Am J Emergency Med* (2017) **35**. DOI: 10.1016/j.ajem.2017.03.049 17. Gu M, Fan J, Xu P, Xiao L, Wang J, Li M. **Effects of perioperative glycemic indicators on outcomes of endovascular treatment for vertebrobasilar artery occlusion**. *Front In Endocrinol* (2022) **13**. DOI: 10.3389/fendo.2022.1000030 18. Huang Y-W, Yin X-S, Li Z-P. **Association of the stress hyperglycemia ratio and clinical outcomes in patients with stroke: A systematic review and meta-analysis**. *Front In Neurol* (2022) **13**. DOI: 10.3389/fneur.2022.999536 19. Mi D, Li Z, Gu H, Jiang Y, Zhao X, Wang Y. **Stress hyperglycemia is associated with in-hospital mortality in patients with diabetes and acute ischemic stroke**. *CNS Neurosci Ther* (2022) **28**. DOI: 10.1111/cns.13764 20. Feng X, Yu F, Zhou X, Liu Z, Liao D, Huang Q. **MMP9 rs17576 is simultaneously correlated with symptomatic intracranial atherosclerotic stenosis and white matter hyperintensities in Chinese population**. *Cerebrovascular Dis (Basel Switzerland)* (2021) **50**. DOI: 10.1159/000511582 21. Wu C, Xue F, Lian Y, Zhang J, Wu D, Xie N. **Relationship between elevated plasma trimethylamine n-oxide levels and increased stroke injury**. *Neurology* (2020) **94**. DOI: 10.1212/WNL.0000000000008862 22. Schwedhelm E, Schwieren L, Tiedt S, von Lucadou M, Gloyer N-O, Böger R. **Serum sphingosine-1-Phosphate levels are associated with severity and outcome in patients with cerebral ischemia**. *Stroke* (2021) **52**. DOI: 10.1161/STROKEAHA.120.033414 23. Adams HP, Bendixen BH, Kappelle LJ, Biller J, Love BB, Gordon DL. **Classification of subtype of acute ischemic stroke. definitions for use in a multicenter clinical trial. TOAST. trial of org 10172 in acute stroke treatment**. *Stroke* (1993) **24** 35-41. DOI: 10.1161/01.str.24.1.35 24. Deng Y, Wu S, Liu J, Liu M, Wang L, Wan J. **The stress hyperglycemia ratio is associated with the development of cerebral edema and poor functional outcome in patients with acute cerebral infarction**. *Front In Aging Neurosci* (2022) **14**. DOI: 10.3389/fnagi.2022.936862 25. Zhu B, Pan Y, Jing J, Meng X, Zhao X, Liu L. **Neutrophil counts, neutrophil ratio, and new stroke in minor ischemic stroke or TIA**. *Neurology* (2018) **90**. DOI: 10.1212/WNL.0000000000005554 26. Perez-de-Puig I, Miró-Mur F, Ferrer-Ferrer M, Gelpi E, Pedragosa J, Justicia C. **Neutrophil recruitment to the brain in mouse and human ischemic stroke**. *Acta Neuropathologica* (2015) **129**. DOI: 10.1007/s00401-014-1381-0 27. Kang L, Yu H, Yang X, Zhu Y, Bai X, Wang R. **Neutrophil extracellular traps released by neutrophils impair revascularization and vascular remodeling after stroke**. *Nat Commun* (2020) **11** 2488. DOI: 10.1038/s41467-020-16191-y 28. Li C, Xing Y, Zhang Y, Hua Y, Hu J, Bai Y. **Neutrophil extracellular traps exacerbate ischemic brain damage**. *Mol Neurobiol* (2022) **59**. DOI: 10.1007/s12035-021-02635-z 29. Laridan E, Denorme F, Desender L, François O, Andersson T, Deckmyn H. **Neutrophil extracellular traps in ischemic stroke thrombi**. *Ann Neurol* (2017) **82**. DOI: 10.1002/ana.24993 30. Macrez R, Ali C, Toutirais O, Le Mauff B, Defer G, Dirnagl U. **Stroke and the immune system: from pathophysiology to new therapeutic strategies**. *Lancet Neurol* (2011) **10**. DOI: 10.1016/S1474-4422(11)70066-7 31. Xiao J, Qiu Q-W, Qin C, Tao R, Qiao S-Y, Chen M. **Dynamic changes of peripheral blood lymphocyte subsets in acute ischemic stroke and prognostic value**. *Brain Behav* (2021) **11** e01919. DOI: 10.1002/brb3.1919 32. Goyal N, Tsivgoulis G, Chang JJ, Malhotra K, Pandhi A, Ishfaq MF. **Admission neutrophil-to-Lymphocyte ratio as a prognostic biomarker of outcomes in Large vessel occlusion strokes**. *Stroke* (2018) **49**. DOI: 10.1161/STROKEAHA 33. Song Q, Pan R, Jin Y, Wang Y, Cheng Y, Liu J. **Lymphocyte-to-monocyte ratio and risk of hemorrhagic transformation in patients with acute ischemic stroke**. *Neurological Sciences: Off J Ital Neurological Soc Ital Soc Clin Neurophysiol* (2020) **41**. DOI: 10.1007/s10072-020-04355-z 34. Marik PE, Bellomo R. **Stress hyperglycemia: an essential survival response**. *Crit Care (London England)* (2013) **17** 305. DOI: 10.1097/CCM.0b013e318283d124 35. Dungan KM, Braithwaite SS, Preiser J-C. **Stress hyperglycaemia**. *Lancet (London England)* (2009) **373**. DOI: 10.1016/S0140-6736(09)60553-5 36. Yuan C, Chen S, Ruan Y, Liu Y, Cheng H, Zeng Y. **The stress hyperglycemia ratio is associated with hemorrhagic transformation in patients with acute ischemic stroke**. *Clin Interventions In Aging* (2021) **16**. DOI: 10.2147/CIA.S280808 37. Zhao M, Wang S, Zuo A, Zhang J, Wen W, Jiang W. **HIF-1α/JMJD1A signaling regulates inflammation and oxidative stress following hyperglycemia and hypoxia-induced vascular cell injury**. *Cell Mol Biol Lett* (2021) **26** 40. DOI: 10.1186/s11658-021-00283-8 38. Volpe CMO, Villar-Delfino PH, Dos Anjos PMF, Nogueira-Machado JA. **Cellular death, reactive oxygen species (ROS) and diabetic complications**. *Cell Death Dis* (2018) **9** 119. DOI: 10.1038/s41419-017-0135-z 39. Byun K, Yoo Y, Son M, Lee J, Jeong G-B, Park YM. **Advanced glycation end-products produced systemically and by macrophages: A common contributor to inflammation and degenerative diseases**. *Pharmacol Ther* (2017) **177** 44-55. DOI: 10.1016/j.pharmthera.2017.02.030 40. Nonaka K, Kajiura Y, Bando M, Sakamoto E, Inagaki Y, Lew JH. **Advanced glycation end-products increase IL-6 and ICAM-1 expression**. *J Periodontal Res* (2018) **53**. DOI: 10.1111/jre.12518 41. Zhang H, Li Z, Dai Y, Guo E, Zhang C, Wang Y. **Ischaemic stroke etiological classification system: the agreement analysis of CISS, SPARKLE and TOAST**. *Stroke Vasc Neurol* (2019) **4**. DOI: 10.1136/svn-2018-000226 42. Zhu Y, Xian X, Wang Z, Bi Y, Chen Q, Han X. **Research progress on the relationship between atherosclerosis and inflammation**. *Biomolecules* (2018) **8** 80. DOI: 10.3390/biom8030080 43. Yuan T, Yang T, Chen H, Fu D, Hu Y, Wang J. **New insights into oxidative stress and inflammation during diabetes mellitus-accelerated atherosclerosis**. *Redox Biol* (2019) **20**. DOI: 10.1016/j.redox.2018.09.025 44. Fuentes B, Ntaios G, Putaala J. **Editorial: Current challenges in cardioembolic stroke**. *Front In Neurol* (2021) **12**. DOI: 10.3389/fneur.2021.688371 45. Yuan T, Yang T, Chen H, Fu D, Hu Y, Wang J. **Neuroinflammatory mechanisms in ischemic stroke: Focus on cardioembolic stroke, background, and therapeutic approaches**. *Int J Mol Sci* (2020) **21** 6454. DOI: 10.3390/ijms21186454
--- title: Brain system segregation and pain catastrophizing in chronic pain progression authors: - Selma Delgado-Gallén - MD Soler - María Cabello-Toscano - Kilian Abellaneda-Pérez - Javier Solana-Sánchez - Goretti España-Irla - Alba Roca-Ventura - David Bartrés-Faz - Josep M. Tormos - Alvaro Pascual-Leone - Gabriele Cattaneo journal: Frontiers in Neuroscience year: 2023 pmcid: PMC10060861 doi: 10.3389/fnins.2023.1148176 license: CC BY 4.0 --- # Brain system segregation and pain catastrophizing in chronic pain progression ## Abstract Pain processing involves emotional and cognitive factors that can modify pain perception. Increasing evidence suggests that pain catastrophizing (PC) is implicated, through pain-related self-thoughts, in the maladaptive plastic changes related to the maintenance of chronic pain (CP). Functional magnetic resonance imaging (fMRI) studies have shown an association between CP and two main networks: default mode (DMN) and dorsoattentional (DAN). Brain system segregation degree (SyS), an fMRI framework used to quantify the extent to which functional networks are segregated from each other, is associated with cognitive abilities in both healthy individuals and neurological patients. We hypothesized that individuals suffering from CP would show worst health-related status compared to healthy individuals and that, within CP individuals, longitudinal changes in pain experience (pain intensity and affective interference), could be predicted by SyS and PC subdomains (rumination, magnification, and helplessness). To assess the longitudinal progression of CP, two pain surveys were taken before and after an in-person assessment (physical evaluation and fMRI). We first compared the sociodemographic, health-related, and SyS data in the whole sample (no pain and pain groups). Secondly, we ran linear regression and a moderation model only in the pain group, to see the predictive and moderator values of PC and SyS in pain progression. From our sample of 347 individuals (mean age = 53.84, $55.2\%$ women), 133 responded to having CP, and 214 denied having CP. When comparing groups, results showed significant differences in health-related questionnaires, but no differences in SyS. Within the pain group, helplessness (β = 0.325; $$p \leq 0.003$$), higher DMN (β = 0.193; $$p \leq 0.037$$), and lower DAN segregation (β = 0.215; $$p \leq 0.014$$) were strongly associated with a worsening in pain experience over time. Moreover, helplessness moderated the association between DMN segregation and pain experience progression ($$p \leq 0.003$$). Our findings indicate that the efficient functioning of these networks and catastrophizing could be used as predictors of pain progression, bringing new light to the influence of the interplay between psychological aspects and brain networks. Consequently, approaches focusing on these factors could minimize the impact on daily life activities. ## 1. Introduction Chronic pain (CP) is an unpleasant sensory and emotional experience associated with negative cognitive and emotional aspects like the feeling of unpleasantness, pain catastrophizing (PC), and decreased physical and psychological functioning across the lifespan (Dahlhamer, 2018). This prolonged pain experience encompasses pain intensity (i.e., how much a patient is in pain) and pain affection aspects, the degree of emotional arousal caused by the sensory experience of pain (Haefeli and Elfering, 2006). These aspects influence pain perception and modulation, and vice-versa (Wiech, 2016, 2018; Opdebeeck et al., 2018; Wiech and Shriver, 2018; Delgado-Gallén et al., 2021), resulting in large inter-individual differences in terms of pain assessment and treatment. Thus, in the past decades, PC and functional brain connectivity (FC) has been shown to play an important role in understanding pain experience, its chronification, and treatment response. Pain catastrophizing, intended as the tendency to magnify and ruminate about pain, boasts attentional biases to threatening aspects of painful experience (Gilliam et al., 2017) and, consequently, impacts self-reported pain intensity and pain-related psychological aspects (Jensen et al., 2017; Suso-Ribera et al., 2017; Häggman-Henrikson et al., 2021). Therefore, the combination of PC and unpleasantness can lead to suffering, anger, fear, frustration, or anxiety, having an important role in adjustment to CP (Ziadni et al., 2018) and pain management (Gilliam et al., 2017). In this line, it has been suggested that the relationship between pain and depressed mood is mediated by PC (Dong et al., 2020). In addition, studies exploring the differential relationships among individual components of PC (helplessness, magnification, and rumination) and pain outcomes (Müller, 2011; Craner et al., 2016; Stensland, 2021), indicated that catastrophic thoughts and behavior are as well linked with pain co-morbidities as sleep (Abeler et al., 2020), mental health (Dong et al., 2020) and physical activity. Furthermore, the predictive value of PC in pain evolution has been found in several studies. For instance, high levels of PC predicted a decrease in physical activity due to sedentary behavior (Zhaoyang et al., 2020), or higher levels of acute or persistent pain after surgery for knee osteoarthritis (Burns et al., 2015). Also, the neural correlates of these phenomena have been explored in multiple studies. Altered brain connectivity in pain disorders in task-oriented and resting-state functional magnetic resonance imaging (rs-fMRI) has been shown altered in the presence of acute and CP (Baliki et al., 2014; Kucyi et al., 2014; Yu et al., 2014; Hemington et al., 2016; Pfannmöller and Lotze, 2019; Spisak et al., 2019; You et al., 2021; De Ridder et al., 2022; Solé-Padullés et al., 2022). Crucially it has been suggested that two specific main large-scale brain networks are related to altered pain processing and pain chronification: the dorsoattentional network (DAN), and the default mode network (DMN) (Spreng et al., 2013; Baliki et al., 2014; Becerra et al., 2014; Kucyi et al., 2014; Kilpatrick et al., 2015; Seidler et al., 2015; Franzmeier et al., 2017; Kim et al., 2019; van Ettinger-Veenstra et al., 2019; Jones et al., 2020; De Ridder et al., 2022). DAN network is prominently involved in goal-directed attention and top-down selection of stimuli and responses, interacting dynamically with the salience network (SN) and control executive network. Concretely, DAN is responsible for the maintenance of spatial priority maps for covert spatial attention, saccade planning, and visual working memory (Vossel et al., 2014). In CP patients it has been found that the functional connectivity of this network, especially concerning its connectivity with other large-scale networks, is consistently altered (Coppola et al., 2019; Mao et al., 2022), and could normalize after pain therapy (Yoshino et al., 2018). On the other hand, the DMN, which controls self-representational processing, is normally deactivated during task or stimulus exposure, but not in CP patients (Legrain et al., 2009; Seminowicz et al., 2011; Kucyi et al., 2014; De Ridder et al., 2022), exhibiting abnormal DMN resting-state functional connectivity (rs-FC). Alteration in the connectivity of this network has been coupled to several pain types (e.g., low back pain, complex regional pain syndrome (CRPS), chronic widespread pain, or osteoarthritic pain), potentially explaining why pain becomes an integral part of the self. Finally, DMN has been also consistently associated with pain anticipation, pain intensity, and PC (Napadow et al., 2010; Ter Minassian et al., 2012; Loggia et al., 2013; Kucyi et al., 2014; De Ridder et al., 2022). In this study we aim to use a novel approach in the study of the relationship between CP, PC, and brain networks functional connectivity, using rs-fMRI combined with graph theory methods (i.e., the topological organization of brain networks) (Wang et al., 2010; Ewers et al., 2021). Concretely we will study networks system segregation (SyS) (Wig, 2017), a paradigm based on the idea that effective network functioning is supported by maintaining subnetworks’ segregation while simultaneously allowing integration between them (Wig, 2017), resulting in a brain more adaptable to task demands. Brain networks’ segregation degree has been previously associated with cognitive abilities in healthy adults and patients affected by neurologic diseases (van den Heuvel and Sporns, 2013; Malagurski et al., 2020; Ewers et al., 2021; Riedel et al., 2021). From our knowledge, this is the first study that explores how SyS, the ratio of rs-FC within brain networks and their connection with the rest of the cortex, could be related to the presence of pain, and the potential role of SyS and pain-related psychological factors in longitudinal changes in the pain experience. ## 2.1. Participants and study design This study was performed in the framework of the Barcelona Brain Health Initiative (BBHI) (Cattaneo et al., 2018, 2020), an ongoing prospective longitudinal study that started in 2017 to identify lifestyle factors and biological mechanisms underlying good brain health in middle-aged adults (40–65 years). Between 2018 and 2021 participants underwent an online self-assessment (through the BBHI web-based platform) of sociodemographic characteristics, mental health (MH), quality of life (QoL), and self-perceived cognitive concerns (see Figure 1 for more details). Between 2019 and 2020 they answered the baseline pain questionnaire (T1), and from 2020 to 2021 the second one (T2). **FIGURE 1:** *Flowchart of the selection and distribution of patients in this study. Chart illustrating how participants were asked to fill questionnaires (T1 and T2) and undertake an MRI (T1′), and then formed the pain group (YES-YES) and the no pain group (NO-NO). Those participants who did not maintain pain on both questionnaires were not included on this study (YES-NO, NO-YES). Mean time between first questionnaire (T1) and the MRI (T1′) was approximately 4 months, and between the MRI and the second questionnaire (T2), 14 months. *Two participants were excluded from the no pain group for having excessive motion (at least the 50% of the between scans movements were above a 0.5 mm threshold).* Parallelly, between May 2018 and February 2021 (T1′), participants performed an in-person assessment, including a medical exam and MRI. The same in-person evaluation protocol was applied to all participants. Online questionnaires and in-person assessments were not paralleled in time and were not equal for all participants. The time gap between T1 and T1′ was approximately 4 months, and between T1′ and T2, 14 months. However, the minimum distance between questionnaires, and between the fMRI scan and T2 was fixed at 3 months to leave room for a reliable pain progression and assure that the criteria for the diagnosis of CP (3 months) were satisfied in each questionnaire independently from the other. Participants with CP were excluded if [1] pain was due to cancer, fracture, infection, or diagnosis of neurologic or psychiatric disease, [2] made active substance abuse disorder in the past 2 years, [3] uses of prescription opioids exceeding 60 mg morphine equivalents per day, [4] were not suitable for MRI scan and [5] they had excessive head motion during the MRI (at least in the $50\%$ of the scans movements were above a 0.5 mm threshold). From our sample, 450 participants completed online and in-person assessments (see Figure 1): 133 consistently answered having CP (see below for a CP definition) that persisted for at least 3 months in both questionnaires, 216 without CP in any time-point (where 2 participants were excluded for excessive head motion during MRI), 39 participants that developed CP (that is, they first answered not having CP, and in the second questionnaire they did), and 62 that recovered from a CP (i.e., they started with CP and then they reported not having pain anymore). Considering our principal aim to study pain chronification and its maintenance, the present work included 347 volunteers (133 with CP and 214 without CP). The protocol was approved by the “Comité d’Ètica i Investigació Clínica de la Unió Catalana d’Hospitals” and was carried out following the Declaration of Helsinki (World Medical Association, 2013). Written informed consent was obtained from all participants before inclusion in the study. ## 2.2.1. Pain: Clinical symptoms Participants were screened online for CP, which was considered the pain that persists or recurs for > 3 months according to the International Association for the Study of Pain (IASP) criteria (Treede et al., 2015). Only those participants who answered to have recurring or persisting pain underwent the posterior pain assessment explained below. Thus, the pain group in this study was formed by participants who answered to have pain during at least 3 months in both questionnaires (T1 and T2), while participants who had not CP in either of the questionnaires were included in the no pain group. Non-recurrent or non-persistent pain (i.e., acute pain) was not assessed in this study, neither in healthy nor in CP subjects. The Brief Pain Inventory-Short Form (BPI-SF) (Keller et al., 2004) was used to assess the intensity and severity of pain and pain onset. Validation studies among CP patients and the published Spanish translation demonstrated good psychometric properties (Badia et al., 2003). Pain intensity was estimated considering the mean of pain intensity in the last week, considering that asking for a short past period (i.e., 1 week) is more reliable than asking for “current” pain (Haefeli and Elfering, 2006). Pain interference was estimated through seven domains divided into two subdimensions (with the arithmetic mean): affective (relations with others, enjoyment of life, sleep, and mood) and activity (walking, general activity, and work), according to the BPI user guide (Cleeland and Ryan, 1991). Although sleep is seen as a third domain in some studies, we used it in the affective domain (Miettinen et al., 2019). Questions about the number of pain sites (one, two, three, or four or more pain sites), and pain medication (non-steroidal anti-inflammatory, anti-migraines…) were added to the questionnaire. The first and second assessments were conducted during 2019–2020 (T1) and 2020–2021 (T2), respectively, (approximately 10 months between both, see Figure 1). ## 2.2.2. Pain experience over time: Intensity and affective interference To calculate the longitudinal progression of the pain experience, we calculated the mean between pain intensity and pain affective interference at each time point: Then we subtracted the numerical value of the first assessment from the second assessment, resulting in a worsening if the final score was positive or an amelioration if it was negative. ## 2.2.3. Pain catastrophizing Pain catastrophizing can be understood as an expansion of maladaptive cognitive response during actual or perceived painful stimuli, comprising negative cognitive and emotional processes. The PC scale is a multidimensional construct that encompasses elements of rumination, magnification, and helplessness. It can be computed by summing responses to all 13 items, and uses a 5-point scale, ranging from 0 (not at all) to 4 (all the time). The total scale score ranges from 0 to 52, with higher scores representing greater catastrophic thinking. Participants are asked to indicate the degree to which they experienced each of the 13 thoughts or feelings when experiencing pain. This scale has been demonstrated to have good psychometric proprieties (Sullivan et al., 1995; García Campayo et al., 2008; Olmedilla Zafra et al., 2013) and it has been validated in the Spanish language. ## 2.2.4. Health-related questionnaires We employed the ultra-brief self-reported Patient Health Questionnaire-4 (PHQ-4) to assess MH. The PHQ-4 had four items asking about mood disorder symptoms (two items for depression and the other two items for anxiety) in the past 2 weeks. All items were rated on a four-point scale ranging from 0 (not at all) to 3 (nearly every day). The published Spanish translation also demonstrated good psychometric properties in a validation study (Kocalevent et al., 2014). To assess the QoL, we used WHO-QoL-AGE, a 13-item questionnaire scored on a 5-point Likert scale. This questionnaire attempts to assess satisfaction with one’s life, living place, general health (i.e., hearing, vision…), daily activities, personal relationships, goal achievements, or economic status (The Whoqol Group, 1998). The published Spanish translation has good psychometric data in a validation study (Lucas-Carrasco, 2012). We used PROMIS® Cognitive Abilities and Cognitive Concerns scales to assess cognitive troubles. This scale consists of 12-item, extracted from the PROMIS item bank, measuring self-reported cognitive deficits in memory, working memory, attention, processing speed, or cognitive flexibility (Fieo et al., 2016). Each item asked participants to answer “within the last 7 days” using five response options. ## 2.3.1. MRI acquisition parameters Magnetic resonance imaging data were acquired in a 3T Siemens scanner (MAGNETOM Prisma) (Siemens Healthcare GmbH, Erlangen, Germany) with a 32-channel head coil, at the Unitat d’Imatge per Ressonància Magnètica IDIBAPS (Institut d’Investigacions Biomèdiques August Pi i Sunyer) at Hospital Clínic de Barcelona, Barcelona. MRI session included accelerated multi-band sequences adapted from the Human Connectome Project and provided by the Center of Magnetic Resonance Research at the University of Minnesota. For all participants, a high-resolution T1-weighted structural image was obtained with a magnetization-prepared rapid acquisition gradient-echo (MPRAGE) three-dimensional protocol, and a total of 208 contiguous axial slices were obtained in ascending fashion [repetition time (TR) = 2,400 ms, echo time (TE) = 2.22 ms, inversion time = 1,000 ms, flip angle = 8°, a field of view (FOV) = 256 mm and 0.8 mm isotropic voxel]. Additionally, a high-resolution 3D SPC T2 weighted acquisition was undertaken (TR = 3,200 ms, TE = 563 ms, flip angle = 120°, 0.8 mm isotropic voxel, FOV = 256 mm). In the same session, they also underwent rs-fMRI multiband (anterior-posterior phase-encoding; acceleration factor = 8) interleaved acquisitions (T2*weighted EPI scans, TR = 800 ms, TE = 37 ms, 750 volumes, 72 slices, slice thickness = 2 mm, FOV = 208 mm). All the MRI images were examined by a senior neuroradiologist (NB) to detect any clinically significant pathology (none found). Then, all the acquisitions were visually inspected before analysis (MC-T and LM-P.) to ensure that they did not contain MRI artifacts or excessive motion. ## 2.3.2. MRI preprocessing The rs-fMRI preprocessing pipeline comprised spatial standardization and nuisance correction by making use of functions from FMRIB Software Library (FSL; version 5.0.11)1, FreeSurfer (version 6.0)2 and Statistical Parametric Mapping (SPM12).3 To start with, the first 10 scans were removed to ensure magnetization equilibrium. After that, all images were field inhomogeneity corrected (FSL topup tool), all scans realigned to a reference image (FSL MCFLIRT), and then standardized into native T1-weighted space (SPM Coregister). Finally, normalization (SPM Normalize) of all fMRI images to Montreal Neuroscience Institute (MNI152) standard space was performed to ensure among-subjects comparability. As for nuisance correction, different components were defined and manually removed from the rs-fMRI images by the “fsl_regfilt” tool implemented in FSL. These components correspond to [1] motion regressors of rotation, translation, and their derivatives, as estimated during scans’ realignment, [2] a drift estimated by a discrete cosine transform (DCT) as a low-pass frequency filter (< 0.01), and [3] signals from white matter (WM) and cerebrospinal fluid (CSF). To extract these, CSF and WM masks were obtained from automatic subcortical segmentation of brain volume, based on the existence of an atlas containing probabilistic information on the location of structures (Fischl et al., 2002). This step was part of the FreeSurfer “recon-all” processing stream, which was run with default parameters, except for the addition of the T2 flag for the improvement of pial surfaces reconstruction. Both T1-w and T2-w images were used for processing anatomical information. As head movement may affect rs-fMRI results (Van Dijk et al., 2012; Power et al., 2013, 2014, 2015), the in-scanner head motion was considered. In this study, the framewise displacement (FWD) mean was calculated for every subject. FWD was computed as in Power et al. [ 2012], using the vectors of rotation and translation estimated during scans’ realignment as part of the preprocessing pipeline (Power et al., 2012). ## 2.3.3. Functional magnetic resonance imaging: System segregation A node-based approach was adopted to quantify subject rs-FC and SyS of seven resting-state networks (RSN) as defined in the Yeo atlas (Thomas Yeo et al., 2011). These functional connectivity measures have been previously used to study brain networks implicated in pain progression (Kastrati et al., 2022; Lee et al., 2022). To increase Yeo-atlas spatial resolution and precision at rs-FC computation, the 100 nodes, and 7 networks Schaeffer-Yeo atlas was used (Figure 2; Schaefer et al., 2018).4 Per each of the 100 regions of interest (ROIs), a BOLD signal was extracted and averaged across all voxels falling within an ROI. Then, ROI-to-ROI rs-FCs were computed as Pearson-Moment correlations, and subsequently, Fisher-z transformed. Negative values were set to zero and autocorrelations were not considered in the further computation. **FIGURE 2:** *Nodes of the seven networks. Nodes of each of the studied networks based on the Schaffer-Yeo atlas of 100 regions of interest and 7 networks.* System segregation (SyS) is a graph theory metric to quantify the extent to which major functional networks are segregated from each other (see Figure 3). As expressed in **FIGURE 3:** *Nodes of the seven networks. A node-based approach was adopted to quantify subject rs-FC and SyS of seven resting-state networks (RSN) as defined in the Yeo atlas (Thomas Yeo et al., 2011). Per each of the 100 regions of interest (ROIs), a BOLD signal was extracted and averaged across all voxels falling within an ROI. Then, ROI-to-ROI rs-FCs were computed as Pearson-moment correlations, and subsequently, Fisher-z transformed. Negative values were set to zero and autocorrelations were not considered in the further computation. SyS captures the balance between within-network (Wnet) and between-networks (Bnet) rs-FC. Within-network rs-FC was computed as the average rs-FC connecting all the nodes within the same network. Between-network rs-FC was computed as the average rs-FC connecting nodes of a particular network to nodes from the rest of the cortex.* System segregation (SyS) captures the balance between within-network (Wnet) and between-networks (Bnet) rs-FC. Within-network rs-FC was computed as the average rs-FC connecting all the nodes within the same network. Between-network rs-FC was computed as the average rs-FC connecting nodes of a particular network to nodes from the rest of the cortex. ## 2.4.1. Comparisons between no pain and pain groups We first analyzed group differences (no pain and CP) in sociodemographic data [age, biological sex, and body mass index (BMI)], health-related questionnaire scores (MH, cognitive complaints, and QoL), and head movements during the scan using Welch t-tests and chi-square statistics. In addition, we also ran a multivariate analysis between these groups on the segregation of networks, correcting for the delay between the first questionnaire and MRI, head movement during MRI, age, and biological sex. ## 2.4.2. Correlations between pain variables, psychological aspects, and system segregation We ran bivariate correlations to explore the association between psychological variables (MH, QoL, cognitive concerns), PC subscales, and pain variables at baseline (intensity, interference, number of pain sites, duration, and medication intake). Besides, we ran partial correlations between SyS and pain factors (intensity, interference, and the number of pain sites), correcting for age, gender, and head motion. ## 2.4.3. Description of longitudinal pain progression We explored the differences between pain characteristics (intensity, affective and activity interference, number of pain sites, pain duration, and pain-related medication) in both time points using t-tests and chi-square statistics. ## 2.4.4. Regression models within the chronic pain group To explore which variables affected pain experience over time, we ran a first multiple regression model with pain experience as the primary outcome, and sociodemographic data (age, biological sex, and BMI), pain baseline ratings (pain-related medication, intensity, total interference, duration, and the number of pain sites), PC scale total score and health-related questionnaires (MH, QoL, and cognitive concerns) as regressors. Then, we ran different multiple regression models using pain experience progression (Pain experience 2- Pain experience 1) as the dependent variable and sociodemographic (age and biological sex), PC subscales, pain characteristics at baseline (intensity, affective interference, and activity interference), and SyS. We also added covariable head motion during the MRI, as it has an important influence on intrinsic functional connectivity and its interpretation (Van Dijk et al., 2012), in the model and the time between the first questionnaire (T1) and MRI (T1′). Finally, we added a multicollinearity diagnostic to ensure the interdependence of regressors using the variance inflation factor coefficient. Variables in this step were selected from previous analyses depending on if they had achieved significant effects (like pain intensity, interference, and catastrophizing) or if they could alter SyS results (age, biological sex, distance between first questionnaire and fMRI, and head motion during fMRI). To explore the interaction between catastrophism’s helplessness and brain networks related to pain progression, we run moderation models through SPSS Process®, and we look for the Johnson-Neyman interval. Finally, to explore the possible effect of pain experience in interaction with catastrophism’s helplessness on brain connectivity we repeated the models using brain SyS as the dependent variable and pain experience as the independent variable. ## 2.4.5. Statistical analyses Statistical analyses were performed using SPSS version 20.0 (Statistical Package for Social Sciences, Chicago, IL, USA). Regression models’ graphics were created through R v.3.6.3 (R Foundation for Statistical Computing, Vienna, Austria). Brain nodes and segregation graphic representation were created with the Surf Ice tool (version 6-October-2021; v1.0.20211006), which is an OpenGL Shading Language surface rendering source code. ## 3.1. Differences between no pain and chronic pain group When we compared the no pain and CP group for sociodemographic data and health-related questionnaires, we found significant differences in age ($F = 6$,271; $$p \leq 0.013$$), gender (x2 = 8.396; $$p \leq 0.003$$), BMI ($F = 9$,352; $$p \leq 0.002$$), MH ($F = 24$,772; $p \leq 0.001$), cognitive complaints ($F = 31$,945; $p \leq 0.001$), QoL ($F = 40$,392; $p \leq 0.001$) and head movement during the scan ($F = 4$,652; $$p \leq 0.032$$). CP group was composed predominantly of women ($63.2\%$) and older people when compared with the no pain group (mean = 54.81; SD = 7.04) (detailed information is in Table 1). **TABLE 1** | Unnamed: 0 | No pain (214) | Chronic pain (133) | F | p-value | | --- | --- | --- | --- | --- | | Sociodemographic | Mean (SD) | Mean (SD) | | | | Age | 52.87 (7.02) | 54.81 (7.04) | 6271 | 0.013* | | Biological sex (% women) | 47.2% | 63.2 % | x2 = 8.396 | 0.003* | | BMI | 25.36 (3.72) | 26.86 (5.40) | 9362 | 0.002* | | Health-related questionnaires | Health-related questionnaires | Health-related questionnaires | Health-related questionnaires | Health-related questionnaires | | MH | 1.21 (1.59) | 2.18 (2.02) | 24772 | < 0.001** | | Cognitive complaints | 53.26 (6.54) | 48.41 (9.28) | 31945 | < 0.001** | | QoL | 39.90 (7.93) | 34.32 | 40392 | < 0.001** | | Brain networks’ system segregation | Brain networks’ system segregation | Brain networks’ system segregation | Brain networks’ system segregation | Brain networks’ system segregation | | Default mode | 0.268 (0.098) | 2.66 (0.095) | | 0.831 | | Somatomotor | 0.415 (0.082) | 0.410 (0.076) | | 0.871 | | Control | 0.247 (0.084) | 0.253 (0.087) | | 0.380 | | Dorsoattentional | 0.305 (0.067) | 0.305 (0.066) | | 0.533 | | Salience ventral attentional | 0.293 (0.077) | 0.304 (0.077) | | 0.117 | | Limbic | 0.275 (0.104) | 0.279 (0.096) | | 0.822 | | Visual | 0.396 (0.093) | 0.393 (0.096) | | 0.999 | | Head movement | 0.163 (−0.035) | 0.179 (0.003) | 4652 | 0.032* | Regarding SyS, the multivariate analysis (corrected by head movement, the time between the first questionnaire, biological sex, and age; see Table 1) showed no differences between groups in DMN ($F = 0.046$; $$p \leq 0.831$$), somatomotor network ($F = 0.026$; $$p \leq 0.871$$), control network ($F = 0.772$; $$p \leq 0.380$$), DAN ($F = 0.390$; $$p \leq 0.533$$), SN ($F = 2.466$; $$p \leq 0.117$$), limbic network ($F = 0.050$; $$p \leq 0.822$$) and visual network ($F = 0.000$; $$p \leq 0.999$$) (Table 1). ## 3.2. Correlations in psychological aspects and pain variables Quality of life significantly correlated with multiple pain sites (r = −0.243 $$p \leq 0.005$$), pain intensity (r = −0.282, $$p \leq 0.001$$) and interference (r = −0.521, $p \leq 0.001$), cognitive complaints ($r = 0.398$, $p \leq .001$), MH (r = −0.533, $p \leq 0.001$), rumination (r = −0.250, $$p \leq 0.004$$), magnification (r = −0.442, $p \leq 0.001$) and helplessness (r = −0.478, $p \leq 0.001$). No correlation was present between QoL and pain duration (r = −0.011, $$p \leq 0.900$$) or pain medication (r = −0.087, $$p \leq 0.321$$). For MH, we found significant correlations with multiple pain sites ($r = 0.184$, $$p \leq 0.034$$), pain medication ($r = 0.187$, $$p \leq 0.031$$), pain intensity ($r = 0.213$, $$p \leq 0.014$$), pain interference ($r = 0.371$, $p \leq 0.001$), QoL, cognitive concerns (r = −0.423, $p \leq 0.001$), rumination ($r = 0.288$, $$p \leq 0.001$$), magnification ($r = 0.471$, $p \leq 0.001$) and helplessness ($r = 0.422$, $p \leq 0.001$). No correlation was found with pain duration. Finally, cognitive complaints were positively correlated with multiple pain sites (r = −0.182, $$p \leq 0.038$$), MH, QoL, magnification (r = −0.222, $$p \leq 0.011$$), and helplessness (r = −0.191, $$p \leq 0.029$$). Concerning SyS and pain factors (intensity, interference, and the number of pain sites) associations, we only found pain interference and SyS of DMN ($r = 0.186$, $$p \leq 0.034$$) and SN ($r = 0.215$, $$p \leq 0.014$$), and a tendency of an association with DAN ($r = 0.161$, $$p \leq 0.068$$). ## 3.3. Description of longitudinal pain progression Pain characteristics at baseline in our sample are drawn in Figure 4. The most common location was back pain ($44.4\%$), followed by lower ($24.8\%$) and upper limbs ($12.8\%$). During the in-person assessment, participants with CP were asked if they had a diagnosis: 29 suffered from migraine, 15 from cervicalgia, 8 from fibromyalgia, 1 from polymyositis traumatic, 16 from knee pathologies, 54 from low back pain, 15 from other diagnoses that were cursing with CP and 36 had no specific diagnosis. Several participants had more than one diagnosis. **FIGURE 4:** *Pain characteristics. (A) Percentages of participants’ pain location. (B) Mean of interference (sum, activity, and affective) and intensity ratings on a numerical rating scale (0–10). (C) Percentages of the number of pain sites. (D) Pain duration of the CP in our sample.* Regarding the number of pain sites, in the first assessment, $75.2\%$ of participants with CP had more than one single painful site and more than half of the sample had their pain for more than 3 years ($58.7\%$). The total interference summed mean was 2.66 (SD = 2.21), 2.76 (SD = 2.36) in the activity subdomain, and 2.58 (SD = 2.39) in the affective subdomain. Finally, the mean intensity for the last week was 4.17 (SD = 1.47). The pain-relief drugs were the most used treatment at baseline ($31.6\%$), although $31.6\%$ of participants were not enrolled in any kind of treatment at that moment. Finally, $32.3\%$ of our sample treated their pain with other techniques, like physical or psychological therapies. The mean catastrophizing total score was 16.44 (SD = 8.65), helplessness 6.41 (SD = 4.19), rumination 6.44 (SD = 3.37), and magnification 3.60 (SD = 2.26). When comparing two-time points (Table 2), we found significant differences in intensity ($$p \leq 0.004$$), interference (total, affective, and activity) ($$p \leq 0.021$$; $$p \leq 0.033$$; $$p \leq 0.047$$), and medication intake ($$p \leq 0.024$$). The number of pain sites and pain duration instead showed no differences ($$p \leq 0.800$$). Regarding the worsening or amelioration of pain intensity and affective interference, that is, what we considered as pain experience, $53.3\%$ showed improvements during this period, and 47.7 % exhibited worsening symptoms. **TABLE 2** | Unnamed: 0 | Pain T1 (mean) | Pain T2 (mean) | t | p-value | | --- | --- | --- | --- | --- | | Intensity (last week) | 4.17 (1.89) | 3.70 (1.95) | 2.923 | 0.004** | | Interference total | 2.66 (2.21) | 2.32 (2.16) | 2.332 | 0.021* | | Interference affective | 2.59 (2.37) | 2.23 (2.22) | 2.156 | 0.033* | | Interference activity | 2.76 (2.36) | 2.44 (2.36) | 2.004 | 0.047* | | Number of pain sites | 2.35 (1.07) | 2.38 (1.063) | −0.254 | 0.800 | | Duration (< 3 years) (%) | 58.7% | 36.8% | 0.590 | 0.556 | | Duration (3–9 years) (%) | 28.6% | 36.8% | | | | Duration (> 9 years) (%) | 12.7% | 26.4% | | | | Medication intake (% of yes) | 31.6% | 42.9% | X2 = 5.115 | 0.024* | | Catastrophizing | | 6.34 (9.64) | | | | Helplessness | | 6.41 (4.19) | | | | Rumination (maximum scoring 16) | | 6.44 (3.37) | | | | Magnification (maximum scoring 12) | | 3.60 (2.26) | | | ## 3.4. Chronic pain and the role of brain networks in pain progression When we adjusted the first regression model, where all sociodemographic (BMI, age, and biological sex), health-related questionnaires (MH, QoL, and cognitive concerns), and pain characteristics (PC, pain medication, intensity, interference, duration, and the number of pain sites) were independent variables, we found that PC (β = 0.481; $p \leq 0.001$), pain intensity (β = −0.299; $$p \leq 0.002$$) and total interference (β = −0.562; $p \leq 0.001$) were associated with pain experience over time. The second regression model crucially showed that more segregation of DMN (β = 0.193; $$p \leq 0.037$$), and less segregation in the DAN (β = −0.215; $$p \leq 0.014$$) were associated with worst pain experience progression. Moreover, activity and affective interference (β = 0.255; $$p \leq 0.022$$; β = −0.678; $p \leq 0.001$, respectively), pain intensity at baseline (β = −0.428; $p \leq 0.001$), and helplessness (β = 0.325; $p \leq 0.003$) were also related with worst pain experience progression. Finally, when we explored the potential moderator role of helplessness on the relation between SyS and pain experience, we found that helplessness moderates the effect of DMN segregation on pain experience progression ($$p \leq 0.0313$$, 5,000 bootstrap samples, $95\%$ CI 0.57–1.20), and a tendency in moderate the relation between DAN SyS ($$p \leq 0.098$$, 5,000 bootstrap samples, $95\%$ CI −0.13–1.52) (see Figure 5) and pain experience progression. Johnson-Neyman plots revealed that a score of 7 in the helplessness subscale is the critical value to have a significant association between DMN segregation and pain experience progression (see Figure 6). **FIGURE 5:** *System segregation and helplessness predict chronic pain progression. On top, a chart explaining how pain progression has been calculated (subtracting pain experience T1 from pain experience T2). Positive values indicates a worsening while negative results an amelioration. In the middle, two scatter plots reflecting how each network [default mode network (DMN) at left and dorsoattentional network (DAN) at right] was associated with CP progression depending on low/high helplessness (violet, and orange, respectively). At the bottom of the figure, nodes in the graph represent studied regions of interest (ROIs) as defined by the Schaefer-Yeo atlas of 100 nodes and 7 networks. The nodes and edges illustrate ROIs blue for DMN (left) and yellow for DAN (right). Within network connectivity is represented in black (only representative) and refers to outside network ROIs and the connectivity between them and the DAN or DMN.* **FIGURE 6:** *Helplessness moderation. At the left, Johnson-Neyman plot show how helplessness moderates the default mode network (DMN) slope since the score 6, where it accentuates the impact of DMN segregation on CP progression. At the right, representative chart of moderation of pain helplessness in the relationship between DMN segregation and pain progression.* When we swap the dependent and independent variables to explore catastrophism’s helplessness, in interaction with pain experience, and modulated brain SyS, we found no significant associations. ## 4. Discussion In this paper, we explored the role of SyS and psychological aspects in the evolution of pain experience over time. First, significant differences in self-perceived health status were found between the pain and no pain groups. Second, PC and brain network segregation of DMN and DAN were the main predictors of pain progression. Finally, moderation analyses revealed that helplessness, a PC’s subdomain, moderated the relationship between DMN and the progression of the pain experience. To our knowledge, this is the first study using the fMRI SyS model to identify biomarkers that can predict the progression of CP. ## 4.1. Differences between groups and pain characteristics When we compared participants with and without CP, we found a high prevalence of CP ($38.3\%$) in our sample, according to previous reports of a peak prevalence in late middle-aged adults (50–65 years), affecting up to 20–$80\%$ of people (Gibson and Lussier, 2012). The pain group was older, formed predominantly by women, and had an average BMI in the overweight range (bordering on mild obesity), also showing the worst health status (MH, cognition, QoL). This is in line with previous studies that found an association between overweight/obesity and low back pain, particularly stronger in women than men (Breivik et al., 2006; Stone and Broderick, 2012; Dahlhamer, 2018). Another difference observed was an increased head motion during the scan for the pain group, possibly related to an increased difficulty to keep still due to their condition. We did not observe differences in SyS in any network, probably due to the non-clinical characteristics of our pain sample (i.e., low disability or low impact in their daily lives, low to mild fluctuations in pain intensity, and well-controlled pain). ## 4.2. Pain characteristics and their correlations with psychological aspects We found several correlations between pain factors and psychological variables (QoL, MH, and self-perceived cognitive complaints), as has been widely described in pain literature (Edwards et al., 2016; Kawai et al., 2017). The subjective and prolonged experience of pain in our cohort was associated with significant mood consequences, such as light depressive symptoms and less capacity to enjoy life. Unquestionably, feeling pain may evolve from normal reactive emotional symptoms, related to stress (Hammen, 2005; Abdallah and Geha, 2017), to clinically relevant depression associated with CP (DeVeaugh-Geiss et al., 2010). Increasing evidence suggests that the pain-related plasticity and depression-related neural circuits are responsible for subtle changes in areas involved in the emotional and cognitive aspects of pain over time that contributes to the behavioral manifestation of altered affective processes (Doan et al., 2015). Finally, as will be discussed later, PC is an important risk factor for a wide range of pain-related effects, including increased pain intensity, increased emotional distress, depression, decreased physical function, and prolonged disability (Schütze et al., 2018). ## 4.3. Catastrophizing is associated with pain progression Our results indicated that changes in pain experience over time were associated with higher levels of PC and, helplessness was the only domain that vaticinated greater pain impact. Pain catastrophizing is defined as “an exaggerated negative mental set brought to bear during actual or anticipated painful experience” (Sullivan et al., 2001), and can be divided into three main dimensions: magnification (an exaggerated threat value of pain), rumination (excessive focus pain-related stimuli) and helplessness. Sullivan (Sullivan, 2012) described how magnification and rumination could overlap with features from primary appraisals (i.e., evaluating the pain stimulus), while helplessness overlaps with secondary appraisal (i.e., the evaluation of oneself to effectively deal with a stressful situation, like pain stimulus). Consequently, cognitions of helplessness are related to higher perceived pain intensity, possibly due to feelings of loss of control, negative future expectations, or rumination, factors that enhance affective pain experience (Müller, 2011). Pain catastrophizing is related not only to psychological factors but also to altered brain connectivity in pain-related areas and top-down inhibition mechanisms [e.g., a reduced engagement of the descending pain modulatory system; see Malfliet et al. [ 2017] for review]. Rumination in PC can be considered as a cognitive style (“mind-wandering like”) (Èeko et al., 2015), and is responsible for the occurrence of thoughts not related to a given task and not tied to the immediate environment (Häggman-Henrikson et al., 2021). *In* general terms, a rumination is a form of circular thinking that swallows the individual in a path without a way out, and it can be broadly defined as a perseverative self-focused thinking process, whereby an individual goes over and over the same thoughts in his or her mind. This process, which can be activated during or after a painful event, generally interferes with a person’s ability to inhibit thoughts, generate alternative ways of thinking, and switch the focus of attention. PC, which possibly appears as a kind of fear of pain, helps perpetuate the cognitive-behavioral pain cycle, collaborating in the activation of negative cognitive and meta-cognitive processes, that in turn can lead to worse coping behaviors and an exacerbation of pain (Ziadni et al., 2018). This is consistent with previous studies focused on cognitive-affective processes of pain (Gentili et al., 2019; Gonzalez et al., 2019; You et al., 2021). You et al. [ 2021] demonstrated the existence of pain-specific resilience, referring to the ability to maintain relatively stable and healthy levels of psychological functioning in face of ongoing and persistent pain (You et al., 2021). Sociodemographic (Tanner et al., 2021), structural and functional MRI (Tanner et al., 2021; You et al., 2021), clinical pain symptoms, negative pain-related emotions, and PC (Sturgeon and Zautra, 2013; Gonzalez et al., 2019) have been related to resilience and better outcomes in the presence of recurrent pain, while to our knowledge, this is the first study to explore the interaction between all these variables. ## 4.4. System segregation and pain experience progression We found associations between pain interference and DMN, and SN, as well as a significant tendency in DAN. One important point, that distinguishes our results from other studies, is that higher segregation of DMN and lower segregation of DAN were associated with this pain experience evolution over time, which is coherent and match with previous results. Besides, helplessness was found to moderate the relationship between DMN and pain experience progression. When we explore the role of the interaction between helplessness and pain experience on brain SyS we found no significant effects, suggesting that brain SyS in certain networks, in interaction with helplessness, affects pain experience evolution over time, and not the reverse. In this section, we aim to address all the points by substantially revising the main findings on each network individually and collectively, as well as their relationship with helplessness. First, concerning DAN, its alteration has already been shown to predict pain intensity progression, possibly due to a cognitive evaluation of pain as a permanent threat to the body (Pfannmöller and Lotze, 2019). In this view, our results could be interpreted as dysregulated DAN internal and external connectivity could reduce the ability to correctly process nociceptive stimuli and, consequently, increase the perception of pain intensity over time. Thus, it could be possible that an imbalance in FC between networks causes a sort of “attentional resources kidnapping,” increasing the aversive response to pain. This constant painful input and its maladaptive brain changes (i.e., malfunction of the descending pain pathways), reinforced by catastrophic thoughts, ultimately provoke an increase in self-perceived pain intensity over time. Similarly, we found a correlation between pain interference and SN segregation, suggesting that, as DAN, altered response in this network could be a contributing factor to the maintenance and chronicity of pain. SN also exhibits task- and resting-state abnormalities in some CP populations (Otti et al., 2013; Becerra et al., 2014; Cauda et al., 2014; Qiu et al., 2021), and may be dysregulated due to constant pain (Borsook et al., 2013), giving rise to a “salient state,” mostly divided into two processes: bottom-up saliency and top-down control (Melloni et al., 2012). Nonetheless, it is well justified that there is an association between a dysregulated SN and the interplay between pain symptoms and psychological aspects, as several studies have also seen before (Legrain et al., 2011; Coppieters et al., 2016; van Ettinger-Veenstra et al., 2019). Indeed, the function of the SN is essential in the processing of sensory stimuli, as the SN plays a key role in the assessment of the inherent danger of such stimuli (and how one should respond to them), and plays a central role in the memory of painful events (Kim et al., 2019). In the same line DMN alteration in CP patients (Malfliet et al., 2017) has been associated with the “pain state” (Èeko et al., 2020), which facilitates stimulus-independent thoughts, or internally directed, spontaneous or autobiographical thoughts, also resumed in the term “self-generated thought” (Andrews-Hanna et al., 2014). Repetitive negative thinking is considered a form of avoidant coping strategy (Flink et al., 2013). Occupying one’s thoughts with repetitive negative thinking prevents the confrontation with the threat (e.g., What I could have done to prevent this situation?). Additionally, repetitive negative thinking can be negatively reinforced by abstract cognitive activity (e.g., Why do I suffer from pain?). This form of abstract thinking impedes the activation and processing of emotional and somatic responses. The process of suppression/avoidance could magnify the negative emotions and consequently fuel the catastrophic worry cycle. Usually, some of these thoughts are related to personal significance, temporal or social orientation, or somatosensory awareness. Ergo, DMN seems to play an important active role in the self-generation of cognition, thinking about one-self and future thinking, but also with rumination and PC (Èeko et al., 2020). The predictive value of DMN rs-FC on pain management was already shown by Baliki et al. [ 2014], where CP disrupted the dynamics of DMN and frontoparietal network (related to attention and working memory), and other regions related to pain modulation (insula; anterior cingulate cortex, ACC) (Baliki et al., 2014). Specific nodes of DMN, like the hippocampus and medial prefrontal cortex (mPFC), are importantly implicated in pain processing. Similarly, Kucyi et al. [ 2014] demonstrated an enhanced mPFC connectivity in DMN, suggesting its role in the descending modulatory system underlying the degree to which patients ruminate about their CP (Kucyi et al., 2014). Hashmi et al. [ 2013] also found that strengthening mPFC-nucleus accumbens FC predicts the extension in which brain activity shifts from pain-related to emotion-related regions in patients with persistent subacute back pain, compared to those that get total recovery (Hashmi et al., 2013). In sum, the mPFC can be seen as a hub for the development of mental comorbidities associated with CP. The impaired cholinergic activity contributes to the deactivation of mPFC, possibly leading to cognitive and emotional deficits in CP patients (Kummer et al., 2020). Overall, our results are consistent with findings reported by Ter Minassian et al. [ 2012] in their study about brain activity during pain anticipation and pain perception (Ter Minassian et al., 2012). They found that during pain anticipation, DAN was activated while DMN was deactivated, possibly due to the search for strategies to avoid pain. In contrast, during pain perception, DMN was reactivated, whereas DAN remained activated, supporting the fact that DMN and attentional networks cooperate to integrate pain-related stimuli and thoughts, as DMN is at the top of the networks that perform hierarchical integration. The authors concluded that the ACC could be the structure most involved in the coordination between these two networks, given that it can be anatomically and functionally divided during pain anticipation (cognitive; caudal ACC) and pain perception (emotional; rostral ACC). ## 4.5. Default mode and dorsoattentional networks interactions with helplessness Our results indicate that brain network functioning and cognitive-emotional strategies can interact in modulating pain experience, and both can be understood as possible protectors/risk factors. Once CP is established, catastrophizing and segregation are not independent factors acting in the pain progression. Segregation of DMN and DAN, indeed, may represent some aspects of brain resilience that could help to maintain or get better from pain, allowing the brain to effectively process pain, or at least avoid areas not involved in pain processing. On the other hand, modifiable psychologic aspects like catastrophizing, related to cognitive reserve, may have a crucial role in pain evolution and the impact of MH. Both kinds of processes can be treated separately but in a coordinated way, as seen in studies of combined behavioral therapies (e.g., pain neuroscience education) with brain stimulation (Meeker et al., 2020; Alcon and Wang-Price, 2022), and could reduce the negative impact of emotions in the evolution of pain over time, collaborating in the selection of effective coping strategies or avoiding negative thinking. Thereby, descending modulation might be more effective, decreasing levels of pain intensity and affective interference. Finally, resilience-focused cognitive-affective approaches, including this holistic view, are important to be applied not only in highly affected CP patients, but also in individuals with a low rating in pain parameters, as they can already show lower QoL, cognition, and MH, or FC alterations. ## 4.6. Limitations and future directions Our main limitation was that we investigated a non-homogeneous (e.g., different kinds of pain) and non-clinical sample (low ratings in pain intensity, interference, and PC). Thus, in future work, investigating how higher catastrophizing scores, as well as intensity or interference ratings, interact with networks’ segregation in the progression of pain might shed light on how pain alters different networks and how it’s linked to emotional and cognitive aspects. Another limitation of our study was the lack of information about pain during MRI or the presence of acute pain in the no-pain sample. Further work is certainly required to disentangle these complexities in acute (non-experimentally provoked) and CP. For example, the possibility of prognostic biomarkers of the transition from acute pain to CP, within the framework of pain-related resilience (SyS and psychological factors) is described in this manuscript. An additional potential limitation was related to time gaps between questionnaires and MRI. Even though we used this gap as a covariable in our analysis, we cannot exclude that this aspect can introduce some bias in the results. Although we excluded subjects with excess motion and added the mean head motion of each participant as a covariate in all the analyses related to fMRI, we cannot fully dismiss that subject motion to some degree has influenced the present results. Finally, due to the exploratory nature of our analyses, we did not correct functional connectivity analysis for multiple comparisons. Future studies need to confirm these results with better controlling for all these aspects and overcome present limitations. ## 5. Conclusion In summary, present results indicate that non-clinical CP conditions are associated with the segregation of brain networks during rs-fMRI, more concretely SN, DAN, and DMN. Moreover, the latter effect is moderated by helplessness, a catastrophizing domain, that can be seen as a protective factor against pain impact over time. Our result casts a new light on the interplay among the influence of the interaction between psychological aspects and brain networks in pain management. The extent of the reorganization of these networks and their functioning is critical for the evolution of pain. Besides, the reorganization of spatial properties of the DMN, DAN, and SN may reflect different emotional, attentional, and cognitive abnormalities observed in CP conditions. Under certain assumptions, this can be construed as the common reorganization among pain patients is the extent of association of the component of the DAN, and its dissociation from the posterior components of the DMN, which seems to disrupt the competitive inhibition between the DMN and the brain networks underlying attention. Finally, our results highlight the predictive value of fMRI that can orient researchers and clinicians about what form of treatable pathophysiology an individual patient with CP may have. Given that other fMRI techniques are not suitable to measure certain characteristics, such as the effect of attentional fluctuation or the degree of self-generated thoughts, we conclude that SyS networks are an effective metric to understand brain connectivity and neural correlates of the pain state. SyS could be used as biomarkers indicating a predisposition of pain maintenance and allowing disease progression monitoring, as well as the reversion or compensation of these alterations in different kinds of interventions, that should, in the future, be considered for more deep research. We have demonstrated that these variables should be assessed without considering the etiology of pain, giving more importance to the pain state than the pain condition per se. ## Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. ## Ethics statement The protocol was approved by the “Comité d’Ètica i Investigació Clínica de la Unió Catalana d’Hospitals” and was carried out following the Declaration of Helsinki (World Medical Association, 2013). Written informed consent was obtained from all participants before inclusion in the study. ## Author contributions MDS, GC, GE-I, AR-V, MC-T, KA-P, JS-S, AP-L, DB-F, JMT, and SD-G have made substantial contributions to conception, design, and interpretation of data. GC, MDS, and SD-G have made substantial contributions to analysis and interpretation of data. GE-I, AR-V, MC-T, KA-P, JS-S, and SD-G have made substantial contribution to acquisition of data. MDS, GC, and SD-G participated in drafting the manuscript. AP-L, DB-F, JMT, GE-I, AR-V, MC-T, KA-P, and JS-S contributed to revising it critically for important intellectual content. All authors have given final approval of the version to be submitted. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. AP-L received funding from MagStim Inc. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit It for publication. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## References 1. Abdallah C. G., Geha P.. **Chronic pain and chronic stress: two sides of the same coin?**. (2017) **1**. DOI: 10.1177/2470547017704763 2. Abeler K., Friborg O., Engstrøm M., Sand T., Bergvik S.. **Sleep characteristics in adults with and without chronic musculoskeletal pain: the role of mental distress and pain catastrophizing.**. (2020) **36** 707-715. DOI: 10.1097/AJP.0000000000000854 3. Alcon C. A., Wang-Price S.. **Non-invasive brain stimulation and pain neuroscience education in the cognitive-affective treatment of chronic low back pain: evidence and future directions.**. (2022) **3**. DOI: 10.3389/fpain.2022.959609 4. Andrews-Hanna J. R., Smallwood J., Spreng R. N.. **The default network and self-generated thought: component processes, dynamic control, and clinical relevance.**. (2014) **1316** 29-52. DOI: 10.1111/nyas.12360 5. Badia X., Muriel C., Gracia A., Manuel Núñez-Olarte J., Perulero N., Gálvez R.. **Validación española del cuestionario brief pain inventory en pacientes con dolor de causa neoplásica.**. (2003) **120** 52-59. DOI: 10.1016/S0025-7753(03)73601-X 6. Baliki M. N., Mansour A. R., Baria A. T., Apkarian A. V.. **Functional reorganization of the default mode network across chronic pain conditions.**. (2014) **9**. DOI: 10.1371/journal.pone.0106133 7. Becerra L., Sava S., Simons L. E., Drosos A. M., Sethna N., Berde C.. **Intrinsic brain networks normalize with treatment in pediatric complex regional pain syndrome.**. (2014) **6** 347-369. DOI: 10.1016/j.nicl.2014.07.012 8. Borsook D., Edwards R., Elman I., Becerra L., Levine J.. **Pain and analgesia: the value of salience circuits.**. (2013) **104** 93-105. DOI: 10.1016/j.pneurobio.2013.02.003 9. Breivik H., Collett B., Ventafridda V., Cohen R., Gallacher D.. **Survey of chronic pain in Europe: prevalence, impact on daily life, and treatment.**. (2006) **10** 287-333. DOI: 10.1016/j.ejpain.2005.06.009 10. Burns L. C., Ritvo S. E., Ferguson M. K., Clarke H., Seltzer Z., Katz J.. **Pain catastrophizing as a risk factor for chronic pain after total knee arthroplasty: a systematic review.**. (2015) **8** 21-32. DOI: 10.2147/JPR.S64730 11. Cattaneo G., Bartrés-Faz D., Morris T. P., Sánchez J. S., Macià D., Tarrero C.. **The barcelona brain health initiative: a cohort study to define and promote determinants of brain health.**. (2018) **10**. DOI: 10.3389/fnagi.2018.00321 12. Cattaneo G., Bartrés-Faz D., Morris T. P., Sánchez J. S., Macià D., Tormos J. M.. **The barcelona brain health initiative: cohort description and first follow-up.**. (2020) **15**. DOI: 10.1371/journal.pone.0228754 13. Cauda F., Palermo S., Costa T., Torta R., Duca S., Vercelli U.. **Gray matter alterations in chronic pain: a network-oriented meta-analytic approach.**. (2014) **4** 676-686. DOI: 10.1016/j.nicl.2014.04.007 14. Èeko M., Frangos E., Gracely J., Richards E., Wang B., Schweinhardt P.. **Default mode network changes in fibromyalgia patients are largely dependent on current clinical pain.**. (2020) **216**. DOI: 10.1016/j.neuroimage.2020.116877 15. Èeko M., Gracely J. L., Fitzcharles M.-A., Seminowicz D. A., Schweinhardt P., Bushnell M. C.. **Is a responsive default mode network required for successful working memory task performance?**. (2015) **35** 11595-11605. DOI: 10.1523/JNEUROSCI.0264-15.2015 16. Cleeland C. S.. (2009) 17. Coppieters I., Meeus M., Kregel J., Caeyenberghs K., De Pauw R., Goubert D.. **Relations between brain alterations and clinical pain measures in chronic musculoskeletal pain: a systematic review.**. (2016) **17** 949-962. DOI: 10.1016/j.jpain.2016.04.005 18. Coppola G., Di Renzo A., Petolicchio B., Tinelli E., Di Lorenzo C., Parisi V.. **Aberrant interactions of cortical networks in chronic migraine: a resting-state fMRI study.**. (2019) **92** e2550-e2558. DOI: 10.1212/WNL.0000000000007577 19. Craner J. R., Gilliam W. P., Sperry J. A.. **Rumination, magnification, and helplessness: how do different aspects of pain catastrophizing relate to pain severity and functioning?**. (2016) **32** 1028-1035. DOI: 10.1097/AJP.0000000000000355 20. Dahlhamer J.. **Prevalence of chronic pain and high-impact chronic pain among adults — United States, 2016.**. (2018) **67** 1001-1006. DOI: 10.15585/mmwr.mm6736a2 21. De Ridder D., Vanneste S., Smith M., Adhia D.. **Pain and the triple network model.**. (2022) **13**. DOI: 10.3389/fneur.2022.757241 22. Delgado-Gallén S., Soler M. D., Albu S., Pachón-García C., Alviárez-Schulze V., Solana-Sánchez J.. **Cognitive reserve as a protective factor of mental health in middle-aged adults affected by chronic pain.**. (2021) **12**. DOI: 10.3389/fpsyg.2021.752623 23. DeVeaugh-Geiss A. M., West S. L., Miller W. C., Sleath B., Gaynes B. N., Kroenke K.. **The adverse effects of comorbid pain on depression outcomes in primary care patients: results from the ARTIST trial.**. (2010) **11** 732-741. DOI: 10.1111/j.1526-4637.2010.00830.x 24. Doan L., Manders T., Wang J.. **Neuroplasticity underlying the comorbidity of pain and depression.**. (2015) **2015**. DOI: 10.1155/2015/504691 25. Dong H.-J., Gerdle B., Bernfort L., Levin L. -Å, Dragioti E.. **Pain catastrophizing in older adults with chronic pain: the mediator effect of mood using a path analysis approach.**. (2020) **9**. DOI: 10.3390/jcm9072073 26. Edwards R. R., Dworkin R. H., Sullivan M. D., Turk D. C., Wasan A. D.. **The role of psychosocial processes in the development and maintenance of chronic pain.**. (2016) **17** T70-T92. DOI: 10.1016/j.jpain.2016.01.001 27. Ewers M., Luan Y., Frontzkowski L., Neitzel J., Rubinski A., Dichgans M.. **Segregation of functional networks is associated with cognitive resilience in Alzheimer’s disease.**. (2021) **144** 2176-2185. DOI: 10.1093/brain/awab112 28. Fieo R., Ocepek-Welikson K., Kleinman M., Eimicke J. P., Crane P. K., Cella D.. **Measurement equivalence of the patient reported outcomes measurement information system**. (2016) **58** 255-307. PMID: 28523238 29. Fischl B., Salat D. H., Busa E., Albert M., Dieterich M., Haselgrove C.. **Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain.**. (2002) **33** 341-355. DOI: 10.1016/S0896-6273(02)00569-X 30. Flink I. L., Boersma K., Linton S. J.. **Pain catastrophizing as repetitive negative thinking: a development of the conceptualization.**. (2013) **42** 215-223. DOI: 10.1080/16506073.2013.769621 31. Franzmeier N., Göttler J., Grimmer T., Drzezga A., Áraque-Caballero M. A., Simon-Vermot L.. **Resting-state connectivity of the left frontal cortex to the default mode and dorsal attention network supports reserve in mild cognitive impairment.**. (2017) **9**. DOI: 10.3389/fnagi.2017.00264 32. García Campayo J., Rodero B., Alda M., Sobradiel N., Montero J., Moreno S.. **[Validation of the Spanish version of the pain catastrophizing scale in fibromyalgia].**. (2008) **131** 487-492. DOI: 10.1157/13127277 33. Gentili C., Rickardsson J., Zetterqvist V., Simons L. E., Lekander M., Wicksell R. K.. **Psychological flexibility as a resilience factor in individuals with chronic pain.**. (2019) **10**. DOI: 10.3389/fpsyg.2019.02016 34. Gibson S. J., Lussier D.. **Prevalence and relevance of pain in older persons.**. (2012) **13** S23-S26. DOI: 10.1111/j.1526-4637.2012.01349.x 35. Gilliam W. P., Craner J. R., Morrison E. J., Sperry J. A.. **The mediating effects of the different dimensions of pain catastrophizing on outcomes in an interdisciplinary pain rehabilitation program.**. (2017) **33** 443-451. DOI: 10.1097/AJP.0000000000000419 36. Gonzalez C. E., Okunbor J. I., Parker R., Owens M. A., White D. M., Merlin J. S.. **Pain-specific resilience in people living with HIV and chronic pain: beneficial associations with coping strategies and catastrophizing.**. (2019) **10**. DOI: 10.3389/fpsyg.2019.02046 37. Haefeli M., Elfering A.. **Pain assessment.**. (2006) **15** S17-S24. DOI: 10.1007/s00586-005-1044-x 38. Häggman-Henrikson B., Visscher C. M., Wänman A., Ljótsson B., Peck C. C., Lövgren A.. **Even mild catastrophic thinking is related to pain intensity in individuals with painful temporomandibular disorders.**. (2021) **48** 1193-1200. DOI: 10.1111/joor.13251 39. Hammen C.. **Stress and depression.**. (2005) **1** 293-319. DOI: 10.1146/annurev.clinpsy.1.102803.143938 40. Hashmi J. A., Baliki M. N., Huang L., Baria A. T., Torbey S., Hermann K. M.. **Shape shifting pain: chronification of back pain shifts brain representation from nociceptive to emotional circuits.**. (2013) **136** 2751-2768. DOI: 10.1093/brain/awt211 41. Hemington K. S., Wu Q., Kucyi A., Inman R. D., Davis K. D.. **Abnormal cross-network functional connectivity in chronic pain and its association with clinical symptoms.**. (2016) **221** 4203-4219. DOI: 10.1007/s00429-015-1161-1 42. Jensen M. P., Tomé-Pires C., de la Vega R., Galán S., Solé E., Miró J.. **What determines whether a pain is rated as mild, moderate, or severe? the importance of pain beliefs and pain interference.**. (2017) **33** 414-421. DOI: 10.1097/AJP.0000000000000429 43. Jones S. A., Morales A. M., Holley A. L., Wilson A. C., Nagel B. J.. **Default mode network connectivity is related to pain frequency and intensity in adolescents.**. (2020) **27**. DOI: 10.1016/j.nicl.2020.102326 44. Kastrati G., Thompson W. H., Schiffler B., Fransson P., Jensen K. B.. **Brain network segregation and integration during painful thermal stimulation.**. (2022) **32** 4039-4049. DOI: 10.1093/cercor/bhab464 45. Kawai K., Kawai A. T., Wollan P., Yawn B. P.. **Adverse impacts of chronic pain on health-related quality of life, work productivity, depression and anxiety in a community-based study.**. (2017) **34** 656-661. DOI: 10.1093/fampra/cmx034 46. Keller S., Bann C. M., Dodd S. L., Schein J., Mendoza T. R., Cleeland C. S.. **Validity of the brief pain inventory for use in documenting the outcomes of patients with noncancer pain.**. (2004) **20** 309-318. DOI: 10.1097/00002508-200409000-00005 47. Kilpatrick L. A., Istrin J. J., Gupta A., Naliboff B., Tillisch K., Labus J. S.. **Sex commonalities and differences in the relationship between resilient personality and the intrinsic connectivity of the salience and default mode networks.**. (2015) **112** 107-115. DOI: 10.1016/j.biopsycho.2015.09.010 48. Kim J., Mawla I., Kong J., Lee J., Gerber J., Ortiz A.. **Somatotopically specific primary somatosensory connectivity to salience and default mode networks encodes clinical pain.**. (2019) **160** 1594-1605. DOI: 10.1097/j.pain.0000000000001541 49. Kocalevent R.-D., Finck C., Jimenez-Leal W., Sautier L., Hinz A.. **Standardization of the Colombian version of the PHQ-4 in the general population.**. (2014) **14**. DOI: 10.1186/1471-244X-14-205 50. Kucyi A., Moayedi M., Weissman-Fogel I., Goldberg M. B., Freeman B. V., Tenenbaum H. C.. **Enhanced medial prefrontal-default mode network functional connectivity in chronic pain and its association with pain rumination.**. (2014) **34** 3969-3975. DOI: 10.1523/JNEUROSCI.5055-13.2014 51. Kummer K. K., Mitriæ M., Kalpachidou T., Kress M.. **The medial prefrontal cortex as a central hub for mental comorbidities associated with chronic pain.**. (2020) **21**. DOI: 10.3390/ijms21103440 52. Lee J.-J., Lee S., Lee D. H., Woo C.-W.. **Functional brain reconfiguration during sustained pain.**. (2022) **11**. DOI: 10.7554/eLife.74463 53. Legrain V., Damme S. V., Eccleston C., Davis K. D., Seminowicz D. A., Crombez G.. **A neurocognitive model of attention to pain: behavioral and neuroimaging evidence.**. (2009) **144** 230-232. DOI: 10.1016/j.pain.2009.03.020 54. Legrain V., Iannetti G. D., Plaghki L., Mouraux A.. **The pain matrix reloaded: a salience detection system for the body.**. (2011) **93** 111-124. DOI: 10.1016/j.pneurobio.2010.10.005 55. Loggia M. L., Kim J., Gollub R. L., Vangel M. G., Kirsch I., Kong J.. **Default mode network connectivity encodes clinical pain: an arterial spin labeling study.**. (2013) **154** 24-33. DOI: 10.1016/j.pain.2012.07.029 56. Lucas-Carrasco R.. **The WHO quality of life (WHOQOL) questionnaire: Spanish development and validation studies.**. (2012) **21** 161-165. DOI: 10.1007/s11136-011-9926-3 57. Malagurski B., Liem F., Oschwald J., Mérillat S., Jäncke L.. **Functional dedifferentiation of associative resting state networks in older adults – A longitudinal study**. (2020) **214**. DOI: 10.1016/j.neuroimage.2020.116680 58. Malfliet A., Coppieters I., Van Wilgen P., Kregel J., De Pauw R., Dolphens M.. **Brain changes associated with cognitive and emotional factors in chronic pain: a systematic review.**. (2017) **21** 769-786. DOI: 10.1002/ejp.1003 59. Mao C. P., Yang H. J., Zhang Q. J., Yang Q. X., Li X. H.. **Altered effective connectivity within the cingulo-frontal-parietal cognitive attention networks in chronic low back pain: a dynamic causal modeling study.**. (2022) **16** 1516-1527. DOI: 10.1007/s11682-021-00623-4 60. Meeker T. J., Jupudi R., Lenz F. A., Greenspan J. D.. **New developments in non-invasive brain stimulation in chronic pain.**. (2020) **8** 280-292. DOI: 10.1007/s40141-020-00260-w 61. Melloni L., van Leeuwen S., Alink A., Müller N. G.. **Interaction between bottom-up saliency and top-down control: how saliency maps are created in the human brain.**. (2012) **22** 2943-2952. DOI: 10.1093/cercor/bhr384 62. Miettinen T., Kautiainen H., Mäntyselkä P., Linton S. J., Kalso E.. **Pain interference type and level guide the assessment process in chronic pain: categorizing pain patients entering tertiary pain treatment with the brief pain inventory.**. (2019) **14**. DOI: 10.1371/journal.pone.0221437 63. Müller M. J.. **Helplessness and perceived pain intensity: relations to cortisol concentrations after electrocutaneous stimulation in healthy young men.**. (2011) **5**. DOI: 10.1186/1751-0759-5-8 64. Napadow V., LaCount L., Park K., As-Sanie S., Clauw D. J., Harris R. E.. **Intrinsic brain connectivity in fibromyalgia is associated with chronic pain intensity.**. (2010) **62** 2545-2555. DOI: 10.1002/art.27497 65. Olmedilla Zafra A., Ortega Toro E., Abenza Cano L.. **Validación de la escala de catastrofismo ante el dolor (pain catastrophizing scale) en deportistas españoles.**. (2013) **13** 83-94. DOI: 10.4321/S1578-84232013000100009 66. Opdebeeck C., Matthews F. E., Wu Y.-T., Woods R. T., Brayne C., Clare L.. **Cognitive reserve as a moderator of the negative association between mood and cognition: evidence from a population-representative cohort.**. (2018) **48** 61-71. DOI: 10.1017/S003329171700126X 67. Otti A., Guendel H., Wohlschläger A., Zimmer C., Noll-Hussong M.. **Frequency shifts in the anterior default mode network and the salience network in chronic pain disorder.**. (2013) **13**. DOI: 10.1186/1471-244X-13-84 68. Pfannmöller J., Lotze M.. **Review on biomarkers in the resting-state networks of chronic pain patients.**. (2019) **131** 4-9. DOI: 10.1016/j.bandc.2018.06.005 69. Power J. D., Barnes K. A., Snyder A. Z., Schlaggar B. L., Petersen S. E.. **Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion.**. (2012) **59** 2142-2154. DOI: 10.1016/j.neuroimage.2011.10.018 70. Power J. D., Barnes K. A., Snyder A. Z., Schlaggar B. L., Petersen S. E.. **Steps toward optimizing motion artifact removal in functional connectivity MRI; a reply to carp.**. (2013) **76** 439-441. DOI: 10.1016/j.neuroimage.2012.03.017 71. Power J. D., Mitra A., Laumann T. O., Snyder A. Z., Schlaggar B. L., Petersen S. E.. **Methods to detect, characterize, and remove motion artifact in resting state fMRI.**. (2014) **84** 320-341. DOI: 10.1016/j.neuroimage.2013.08.048 72. Power J. D., Schlaggar B. L., Petersen S. E.. **Recent progress and outstanding issues in motion correction in resting state fMRI.**. (2015) **105** 536-551. DOI: 10.1016/j.neuroimage.2014.10.044 73. Qiu J., Du M., Yang J., Lin Z., Qin N., Sun X.. **The brain’s structural differences between postherpetic neuralgia and lower back pain.**. (2021) **11**. DOI: 10.1038/s41598-021-01915-x 74. Riedel L., van den Heuvel M. P., Markett S.. **Trajectory of rich club properties in structural brain networks.**. (2021) **43** 4239-4253. DOI: 10.1002/hbm.25950 75. Schaefer A., Kong R., Gordon E. M., Laumann T. O., Zuo X.-N., Holmes A. J.. **Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI.**. (2018) **28** 3095-3114. DOI: 10.1093/cercor/bhx179 76. Schütze R., Rees C., Smith A., Slater H., Campbell J. M., O’Sullivan P.. **How can we best reduce pain catastrophizing in adults with chronic noncancer pain? a systematic review and meta-analysis.**. (2018) **19** 233-256. DOI: 10.1016/j.jpain.2017.09.010 77. Seidler R., Erdeniz B., Koppelmans V., Hirsiger S., Mérillat S., Jäncke L.. **Associations between age, motor function, and resting state sensorimotor network connectivity in healthy older adults.**. (2015) **108** 47-59. DOI: 10.1016/j.neuroimage.2014.12.023 78. Seminowicz D. A., Wideman T. H., Naso L., Hatami-Khoroushahi Z., Fallatah S., Ware M. A.. **Effective treatment of chronic low back pain in humans reverses abnormal brain anatomy and function.**. (2011) **31** 7540-7550. DOI: 10.1523/JNEUROSCI.5280-10.2011 79. Solé-Padullés C., Cattaneo G., Marchant N. L., Cabello-Toscano M., Mulet-Pons L., Solana J.. **Associations between repetitive negative thinking and resting-state network segregation among healthy middle-aged adults.**. (2022) **14**. DOI: 10.3389/fnagi.2022.1062887 80. Spisak T., Kincses B., Schlitt F., Zunhammer M., Schmidt-Wilcke T., Kincses Z. T.. **Pain-free resting-state functional brain connectivity predicts individual pain sensitivity.**. (2019). DOI: 10.1101/790709 81. Spreng R. N., Sepulcre J., Turner G. R., Stevens W. D., Schacter D. L.. **Intrinsic architecture underlying the relations among the default, dorsal attention, and frontoparietal control networks of the human brain.**. (2013) **25** 74-86. DOI: 10.1162/jocn_a_00281 82. Stensland M.. **“If you don’t keep going, you’re gonna die”: helplessness and perseverance among older adults living with chronic low back pain.**. (2021) **61** 907-916. DOI: 10.1093/geront/gnaa150 83. Stone A. A., Broderick J. E.. **Obesity and pain are associated in the United States.**. (2012) **20** 1491-1495. DOI: 10.1038/oby.2011.397 84. Sturgeon J. A., Zautra A. J.. **Psychological resilience, pain catastrophizing, and positive emotions: perspectives on comprehensive modeling of individual pain adaptation.**. (2013) **17**. DOI: 10.1007/s11916-012-0317-4 85. Sullivan M. J., Thorn B., Haythornthwaite J. A., Keefe F., Martin M., Bradley L. A.. **Theoretical perspectives on the relation between catastrophizing and pain.**. (2001) **17** 52-64. DOI: 10.1097/00002508-200103000-00008 86. Sullivan M. J. L.. **The communal coping model of pain catastrophising: clinical and research implications.**. (2012) **53** 32-41. DOI: 10.1037/a0026726 87. Sullivan M. J. L., Bishop S. R., Pivik J.. **The pain catastrophizing scale: development and validation.**. (1995) **7** 524-532. DOI: 10.1037/1040-3590.7.4.524 88. Suso-Ribera C., García-Palacios A., Botella C., Ribera-Canudas M. V.. **Pain catastrophizing and its relationship with health outcomes: does pain intensity matter?**. (2017) **2017**. DOI: 10.1155/2017/9762864 89. Tanner J. J., Johnson A. J., Terry E. L., Cardoso J., Garvan C., Staud R.. **Resilience, pain, and the brain: relationships differ by sociodemographics.**. (2021) **99** 1207-1235. DOI: 10.1002/jnr.24790 90. Ter Minassian A., Ricalens E., Humbert S., Duc F., Aubé C., Beydon L.. **Dissociating anticipation from perception: Acute pain activates default mode network.**. (2012) **34** 2228-2243. DOI: 10.1002/hbm.22062 91. **The world health organization quality of life assessment (WHOQOL): development and general psychometric properties.**. (1998) **46** 1569-1585. DOI: 10.1016/S0277-9536(98)00009-4 92. Thomas Yeo B. T., Krienen F. M., Sepulcre J., Sabuncu M. R., Lashkari D., Hollinshead M.. **The organization of the human cerebral cortex estimated by intrinsic functional connectivity.**. (2011) **106** 1125-1165. DOI: 10.1152/jn.00338.2011 93. Treede R.-D., Rief W., Barke A., Aziz Q., Bennett M. I., Benoliel R.. **A classification of chronic pain for ICD-11.**. (2015) **156** 1003-1007. DOI: 10.1097/j.pain.0000000000000160 94. van den Heuvel M. P., Sporns O.. **An anatomical substrate for integration among functional networks in human cortex.**. (2013) **33** 14489-14500. DOI: 10.1523/JNEUROSCI.2128-13.2013 95. Van Dijk K. R. A., Sabuncu M. R., Buckner R. L.. **The influence of head motion on intrinsic functional connectivity MRI.**. (2012) **59** 431-438. DOI: 10.1016/j.neuroimage.2011.07.044 96. van Ettinger-Veenstra H., Lundberg P., Alföldi P., Södermark M., Graven-Nielsen T., Sjörs A.. **Chronic widespread pain patients show disrupted cortical connectivity in default mode and salience networks, modulated by pain sensitivity.**. (2019) **12** 1743-1755. DOI: 10.2147/JPR.S189443 97. Vossel S., Geng J. J., Fink G. R.. **Dorsal and ventral attention systems.**. (2014) **20** 150-159. DOI: 10.1177/1073858413494269 98. Wang J., Zuo X., He Y.. **Graph-based network analysis of resting-state functional MRI.**. (2010) **4**. DOI: 10.3389/fnsys.2010.00016 99. Wiech K.. **Deconstructing the sensation of pain: the influence of cognitive processes on pain perception.**. (2016) **354** 584-587. DOI: 10.1126/science.aaf8934 100. Wiech K.. **Biased perception and learning in pain.**. (2018) **2** 804-805. DOI: 10.1038/s41562-018-0468-3 101. Wiech K., Shriver A.. **Cognition doesn’t only modulate pain perception; it’s a central component of it.**. (2018) **9** 196-198 102. Wig G. S.. **Segregated systems of human brain networks.**. (2017) **21** 981-996. DOI: 10.1016/j.tics.2017.09.006 103. **World medical association declaration of Helsinki: Ethical principles for medical research involving human subjects**. (2013) **310** 2191-2194. DOI: 10.1001/jama.2013.281053 104. Yoshino A., Okamoto Y., Okada G., Takamura M., Ichikawa N., Shibasaki C.. **Changes in resting-state brain networks after cognitive-behavioral therapy for chronic pain.**. (2018) **48** 1148-1156. PMID: 28893330 105. You B., Wen H., Jackson T.. **Identifying resting state differences salient for resilience to chronic pain based on machine learning multivariate pattern analysis.**. (2021) **58**. DOI: 10.1111/psyp.13921 106. Yu R., Gollub R. L., Spaeth R., Napadow V., Wasan A., Kong J.. **Disrupted functional connectivity of the periaqueductal gray in chronic low back pain.**. (2014) **6** 100-108. DOI: 10.1016/j.nicl.2014.08.019 107. Zhaoyang R., Martire L. M., Darnall B. D.. **Daily pain catastrophizing predicts less physical activity and more sedentary behavior in older adults with osteoarthritis.**. (2020) **161** 2603-2610. DOI: 10.1097/j.pain.0000000000001959 108. Ziadni M. S., Sturgeon J. A., Darnall B. D.. **The relationship between negative metacognitive thoughts, pain catastrophizing and adjustment to chronic pain.**. (2018) **22** 756-762. DOI: 10.1002/ejp.1160
--- title: Identification of five hub immune genes and characterization of two immune subtypes of osteoarthritis authors: - Lifeng Pan - Feng Yang - Xianhua Cao - Hongchang Zhao - Jian Li - Jinxi Zhang - Jiandong Guo - Zhijiang Jin - Zhongning Guan - Feng Zhou journal: Frontiers in Endocrinology year: 2023 pmcid: PMC10060864 doi: 10.3389/fendo.2023.1144258 license: CC BY 4.0 --- # Identification of five hub immune genes and characterization of two immune subtypes of osteoarthritis ## Abstract ### Background Osteoarthritis (OA) is one of the most prevalent chronic diseases, leading to degeneration of joints, chronic pain, and disability in the elderly. Little is known about the role of immune-related genes (IRGs) and immune cells in OA. ### Method Hub IRGs of OA were identified by differential expression analysis and filtered by three machine learning strategies, including random forest (RF), least absolute shrinkage and selection operator (LASSO), and support vector machine (SVM). A diagnostic nomogram model was then constructed by using these hub IRGs, with receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and clinical impact curve analysis (CICA) estimating its performance and clinical impact. Hierarchical clustering analysis was then conducted by setting the hub IRGs as input information. Differences in immune cell infiltration and activities of immune pathways were revealed between different immune subtypes. ### Result Five hub IRGs of OA were identified, including TNFSF11, SCD1, PGF, EDNRB, and IL1R1. Of them, TNFSF11 and SCD1 contributed the most to the diagnostic nomogram model with area under the curve (AUC) values of 0.904 and 0.864, respectively. Two immune subtypes were characterized. The immune over-activated subtype showed excessively activated cellular immunity with a higher proportion of activated B cells and activated CD8 T cells. The two phenotypes were also seen in two validation cohorts. ### Conclusion The present study comprehensively investigated the role of immune genes and immune cells in OA. Five hub IRGs and two immune subtypes were identified. These findings will provide novel insights into the diagnosis and treatment of OA. ## Introduction Osteoarthritis (OA) is one of the most prevalent chronic diseases worldwide, leading to degeneration of joints, chronic pain, and disability in the elderly [1]. Novel insights suggested that OA is a syndrome of joint destruction caused by different risk factors, and each of the factors could promote OA by instigating different mechanistic pathways [2]. Typical processes involved in OA development contain mechanical [3], inflammatory [4], metabolic [5], and senescent [6] signaling pathways. Interestingly, synovitis is found in the majority of patients with OA. Moreover, the infiltration of T cells and activated macrophages in synovial tissue has a strong correlation with bone erosion and pain in OA patients [7]. Little is known, however, about the osteo-immune microenvironment (OIME) of OA, and the role of immune-related genes has hardly been studied in this disease. Hereby, we investigated the role of immune-related genes (IRGs) in OA from the aspects of OIME, disease classification, and diagnostic value. First, hub IRGs were identified by differential expression analysis and three strategies of feature selection, including random forest (RF), least absolute shrinkage and selection operator (LASSO), and support vector machine (SVM). Then, these hub IRGs were used to construct a diagnostic nomogram model with receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and clinical impact curve analysis (CICA) estimating its diagnostic performance and clinical impact for OA. These hub IRGs were then subjected to hierarchical clustering analysis, and two immune subtypes of OA were characterized. The immune over-activated subtype showed a higher proportion of activated B-cell and activated CD8 T-cell infiltration, underlying an OIME with excessively activated cellular immunity for this group. Finally, two external cohorts of OA were utilized to validate the existence of the two immune subtypes of OA. In all, the present study conducted a comprehensive analysis of the role of immune genes and immune cells in OA. An immune over-activated subtype of OA was identified, and a nomogram model was built for clinical practice. It was found that regulatory T-cell infiltration was positively correlated with TNFSF11 and IL1R1 and negatively correlated with EDNRB. These findings provided novel insights to understand the role of the osteo-immune microenvironment in the development of OA. ## Data collection and processing The microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) using “Osteoarthritis”, “Tissue”, and “Homo sapiens” as keywords. The microarray datasets GSE55235 and GSE55457 (doi: 10.1186/ar4526) and GSE82107 (doi: 10.1371/journal.pone.0167076) contained 27 healthy controls and 30 OA patients. A dataset of identified IRGs was acquired from the ImmPort database (http://www.immport.org). We then performed log2 transformed for gene expression profiling and matched the probes to their gene symbols according to the annotation document of corresponding platforms. Finally, the gene matrix with row names as sample names and column names as gene symbols were obtained for subsequent analyses. ## Identification of differentially expressed immune-related genes These three datasets were merged and normalized by the “limma” package8 of R software (doi: 10.1093/nar/gkv007). The batch effect amid different arrays was eliminated by using the ComBat function of R (version 4.1.3) package sva9. We extracted the expression profiles of immune-related genes from this merged dataset. Then, we identified differentially expressed IRGs in OA and normal samples by the “limma” package. p-value <0.05 was considered a significant difference. Heatmap was generated using the R package “pheatmap” to visualize the differentially expressed IRGs. ## Functional and pathway enrichment analyses To investigate the functional and molecular pathways of differentially expressed IRGs, we performed Gene Ontology (GO) [8], Kyoto Encyclopedia of Genes and Genomes (KEGG) [9], and gene set enrichment analysis (GSEA) [10] enrichment analyses by the “colorspace”, “stringi”, and “ggplot2” packages in R (doi: 10.7717/peerj.11534). $p \leq 0.05$ was considered statistically significant. ## Screening of OA-related biomarker characteristic genes The protein–protein interaction (PPI) network was constructed to predict protein–protein interactions of differentially expressed IRGs using the Search Tool for the Retrieval of Interacting Genes database (STRING, http://www.stringdb.org). *The* gene with an interaction score >0.9 was retained, and Cytoscape software v3.6.0 is used to visualize the PPI network. Based on these IRGs, three feature selection algorithms including SVM–recursive feature elimination (SVM-RFE), LASSO logistic regression, and RF were adapted to screen OA-related biomarkers. The SVM-RFE algorithm was performed by R packages “e1071” and “caret” with fivefold cross-validation. The LASSO logistic regression was employed with the R package “glmnet” [11]. The RF algorithm was analyzed by the “randomForest” package in R (https://CRAN.R-project.org/package=beeswarm). Then, the “venn” R package [12] (version 1.7) was used to select overlapping genes from the three algorithms as signature genes for further analysis. ## Construction of a nomogram model The ROC and area under the curve (AUC) were also calculated to evaluate the predictive effectiveness of the algorithm. We constructed a nomogram model based on OA-related signature genes to predict the occurrence of OA patients with the “rms” package in R. The calibration curve was used to assess the predictive performance of the nomogram model. Then, we further performed DCA and CICA to estimate the clinical utility of the nomogram model. ## Consensus clustering Consensus clustering is an algorithm for identifying the cluster of each member and their number in datasets. We utilized the consensus clustering method to distinguish distinct immune-related clinical subtypes of OA and identify different IRG patterns based on the significant differentially expressed IRGs with the R package “ConsensusClusterPlus” [13]. In the correlation between significant OA-related IRG expression and clinical features in subtypes of OA patients. “ Points” represents the score of the corresponding factor below, and “Total Points” indicates the summation of all the scores of factors above. ## Estimation of immune cell infiltration The single-sample gene set enrichment analysis (ssGSEA) was employed to measure the relative abundance of immune cells in OA samples via the R packages “limma”, “GSVA” [10], and “GSEABase”. *The* gene set for marking each immune cell type was obtained from the study of Charoentong [14]. We also conducted a correlation analysis of immune cells with OA-related genes. ## Calculation of immune score We used principal component analysis (PCA) algorithms to construct the signature of immune-related genes for OA samples (doi: 10.1038/nbt0308-303). Principal Component 1 (PC1) and Principal Component 2 (PC2) were chosen as the signature scores. Immune scores for each OA patient were calculated using the formula Immune Score = Σ(PC1i + PC2i), where i is the expression of immune-related genes. We calculated the relationship between different classifications and immune scores. We used limma and ggpubr packages to study the relationship between the different classifications and the expression level of notable molecules. ## Statistics and software Data processing and bioinformatics analyses were accomplished by R (version 4.1.3). Packages limma, ggplot2, rmda, clusterProfiler, ssGSEA, rsm, and glmnet were employed for analyses with proper citations. The Wilcoxon or Kruskal–Wallis test was applied for comparisons between two or more groups involved in this study. Pearson’s and Spearman’s rank correlation tests were adopted to estimate the statistical correlation of parametric or non-parametric variables. Two-sided $p \leq 0.05$ was considered a significant threshold for all statistical tests. ## Hub IRGs and their biological function in OA Between the OA samples and the control samples, there was a significant difference in the expression of 2,483 IRGs (Figure 1A). As was to be predicted, enrichment of these genes was found in a number of processes related to bone production and resorption. These processes include MAPK, Osteoclast Differentiation, and Ras Signaling Pathways. In addition to this, the Th17 cell differentiation pathway was shown to be active in OA patients, which suggests the possible involvement of immune cells in the development of OA (Figures 1B, C). **Figure 1:** *Differentially expressed immune genes in osteoarthritis (OA). (A) The heatmap shows the differentially expressed immune genes between OA and control samples (GSE55235). (B, C) Gene Ontology (B) and Kyoto Encyclopedia of Genes and Genomes (C) enrichment analyses revealed the biological function and downstream pathways of the differentially expressed immune genes.* ## Diagnostic value of the hub IRGs in OA There were intense interactions amid these IRGs, and several genes seemed to be key regulators in OA, including VEGFA, EDN1, JUN, and MAPK8 (Figure 2A, Figure S1). Three machine learning strategies were then utilized for feature selection by inputting these IRGs and patients’ diagnostic information (Figures 2B-D). Finally, 17, 11, and 21 core genes were authenticated by LASSO, SVM, and RF algorithms, respectively (Figure 2E). Of them, five intersected genes were submitted to the final diagnostic model, including PGF, TNFSF11, EDNRB, SDC1, and IL1R1 (Figure 2E). **Figure 2:** *Hub immune-related genes (IRGs) and their diagnostic value. (A) Protein–protein interaction network of the IRGs. (B–D) Hub IRGs were filtered by three machine learning strategies of feature selection, including least absolute shrinkage and selection operator (B), random forest (C), and support vector machine (D). (E) Five hub IRGs were identified by the three machine learning strategies. (F) The five-IRG-based nomogram model showed good diagnostic performance.* In the end, TNFSF11 and SDC1 appeared to contribute the most in the diagnostic model to distinguish OA samples from control samples, suggesting that these two genes play an important role in the progression of OA (Figure S2). The AUC for TNFSF11 was 0.904 (0.806–0.979), and the AUC for SDC1 was 0.864 (0.744–0.959) (Figures S2D,E). The nomogram then quantified the contribution of each gene, and as a result, the patients’ disease risk was quickly calculated by adding up the points from all five genes (Figure 2F). In the calibration curve, the nomogram’s predicted disease risk and the actual disease condition were quite congruent with one another (Figure S3A). The subsequent DCA study demonstrated a significant internal advantage for this approach (Figure S3B). When the value of the threshold was greater than 0.6, the estimated number of patients came closer to matching the actual positive patient count (Figure S3C). ## Characterization of the immune over-activated and immune-inhibited subtypes of OA Two subtypes of OA were identified by executing hierarchical clustering analysis with the IRGs mentioned above (Figures 3A, C). Cluster A displayed higher expression of TNFSF11 and IL1R1, while Cluster B demonstrated an increased level of EDNRB (Figure 3B). Moreover, Cluster B was seen with increased infiltration of activated B cells and activated CD8 T cells and decreased infiltration of regulatory T cells, suggesting a microenvironment with excessively activated cellular immunity for this group (Figure 3D). On the contrary, Cluster A seemed to be the immune-inhibited subtype of OA with more infiltration of regulatory T cells. Correspondingly, TNFSF11 and IL1R1 were found positively correlated with the infiltration of regulatory T cells, partly accounting for its reduction in Cluster B (Figure 3E). In addition, Clusters A and B differed in many biological processes (Figure 3F) such as regulation of anatomical structure size (go:0090066), endoplasmic reticulum lumen (go:0005788), potassium channel activity (go:0005267), and heat generation (go:0031649). **Figure 3:** *Clustering analysis and immune infiltration analysis. (A) Clustering analysis stratified patients into two subtypes. (B, C) The two immune subtypes differ in gene expression pattern (B) and geometrical distance (C). (D) Immune subtype B showed higher infiltration of activated B cells and activated T cells than subtype (A). (E) Correlation analysis between five hub immune genes and immune cells. (F) Gene Ontology enrichment analysis revealed the functional differences between the two immune subtypes. * means P < 0.05, *** means P < 0.001.* ## External validation for the two immune subtypes in GSE55457 and GSE82107 Similar classifications were seen in two external validation cohorts: GSE55457 ($$n = 33$$) and GSE82107 ($$n = 17$$). The processes of clustering analyses for these two cohorts were illustrated in supplementary pictures (Figures S4, S5) with consensus matrix, CDF, and delta area determining the optimal number of clusters. Distinguishable two clusters were identified in GSE55457 with a group of genes upregulated in Cluster A (Figures 4A, B). Keeping consistent with the former results of the training cohort, TNFSF11, IL1R1, and regulatory T cells also showed a marked decrease in Cluster B (Figures 4C, D), implying a phenotype of immune over-activation with advanced bone absorption. In GSE55457, Cluster B was seen with an increased immune score in both the immune gene cluster and the gene cluster, supporting the immune-activated phenotype of this group. The Sankey diagram demonstrated the overlap of patients between the different clusters (Figures 5A, B). In parallel, Cluster B showed a distinct decline of TNFSF11 and GDF5, accompanied by significant ascending of FRZB and TRAPPC2 (Figures 5C, D). **Figure 4:** *External validation for the two immune subtypes in GSE55457. (A) Two immune subtypes were found in GSE55457 by clustering analysis. (B) The heatmap showed the differentially expressed genes between the two subtypes. (C, D) The two immune subtypes differ in the pattern of immune gene expression (C) and immune cell infiltration (D). Cluster B also displayed higher infiltration of activated B cells and T cells as the subtype B in GSE55235. * means P < 0.05, ** means P < 0.01, *** means P < 0.001.* **Figure 5:** *External validation for the two immune subtypes in GSE82107. (A) Boxplot showed the difference in immune score in the immune gene cluster and gene cluster in GSE82107. (B) The Sankey diagram showed the distribution of patients in different clusters. (C, D) Expression difference of five osteoarthritis-related genes in the immune gene cluster (C) and gene cluster (D). * means P < 0.05, ** means P < 0.01, *** means P < 0.001.* ## Discussion Non-infectious chronic inflammation, which occurs when inflammatory cells invade synovial tissue or synovial fluid, especially in the early stages of the illness, is the main clinical hallmark of OA (doi: 10.1053/joca.1998.0224, 10.1002/art.10768). Immunity plays a key role in the emergence and progression of OA. The present study comprehensively investigated the role of immune genes and immune cells in OA, revealing the immune over-activated and immune-inhibited subtypes of OA. The former subtype showed higher infiltration of activated B cells and CD8 T cell, compared with lesser infiltration of regulatory T cells, underlying a microenvironment with excessive cellular immunity. A nomogram model was also constructed by using five immune genes, showing rather good diagnostic performance. These findings will help understand the crosstalk between immune cells and bone tissue, providing novel insights for the diagnosis and treatment of OA. First, five critical IRGs were identified in our study, including PGF, TNFSF11, EDNRB, SDC1, and IL1R1. It was shown that the presence of regulatory T cells was inversely connected with EDNRB and positively correlated with TNFSF11 and IL1R1. Of them, TNFSF11 contributed most significantly to the diagnosis of OA, followed by SCD1. Reportedly, TNFSF11 (TNF Superfamily Member 11) is a key factor responsible for osteoclast differentiation and activation, encoding RANKL, the ligand of osteoprotegerin (OPG) [15, 16], to regulate bone density. Moreover, TNFSF11 has already been linked to a series of diseases with osteoproliferation or osteolysis, including osteopetrosis, dysosteosclerosis, Paget disease of bone 2, and familial expansile osteolysis [17]. Therefore, it is reasonable to see the significant upregulation of TNFSF11 in osteoarthritis. Correspondingly, reducing TNFSF11 expression could relieve the progression of cartilage degradation in OA [18]. Meanwhile, TNFSF11 is key in the processes of lymph node development and production of activated B cells and T cells [19, 20]. This is consistent with the results of our study: TNFSF11 was observed to be correlated with the infiltration of activated T cells, B cells, natural killer T cells, neutrophils, monocytes, etc. Similarly, elevated TNFSF11 was reported to induce a pro-inflammatory phenotype of OA [21], resulting in accelerated joint destruction and deteriorated clinical symptoms [22]. SCD1, stearoyl CoA desaturase 1, was found to promote the function of osteogenesis in bone marrow mesenchymal stem cells [23], and inhibition of SCD1 could prevent postmenopausal osteoporosis to some extent [24]. Keeping consistent with these studies, we found that SCD1 also played a pivotal role in OA. SCD1 expression was positively correlated with the infiltration of monocyte, activated CD4 T cell, and gamma delta T cell, underlying an inflamed microenvironment. Potential mechanisms accounting for this correlation between SCD1 and immune imbalance are the activation of miR-203a/FOS and miR-1908/EXO1 signaling pathways by SCD1 [25]. The present study has several advantages. Comprehensive investigations were conducted on the role of immune genes and immune cells in OA. Several critical immune genes were identified, and a diagnostic nomogram was constructed with quite good performance. Immune over-activated and immune-inhibited subtypes of OA were revealed. The former subtype showed higher infiltration of activated B cells and CD8 T cells, underlying a microenvironment with excessive cellular immunity. These findings will provide novel insights into the diagnosis and treatment of OA. There were also some limitations to our study. First, it would be more convincing if there were some in vitro experiments. Second, the expression of TNFSF11, SCD1, and the two immune subtypes of OA could be tested in actual patient cohorts. Lastly, analysis of the pathways related to osteogenesis can be added to further explain the difference between the two immune subtypes of OA. ## Data availability statement The original contributions presented in the study are included in the article/ Supplementary Material. Further inquiries can be directed to the corresponding authors. ## Author contributions FZ, ZG, and ZJ designed the study. LP, FY, XC, HZ, JL, JZ, and JG performed data analysis. LP and FY drafted the manuscript. FZ, ZG, and ZJ revised the manuscript. All authors read and approved the final manuscript. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2023.1144258/full#supplementary-material ## References 1. Prieto-Alhambra D, Judge A, Javaid MK, Cooper C, Diez-Perez A, Arden NK. **Incidence and risk factors for clinically diagnosed knee, hip and hand osteoarthritis: Influences of age, gender and osteoarthritis affecting other joints**. *Ann Rheum Dis* (2014) **73**. DOI: 10.1136/annrheumdis-2013-203355 2. Barnett R. **Osteoarthritis**. *Lancet* (2018) **391** 1985. DOI: 10.1016/s0140-6736(18)31064-x 3. Scanzello CR. **Role of low-grade inflammation in osteoarthritis**. *Curr Opin Rheumatol* (2017) **29** 79-85. DOI: 10.1097/bor.0000000000000353 4. Bierma-Zeinstra SM, van Middelkoop M. **Osteoarthritis: In search of phenotypes**. *Nat Rev Rheumatol* (2017) **13**. DOI: 10.1038/nrrheum.2017.181 5. Courties A, Sellam J, Berenbaum F. **Metabolic syndrome-associated osteoarthritis**. *Curr Opin Rheumatol* (2017) **29**. DOI: 10.1097/bor.0000000000000373 6. Jeon OH, Kim C, Laberge RM, Demaria M, Rathod S, Vasserot AP. **Local clearance of senescent cells attenuates the development of post-traumatic osteoarthritis and creates a pro-regenerative environment**. *Nat Med* (2017) **23**. DOI: 10.1038/nm.4324 7. Lopes EBP, Filiberti A, Husain SA, Humphrey MB. **Immune contributions to osteoarthritis**. *Curr Osteoporos Rep* (2017) **15** 593-600. DOI: 10.1007/s11914-017-0411-y 8. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM. **Gene ontology: Tool for the unification of biology. the gene ontology consortium**. *Nat Genet* (2000) **25**. DOI: 10.1038/75556 9. Kanehisa M, Goto S. **KEGG: kyoto encyclopedia of genes and genomes**. *Nucleic Acids Res* (2000) **28** 27-30. DOI: 10.1093/nar/28.1.27 10. Hänzelmann S, Castelo R, Guinney J. **GSVA: Gene set variation analysis for microarray and RNA-seq data**. *BMC Bioinf* (2013) **14** 7. DOI: 10.1186/1471-2105-14-7 11. Lu X, Meng J, Zhu J, Zhou Y, Jiang L, Wang Y. **Prognosis stratification and personalized treatment in bladder cancer through a robust immune gene pair-based signature**. *Clin Transl Med* (2021) **11**. DOI: 10.1002/ctm2.453 12. Chen H, Boutros PC. **VennDiagram: A package for the generation of highly-customizable Venn and Euler diagrams in R**. (2011) **12** 35. DOI: 10.1186/1471-2105-12-35 13. Wilkerson MD, Hayes DN. **ConsensusClusterPlus: A class discovery tool with confidence assessments and item tracking**. *Bioinformatics* (2010) **26**. DOI: 10.1093/bioinformatics/btq170 14. Charoentong P, Finotello F, Angelova M, Mayer C, Efremova M, Rieder D. **Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade**. *Cell Rep* (2017) **18**. DOI: 10.1016/j.celrep.2016.12.019 15. Xiao L, Zhong M, Huang Y, Zhu J, Tang W, Li D. **Puerarin alleviates osteoporosis in the ovariectomy-induced mice by suppressing osteoclastogenesis**. *Aging (Albany NY)* (2020) **12**. DOI: 10.18632/aging.103976 16. Udagawa N, Koide M, Nakamura M, Nakamichi Y, Yamashita T, Uehara S. **Osteoclast differentiation by RANKL and OPG signaling pathways**. *J Bone Miner Metab* (2021) **39** 19-26. DOI: 10.1007/s00774-020-01162-6 17. Xue JY, Ikegawa S, Guo L. **Genetic disorders associated with the RANKL/OPG/RANK pathway**. *J Bone Miner Metab* (2021) **39** 45-53. DOI: 10.1007/s00774-020-01148-4 18. Li J, Jiang M, Yu Z, Xiong C, Pan J, Cai Z. **Artemisinin relieves osteoarthritis by activating mitochondrial autophagy through reducing TNFSF11 expression and inhibiting PI3K/AKT/mTOR signaling in cartilage**. *Cell Mol Biol Lett* (2022) **27** 62. DOI: 10.1186/s11658-022-00365-1 19. Hess E, Duheron V, Decossas M, Lézot F, Berdal A, Chea S. **RANKL induces organized lymph node growth by stromal cell proliferation**. *J Immunol* (2012) **188**. DOI: 10.4049/jimmunol.1101513 20. Kawai T, Matsuyama T, Hosokawa Y, Makihira S, Seki M, Karimbux NY. **B and T lymphocytes are the primary sources of RANKL in the bone resorptive lesion of periodontal disease**. *Am J Pathol* (2006) **169**. DOI: 10.2353/ajpath.2006.060180 21. Massicotte F, Lajeunesse D, Benderdour M, Pelletier JP, Hilal G, Duval N. **Can altered production of interleukin-1beta, interleukin-6, transforming growth factor-beta and prostaglandin E(2) by isolated human subchondral osteoblasts identify two subgroups of osteoarthritic patients**. *Osteoarthritis Cartilage* (2002) **10** 491-500. DOI: 10.1053/joca.2002.0528 22. Tat SK, Pelletier JP, Lajeunesse D, Fahmi H, Duval N, Martel-Pelletier J. **Differential modulation of RANKL isoforms by human osteoarthritic subchondral bone osteoblasts: influence of osteotropic factors**. *Bone* (2008) **43**. DOI: 10.1016/j.bone.2008.04.006 23. Tao J, Shi J, Lu Y, Dou B, Zhou Z, Gao M. **Overexpression of stearoyl-CoA desaturase 1 in bone-marrow mesenchymal stem cells increases osteogenesis**. *Panminerva Med* (2013) **55** 24. Lu L, Wang L, Wu J, Yang M, Chen B, Wang H. **DNMT3a promotes osteoblast differentiation and alleviates osteoporosis**. *Epigenomics* (2022) **14**. DOI: 10.2217/epi-2021-0391 25. Chen YS, Kang XR, Zhou ZH, Yang J, Xin Q, Ying CT. **MiR-1908/EXO1 and MiR-203a/FOS, regulated by scd1, are associated with fracture risk and bone health in postmenopausal diabetic women**. *Aging (Albany NY)* (2020) **12**. DOI: 10.18632/aging.103227
--- title: 'Association between glycolipids and risk of obstructive sleep apnea: A population-based study' authors: - Murui Zheng - Xueru Duan - Huanning Zhou - Weidi Sun - Guoqiang Sun - Jianying Chen - Xiuyi Wu - Sijing Rong - Jun Huang - Wengjing Zhao - Hai Deng - Xudong Liu journal: Frontiers in Nutrition year: 2023 pmcid: PMC10060897 doi: 10.3389/fnut.2023.974801 license: CC BY 4.0 --- # Association between glycolipids and risk of obstructive sleep apnea: A population-based study ## Abstract ### Background This study aimed to investigate the associations between multiple glycolipid biomarkers and the risk of obstructive sleep apnea (OSA). ### Methods Participants [10,286] aged from 35 to 74 years old were included in this cross-sectional study from the baseline survey of the Guangzhou Heart Study. OSA was ascertained using both Berlin Questionnaire and STOP-BANG Questionnaire. Fasting blood samples were collected from each participant; fasting blood glucose (FBG) and serum concentrations of high-density lipoprotein cholesterol (HDL-CH), low-density lipoprotein cholesterol (LDL-CH), total cholesterol (TC), and triglyceride (TG) were determined. Odds ratio (OR) with $95\%$ confidence interval (CI) was calculated using the multivariate logistic regression model after adjustment for covariates. ### Results Of the participants included, $15.56\%$ were categorized into the pre-OSA group, and $8.22\%$ into the OSA group. When comparing the highest with the lowest quartiles, HDL-HC was associated with a $22\%$ (OR: 0.78, $95\%$ CI: 0.65–0.94) and $41\%$ (OR: 0.59, $95\%$ CI: 0.45–0.78) reduced risk of pre-OSA and OSA, triglyceride was associated with a $32\%$ (OR 1.32, $95\%$ CI 1.08–1.60) and a $56\%$ (OR 1.56, $95\%$ CI 1.18–2.07) increased risk of pre-OSA and OSA, and FBG was associated with a 1.37-fold ($95\%$ CI 1.13–1.67) risk of pre-OSA and 1.38-fold ($95\%$ CI 1.03–1.85) risk of OSA. A significant exposure-response trend was observed for HDL-HC, TG, and FBG with both OSA and Pre-OSA (all $p \leq 0.05$). No significant association of LDL-CH and TC with the risk of both pre-OSA and OSA was observed. ### Conclusion The findings suggest that serum HDL-CH was inversely associated with OSA risk, while elevated serum TG and FBG could increase the risk of OSA. Healthy glycolipid metabolism warrants more attention in the field of OSA prevention. ## Introduction Obstructive sleep apnea (OSA) is a common sleep disorder characterized by recurrent episodes of upper airway obstruction and hypopnea during sleep [1]. It is an adverse health condition that has become increasingly prevalent worldwide [2]. The overall number of affected adults has reached approximately 1 billion [3]. In the United States, the estimated prevalence of OSA ranged from 7 to $12\%$ among men aged 30–49 years old and was even higher as the age went up [4]. China was considered to have the largest number of individuals with OSA among 16 countries [3]. OSA was well recognized as an independent risk factor for multifactorial consequences including cardiovascular diseases, cognitive impairment, [5, 6]. Previous studies have shown that OSA is associated with multiple risk factors, including a low level of physical activity, obesity, and metabolic syndrome [7, 8]. OSA can be both a sleep disorder and a heterogeneous metabolic disorder [9]. Glycolipid biomarkers are considerable screening tools in many chronic diseases [10, 11], the level of which is also considered to be independently associated with the risk of OSA. Pathogenic pathways of the association are consist of activation of the sympathetic nervous system, changes in hypothalamic–pituitary–adrenal axis activity, and formation of reactive oxygen species, etc. [ 12, 13]. Previous studies have shown that individuals with OSA had a higher prevalence of elevated total cholesterol (TC) and triacylglycerol (TG) [14], and a lower level of high-density lipoprotein cholesterol (HDL-CH) [15]. Dyslipidemia and diabetes, two types of diseases resulting from the dysfunction of glycolipids, were also observed to be associated with OSA in several reports [8, 16, 17]. However, no conclusive results have been found in research on the associations between glycolipid biomarkers and OSA up till now. In a multiethnic study among American people, lower HDL-CH was associated with a higher apnea-hypopnea index (AHI), the key metric used to define OSA severity [18]. In contrast, a remarkable association with AHI was observed in low-dense lipoprotein cholesterol (LDL-CH) but not in HDL-CH among Chinese people [19]. Besides, data from the Heart Institute in America did not present a significant association between HDL-CH and OSA [20]. Therefore, this study aimed to assess the association between glycolipid biomarkers and the risk of OSA using data from the Guangzhou Heart Study (GZHS). ## Setting and subjects Participants in this cross-sectional study were recruited from baseline survey of the Guangzhou Heart Study (GZHS), which recruited 12,013 individuals aged 35 years old and above using a randomized multistage cluster sampling between July 2015 and August 2017. Detailed information of the cohort has been reported elsewhere [7, 21–23]. The inclusion criteria for this study were: [1] Guangzhou permanent residents, [2] aged 35 years old and above; [3] having lived in the selected communities for at least 6 months before being involved into the study. Those who were aged 75 years old and above, were pregnant or lactating women, were non-permanent residents in Guangzhou, had mental or cognitive disorders including dementia, disturbance of understanding and deaf-mutters, had mobility difficulties including high paraplegia, and had any cancer history, were excluded. Finally, a total of 10,826 individuals from the GZHS baseline survey were involved for further analysis. This study was approved by the Ethical Review Committee for Biomedical Research, School of Public Health, Sun Yat-sen University, and Ethics Committee of Guangzhou Centre for Disease Control and Prevention. It was conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants. ## Ascertainment of OSA Obstructive sleep apnea was ascertained by Berlin Questionnaire (BQ) and STOP-BANG Questionnaire (SBQ), which are both widely used as screening tools for identifying OSA. The Chinese versions of both questionnaires had been proved to have superior predictive validity and reliability [24–27]. Berlin Questionnaire contains ten questions in three categories: snoring and cessation of breathing (category 1), symptoms of excessive daytime sleepiness (category 2), body mass index (BMI), and hypertension (category 3). High risk in category 1 and category 2 was defined by persistent symptoms (> 3–4 times/week) in at least two questions of each category, and high risk in category 3 was defined by the history of hypertension or BMI higher than 30 kg/m2. When two or more categories were considered positive, it indicated a high risk for OSA, otherwise a low-risk for OSA [28]. The SBQ included eight questions with dichotomous (yes/no) answers. The questions referred to snoring, fatigue, observed apnea, high blood pressure (or treatment for it), body mass index (BMI > 35 kg/m2), age (> 50 years old), neck circumference (> 40 cm), and gender (male). For each question, answering “yes” scores 1 and a “no” response scores 0. Subjects scoring three or more were considered to be at a high risk of OSA, otherwise were as at a low risk of OSA [24]. Then, participants who were assessed as having a high risk of OSA by both BQ and SBQ were classified into the OSA group, those who were assessed as having a lower risk of OSA by both BQ and SBQ were classified into the non-OSA group, and the remain participants were classified in the pre-OSA group. ## Measurements of glycolipid biomarkers Fasting blood samples were collected in the morning from each participant at the baseline survey and then detected within 4 h after collection in a qualified third-party medical laboratory (Guangzhou KingMed Diagnostics Group Co., Ltd). Fasting blood glucose (FBG) and serum lipids, including high-density lipoprotein cholesterol (HDL-CH), low-density lipoprotein cholesterol (LDC-CH), total cholesterol (TC), and triglyceride, were detected. $5\%$ samples were randomly selected for parallel double-sample detection, and the results showed that the detection results were reliable. ## Measurements of covariates A face-to-face interview approach and a medical examination were adopted to collect information. A structured questionnaire was used to collect each participant’s social-demographic characteristics and lifestyle factors, including age, gender, marital status (married or others), educational status (primary school and lower, junior high school, senior high school, college and above), work intensity (light, moderate, vigorous, retirement), active smoking (never, occasional, frequent), passive smoking (yes or no), alcohol drinking (yes or no), fresh vegetable and fruit intake (< once/day or ≥ once/day). Leisure-time physical activity (LTPA) was assessed by a modified Global Physical Activity Questionnaire and the total volume of LTPA was calculated according to the method we reported [7, 22]. Personal history of chronic diseases, including hypertension, dyslipidemia, diabetes, chronic obstructive pulmonary disease (COPD), and cardiovascular diseases (CVDs) was collected. Hypertension was confirmed if the participant reported having physician-diagnosed hypertension, and/or had a mean systolic blood pressure (SBP) ≥ 140 mmHg, and/or mean diastolic blood pressure (DBP) ≥ 90 mmHg, and/or was on anti-hypertensive drugs [29]. A participant would be defined as dyslipidemia if he/she self-reported dyslipidemia diagnosed by a physician, or had serum cholesterol ≥ 5.2 mmol/l, LDC-CH ≥ 3.4 mmol/l, or TG ≥ 1.7 mmol/l [30]. A participant who had fasting plasma glucose ≥ 7.0 mmol/l and/or HbA1c ≥ $6.5\%$, and/or self-reported physician-diagnosed diabetes, and/or on diabetes treatment would be defined as having diabetes [31]. Height and weight were measured to calculate body mass index (BMI). Waist circumference and hip circumference were measured to calculate the waist-hip ratio (WHR). In addition, individual exposure to PM2.5 was assessed by 4-year average PM2.5 concentration from 2014 to 2017 within a 1,000 m circular buffer of each participant’s residential address. We obtained daily average PM2.5 data from 142 monitoring stations within or around Guangzhou City by the inverse distance weighting interpolation method [32]. ## Statistical analysis The Kolmogorov–Smirnov test was used to test for normality. Continuous variables that were normally distributed are presented as mean and standard deviation (SD), otherwise were presented as median and interquartile range (IQR). Distributions of categorical variables were presented as frequencies and percentages. The distribution of demographic and socioeconomic characteristics, history of chronic diseases, and glycolipid biomarkers were described. Comparison of characteristics among the non-OSA group, the pre-OSA group, and the OSA group, was conducted by one-way analysis of variance (ANOVA) for continuous variable and Chi-square tests for categorical variable. Each glycolipid biomarker was transformed into a categorical variable by using quartile methods. Unadjusted and adjusted odds ratios (ORs) with $95\%$ confidence intervals (CIs) were estimated by using multivariate logistic regression models to display the association between glycolipid biomarkers and the risk of OSA and pre-OSA. The linear exposure-response relationship was examined by putting the median of each quartile of exposure as a continuous variable in the model. Sensitivity analysis was carried out by excluding participants with COPD and CVDs. The associations between dyslipidemia and diabetes and risk of the OSA and pre-OSA were also assessed among overall participants and participants without COPD and CVDs, as dyslipidemia and diabetes were diseases resulting from dysfunction of glycolipids. All analyses were performed using the R software (version 3.6.1). All tests were two-sided and a p-value of less than 0.05 was considered to be statistically significant. ## Results Of the 10,826 participants included, $15.56\%$ were categorized into the pre-OSA group, and $8.22\%$ into the OSA group (Table 1). The proportion of males in the OSA group was $76.18\%$ and was the highest among the three groups. Compared with participants without OSA, those with OSA were older and more likely to smoke frequently and drink alcohol, had higher levels of BMI [26.68 (3.98) kg/m2] and the waist-hip ratio [0.93 (0.06)], had a lower volume of LTPA [36.84 (34.77) MET-hours/week] and had a higher prevalence of dyslipidemia ($77.19\%$) and diabetes ($12.13\%$). The mean concentration of HDL-CH significantly decreased from 1.54 (0.43) mmol/L in the non-OSA group to 1.44 (0.41) mmol/L in the pre-OSA group and 1.39 (SD: 0.41) mmol/L in the OSA group. In contrast, the mean levels of LDL-CH, Triglyceride, and FBG increased in sequence from the non-OSA group, pre-OSA group, and OSA group (all $p \leq 0.05$). **Table 1** | Items | Non-OSA group | Pre-OSA group | OSA group | p value | | --- | --- | --- | --- | --- | | Sample, N (%) | 8,252 (76.22) | 1,684 (15.56) | 890 (8.22) | -- | | Age, year, mean (S.D.) | 55.47 (10.14) | 59.33 (8.73) | 59.55 (8.36) | <0.001* | | Body mass index, kg/m2, mean (S.D.) | 23.44 (3.22) | 25.43 (3.68) | 26.68 (3.98) | <0.001* | | Waist-hip ratio, mean (S.D.) | 0.87 (0.07) | 0.91 (0.06) | 0.93 (0.06) | <0.001* | | LTPA, MET-hours/week, mean (S.D.) | 43.89 (35.90) | 37.28 (32.07) | 36.84 (34.77) | <0.001* | | PM2.5 concentration, μg/m3, mean (S.D.) | 49.21 (0.87) | 49.18 (0.89) | 49.16 (0.91) | 0.0786* | | Gender, N (%) | | | | <0.001† | | Male | 1915 (23.21) | 1,148 (68.17) | 678 (76.18) | | | Female | 6,337 (76.79) | 536 (31.83) | 212 (23.82) | | | Marital status, marital, N (%) | | | | <0.001† | | Married | 7,156 (86.72) | 1,547 (91.86) | 825 (92.70) | | | Others | 1,096 (13.28) | 137 (8.14) | 65 (7.30) | | | Educational status, mean (S.D.) | | | | 0.013† | | Primary school and lower | 2,970 (35.99) | 600 (35.63) | 308 (34.61) | | | Junior high school | 2094 (25.38) | 437 (25.95) | 204 (22.92) | | | Senior high school | 2086 (25.28) | 416 (24.70) | 276 (31.01) | | | College and above | 1,102 (13.35) | 231 (13.72) | 102 (11.46) | | | Active smoking, N (%) | | | | <0.001† | | Never | 7,055 (85.49) | 1,002 (59.5) | 485 (54.49) | | | Occasional | 257 (3.11) | 189 (11.22) | 131 (14.72) | | | Frequent | 940 (11.39) | 493 (29.28) | 274 (30.79) | | | Passive smoking, N (%) | | | | <0.001† | | Yes | 3,120 (37.81) | 615 (36.52) | 367 (41.24) | | | No | 5,132 (62.19) | 1,069 (63.48) | 523 (58.76) | | | Alcohol drinking, N (%) | | | | <0.001† | | Yes | 1,538 (18.64) | 545 (32.36) | 328 (36.85) | | | No | 6,714 (81.36) | 1,139 (67.64) | 562 (63.15) | | | Work intensity, N (%) | | | | <0.001† | | Light | 2,789 (33.80) | 466 (27.67) | 233 (26.18) | | | Moderate | 855 (10.36) | 186 (11.05) | 95 (10.67) | | | Vigorous | 448 (5.43) | 84 (4.99) | 55 (6.18) | | | Retirement | 4,160 (50.41) | 948 (56.29) | 507 (56.97) | | | Fresh vegetable intake, N (%) | | | | <0.001† | | < once/day | 107 (1.30) | 43 (2.55) | 25 (2.81) | | | ≥ once/day | 8,145 (98.70) | 1,641 (97.45) | 865 (97.19) | | | Fruit intake, N (%) | | | | <0.001† | | < once/day | 1,594 (19.32) | 390 (23.16) | 267 (30.00) | | | ≥ once/day | 6,658 (80.68) | 1,294 (76.84) | 623 (70.00) | | | Hypertension, N (%) | | | | <0.001† | | Yes | 1,375 (16.66) | 972 (57.72) | 821 (92.25) | | | No | 6,877 (83.34) | 712 (42.28) | 69 (7.75) | | | Dyslipidemia, N (%) | | | | <0.001† | | Yes | 5,673 (68.75) | 1,219 (72.39) | 687 (77.19) | | | No | 2,579 (31.25) | 465 (27.61) | 203 (22.81) | | | Diabetes, N (%) | | | | <0.001† | | Yes | 603 (7.31) | 159 (9.44) | 108 (12.13) | | | No | 7,649 (92.69) | 1,525 (90.56) | 782 (87.87) | | | COPD, N (%) | | | | 0.008† | | Yes | 7,870 (95.37) | 1,577 (93.65) | 840 (94.38) | | | No | 382 (4.63) | 107 (6.35) | 50 (5.62) | | | CVDs, N (%) | | | | <0.001† | | Yes | 7,868 (95.35) | 1,538 (91.33) | 805 (90.45) | | | No | 384 (4.65) | 146 (8.67) | 85 (9.55) | | | HDL-CH, mmol/L, mean (S.D.) | 1.54 (0.43) | 1.44 (0.41) | 1.39 (0.41) | <0.001* | | LDL-CH, mmol/L, mean (S.D.) | 3.62 (0.99) | 3.65 (0.97) | 3.69 (0.96) | 0.034* | | Cholesterol, mmol/L, mean (S.D.) | 5.47 (1.10) | 5.47 (1.07) | 5.54 (1.08) | 0.065* | | Triglyceride, mmol/L, mean (S.D.) | 1.62 (1.40) | 1.82 (1.48) | 1.99 (1.79) | <0.001* | | Fasting blood glucose, mmol/L, mean (S.D.) | 5.49 (1.47) | 5.71 (1.72) | 5.88 (1.90) | <0.001* | The associations between glycolipid biomarkers and the risk of pre-OSA and OSA were presented in Table 2. Every 1 mmol/l increment of HDL-CH was associated with a decreased risk of both pre-OSA (OR: 0.82, $95\%$ CI: 0.70–0.96) and OSA (OR: 0.63, $95\%$ CI: 0.50–0.79) after adjustment for covariates. Compared with subjects within the lowest quartile of HDL-CH, the adjusted OR of pre-OSA and OSA in subjects within the highest quartile (>1.76 mmol/l) was 0.78 ($95\%$ CI: 0.65–0.94) and 0.59 ($95\%$ CI, 0.45–0.78), respectively; and a significant exposure-response trend was observed for both pre-OSA and OSA (both $p \leq 0.05$). **Table 2** | Unnamed: 0 | Sample size | Sample size.1 | Sample size.2 | Crude OR (95% CI) | Crude OR (95% CI).1 | Adjusted OR (95% CI)* | Adjusted OR (95% CI)*.1 | | --- | --- | --- | --- | --- | --- | --- | --- | | | Non-OSA | Pre-OSA | OSA | Pre-OSA vs. Non-OSA | OSA vs. Non-OSA | Pre-OSA vs. Non-OSA | OSA vs. Non-OSA | | HDL-CH | | | | | | | | | Quartile 1 (≤ 1.21) | 1943 | 517 | 332 | 1.00 | 1.00 | 1.00 | 1.00 | | Quartile 2 (> 1.21 ~ ≤ 1.46) | 2004 | 450 | 231 | 0.84 (0.73, 0.97) | 0.68 (0.56, 0.81) | 0.96 (0.81, 1.14) | 0.79 (0.62, 1.01) | | Quartile 3 (> 1.46 ~ ≤ 1.76) | 2118 | 406 | 181 | 0.72 (0.62, 0.83) | 0.50 (0.41, 0.61) | 0.97 (0.81, 1.15) | 0.70 (0.54, 0.91) | | Quartile 4 (> 1.76) | 2187 | 311 | 146 | 0.53 (0.46, 0.62) | 0.39 (0.32, 0.48) | 0.78 (0.65, 0.94) | 0.59 (0.45, 0.78) | | P for trend | | | | < 0.001 | < 0.001 | 0.019 | < 0.001 | | Every 1 mmol/l increment | | | | 0.58 (0.51, 0.66) | 0.42 (0.35, 0.50) | 0.82 (0.70, 0.96) | 0.63 (0.50, 0.79) | | LDL-CH | | | | | | | | | Quartile 1 (≤ 2.95) | 2106 | 408 | 201 | 1.00 | 1.00 | 1.00 | 1.00 | | Quartile 2 (> 2.95 ~ ≤3.56) | 2104 | 409 | 219 | 1 (0.86, 1.17) | 1.09 (0.89, 1.33) | 1.11 (0.92, 1.34) | 1.19 (0.91, 1.57) | | Quartile 3 (> 3.56 ~ ≤4.24) | 2037 | 422 | 239 | 1.07 (0.92, 1.24) | 1.23 (1.01, 1.5) | 1.14 (0.92, 1.43) | 1.18 (0.86, 1.62) | | Quartile 4 (> 4.24) | 2005 | 445 | 231 | 1.15 (0.99, 1.33) | 1.21 (0.99, 1.47) | 1.13 (0.90, 1.41) | 0.98 (0.72, 1.35) | | P for trend | | | | 0.0456 | 0.0335 | 0.360 | 0.684 | | Every 1 mmol/l increment | | | | 1.03 (0.98, 1.09) | 1.07 (0.99, 1.15) | 1.01 (0.93, 1.09) | 0.99 (0.89, 1.10) | | Total cholesterol | | | | | | | | | Quartile 1 (> 4.72) | 2083 | 428 | 196 | 1.00 | 1.00 | 1.00 | 1.00 | | Quartile 2 (> 4.72 ~ ≤ 5.39) | 2083 | 407 | 234 | 0.95 (0.82, 1.10) | 1.19 (0.98, 1.46) | 0.94 (0.77, 1.14) | 1.09 (0.82, 1.45) | | Quartile 3 (> 5.39 ~ ≤ 6.12) | 2068 | 414 | 220 | 0.97 (0.84, 1.13) | 1.13 (0.92, 1.38) | 0.96 (0.76, 1.21) | 0.93 (0.66, 1.30) | | Quartile 4 (> 6.12) | 2018 | 435 | 240 | 1.05 (0.91, 1.22) | 1.26 (1.04, 1.54) | 0.93 (0.74, 1.18) | 0.89 (0.64, 1.24) | | P for trend | | | | 0.478 | 0.047 | 0.625 | 0.295 | | Every 1 mmol/l increment | | | | 1 (0.96, 1.05) | 1.06 (0.99, 1.13) | 0.98 (0.91, 1.05) | 0.97 (0.88, 1.07) | | Triglyceride | | | | | | | | | Quartile 1 (≤ 0.95) | 2218 | 348 | 150 | 1.00 | 1.00 | 1.00 | 1.00 | | Quartile 2 (> 0.95 ~ ≤ 1.34) | 2140 | 391 | 187 | 1.17 (1.01, 1.36) | 1.29 (1.03, 1.62) | 1.04 (0.86, 1.25) | 1.02 (0.76, 1.37) | | Quartile 3 (> 1.34 ~ ≤ 1.95) | 2003 | 433 | 263 | 1.38 (1.18, 1.61) | 1.94 (1.57, 2.39) | 1.22 (1.01, 1.47) | 1.41 (1.05, 1.89) | | Quartile 4 (> 1.95) | 1891 | 512 | 290 | 1.73 (1.49, 2.00) | 2.27 (1.84, 2.79) | 1.32 (1.08, 1.60) | 1.56 (1.18, 2.07) | | P for trend | | | | < 0.001 | < 0.001 | 0.002 | 0.002 | | Every 1 mmol/l increment | | | | 1.10 (1.07, 1.14) | 1.15 (1.10, 1.19) | 1.01 (0.98, 1.05) | 1.03 (0.98, 1.09) | | Fasting blood glucose | | | | | | | | | Q1 (≤ 4.84) | 2256 | 345 | 166 | 1.00 | 1.00 | 1.00 | 1.00 | | Q2 (> 4.84 ~ ≤ 5.18) | 2085 | 391 | 179 | 1.23 (1.05, 1.43) | 1.17 (0.94, 1.45) | 1.11 (0.92, 1.34) | 1.01 (0.76, 1.35) | | Q3 (> 5.18 ~ ≤ 5.68) | 2031 | 441 | 250 | 1.42 (1.22, 1.66) | 1.67 (1.36, 2.05) | 1.07 (0.89, 1.28) | 1.13 (0.86, 1.48) | | Q4 (> 5.68) | 1880 | 507 | 295 | 1.76 (1.52, 2.05) | 2.13 (1.75, 2.60) | 1.37 (1.13, 1.67) | 1.38 (1.03, 1.85) | | P for trend | | | | < 0.001 | < 0.001 | 0.006 | 0.022 | | Every 1 mmol/l increment | | | | 1.09 (1.06, 1.12) | 1.14 (1.10, 1.18) | 1.05 (0.99, 1.11) | 1.03 (0.96, 1.12) | Conversely, when comparing the highest with the lowest quartile, triglyceride was associated with a $32\%$ (OR: 1.32, $95\%$ CI: 1.08–1.60) and a $41\%$ (OR: 1.59, $95\%$ CI: 1.18–2.07) increased risk of pre-OSA and OSA, separately; FBG was associated with a $37\%$ (OR: 1.37, $95\%$ CI: 1.13–1.67) and a $38\%$ (OR: 1.38, $95\%$ CI: 1.03–1.85) increased risk of pre-OSA and OSA, separately; significant exposure-response trends were also observed for pre-OSA and OSA (all $p \leq 0.05$). However, every 1 mmol/l increment of Triglyceride or FBG was associated with a slightly increased risk of pre-OSA and OSA, despite that the associations were nonsignificant. No significant association of hemoglobin, LDL-CH, or cholesterol with the risk of both pre-OSA and OSA was observed. Besides, subjects with dyslipidemia had a higher risk of pre-OSA (OR 1.19, $95\%$ CI 1.06–1.34) and OSA (OR 1.54, $95\%$ CI 1.31–1.81) in the crude model. However, the association disappeared after adjusting for covariates (Table 3). Similarly, there was no significant association between diabetes and the risk of pre-OSA (OR 1.08, $95\%$ CI 0.87–1.25) or OSA (OR 1.29, $95\%$ CI 0.96–1.74) after adjustment for covariates. **Table 3** | Unnamed: 0 | Crude OR (95% CI) | Crude OR (95% CI).1 | Adjusted OR (95% CI)* | Adjusted OR (95% CI)*.1 | | --- | --- | --- | --- | --- | | | Pre-OSA vs. Non-OSA | OSA vs. Non-OSA | Pre-OSA vs. Non-OSA | OSA vs. Non-OSA | | Dyslipidemia | | | | | | No | 1.00 | 1.00 | 1.00 | 1.00 | | Yes | 1.19 (1.06, 1.34) | 1.54 (1.31, 1.81) | 1.00 (0.86, 1.14) | 1.11 (0.90, 1.38) | | Diabetes | | | | | | No | 1.00 | 1.00 | 1.00 | 1.00 | | Yes | 1.32 (1.10, 1.59) | 1.75 (1.41, 2.18) | 1.08 (0.87, 1.25) | 1.29 (0.96, 1.74) | The sensitivity analysis was conducted by excluding participants with COPD and CVDs, and similar results were obtained between five glycolipid biomarkers and risk of Pre-OSA and OSA (Table 4), and between dyslipidemia and diabetes and risk of Pre-OSA and OSA (Table 5). ## Discussion To our best knowledge, this is the first study to assess the association between glycolipid biomarkers and the risk of OSA identified by the combination of the Berlin Questionnaire and STOP-BANG Questionnaire in China. Given the difficulty of OSA diagnosis by polysomnography in a large-scale population [33], the use of double questionnaires could improve the specificity of OSA. This present study found that a higher level of HDL-CH was associated with a decreased risk of both pre-OSA and OSA, while triglyceride and FBG were positively associated with the risk of both pre-OSA and OSA. No significant association of LDL-CH, cholesterol, dyslipidemia, or diabetes with the risk of both pre-OSA and OSA was observed. Metabolic abnormalities could increase the chance of upper airway collapsibility [8, 34]. Previous studies used serum lipid parameters to predict the risk of OSA, such as glucose [35], TG [9], and HDL-CH [14]. Novel composite parameters of glycolipids such as lipid accumulation product (LAP) also showed strong associations with OSA [36]. Some clinical OSA cohorts reported a positive association between TG and AHI [9, 37]. Moreover, a twin study analyzed the heritability of the relationship between OSA and hypertriglyceridemia and found that common genetic factors significantly determined the relationship between indices of chronic intermittent hypoxia and serum TG levels [38]. A genome-wide association study on the United Kingdom Biobank also indicated that genetically increased TG levels have independent causal effects on the risk of sleep apnea without the confounding effects of obesity [39]. Consistently, our study found similar results that triglyceride and fasting blood glucose were positively associated with the risk of both OSA and pre-OSA, whereas HDL-CH was negatively associated with the risk of both OSA and pre-OSA, further suggesting that regardless of the number of OSA-related symptoms or severity of OSA, glycolipid biomarkers may help determine risk and should be controlled during daily life. In this study, we took both BMI and WHR into consideration to exclude the confounding effect of peripheral and abdominal adiposity, which indicated that elevated level of HDL and TG was independently associated with OSA risk. Compared with peripheral obesity, abdominal obesity has a greater effect on upper airway function [40, 41]. In adult individuals, a large number of cross-sectional studies have shown independent associations between fasting levels of TC and the severity of OSA, particularly the frequency of intermittent hypoxic events [34]. Evidence from a Chinese large-scale cross-sectional study showed that, of the various components in serum lipid, only LDL-CH was independently associated with OSA [42]. However, we did not find a significant association of TC or LDL-CH with OSA risk. Furthermore, the results from our study were opposite to results from the ELSA-Brazil cohort study that drew the conclusion that OSA was independently associated with total cholesterol but not with HDL levels [37]. The conflicts may be connected with the reason that the possible association of TC and LDL-CH with OSA can be covered by comorbidities of OSA, such as hypertension and multiple cardiovascular diseases, due to their shared risk factors [34, 43]. Moreover, evidence showed that oxidized LDL (oxLDL) was associated with OSA. OxLDL is no longer recognized by cellular receptors with consequent inflammation and plaque formation on the internal surfaces of blood vessels [44]. This could be due to the fact that LDL particles are not removed by the liver and peripheral cells due to the depletion of LDL receptor-related protein-1 (LRP-1) in OSA [45]. In addition, emerging randomized trials for patients with OSA found that plasma levels of lipid biomarkers were reversed by CPAP treatment, which suggests causality [34]. Taking these elements together, we found that pre-OSA and OSA were associated with HDL and TG levels, but not with dyslipidemia. The possible explanation may be that even a minor change of blood lipid can initiate the pathologic development of OSA, and this change does not necessarily depend on the presence of specific symptoms, whereas dyslipidemia is widely believed to be closely associated with OSA [8, 9]. Noticeably, we did not find any association of diabetes with pre-OSA risk and OSA risk. Nevertheless, we found that the highest quantile of blood glucose (> 5.68 mmol/l) was linked with an increased risk of pre-OSA and OSA. Although diabetes was commonly assessed by fasting blood glucose higher than ≥7.0 mmol/l, our findings suggested that even a lower level of FBG could lead to a higher risk of pre-OSA and OSA. OSA is commonly considered as frequent comorbidity in patients with type 2 diabetes, and cardinal features of OSA, including intermittent hypoxemia and sleep fragmentation, have been linked to abnormal glucose metabolism in laboratory-based experiments [46]. The relationship between OSA and type 2 diabetes may be bidirectional in nature given that diabetic neuropathy can affect the central control of respiration and upper airway neural reflexes, promoting sleep-disordered breathing [46]. Early attention to individual blood glucose levels may have significant preventive implications for reducing OSA prevalence. Our study has some strengths. First, we conjunctively used the Berlin Questionnaire and the STOP-BANG Questionnaire, two widely used questionnaires with high validity and reliability, to identify OSA statuses, which enhanced the screening specificity. Second, we studied the association between several glycolipid biomarkers and the risk of pre-OSA and OSA among a representative population in Guangdong, which attenuated selection bias. Third, we took a large number of confounding factors into account, including PM2.5 exposure, individual lifestyles, and abdominal adiposity indicator, which could eliminate confounders to a large degree and reveal the independent associations between those indicators and OSA. Finally, sensitivity analyses yielded similar results, demonstrating the robustness of our findings. However, there are also some limitations. First, we used questionnaires to define OSA rather than overnight polysomnography, for it was difficult to implement in a large-scale population study. Nevertheless, the questionnaires used in this study showed high validity and reliability in predicting OSA [26, 27] and were commonly implemented in previous studies [26, 27, 47]. Second, the nature of a cross-sectional study could not support causality inference. Since OSA often coexists with various chronic diseases such as hyperlipidemia, diabetes, and cardiovascular disease, or acts as an intermediate link in the occurrence and development of these diseases, it is difficult to clarify the mechanism of glucose and lipid metabolism and OSA. Further studies with longitudinal design are needed to confirm this relationship. ## Conclusion The findings suggest that the level of HDL-CH was inversely associated with OSA risk, while elevated serum triglyceride and FBG could increase the risk of OSA. Healthy glycolipid metabolism warrants more attention in the field of OSA prevention, and more reports from rigorous longitudinal studies are expected. ## Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. ## Ethics statement The studies involving human participants were reviewed and approved by the Ethical Review Committee for Biomedical Research, School of Public Health, Sun Yat-sen University, and Ethics Committee of Guangzhou Centre for Disease Control and Prevention. The patients/participants provided their written informed consent to participate in this study. ## Author contributions XL and HD conceived and designed the study. HD, HZ, MZ, GS, JC, XW, SR, JH, and XL collected the data. MZ and HZ analyzed the data. MZ, XD, and WS drafted the manuscript. XL, HZ, HD, JH, and WZ reviewed and edited the manuscript. All authors contributed to the article and approved the submitted version. ## Funding This work was supported by the Guangdong Basic and Applied Basic Research Foundation (No. 2022A1515010686), the Medical Science and Technology Research Foundation of Guangdong Province (No. A2023408), the Guangdong Provincial Key R&D Program (No.2019B020230004) and the National Key R&D Program of China (No.2018YFC1312502). The founder had no role in the design, analysis, or writing of this manuscript. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## References 1. Jordan AS, McSharry DG, Malhotra A. **Adult obstructive sleep apnoea**. *Lancet* (2014) **383** 736-47. DOI: 10.1016/S0140-6736(13)60734-5 2. Punjabi NM. **The epidemiology of adult obstructive sleep apnea**. *Proc Am Thorac Soc* (2008) **5** 136-43. DOI: 10.1513/pats.200709-155MG 3. Benjafield AV, Ayas NT, Eastwood PR, Heinzer R, Ip MSM, Morrell MJ. **Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis**. *Lancet Respir Med* (2019) **7** 687-98. DOI: 10.1016/S2213-2600(19)30198-5 4. Peppard PE, Young T, Barnet JH, Palta M, Hagen EW, Hla KM. **Increased prevalence of sleep-disordered breathing in adults**. *Am J Epidemiol* (2013) **177** 1006-14. DOI: 10.1093/aje/kws342 5. Arnaud C, Bochaton T, Pépin JL, Belaidi E. **Obstructive sleep apnoea and cardiovascular consequences: pathophysiological mechanisms**. *Arch Cardiovasc Dis* (2020) **113** 350-8. DOI: 10.1016/j.acvd.2020.01.003 6. Andrade AG, Bubu OM, Varga AW, Osorio RS. **The relationship between obstructive sleep apnea and Alzheimer's disease**. *J Alzheimers Dis* (2018) **64** S255-s270. DOI: 10.3233/JAD-179936 7. Duan X, Zheng M, He S, Lao L, Huang J, Zhao W. **Association between physical activity and risk of obstructive sleep apnea**. *Sleep Breath* (2021) **25** 1925-34. DOI: 10.1007/s11325-021-02318-y 8. Castaneda A, Jauregui-Maldonado E, Ratnani I, Varon J, Surani S. **Correlation between metabolic syndrome and sleep apnea**. *World J Diabetes* (2018) **9** 66-71. DOI: 10.4239/wjd.v9.i4.66 9. Kim DH, Kim B, Han K, Kim SW. **The relationship between metabolic syndrome and obstructive sleep apnea syndrome: a nationwide population-based study**. *Sci Rep* (2021) **11** 8751. DOI: 10.1038/s41598-021-88233-4 10. Dehelean L, Sarbu M, Petrut A, Zamfir AD. **Trends in glycolipid biomarker discovery in neurodegenerative disorders by mass spectrometry**. *Adv Exp Med Biol* (2019) **1140** 703-29. DOI: 10.1007/978-3-030-15950-4_42 11. Li G, Li L, Joo EJ, Son JW, Kim YJ, Kang JK. **Glycosaminoglycans and glycolipids as potential biomarkers in lung cancer**. *Glycoconj J* (2017) **34** 661-9. DOI: 10.1007/s10719-017-9790-7 12. Almendros I, García-Río F. **Sleep apnoea, insulin resistance and diabetes: the first step is in the fat**. *Eur Respir J* (2017) **49** 1700179. DOI: 10.1183/13993003.00179-2017 13. Meszaros M, Bikov A. **Obstructive sleep Apnoea and lipid metabolism: the summary of evidence and future perspectives in the pathophysiology of OSA-associated Dyslipidaemia**. *Biomedicine* (2022) **10** 2754. DOI: 10.3390/biomedicines10112754 14. Karadeniz Y, Onat A, Akbaş T, Şimşek B, Yüksel H, Can G. **Determinants of obstructive sleep apnea syndrome: pro-inflammatory state and dysfunction of high-density lipoprotein**. *Nutrition* (2017) **43-44** 54-60. DOI: 10.1016/j.nut.2017.06.021 15. Sertogullarindan B, Komuroglu AU, Ucler R, Gunbatar H, Sunnetcioglu A, Cokluk E. **Betatrophin association with serum triglyceride levels in obstructive sleep apnea patients**. *Ann Thorac Med* (2019) **14** 63-8. DOI: 10.4103/atm.ATM_52_18 16. Patel SR. **Obstructive sleep apnea**. *Ann Intern Med* (2019) **171** Itc81-Itc96. DOI: 10.7326/AITC201912030 17. Drager LF, Santos RB, Silva WA, Parise BK, Giatti S, Aielo AN. **OSA, short sleep duration, and their interactions with sleepiness and Cardiometabolic risk factors in adults: the ELSA-Brasil study**. *Chest* (2019) **155** 1190-8. DOI: 10.1016/j.chest.2018.12.003 18. Patel SR. **Obstructive sleep apnea**. *Ann Intern Med* (2019) **171** Itc81-itc96. PMID: 31791057 19. Zhao X, Li X, Xu H, Qian Y, Fang F, Yi H. **Relationships between cardiometabolic disorders and obstructive sleep apnea: implications for cardiovascular disease risk**. *J Clin Hypertens (Greenwich)* (2019) **21** 280-90. DOI: 10.1111/jch.13473 20. Drager LF, Lopes HF, Maki-Nunes C, Trombetta IC, Toschi-Dias E, Alves MJNN. **The impact of obstructive sleep apnea on metabolic and inflammatory markers in consecutive patients with metabolic syndrome**. *PLoS One* (2010) **5** e12065. DOI: 10.1371/journal.pone.0012065 21. Deng H, Guo P, Zheng M, Huang J, Xue Y, Zhan X. **Epidemiological characteristics of atrial fibrillation in southern China: results from the Guangzhou heart study**. *Sci Rep* (2018) **8** 17829. DOI: 10.1038/s41598-018-35928-w 22. Duan X, Huang J, Zheng M, Zhao W, Lao L, Li H. **Association of healthy lifestyle with risk of obstructive sleep apnea: a cross-sectional study**. *BMC Pulm Med* (2022) **22** 33. DOI: 10.1186/s12890-021-01818-7 23. Duan X, Zheng M, Zhao W, Huang J, Lao L, Li H. **Associations of depression, anxiety, and life events with the risk of obstructive sleep apnea evaluated by Berlin questionnaire**. *Front Med* (2022) **9** 9. DOI: 10.3389/fmed.2022.799792 24. Chung F, Abdullah HR, Liao P. **STOP-Bang questionnaire: a practical approach to screen for obstructive sleep apnea**. *Chest* (2016) **149** 631-8. DOI: 10.1378/chest.15-0903 25. Chiu HY, Chen PY, Chuang LP, Chen NH, Tu YK, Hsieh YJ. **Diagnostic accuracy of the Berlin questionnaire, STOP-BANG, STOP, and Epworth sleepiness scale in detecting obstructive sleep apnea: a bivariate meta-analysis**. *Sleep Med Rev* (2017) **36** 57-70. DOI: 10.1016/j.smrv.2016.10.004 26. Hu YY, Yu Y, Wang ZB, Liu C, Cui YH, Xiao WM. **Reliability and validity of simplified Chinese STOP-BANG questionnaire in diagnosing and screening obstructive sleep apnea hypopnea syndrome**. *Curr Med Sci* (2019) **39** 127-33. DOI: 10.1007/s11596-019-2010-x 27. Ha SC, Lee DLY, Abdullah VJ, van Hasselt CA. **Evaluation and validation of four translated Chinese questionnaires for obstructive sleep apnea patients in Hong Kong**. *Sleep Breath* (2014) **18** 715-21. DOI: 10.1007/s11325-013-0889-1 28. Tan A, Yin JD, Tan LW, van Dam R, Cheung YY, Lee CH. **Using the Berlin questionnaire to predict obstructive sleep apnea in the general population**. *J. Clin. Sleep Med.* (2017) **13** 427-32. DOI: 10.5664/jcsm.6496 29. 29.National High Blood Pressure Education, P. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. Bethesda (MD): National Heart, Lung, and Blood Institute (US) (2004).. *The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure* (2004) 30. **[2016 Chinese guideline for the management of dyslipidemia in adults]**. *Zhonghua Xin Xue Guan Bing Za Zhi* (2016) **44** 833-853. DOI: 10.3760/cma.j.issn.0253-3758.2016.10.005 31. **Standards of medical care in diabetes–2010**. *Diabetes Care* (2010) **33** S11-61. DOI: 10.2337/dc10-S011 32. Xu R, Tian Q, Lu W, Yang Z, Ye Y, Li Y. **Association of short-term exposure to air pollution with recurrent ischemic cerebrovascular events in older adults**. *Int J Hyg Environ Health* (2022) **240** 113925. DOI: 10.1016/j.ijheh.2022.113925 33. Barros D, García-Río F. **Obstructive sleep apnea and dyslipidemia: from animal models to clinical evidence**. *Sleep* (2019) **42** zsy236. DOI: 10.1093/sleep/zsy236 34. Gileles-Hillel A, Kheirandish-Gozal L, Gozal D. **Biological plausibility linking sleep apnoea and metabolic dysfunction**. *Nat Rev Endocrinol* (2016) **12** 290-8. DOI: 10.1038/nrendo.2016.22 35. Xu H, Zhao X, Shi Y, Li X, Qian Y, Zou J. **Development and validation of a simple-to-use clinical nomogram for predicting obstructive sleep apnea**. *BMC Pulm Med* (2019) **19** 18. DOI: 10.1186/s12890-019-0782-1 36. Bikov A, Frent S, Reisz D, Negru A, Gaita L, Breban Schwarzkopf D. **Comparison of composite lipid indices in patients with obstructive sleep Apnoea**. *Nat Sci Sleep* (2022) **14** 1333-40. DOI: 10.2147/NSS.S361318 37. Silva WA, Almeida-Pititto B, Santos RB, Aielo AN, Giatti S, Parise BK. **Obstructive sleep apnea is associated with lower adiponectin and higher cholesterol levels independently of traditional factors and other sleep disorders in middle-aged adults: the ELSA-Brasil cohort**. *Sleep Breath* (2021) **25** 1935-44. DOI: 10.1007/s11325-021-02290-7 38. Meszaros M, Tarnoki AD, Tarnoki DL, Kovacs DT, Forgo B, Lee J. **Obstructive sleep apnea and hypertriglyceridaemia share common genetic background: results of a twin study**. *J Sleep Res* (2020) **29** e12979. DOI: 10.1111/jsr.12979 39. Tang H, Zhou Q, Zheng F, Wu T, Tang YD, Jiang J. **The causal effects of lipid profiles on sleep apnea**. *Front Nutr* (2022) **9** 9. DOI: 10.3389/fnut.2022.910690 40. Schwab RJ, Pasirstein M, Pierson R, Mackley A, Hachadoorian R, Arens R. **Identification of upper airway anatomic risk factors for obstructive sleep apnea with volumetric magnetic resonance imaging**. *Am J Respir Crit Care Med* (2003) **168** 522-30. DOI: 10.1164/rccm.200208-866OC 41. Bikov A, Losonczy G, Kunos L. **Role of lung volume and airway inflammation in obstructive sleep apnea**. *Respir Investig* (2017) **55** 326-33. DOI: 10.1016/j.resinv.2017.08.009 42. Xu H, Guan J, Yi H, Zou J, Meng L. **Elevated low-density lipoprotein cholesterol is independently associated with obstructive sleep apnea: evidence from a large-scale cross-sectional study**. *Sleep Breath* (2016) **20** 627-34. DOI: 10.1007/s11325-015-1262-3 43. Drager LF, McEvoy R, Barbe F, Lorenzi-Filho G, Redline S. **Sleep apnea and cardiovascular disease: lessons from recent trials and need for team science**. *Circulation* (2017) **136** 1840-50. DOI: 10.1161/CIRCULATIONAHA.117.029400 44. Feres MC, Fonseca FAH, Cintra FD, Mello-Fujita L, de Souza AL, de Martino MC. **An assessment of oxidized LDL in the lipid profiles of patients with obstructive sleep apnea and its association with both hypertension and dyslipidemia, and the impact of treatment with CPAP**. *Atherosclerosis* (2015) **241** 342-9. DOI: 10.1016/j.atherosclerosis.2015.05.008 45. Meszaros M, Kunos L, Tarnoki AD, Tarnoki DL, Lazar Z, Bikov A. **The role of soluble low-density lipoprotein receptor-related Protein-1 in obstructive sleep Apnoea**. *J Clin Med* (2021) **10** 1494. DOI: 10.3390/jcm10071494 46. Reutrakul S, Mokhlesi B. **Obstructive sleep apnea and diabetes: a state of the art review**. *Chest* (2017) **152** 1070-86. DOI: 10.1016/j.chest.2017.05.009 47. Kang K, Park KS, Kim JE, Kim SW, Kim YT, Kim JS. **Usefulness of the Berlin questionnaire to identify patients at high risk for obstructive sleep apnea: a population-based door-to-door study**. *Sleep Breathing* (2013) **17** 803-10. DOI: 10.1007/s11325-012-0767-2
--- title: 'System Action Learning: Reorientating Practice for System Change in Preventive Health' authors: - Therese Riley - Liza Hopkins - Maria Gomez - Seanna Davidson - Jessica Jacob journal: Systemic Practice and Action Research year: 2023 pmcid: PMC10060912 doi: 10.1007/s11213-023-09638-y license: CC BY 4.0 --- # System Action Learning: Reorientating Practice for System Change in Preventive Health ## Abstract It is now widely accepted that many of the problems we face in public health are complex, from chronic disease to COVID-19. To grapple with such complexity, researchers have turned to both complexity science and systems thinking to better understand the problems and their context. Less work, however, has focused on the nature of complex solutions, or intervention design, when tackling complex problems. This paper explores the nature of system intervention design through case illustrations of system action learning from a large systems level chronic disease prevention study in Australia. The research team worked with community partners in the design and implementation of a process of system action learning designed to reflect on existing initiatives and to reorient practice towards responses informed by system level insights and action. We were able to observe and document changes in the mental models and actions of practitioners and in doing so shine a light on what may be possible once we turn our attention to the nature and practice of system interventions. ## Background Nearly half of all Australians have one or more chronic conditions, such as cardiovascular disease and chronic obstructive pulmonary disease (Australian Bureau of Statistics 2018). Many of these are preventable. Prevention efforts have traditionally focused on lifestyle programs designed to reduce chronic disease risk factors such as unhealthy eating or harmful use of alcohol. However, such approaches are not yielding the gains needed at a population level and calls to take a radically different approach are mounting (Carey et al. 2015; Rutter et al. 2017). Over the past decade public health scholars have been calling for a shift away from reductionist and linear approaches to understanding public health problems and their solutions (Hawe et al. 2004; Hawe 2015a; Plsek and Greenhalgh 2001). It is now believed that embracing the complexity of chronic disease and looking to system science as a new worldview and methodology may get us closer to reversing trends such as obesity which contribute to the burden of chronic disease (Rusoja et al. 2018; Rutter et al. 2017). The use of systems thinking methods and tools in public health research is also increasing, ranging from the creation of causal loop diagrams exploring obesity (Allender et al. 2015) through to dynamic agent based modeling which sheds light on efforts to reduce alcohol harm (Atkinson et al. 2018). A recent review of complex systems approaches to public health evaluations identified 74 studies that applied a range of methods highlighting the growth in their application. The authors of the review conclude that in order for the field to progress, methodological innovation will be necessary (McGill et al. 2021). This surge in systems evaluation, however, is not matched by interest in the scholarship of system interventions. This lack of attention to system intervention design and implementation means that many system evaluations are studying multifaceted programs that are not necessarily designed for systemic change. Hawe (2015a) argues that attention needs to be paid to intervention designs that genuinely grapple with complexity, otherwise we are destined to only see modest or ‘negligible’ gains for the health of populations (Hawe 2015b). While there is a paucity of literature regarding the design of system interventions, there are some notable exceptions, particularly in the field of obesity prevention (Allender et al. 2021; Garcia et al. 2021). Interventions described in these studies are designed ‘with’ communities. They build on and strengthen pre-existing work in community capacity building, and involve action learning and systems thinking (Allender et al. 2021; Garcia et al. 2021). This interdisciplinary approach applies systems methods, creates coordinated actions across systems, expects and encourages adaptation and retains a focus on system behavior. At the heart of these interventions are ‘systems practices’ which are designed to shift mental models through learning and action. It is this combination that ensures intervention implementation is dynamic and able to cope with uncertainty. However, embedding a systems mindset across a workforce is not easy. In a study of the Healthy Together Victoria initiative in Australia (2011–2015) Bensberg [2021] found that the introduction of various systems theories, frameworks and methods to a health promotion workforce, did not necessarily translate into easily articulated practical examples of systems change (Bensberg 2021). While systems practices were evident in the initiative (Roussy et al. 2020), many practitioners struggled to describe the connection between systems ideas and their day-to-day work (Bensberg 2021). This suggests that more work is needed to bridge the gap between systems theory (methods and concepts) and practice. Bringing together the tradition of action research and system science has led to the development of system action learning as a way of placing systems thinking in the action orientated contexts of practice. The purpose of action learning is to engage with real-life problems that lack a clear solution. It is a group process of learning and reflection that can produce further system insights and encourage actions which target systems-level changes (Zuber-Skerritt 2002). This process of participatory systemic inquiry fosters a ‘learning architecture’ where multiple inquiries can operate at once, allowing stakeholders to engage in action within and across various parts of the system. The ‘learning architecture’ links these inquiries together creating a space for stakeholders to effect systems change in meaningful ways (Burns 2014). System action learning programs draw on a range of both action learning and systems traditions including Critical Systems Theory (CST) (Aragon and Giles Macedo 2010; Barta et al. 2016; Lewis 2015). Facilitated action learning processes that question assumptions, boundaries, and power dynamics, can create learning opportunities to generate knowledge about a system that otherwise may have not been explored. In doing so, actions and decisions derived from the system, are privileged and become/inform applied practice. Learning systems foster an environment of cooperative learning whereby iterative learning processes are embedded in organisational or community culture and practice (Ison et al. 2007; Aragon and Giles Macedo 2010). Embedding learning processes is also at the heart of systems approaches such as Soft Systems Methodologies (SSM) that highlight the importance of cyclic inquiry (Checkland and Poulter 2010). Systemic learning is not a one-off event! The cyclic nature of these approaches is consistent with continuous quality improvement and the PDSA (Plan Do Study Act) cycle popular in health care (Taylor et al. 2014). Overall, there are a range of approaches, resources and tools within system action learning (Foster-Fishman and Watson 2012; Wadsworth 2010) that could support and sustain positive change. For the purposes of this paper, we are focused on community based prevention efforts, rather than hospital or health care sectors. ## The Study Context Prevention Tracker was a national initiative of the Australian Prevention Partnership Centre (Wilson et al. 2014). It was designed to better understand the nature of prevention efforts in local communities using a systems thinking approach. We worked with four geographically diverse communities across Australia to describe, guide and monitor change efforts in the chronic disease prevention systems. The work occurred across four domains of enquiry: describing a chronic disease prevention system; guiding system change; monitoring system change; and a cross-case comparison (Riley et al. 2020). Community in this study refers to geographic places defined by government boundaries. Prevention Tracker was an ambitious and complex research intervention, which utilised numerous data sets and a multitude of systems thinking tools and approaches. It aimed to trial a new way of developing solutions to complex, systemic problems within chronic disease prevention - a field characterized by diffuse governance, siloed organisations, multiple funding sources and a focus on programmatic interventions (Del Fabbro et al. 2016, Thompson et al. 2015). The full systems methodology of Prevention Tracker, including an outline of our approach to system action learning is described elsewhere (Riley et al. 2020). This paper examines the systems action learning component of Prevention Tracker which was part of the guiding and monitoring systems change domains. The system action learning activities were developed following the identification of a systemic problem in each of the communities and focused on projects already planned or being implemented within the community. System action learning took place in two of the four communities. ## Method System action learning took the following form in Prevention Tracker. We worked with project partners in communities to identify three local projects which offered scope to incorporate a systems-action learning approach. These were prevention initiatives that were underway at the time and which the research team and the project partners jointly identified as ones where a systemic intervention may yield positive results. This phase of Prevention Tracker involved facilitation of an iterative learning process whereby system-level insights could influence day-to-day prevention activities. Through the process, these activities can allow participants to surface additional system insights which provided feedback into their prior understanding of the system and in turn directed action to address system change. We present the system action learning associated with each project as three cases, each made up of a practice focus, 2–3 community team members and cycles of system action learning. Case 2 and 3 are made up of the same community team. The cases were. The functioning of a coalition of community organisations associated with health and wellbeing. The local community team, made up of 3 government practitioners were grappling with how to maximise the role and impact of a community coalition of organisations providing information, advice and support to government. The system action learning cycles provided an opportunity to reflect on and reconsider the role of the coalition. The community was an urban local government area. 2.The delivery of a healthy eating initiative. The local community team, made up of two practitioners, were involved in the implementation of a healthy eating initiative. They were grappling with how to involve more non traditional actors, such as the private sector. The system action learning cycles provided an opportunity to better understand the situation, surface assumptions and identify new practices. The community was a regional local government area. 3.The evaluation of a new health partnership between a local government and a state government organisation. The local community team made up of two practitioners, were involved in the evaluation of the partnership. The system action learning cycles enabled the team to gain new system insights into the partnership and practices to evaluate impact. The community was a regional local government area. System Action Learning (SAL) within Prevention Tracker involved an iterative cycle of the local community team undertaking their usual planning meeting for each project, then the completion of a Prevention Tracker reflection template to capture the operations of the meeting and the agreed actions which resulted. The completed template was forwarded to the Prevention Tracker research team, and used to inform the design of a tailored SAL activity. The SAL activity was then conducted by at least two members of the research team, together with the project partners in the community. This activity was usually undertaken in person for the first instance and then by telephone for subsequent cycles. The activity involved a 1.5- 2 hr facilitated sessions where a systems thinking specialist member of the research team led the local community team through a series of questions and answers. The process was designed to enable the project partners in the community to gain new and additional insights into the system within which they were working, and the local systemic problem they were trying to address. The facilitated systems activities were each audio-recorded and transcribed verbatim. The final step in the SAL cycle involved the project partners completing a second template designed to help them reflect on and better understand their own role in the system. The completed template was again forwarded to the research team to help inform the study as to how the partners were understanding and learning from the process. The community project teams in each community went through 3 or 4 cycles of System Action Learning per project. A total of 10 rounds of system action learning were undertaken (3 in two of the projects and 4 in the other project). Templates were filled out and systems activity sessions were audio recorded with consent. All data was imported into an NVIVO database (QSR 2012). Table 1 describes the codebook that was developed over a number of weeks to code this diverse data. We started with theoretically informed high level codes and then refined the codes and sub codes through a series of coding workshops (Crabtree and Miller 1999). Three researchers (LH, MG, TR) all coded a selection of transcripts and talked through the similarities and differences to refine the interpretation presented in the codebook. Once all researchers were comfortable and confident in the coding and interpretation, the entire data set was coded. Some codes were easier to apply and interpret than others. Table 1Codebook for System Action LearningCodeSub codeActionIntention to actAction undertaken (By SAL Team)Action Undertaken (by others)Relational actionSystem InsightsLeverage PointsReflection on boundariesReflection on relationshipsReflection on different perspectives or view pointsSystem patterns(dynamics of the system)Reflection on system partsUnintended consequences, challenges or barriersDouble Loop LearningSurfacing assumptionsChallenging assumptionsChanging views or perspectivesTwo-way learningSystem ImpactsCreation of new relationshipsChanges in networks/boundariesCreation of new roles/practicesAlignment of system partsExperimentationNew mental modelsIdentifying actions which are intended to have a systems impactConnection to Systemic Problem The coding structure was informed by the theoretical insights of current systems thinking literature (Checkland and Poulter 2010; Wadsworth2010; Midgley 2006). The aim was to analyse the research data (transcripts and templates), to identify the learning which the community project team had gained from the process, in particular, the learning which had occurred at a ‘systemic’ level, or a higher level of abstraction (double loop learning) than learning simply about activities taking place within the system (single loop learning) (Jaaron and Backhouse 2017; Reynolds 2014). The intention of introducing SAL as a complex intervention into the system, was to help practitioners identify and act on systemic problems that were affecting the implementation of the identified project. In comparison to standard health promotion activity, this approach is very different. The intervention point is practice based decision making. Not through prescriptive guidelines or manuals but rather opportunities to learn about the system and shift ‘mental models’ (Senge 2006) about what is possible. It seeks to create a more significant and enduring shift (even if small) by linking systems thinking expertise with real world practitioners’ expert knowledge of the local system. ## Results Our proposition was that injecting systemic action learning (through the Prevention Tracker project) as a complex intervention would facilitate system level learning and enable local community teams to both learn and act on systemic problems through their day to day practice. Our results are promising. We gathered data about ongoing learning, insights and system impacts. The iterative nature of the SAL process (see Fig. 1) means that the results of our study take a circular, rather than linear pattern, whereby the learning at each step shapes and informs the subsequent steps, in an active sequence of feedback and response. The dynamic nature of the learning also means that new issues emerge while others become less important. For this reason, we present the findings of the SAL in two ways. First, we present a snapshot across three cycles of one of the System Action Learning cases (Case 2) according to the inter-relationship between the system activity sessions, insights gained and actions taken. We then describe each of the SAL stages, and examples from the cases. Where relevant, we include examples of how the data were coded. For clarity we have only included one code for each datum, although data may have been coded against a number of codes presented in Table 1. Fig. 1System action learning cycle in Prevention Tracker ## Cycles of System Action Learning Figure 2 presents a snapshot of system activities, insights and actions across three cycles of systems action learning in Case 2. The case was a healthy eating initiative where the systemic problem/practice focus was the engagement of nontraditional actors in prevention efforts. In this case, food providers and the organization engaging their services. In each cycle we present the system activity that was undertaken, the type of system insights gained (via quotations) and the actions that were either taken or intended. These learnings are then taken into the next cycle and so on. Fig. 2A snapshot of three cycles of system action learning Figure 2 highlights the dynamic nature of learning through action over time. We now describe each of the System Action Learning phases (represented in Fig. 1) in greater detail. ## Systemic Problem & Practice Focus The starting point for each of the three SAL projects within Prevention Tracker was the identification of a systemic problem identified through a collaborative process between the researchers and stakeholders in the local community. This was facilitated by a systems thinking expert to help draw out a system level problem which might be hindering the way that chronic disease prevention activities were carried out in the community. Each group of community partners identified a specific problem that was locally relevant to them, however the overarching key themes which appeared across communities in these systemic problem statements included issues of leadership, collaborative practice and partnering between organisations. The process of identifying the systemic problems are described elsewhere and were completed prior to the start of the SAL phase of the project (Riley et al. 2020). Each community project team then identified a practice focus, which was an existing prevention activity which was underway and indicative of the identified systemic problem. The practice focus would shape the systems activity sessions and subsequent cycles of reflection and action. The following quotation is drawn from the third cycle of SAL, highlighting insights gained into the systemic problem of broadening the role of people and organizations involved in prevention. These insights emerged as the community team grappled with the evaluation of a partnership (practice focus)“*Engagement is* difficult if the perspectives, needs and assumptions of different stakeholders aren’t continually assessed and addressed to ensure stakeholders are ‘on the same page’ and working towards the same goal” (Case 3) These insights could then be applied back to the systemic problem, to identify opportunities for small but sustainable shifts. An example of this came from one of the projects which incorporated its primary work into an expansion of role by a partner organization, and then, by extension, to other communities in the local region, thus changing the nature of local relationships as well as the boundaries of the local prevention system.it’s all kind of linked: it’s kind of like [the partnership initiative] started as this pilot project working to build the capacity of [organization 1]. We’re transitioning out of that now so not providing as much intensive support to [organization 1]. Setting it up for sustainability so it can do best practice health promotion, preventative health by their [policy] then [organization 2] is going to expand that support out to other [local] governments in the region using [the partnership initiative] as a kind of learning platform. So the learnings that we do gain through [the partnership initiative] and the Prevention Tracker work around evaluation will then I guess be applying where we can to [organization 1’s policy] but also the support that we provide in the future to other [local] governments in the region in terms of their … planning and evaluation. ( Case 3 code - Reflection on Boundaries) ## Questioning & Reflection Each of the projects had an existing meeting, governance and action structure within its community. The first step in the SAL cycle (as described above in the methods section) involved the local community project team using the SAL template to draw out systemic issues, for example issues pertaining to the role of the community project team at the systemic level, identifying perceptions or biases in the team’s thinking and developing insight into the problem being addressed. The template data informed the development of the SAL activity which was then undertaken with the research team and community partners. One community team described (in Template 1 cycle 3) the issues they were grappling with at the time, in the following way“How do we make the intervention as simple and engaging as possible for [non traditional stakeholder group]?How can we make the [name] program less resource/time intensive?” ( Case 2, Code - Reflection on System Parts) This was followed up with “a ha” moments of making practical changes to the program to make it easier for stakeholders to be involved. ## Insight Informs Actions Participants in this phase of SAL used these insights to identify intentions to act on the system (above and beyond simply carrying out actions pertaining to their particular project). This action may be something they intended to do themselves to address the systemic problem (or practice focus), or action they anticipated others might take which would have an effect at the systems level. In one case this resulted in a restructure of the next meeting that had been organized between two organisations partnering on a health promotion project. The community team had gained insight into the importance of reflecting on values, assumptions and differing perspectives when working across sectors and organisations. These insights informed the intended action – to restructure the next meeting, as the following quotation highlights. Speaker 3 (SAL participant): I’m just thinking in terms of how we will kind of set up this next meeting and how we might run it, as a bit of a discussion … [or] exploratory group, but I think we need to preface it in a way that shows them why we’re wanting to do what we’re about to do in terms of using these probing questions around what their values are, what their capacity is, etc. Speaker 2 (SAL participant): Maybe we can preface it around that it’s just a reflection, given we’ve invested a lot of their time and our time in it around what we’ve done, what’s possible in the future, and you could also link in the [policy] stuff as well, just we’re really trying to make sure this fits in with your needs and your capacities and things. I don’t think you would really need it probed around, “We’re doing a systems exploration-“ (Case 2 code – Intention to Act). In another example the community team gained insight into the strategic engagement of Stakeholders rather than blanket inclusion. In doing so, they began to foster new practices. we thought everyone had to be involved, and you know, if this particular [organization] wasn’t involved, it was a failure. We had done something wrong. Whereas, in actual fact it’s helped … me to step back and think, “Well actually no.“ ( Case 2 code - New roles and practices). ## Action In between cycles of SAL, the teams in communities undertook action in accordance with their local project parameters and constraints. This could include project meetings, actions in the community and reflection within teams. Action undertaken by the community team within the community, was captured through the SAL templates, as well as in reflection during the next facilitated systems activity session. In the following example, the community team were asked whether they had worked on an issue raised in the previous facilitated systems session, that of encouraging community ownership of a collaborative network. “That’s right. And we actually did talk about that at our last [collaborative network] meeting, um, that that would... be a real focus, even though we, you know, had the data and we had our Community Plan” (Case 1 code - Action Undertaken by SAL Team) The community team went on to describe how the desire for community ownership is reflected in a range of policies and intentions, but that the collaborative network may need to work differently in order to bring this about. ## Analyse Outcomes Community action was then followed up by the next cycle of SAL, in which reflection on action was undertaken and successful examples of action at the systemic level were identified. In the following example the community team continued to work with the systems tools and ideas applied in the system activity session. After our last session with you guys we went away and did some more kind of activity work where we mapped those key project shift points for the project. And then, [we], mapped out our assumptions or learnings at each kind of shift point stage and what we did to address those or how we responded to those assumptions or learnings. So that has been useful in terms of incorporating that information to the evaluation. ( Case 2 code – Action undertaken by SAL Team) ## Identify New Insights Helping the community project teams identify systemic actions naturally led to them identifying new insights into the systems as well. The participant’s comments above was followed by the reflection below:we might be explaining [to our partners] some of the assumptions, plus things that we did, and what it’s made us realize about the system and what we can and cannot control or influence or what the best leverage points are that we’ve identified. ( Case 2 code - Surfacing Assumptions) Another community participant also reflected on the way their team was working, and identified opportunities to improve integration of their activities within the community: I think that it’s also changing the way that we operate and … and look at things. Ah, it’s the whole … team, so, we just had a discussion this morning about, basically going back to basics, and really looking at how we operate and how we best integrate into the community. ( Case 1 code - Creation of New Roles and Practices) ## Discussion For system action learning to take hold, practitioners within the community must be open and receptive to a new way of working, which can be very challenging. At the same time, those delivering the intervention must be flexible and open, willing to listen and adapt to the local environment and maintain focus on the systemic level while fighting the tendency to drift back towards the concrete level of the problem at hand. The example of cycles of system action learning presented in Fig. 2 highlights the type of system insights that can be gained through such processes. Each of the sessions surfaced new insights and actions at a system level. Through the cycles, the community team came to realize the importance of surfacing assumptions and drawing other perspectives into their learning. In addition, they committed to ensuring these learnings were shared with others in future meetings. In this way, their learning about the system was translated directly into practice. As a result, new practices, mental models and relationships became a part of the system of interest. These findings highlight the interconnected and relational nature of both gaining and applying system insights in practice. While much has been written about the importance of relationships in community-based prevention efforts (Trickett et al. 2011) including qualities of relationships such as trust (Bagnall et al. 2019), less attention has been paid to the deeply entangled nature of relationships, learning and action. Our findings suggest that while it is possible to observe key concepts, such as the creation of new relationships in practice data, the greatest insights may come from understanding how these concepts interact with each other through cycles of inquiry and over time. Conceptualizing systems action learning as an intervention is challenging because few organisations within communities are funded or rewarded to work at this level. The overarching prevention system as it exists in *Australia is* fragmented and multi-sectoral/ multi-dimensional. No single bureaucratic home oversees prevention activities and the result is a patchwork of interventions and activities across public health, and many other sectors (including those at federal, state, local, non-government and community level). Therefore it requires a leap of faith from practitioners to commit the time and energy to a process which has often been compared to the actions of turning a huge ship by moving a small rudder (Senge 2006). While the overall benefits of undertaking systems change (for example, to work collaboratively to address chronic disease) may be more effective and more durable than single-action interventions (Waterlander et al. 2021), funding, time and governance restrictions tend to favour short-term, single-action interventions with measurable outcomes over longer term, higher level interventions with less predictable systemic impacts (Hopkins et al. 2021). In this regard, system action learning as an intervention may be better aligned with continuous quality improvement, where actions become the focus of reflection and inquiry and are embedded in organizational settings. Systems change is grounded in the day-to-day practices in organisations and collaborations. SAL is a mechanism for that to shift and change, influencing both the micro system of how the organization operates (practicing systems) as well as impact on their action and engagement in the system. Our analysis of system action learning in Prevention Tracker highlighted the importance of cycles of inquiry as practitioners learn over time and build knowledge in and through their action (Checkland and Poulter 2010). The emphasis on decision making in an immediate sense ensured the relevance of the ‘systems’ ideas to practice. Our ability to identify and describe learning processes (via codes such as double loop learning) linked to systemic ideas and systems change, suggests that it is possible to surface otherwise invisible aspects of system practice. In fact, it may be in the ‘private contexts of practice’ (Riley and Hawe 2009) rather than formalized training that systemic ideas take hold. This may go some way to explain Bensberg’s findings from the Healthy Together Victoria initiative (Bensberg 2021). Our ‘cycle of inquiry’ is similar to others used in action research, action learning and improvement science (Foster-Fishman and Watson 2012; Wadsworth 2010, Taylor et al. 2014). All are designed to embed inquiry into practice. Similarly to Foster-Fishman and Watson [2012], we endeavoured to centre the inquiry process around ‘system’ insights. This required expert systems facilitation to enable real time exploration of practice decisions and their relationship to systems problems. The importance of systemic expertise has also been noted by others drawing attention to the effect of a systemic lens on implementation of initiatives (Pescud et al. 2021). Despite the multitude of theoretical systems approaches which have been identified in the existing literature, comparatively few real world interventions have been able to study the cycle of systems action learning in practice, and to observe examples of systemic change in research data. Prevention Tracker offered a unique opportunity to collaborate between researchers and community-based practitioners to undertake systems action learning as well as scrutinize collected data for evidence of systemic change. Developing a thematic code book which was nuanced enough to identify small examples of systemic change in action, thinking, and learning was challenging. Coding required a deep understanding of each of the cases to interpret the data and this may not always be possible. However, the value in pursuing this inquiry lies in making visible and measuring otherwise invisible aspects of the process of change within complex systems. We invite others to apply, refine and strengthen the veracity of our coding scheme, including other forms of coding, which may yield additional insights. ## Conclusion Designing and implementing complex, systems level interventions to address wicked problems lags behind work to identify and unpack those problems. Prevention Tracker offered an opportunity to guide, observe and demonstrate system level change in local community chronic disease prevention activities. The identification of systemic problems within community prevention systems, followed by facilitated learning about the problem and iterative cycles of action and reflection enabled the three Prevention Tracker SAL projects to surface assumptions, re-orient action and create new relationships and boundaries within and between complex systems. Theoretically informed analysis of action learning data in the form of transcripts of facilitated systems activity sessions and reflective templates from project meetings enabled the research team to identify intervention points at which the local community project teams were working, not just to deliver their immediate prevention project, but to work more systemically to address problems which inhibit more effective work across the entire prevention system. The complexity of systemic change and the required time frames to observe such change don’t lend themselves neatly to the timeframes of research funding. Much work remains to be done to continue tracking the cumulative small shifts at the systems level which could lead to more significant and sustainable action in addressing complex public health problems into the future. ## References 1. Allender S, Orellana L, Crooks N, Bolton KA, Fraser P, Brown AD, Le H, Lowe J, de la Haye K, Millar L, Moodie M, Swinburn B, Bell C, Strugnell C. **Four-year Behavioral, Health-Related Quality of Life, and BMI Outcomes from a Cluster Randomized Whole of Systems Trial of Prevention Strategies for Childhood Obesity**. *Obesity* (2021.0) **29** 1022-1035. DOI: 10.1002/oby.23130 2. Allender S, Owen B, Kuhlberg J, Lowe J, Nagorcka-Smith P, Whelan J, Bell C. **A community based Systems Diagram of obesity causes**. *PLoS ONE* (2015.0) **10** e0129683. DOI: 10.1371/journal.pone.0129683 3. Aragón AO, Giles Macedo JC. **A “Systeemic theories of Change” Approach for Purposeful Capacity Development**. *IDS Bull* (2010.0) **41** 87-99. DOI: 10.1111/j.1759-5436.2010.00140.x 4. Atkinson JA, Prodan A, Livingston M, Knowles D, O’Donnell E, Room R, Indig D, Page A, McDonnell G, Wiggers J. **Impacts of licensed premises trading hour policies on alcohol-related harms**. *Addiction* (2018.0) **113** 1244-1251. DOI: 10.1111/add.14178 5. Australian Bureau of Statistics (2018) National Health Survey: First results, 2017–18. Canberra 6. Bagnall AM, Radley D, Jones R, Gately P, Nobles J, Van Dijk M, Blackshaw J, Montel S, Sahota P (2019) Whole systems approaches to obesity and other complex public health challenges: a systematic review. BMC Public Health 19(8). 10.1186/s12889-018-6274-z 7. Barta WD, Shelton D, Cepelak C, Gallagher C. **Promoting a Sustainable Academic-Correctional Health Partnership: Lessons for Systemic Action Research**. *Syst Pract Action Res* (2016.0) **29** 27-50. DOI: 10.1007/s11213-015-9351-6 8. Bensberg M. **Developing a Systems Mindset in Community-Based Prevention**. *Health Promot Pract* (2021.0) **22** 82-90. DOI: 10.1177/1524839919897266 9. Burns D. **Systemic action research: Changing system dynamics to support sustainable change**. *Action Res* (2014.0) **12** 3-18. DOI: 10.1177/1476750313513910 10. Carey G, Malbon E, Carey N, Joyce A, Crammond B, Carey A. **System science and systems thinking for public health: a systematic review of the field**. *BMJ Open* (2015.0) **2** e009002. DOI: 10.1136/bmjopen-2015-009002 11. Checkland P, and Poulter J (2010) Soft Systems Methodology In Systems Approaches to Managing Change: A Practical Guide, edited by M. Reynolds and S. Holwell, 191–242. London: Springer 12. Crabtree B, Miller W (1999) A template approach to text analysis: Developing and using codebooks. In Doing qualitative research edited by B. Crabtree and W. Miller, 163–177. Newbury Park, CA: Sage 13. Del Fabbro, L, Rowe Minniss F, Ehrlich C, Kendall E. **Political Challenges in Complex Place-Based Health Promotion Partnerships: Lessons From an Exploratory Case Study in a Disadvantaged Area of Queensland, Australia**. *Int Q Community Health Educ* (2016.0) **37** 51-60. DOI: 10.1177/0272684X16685259 14. Garcia LMT, Hunter RF, de la Haye K, Economos CD, King AC (2021) An action-oriented framework for systems-based solutions aimed at childhood obesity prevention in US Latinx and Latin American populations. Obes Rev 22(3). 10.1111/obr.13241 15. Hawe P. **Lessons from complex interventions to improve health**. *Annu Rev Public Health* (2015.0) **36** 307-323. DOI: 10.1146/annurev-publhealth-031912-114421 16. Hawe P. **Minimal, negligible and negligent interventions**. *Soc Sci Med Aug* (2015.0) **138** 265-268. DOI: 10.1016/j.socscimed.2015.05.025 17. Hawe P, Shiell A, Riley T (2004) Complex interventions: how “out of control” can a randomised controlled trial be? BMJ 328 (1561-3) 18. Hopkins L, Chamberlain D, Held F, Riley T, Zhou Jing Wang J, Conte K (2021) Collaborative Networks in Chronic Disease Prevention: what factors inhibit partnering for funding? Int J Public Adm 44(2):91-99. 10.1080/01900692.2019.1669177 19. Ison R, Blackmore C, Collins K, Furniss P. *"Systemic Environ Decis making: designing Learn Syst " Kybernetes* (2007.0) **36** 1340-1361 20. Jaaron AAM, Backhouse CJ. **Operationalising “Double-Loop” learning in Service Organisations: a Systems Approach for creating knowledge**. *Syst Pract Action Res* (2017.0) **30** 317-337. DOI: 10.1007/s11213-016-9397-0 21. Lewis S. **Learning from Communities: the local dynamics of formal and informal volunteering in Korogocho, Kenya**. *IDS Bull* (2015.0) **46** 69-82. DOI: 10.1111/1759-5436.12176 22. McGill E, Er V, Penney T, Egan M, White M, Meier P, Whitehead M, Lock K, Anderson de Cuevas R, Smith R, Savona N, Rutter H, Marks D, de Vocht F, Cummins S, Popay J, Petticrew M. **Evaluation of public health interventions from a complex systems perspective: a research methods review**. *Soc Sci Med* (2021.0). DOI: 10.1016/j.socscimed.2021.113697 23. Midgley G. **Systemic intervention for public health**. *Am J Public Health* (2006.0) **96** 466-472. DOI: 10.2105/AJPH.2005.067660 24. Pescud M, Rychetnik L, Allender S, Irving MJ, Finegood DT, Riley T, Ison R, Rutter H, Friel S (2021) From Understanding to Impactful Action: Systems Thinking for Systems Change in Chronic Disease Prevention Research. Systems 9 (61). doi: 10.3390/systems9030061 25. Plsek PE, Greenhalgh T. **Complexity science: the challenge of complexity in health care**. *BMJ* (2001.0) **323** 625-628. DOI: 10.1136/bmj.323.7313.625 26. Reynolds M. **Triple-loop learning and conversing with reality**. *Kybernetes* (2014.0) **43** 1381-1391. DOI: 10.1108/K-07-2014-0158 27. Riley T, Hawe P. **A typology of practice narratives during the implementation of a preventive, community intervention trial**. *Implement Sci* (2009.0) **4** 80. DOI: 10.1186/1748-5908-4-80 28. Riley T, Hopkins L, Gomez M, Davidson S, Chamberlain D, Jacob J, Wutzke S. **A Systems Thinking Methodology for Studying Prevention Efforts in Communities**. *Syst Pract Action Res* (2020.0). DOI: 10.1007/s11213-020-09544-7 29. Roussy V, Riley T, Livingstone C (2020) Together stronger: boundary work within an Australian systems-based prevention initiative. Health Promotion International 35:671-681 30. Rusoja E, Haynie D, Sievers J, Mustafee N, Nelson F, Reynolds M, Sarriot E, Swanson RC, Williams B. **Thinking about complexity in health: a systematic review of the key systems thinking and complexity ideas in health**. *J Eval Clin Pract* (2018.0) **24** 600-606. DOI: 10.1111/jep.12856 31. Rutter H, Savona N, Glonti K, Bibby J, Cummins S, Finegood DT, Greaves F, Harper L, Hawe P, Moore L, Petticrew M, Rehfuess E, Shiell A, Thomas J, White M. **The need for a complex systems model of evidence for public health**. *Lancet* (2017.0) **390** 2602-2604. DOI: 10.1016/S0140-6736(17)31267-9 32. Senge P (2006) The Fifth Discipline: The art and practice of the learning organisation. Second Edition ed. London: Random House Business Boks 33. Taylor MJ, McNicholas C, Nicolay C, Darzi A, Bell D, Reed JE. 2014. Systematic review of the application of the plan–do–study–act method to improve quality in healthcare. BMJ Quality & Safety 23 (4):290-298 34. Thompson VL, Drake B, James AS, Norfolk M, Goodman M, Ashford L, Jackson S, Witherspoon M, Brewster M, Colditz G. **A Community Coalition to address Cancer Disparities: transitions, Successes and Challenges**. *J Cancer Educ* (2015.0) **30** 616-622. DOI: 10.1007/s13187-014-0746-3 35. Trickett EJ, Beehler S, Deutsch C, Green LW, Hawe P, McLeroy K, Miller RL, Rapkin BD, Schensul JJ, Schulz AJ, Trimble JE. **Advancing the science of community-level interventions**. *Am J Public Health* (2011.0) **101** 1410-1419. DOI: 10.2105/AJPH.2010.300113 36. Wadsworth Y. *Building in research and evaluation: human inquiry for living systems* (2010.0) 37. Waterlander WE, Singh A, Altenburg T, Dijkstra C, Luna Pinzon A, Anselma M, Busch V, van Houtum L, Emke H, Overman ML, Chinapaw MJM, Stronks K (2021) Understanding obesity-related behaviors in youth from a systems dynamics perspective: the use of causal loop diagrams. Obes Rev 22(7). 10.1111/obr.13185 38. Wilson A, Wutzke S, Overs M. **The australian Prevention Partnership Centre: systems thinking to prevent lifestyle-related chronic illness**. *Public Health Res Pract* (2014.0) **25** e2511401. DOI: 10.17061/phrp2511401 39. Zuber-Skerritt O. **A model for designing action learning and action research programs**. *Learn Organ* (2002.0) **9** 143-149. DOI: 10.1108/09696470210428868 40. Foster-Fishman P, and Watson E, (2012) The ABLe Change Framework: A Conceptual and Methodological Tool for Promoting Systems Change American Journal of Community Psychology 49(3-4) 503-516 10.1007/s10464-011-9454-x 41. QSR International Pty Ltd. (2012). NVivo qualitative data analysis software. Version 11. Melbourne, Australia
--- title: 'Quality and cost of healthcare services in patients with diabetes in Iran: Results of a nationwide short-term longitudinal survey' authors: - Mohsen Abbasi-Kangevari - Farnam Mohebi - Seyyed-Hadi Ghamari - Mitra Modirian - Nazila Shahbal - Naser Ahmadi - Yosef Farzi - Mehrdad Azmin - Shahin Roshani - Hossein Zokaei - Maryam Khezrian - Shahedeh Seyfi - Mohammad Keykhaei - Fatemeh Gorgani - Saral Rahimi - Negar Rezaei - Shahab Khatibzadeh - Saeid Shahraz journal: Frontiers in Endocrinology year: 2023 pmcid: PMC10060949 doi: 10.3389/fendo.2023.1099464 license: CC BY 4.0 --- # Quality and cost of healthcare services in patients with diabetes in Iran: Results of a nationwide short-term longitudinal survey ## Abstract ### Aims To investigate the journey of patients with diabetes in the healthcare system using nationally-representative patient-reported data. ### Methods Participants were recruited using a machine-learning-based sampling method based on healthcare structures and medical outcome data and were followed up for three months. We assessed the resource utilization, direct/indirect costs, and quality of healthcare services. ### Results One hundred fifty-eight patients with diabetes participated. The most utilized services were medication purchases (276 times monthly) and outpatient visits (231 times monthly). During the previous year, $90\%$ of respondents had a laboratory fasting blood glucose assessment; however, less than $70\%$ reported a quarterly follow-up physician visit. Only $43\%$ had been asked about any hypoglycemia episodes by their physician. Less than $45\%$ of respondents had been trained for hypoglycemia self-management. The annual average health-related direct cost of a patient with diabetes was 769 USD. The average out-of-pocket share of direct costs was 601 USD ($78.15\%$). Medication purchases, inpatient services, and outpatient services summed up $79.77\%$ of direct costs with a mean of 613 USD. ### Conclusion Healthcare services focused solely on glycemic control and the continuity of services for diabetes control was insufficient. Medication purchases, and inpatient and outpatient services imposed the most out-of-pocket costs. ## Introduction Diabetes mellitus (hereafter diabetes) is one of the most significant global public health concerns causing 916 Disability-Adjusted Life-Years (DALYs) per 100,000 population in 2019 worldwide [1]. Despite the previous efforts, diabetes remains the second most significant cause of reduced healthy life expectancy [2]. The International Diabetes Federation (IDF) estimated that unless effective prevention methods are employed, the prevalence of diabetes will increase by $54\%$ in 2045 [3]. On the national scale, the prevalence of diabetes has risen roughly $30\%$ in Iran in the last decade, which is alarmingly higher than the global estimations [4]. The presence of diabetes is associated with increased mortality from infections, cardiovascular diseases, stroke, chronic kidney diseases, chronic liver diseases, and cancers [5]. Uncontrolled diabetes can impose high direct and indirect costs on patients and healthcare systems. The annual costs attributable to diabetes were estimated to be US$1.31 trillion worldwide or $1.8\%$ of the global gross domestic product (GDP), two-thirds of which were direct medical costs, and the one-third were indirect costs [6]. In the meantime, diabetes costs are expected to grow considerably, disproportionately affecting low- and middle-income countries, where $80\%$ of patients with diabetes live [7]. Delivery of essential medications, blood glucose management, cardiometabolic risk factors elimination, and early screening for complications via well-organized care reduce acute and chronic complications and extends healthy life expectancy among patients with diabetes [8, 9]. Nevertheless, the comprehensive, evidence-based diabetes care delivery is suboptimal even in well-resourced health systems [10]. Notably, multicomponent quality improvement programs have been beneficial in achieving diabetes care goals [11]. In this sense, investigating healthcare quality and costs for diabetes is essential to make evidence-based decisions to lower the costs and increase the quality of care. Thus, the objective of this study was to investigate the journey of patients with diabetes in the healthcare system via assessing the healthcare utilization, quality, and costs on a national level in Iran based on the results of the Iran Quality of Care in Medicine Program (IQCAMP). IQCAMP is a series of longitudinal surveys focusing on seven high-prevalence and high-cost diseases, including chronic obstructive pulmonary disease, congestive heart failure, diabetes mellitus, end-stage renal failure, major depressive disorder, myocardial infarction, and stroke [12]. We believe this study serves as guidance on assessing the care for diabetes at the national level in a minimal but sufficient way, particularly in countries with a similar context. It could also shed light on the likely scenarios of diabetic patients go through where the healthcare system resembles Iranian healthcare architecture. ## Overview The current demonstration study is part of a nationally representative IQCAMP survey generating patient-centered real-world data on the utilization, costs, and quality of care for seven high-prevalence and high-cost diseases in Iran from 2016 to 2018. This study reports first-hand data on patient experience regarding healthcare utilization, costs, and quality of care for diabetes. ## Study protocol The patients in the IQCAMP study were selected using a novel sampling method, the details of which are provided elsewhere [12]. A machine-learning-based sampling method was used to divide the 31 provinces into eight clusters considering their similarity in healthcare structure and outcome data. One province from each cluster was selected for data collection. Simulation analysis of the sampling revealed an efficiency of up to $70\%$ [12]. In the selected clusters, patients with diabetes [13] were selected from the participants with diabetes from the STEPwise Approach to NCD Risk Factor Surveillance (STEPS) 2016 study, a national cross-sectional survey carried out by the Non-Communicable Diseases Research Center (NCDRC). Participants of the STEPS survey were selected via multistage cluster sampling, and they were representative of the general population aged ≥18 years living in urban and rural areas in all provinces of Iran. A detailed description of the study population and the sampling method of the STEPS survey has been published elsewhere [14]. Diabetes was defined as the presence of fasting plasma glucose > 7 mmol/L or A1C > $6.5\%$ or a past medical history of confirmed diabetes that is under treatment. All patients with diabetes who were aware of their disease were invited to participate in the study. Trained nurses called the patients and gave them detailed instructions on the study objectives and their right to leave the study at any time. All participants provided written informed consent. We collected the data through the phone. The initial interview included the participants’ current and past medical history. Then, three monthly follow-up interviews were held to collect information on service utilization, quality indicators, and the cost of healthcare services received. Tehran Medical Science University’s ethics committee and the National Institute of Medical Research Development (NIMAD) approved the patient recruitment protocol. ## Variable and data collection The study assessed variables addressing utilization, quality, and costs of healthcare services. Healthcare services were categorized into three major groups: therapeutic, diagnostic, and patient support services. Therapeutic services included inpatient care, ambulatory care, and medication coverage. Lab and imaging services constituted diagnostic services. Rehabilitation could consist of healthcare services such as physiotherapy, occupational therapy, and speech therapy. Data were gathered from the health records of participants who underwent hospitalization and structured questionnaire-based interviews. Domain experts in epidemiology and endocrinology developed the initial draft of the study questionnaire, which patients with diabetes then debriefed. Finally, the study questionnaire was hosted on an android provisioned device and then went through usability testing for the study interviewers. The questionnaire consisted of four cardinal sections, including questions regarding participants’ sociodemographic and health status, frequencies of the utilization of various healthcare services, quality indicators, and healthcare costs. The sociodemographic section included sex, age, literacy, and household wealth index. Principal Component Analysis (PCA) was applied to derive the household wealth index based on questions on key dwelling characteristics and household ownership, as described in the study protocol. PCA is an approach to statistical analysis in which multiple datasets are combined as orthogonal components [15]. The wealth index was used to divide the population into quintiles, whereby the first and fifth quintiles present the least fortunate and wealthiest households, respectively. In the utilization section, participants were asked to declare frequencies of the utilization of any healthcare services throughout the study period. In addition, all medications available in the Iranian Pharmacopoeia were included in the survey. A selection of pre-defined quality indicators was utilized to assess the quality of provided healthcare services. A panel of medical experts managing patients with diabetes considered different quality indicators using the guidelines of the Ministry of Health and Medical Services of Iran, frameworks utilized in developing countries, and the National Qualification Framework (NQF) designed for the United States (US) [16]. The study’s expert panel added several essential indices based on their experience or literature review. The questionnaire was face-validated and then updated after a pilot study with the participation of ten patients. Hospital invoices and patients’ out-of-pocket share were investigated to calculate the healthcare services’ costs. The related travel and accommodation expenses were added to calculate the direct cost of the disease. Lower income due to diabetes, loss of productivity, and wasted time of the patient and their possible accompanying family members during the doctor-patient appointments were calculated as indirect costs. Questions regarding healthcare diagnostic, therapeutic, and patient-support services were asked to help with out-of-pocket share estimations. ## Data analysis The unit of analysis was defined as person-month. Annual average costs of diabetes were calculated by the average cost of each month multiplied by 12. Purchasing Power Parity (PPP) for 2018 was applied to convert Iranian Rials to US Dollars (USD), 1000 USD equaled 16,773,000 Iranian Rials [17]. The number of absent days from work due to diabetes multiplied by the minimum daily wage was calculated the loss due to diabetes. We computed loss of productivity by asking the patients how much less they had earned in a month when they struggled with diabetes complications compared to a normal month. Diabetes care quality was assessed by reporting the percentage of participants meeting the defined quality standards. All quantitative data are reported by mean, standard deviation, number, and percent. ## Results The final sample included 158 patients, among whom 91 ($57.6\%$) were women. All patients underwent three successive monthly follow-ups, summing up to 474 patient months. The sociodemographic characteristics of participants are presented in Table 1. **Table 1** | Total | 159 | | --- | --- | | Variable | N (%) | | Sex | Sex | | Female | 92 (57.8) | | Male | 67 (42.2) | | Age group | Age group | | 18-35 years | 3 (1.9) | | 36-65 years | 99 (62.7) | | >65 years | 45 (28.5) | | Not defined | 12 (4.9) | | Education | Education | | Illiterate | 11 (7.0) | | Primary school | 42 (26.6) | | Middle/high school | 46 (29.1) | | High school diploma | 15 (9.5) | | University graduate | 22 (13.9) | | Post-graduate degree | 9 (5.7) | | Not defined | 13 (8.2) | | Wealth index | Wealth index | | Very low | 9 (5.7) | | Low | 47 (29.8) | | Middle | 38 (24.0) | | High | 39 (24.7) | | Very high | 25 (15.8) | ## Healthcare utilization The most utilized services were medication purchase, with times per month (159 times among women and 117 among men). The second most utilized type of healthcare service was outpatient visits with 231 times per month (137 times among women and 94 among men). Table 2 presents the monthly utilization of various diabetes-related services among 474 visits. **Table 2** | Services | Services.1 | Patients | Events | Events per patient | | --- | --- | --- | --- | --- | | Therapeutic Services | Therapeutic Services | Therapeutic Services | Therapeutic Services | Therapeutic Services | | Inpatient services | Total | 9 | 11 | 0.04 | | Inpatient services | Women | 7 | 8 | 0.04 | | Inpatient services | Men | 2 | 2 | 0.02 | | Outpatient services | Total | 160 | 231 | 0.74 | | Outpatient services | Women | 96 | 137 | 0.74 | | Outpatient services | Men | 64 | 94 | 0.74 | | Laboratory services | Total | 79 | 85 | 0.27 | | Laboratory services | Women | 54 | 60 | 0.32 | | Laboratory services | Men | 25 | 25 | 0.2 | | Diagnosis services | Total | 21 | 28 | 0.09 | | Diagnosis services | Women | 13 | 20 | 0.11 | | Diagnosis services | Men | 8 | 8 | 0.06 | | Patient Support Services | Patient Support Services | Patient Support Services | Patient Support Services | Patient Support Services | | Rehabilitation services | Total | 3 | 3 | 0.01 | | Rehabilitation services | Women | 3 | 3 | 0.02 | | Rehabilitation services | Men | 0 | 0 | 0 | | Medication purchases | Total | 218 | 276 | 0.88 | | Medication purchases | Women | 125 | 159 | 0.86 | | Medication purchases | Men | 93 | 117 | 0.92 | | Home care services | Total | 2 | 2 | 0.01 | | Home care services | Women | 2 | 2 | 0.01 | | Home care services | Men | 0 | 0 | 0 | | Medical equipment | Total | 27 | 27 | 0.09 | | Medical equipment | Women | 14 | 14 | 0.08 | | Medical equipment | Men | 13 | 13 | 0.1 | ## Healthcare quality The mean (SD) time from the last laboratory blood sugar assessment was 4.44 (5.33) months, 3.5 (3.05) months among women, and 6.02 (6.51) months among men. While more than $90\%$ of respondents showed a history of laboratory fasting blood glucose assessment in the previous year, less than $70\%$ reported quarterly follow-up physician visits for diabetes management. During the follow-up visits in the last year, $93\%$ of respondents reported that their blood pressure was measured during the office visit. However, less than half of the respondents reported weight measurement by any healthcare professional. While $75\%$ of respondents reported receiving instructions on medication use from their consulting physicians, less than $50\%$ said that pharmacy staff explained the medication use and timing. Among 129 respondents, 9 ($7.0\%$) had a history of ambulatory care due to hyper or hypoglycemia during the last year: 8 ($10.4\%$) among women and 1 ($1.9\%$) among men. An average of 3.71 (4.04) hypoglycemia episodes resulted in hospital admissions or outpatient visits in the previous year, 4.5 (5.2) among women and 2.67 (1.12) among men. Nevertheless, only $43\%$ the respondents reported being interviewed for hypoglycemia episodes by their consulting physician during the last year. Notably, less than $45\%$ of respondents reported receiving any training about hypoglycemia self-management from their physician or any other healthcare provider. The mean (SD) number of hyperglycemia episodes that have resulted in hospital admission or an outpatient visit in the previous year was 2.26 (1.5), 2.05 (1.4) among women, and 3 (1.7) among men. While $72\%$ of respondents reported receiving medical advice on the significance of blood glucose control during the last follow-up visit, only $54\%$ reported that their physicians had advised them to have more frequent follow-up visits for uncontrolled blood glucose. Regarding lifestyle modification consults, around $70\%$ of respondents received at least one episode of advice for regular physical activity and $62\%$ for proper dietary habits. Only a quarter of respondents reported undergoing foot examinations by healthcare professionals, and less than $8\%$ said that their shoes had been evaluated for diabetic foot prevention. Regarding diabetic foot prevention, less than $30\%$ of respondents reported being trained for a regular self-foot examination and how to select the correct pair of shoes. Only $35\%$ of respondents said they had received patient education materials for future reference. And finally, less than $10\%$ of respondents reported receiving an influenza vaccine during the previous year, and $1.4\%$ reported vaccination against pneumococcal infection during the last five years (Table 3). **Table 3** | Indicator | Women | Women.1 | Men | Men.1 | Both | Both.1 | | --- | --- | --- | --- | --- | --- | --- | | Indicator | n (%) | Total | n (%) | Total | n (%) | Total | | Comprehensive management | Comprehensive management | Comprehensive management | Comprehensive management | Comprehensive management | Comprehensive management | Comprehensive management | | Glycemic control | Glycemic control | Glycemic control | Glycemic control | Glycemic control | Glycemic control | Glycemic control | | Laboratory fasting blood glucose assessment in the last year | 77 (86.5) | 89 | 61 (95.3) | 64 | 138 (90.2) | 153 | | Over the past year, attended quarterly follow-up physician visits for diabetes management | 54 (65.1) | 83 | 45 (70.3) | 64 | 99 (67.3) | 147 | | As a result of uncontrolled blood sugar levels, doctors scheduled medical visits sooner than quarterly follow-ups | 38 (54.3) | 70 | 33 (54.1) | 61 | 71 (54.2) | 131 | | Weight measurement in the previous physician visit | 40 (47.6) | 84 | 31 (49.2) | 63 | 71 (48.3) | 147 | | Consulting physician asking about hypoglycemia episodes in the past year | 38 (45.2) | 84 | 27 (42.2) | 64 | 65 (43.9) | 148 | | Cardiovascular health | Cardiovascular health | Cardiovascular health | Cardiovascular health | Cardiovascular health | Cardiovascular health | Cardiovascular health | | Blood pressure measurement by a healthcare professional during the last year | 81 (93.1) | 87 | 62 (93.9) | 66 | 143 (93.5) | 153 | | Electrocardiography assessment during the last year | 55 (63.2) | 87 | 44 (67.7) | 65 | 99 (65.1) | 152 | | Lipid profile assessment during the last year | 45 (49.5) | 91 | 30 (45.5) | 66 | 75 (47.8) | 157 | | Microvascular complications | Microvascular complications | Microvascular complications | Microvascular complications | Microvascular complications | Microvascular complications | Microvascular complications | | Ever being referred for retinal examination | 56 (62.2) | 90 | 42 (62.7) | 67 | 98 (62.4) | 157 | | Feet examination by a healthcare professional regarding diabetic foot ulcer during the last year | 23 (25) | 92 | 17 (25.4) | 67 | 40 (25.2) | 159 | | Footwear evaluation by a healthcare professional during the last year | 4 (4.6) | 87 | 8 (12.5) | 64 | 12 (7.9) | 151 | | Mental health | Mental health | Mental health | Mental health | Mental health | Mental health | Mental health | | History of prescription of major depressive disorder medications | 12 (85.7) | 14 | 5 (71.4) | 7 | 17 (81) | 21 | | Being diagnosed with major depressive disorder by a psychiatrist | 14 (15.9) | 88 | 7 (10.8) | 65 | 21 (13.7) | 153 | | Infectious diseases prevention | Infectious diseases prevention | Infectious diseases prevention | Infectious diseases prevention | Infectious diseases prevention | Infectious diseases prevention | Infectious diseases prevention | | Vaccination against influenza during the last year | 6 (7.1) | 84 | 8 (12.3) | 65 | 14 (9.4) | 149 | | Vaccination against pneumococcal infection during the last five years | 1 (1.2) | 84 | 1 (1.6) | 64 | 2 (1.4) | 148 | | Dental health | Dental health | Dental health | Dental health | Dental health | Dental health | Dental health | | Referral to a dentist for gingival or dental assessment during the last six months | 22 (25.6) | 86 | 22 (33.8) | 65 | 44 (29.1) | 151 | | Patient education | Patient education | Patient education | Patient education | Patient education | Patient education | Patient education | | Treatment compliance | Treatment compliance | Treatment compliance | Treatment compliance | Treatment compliance | Treatment compliance | Treatment compliance | | Receiving medical consult on medication usage and intervals by consulting physicians | 29 (82.9) | 35 | 13 (61.9) | 21 | 42 (75) | 56 | | Receiving consult or medical consult on medication usage and timing by the pharmacy staff | 19 (55.9) | 34 | 8 (38.1) | 21 | 27 (49.1) | 55 | | Lifestyle promotion | Lifestyle promotion | Lifestyle promotion | Lifestyle promotion | Lifestyle promotion | Lifestyle promotion | Lifestyle promotion | | Healthcare professionals' recommendations on regular physical activity for blood sugar control in the past year | 58 (63) | 92 | 53 (79.1) | 67 | 111 (69.8) | 159 | | Healthcare professionals' recommendations on dietary habits for blood sugar control in the past year | 58 (63) | 92 | 41 (61.2) | 67 | 99 (62.3) | 159 | | Being advised to quit smoke by a healthcare professional during the last year | 12 (13) | 92 | 12 (17.9) | 67 | 24 (15.1) | 159 | | Selfcare promotion | Selfcare promotion | Selfcare promotion | Selfcare promotion | Selfcare promotion | Selfcare promotion | Selfcare promotion | | Medical advice on the significance of blood glucose control and the complications of diabetes during the last follow-up visit | 67 (79.8) | 84 | 39 (61.9) | 63 | 106 (72.1) | 147 | | Being trained about the measures to be taken upon hypoglycemia | 34 (41) | 83 | 32 (50) | 64 | 66 (44.9) | 147 | | Receiving patient education materials for future reference upon questions or concerns | 30 (36.1) | 83 | 22 (34.4) | 64 | 52 (35.4) | 147 | | Being trained about regular foot self-examination by healthcare professionals | 22 (25) | 88 | 21 (32.3) | 65 | 43 (28.1) | 153 | | Receiving a medical consult on the features of an optimized shoe | 19 (21.8) | 87 | 18 (28.1) | 64 | 37 (24.5) | 151 | ## Healthcare costs Table 4 conveys the direct and indirect costs of diabetes. The annual average direct health-related cost of a patient with diabetes was 768.96 USD. The average out-of-pocket share of direct health-related costs was 600.96 ($78.15\%$) USD. Medication purchases, inpatient services, and outpatient services summed up $79.77\%$ of direct health-related costs with a mean of 613.4 USD. **Table 4** | Services | Out-of-pocket costs | Out-of-pocket costs.1 | Total costs | Total costs.1 | | --- | --- | --- | --- | --- | | Services | Mean(SD) | Median(IQR) | Mean(SD) | Median(IQR) | | Direct Costs | Direct Costs | Direct Costs | Direct Costs | Direct Costs | | Health related | Health related | Health related | Health related | Health related | | In-patient services | 131.64 (17,806.20) | 250.44 (250.32) | 246.60 (16,905.84) | 1,430.88 (15,782.40) | | Out-patient services | 83.16 (413.52) | 143.04 (316.56) | 139.56 (1,344.96) | 243.24 (373.92) | | Laboratory services | 45.00 (337.32) | 200.28 (317.64) | 84.84 (1,008.48) | 433.44 (816.96) | | Diagnosis services | 44.76 (1,244.16) | 536.52 (1,171.68) | 30.12 (999.48) | 590.28 (1,799.40) | | Rehabilitation services | 4.44 (328.80) | 697.56 (232.56) | 4.44 (121.92) | 700.80 (86.16) | | Medication purchases | 261.12 (769.32) | 294.72 (416.76) | 227.16 (925.80) | 301.92 (354.24) | | Home care services | 3.48 (NA) | 1,073.16 (0.00) | 0.00 (NA) | NA (NA) | | Medical equipment | 27.36 (410.76) | 400.68 (457.80) | 36.24 (385.32) | 400.68 (457.80) | | Sum | 600.96 | | 768.96 | | | Non-health related | | | 325.08 (325.08) | 214.68 (271.92) | | Indirect Costs | Indirect Costs | Indirect Costs | Indirect Costs | Indirect Costs | | Lower income because of diabetes | | | 0.84 (0.84) | 0.60 (0.36) | | Loss of productivity | | | 2,978.52 (2,978.52) | 2,146.32 (2,861.76) | | Time waste | | | 576.24 (576.24) | 440.52 (630.48) | | Sum | | | 3880.68 | | ## Discussion This study is the first nationally representative research that collects cost and quality information directly from patients at the national level. Medication purchases and outpatient medical visits were the most utilized healthcare services. Most direct costs were for medications and inpatient and outpatient services. Medication purchases and inpatient and outpatient services imposed the most significant proportion of out-of-pocket costs. The healthcare system's primary focus was on glycemic control rather than a fair distribution of services across preventive and therapeutic care according to standard guidelines for diabetes management [18]. Approximately $80\%$ the direct health-related costs of diabetes were for medication purchases, inpatient services, and outpatient services. Similarly, it has been reported that inpatient and medication costs were the most expensive aspects of diabetes care in low- and middle-income countries [19]. On average, out-of-pocket share constituted $78\%$ of the total direct costs, primarily due to medication purchases. Evidence shows that change in out-of-pocket share for diabetes medications across various payer policies impacts diabetes medication usage. Patients sharing fewer drug payments tend to have a significantly higher number of months with apparent active medication coverage, a proxy for medication adherence [20]. Continuous medical care is required to achieve optimal glycemic control among patients with diabetes and prevent diabetes complications. The respondents reported receiving inpatient or outpatient healthcare services at least two times during the previous year. While home care could lead to improved diabetes-related outcomes among patients [21], it was among the least utilized services by respondents. It has been reported that some one-third of the inappropriate all-causes hospitalization stays in Iran were due to lack of home care, $35\%$ of which was attributable to diabetes complications [22]. Thus, establishing appropriate home care in the health system as well as covering home care expenses by insurance could optimize hospital bed use, reduce costs, decrease readmission rates, and prevent hospital-related complications. More than half of the respondents said they were not asked about hypoglycemia episodes by their consulting physician during their routine follow-up visits in the last year. Based on patients’ reports, healthcare professionals did not train the patients in self-management of hypoglycemia episodes. Nevertheless, intensive antidiabetic treatments could impose patients at increased risk of hypoglycemia. While hypoglycemia-associated risk factors are yet to be adequately understood [23], the frequency and severity of hypoglycemia could be decreased via structured patient education [24]. Telemedicine, as a novel and accessible tool, could be along with proper patient education to monitor blood glucose, thus reducing the risk of hypoglycemia [25]. Despite the clear benefit of weight loss in glycemic management [26], only a tiny percentage of patients with diabetes can maintain substantial weight loss [27]. Notably, frequent follow-up visits can better achieve weight management [28]. In our study, less than half of the respondents reported that they underwent weight measurement by healthcare professionals during the follow-up visits. However, $70\%$ of respondents had been advised for regular physical activity and $60\%$ for proper dietary habits. Only a quarter of respondents reported undergoing foot examination by healthcare professionals, and less than one-tenth said their footwear had been evaluated for diabetic foot prevention. Moreover, less than one-third of respondents reported being trained in the regular self-foot examination and the features of an optimized shoe for patients with diabetes. The lifetime incidence of foot ulcers among patients with diabetes could be as high as $25\%$. There is strong supporting evidence for screening all patients with diabetes to identify those at risk for foot ulceration. High-risk patients could benefit from prophylactic interventions, such as patient education and prescription footwear [29]. Less than $10\%$ of respondents reported receiving an influenza vaccine during the previous year, and only $1.4\%$ said being vaccinated against pneumococcal infection during the last five years. Reasons for low utilization of vaccines among patients could be improper knowledge [30], lack of vaccine recommendations by physicians, mistrust of vaccine safety, inconvenience of vaccination procedure, supply, and accessibility [31]. Neglecting optimal long-term diabetes management can result in a higher prevalence of diabetes complications, reducing the patient’s quality of life and increasing healthcare expenditure. So far, there has been suboptimal diabetes management in the country, as reflected in poor glycemic control [32]. Nevertheless, the issue is not specific to Iran, as less than $10\%$ of patients with diabetes in low-income and middle-income countries receive guideline-based comprehensive diabetes treatment [33]. Even in countries with well-established economies like the US, improving diabetes control at the national level is a new challenge [34]. Part of this challenge is justified by the complicated nature of the patient-provider relationship in setting diabetes control goals when a patient visits the physician [35]. These concerns about diabetes care are a call for concerted efforts toward scaling up the capacity of healthcare systems to follow a complete, integrated care model for the management of diabetes, which provides patient-centered, holistic, and continuous healthcare services for patients with diabetes. Holistic approaches towards diabetes management could consist of multidisciplinary teams [36], close follow-ups [37], regular home visits [38], and medication review [39]. Education should become an integral part of diabetes management to empower patients to take control of their disease. Telehealth technology could be utilized for continuous disease monitoring, delivering education materials, and lifestyle promotion as a novel approach. In particular, access to telehealth in addition to in-person visits can promote access to and use of diabetes care and consequently improve health outcomes and quality of life for people with diabetes [40]. The Middle East and North Africa (MENA) region is estimated to have the second-highest global growth rate in the number of affected individuals with diabetes [41]. Since 2004, the National Program for Prevention and Control of Diabetes (NPPCD) of Iran has made endeavors toward diabetes prevention and sustained care for patients with diabetes [42, 43]. Despite adequate access to core medications for diabetes care, significant heterogeneity remains in comprehensive diabetes management, especially in glycemic control and complications management [44]. While $81\%$ of cities in Iran could cover essential diabetes services, $19\%$ could not provide even the lowest coverage level [45]. Without a national system for integrated diabetes control, researchers have attempted to provide quality and cost measures of diabetes care with mostly indirect estimations (46–48). ## Strengths and limitations This study presented a patient-centered disease-specific collection of insights on healthcare utilization, quality, and costs of diabetes in Iran. In developing countries, where integrated health record systems do not exist, such surveys usually consist of small samples from limited geographic areas. However, this study delivers information collected from a diverse geographic area of the country using a model-based clustering method to represent the country. The study’s follow-up modules confirmed the self-reported costs with the medical bills. Nevertheless, the small sample of this demonstration study hindered sound subgroup analyses statistically. While self-reports of service utilization and care quality guarantee the patient-centeredness nature of the responses, self-reports always suffer from various biases, such as recall. This study had a small but representative sample from the entire country. It successfully provided a frame of action and a methodological blueprint for a more extensive national-level study of the exact nature with a larger sample in the future, both in developing and developed countries. ## Conclusion Healthcare services have focused on glycemic control, and the comprehensive management of diabetes is compromised by insufficient continuity of services for diabetes control. Medication purchases and outpatient medical services were the most utilized healthcare services among patients with diabetes. The most direct costs were medication purchases and inpatient and outpatient services. Medication purchases and inpatient and outpatient services imposed the most out-of-pocket costs. ## Data availability statement The raw data supporting the conclusions of this article will be made available by the authors without undue reservation upon request. ## Ethics statement The studies involving human participants were reviewed and approved by the Ethics Committee of the Tehran University of Medical Sciences. The patients/participants provided written informed consent to participate in this study. ## Author contributions Conceptualization, SK and SSh. Data curation, FM, MM, NS, YF, SSe, FG, SRa, SK, and SSh. Formal analysis, MA-K, S-HG, NA, YF, MA, SRo, SSe, FG, and SSh. Funding acquisition, SSh. Investigation, MA-K, YF, SSe, NR, SK, and SSh. Methodology, FM, YF, MA, SSe, NR, SK, and SSh. Project administration, FM, YF, SSe, NR, and SSh. Resources, NR and SSh. Software, YF, MA, HZ, MKh, and FG. Supervision, YF, NR, and SSh. Validation, YF, NR, SK, and SSh. Writing – original draft preparation, MA-K, S-HG, and SSh. Writing – review and editing, MA-K, FM, S-HG, MM, NS, NA, YF, MA, SRo, HZ, MKh, SSe, MKe, FG, SRa, NR, SK, and SSh. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## References 1. Abbafati C, Machado DB, Cislaghi B, Salman OM, Karanikolos M, McKee M. **Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: A systematic analysis for the global burden of disease study 2019**. *Lancet* (2020.0) **396**. DOI: 10.1016/S0140-6736(20)30925-9 2. Chen H, Chen G, Zheng X, Guo Y. **Contribution of specific diseases and injuries to changes in health adjusted life expectancy in 187 countries from 1990 to 2013: Retrospective observational study**. *BMJ* (2019.0) **364**. DOI: 10.1136/bmj.l969 3. Cho NH, Shaw JE, Karuranga S, Huang Y, da Rocha Fernandes JD, Ohlrogge AW. **IDF diabetes atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045**. *Diabetes Res Clin Pract* (2018.0) **138**. DOI: 10.1016/j.diabres.2018.02.023 4. Khamseh ME, Sepanlou SG, Hashemi-Madani N, Joukar F, Mehrparvar AH, Faramarzi E. **Nationwide prevalence of diabetes and prediabetes and associated risk factors among Iranian adults: Analysis of data from PERSIAN cohort study**. *Diabetes Ther* (2021.0) **12**. DOI: 10.1007/S13300-021-01152-5/FIGURES/4 5. Lin X, Xu Y, Pan X, Xu J, Ding Y, Sun X. **Global, regional, and national burden and trend of diabetes in 195 countries and territories: An analysis from 1990 to 2025**. *Sci Rep* (2020.0) **10** 1. 2020. DOI: 10.1038/s41598-020-71908-9 6. Bommer C, Heesemann E, Sagalova V, Manne-Goehler J, Atun R, Bärnighausen T. **The global economic burden of diabetes in adults aged 20–79 years: a cost-of-illness study**. *Lancet Diabetes Endocrinol* (2017.0) **5**. DOI: 10.1016/S2213-8587(17)30097-9 7. Williams R, Karuranga S, Malanda B, Saeedi P, Basit A, Besançon S. **Global and regional estimates and projections of diabetes-related health expenditure: Results from the international diabetes federation diabetes atlas, 9th edition**. *Diabetes Res Clin Pract* (2020.0) **162** 108072. DOI: 10.1016/J.DIABRES.2020.108072 8. Jonas DE, Crotty K, Yun JDY, Middleton JC, Feltner C, Taylor-Phillips S. **Screening for prediabetes and type 2 diabetes: Updated evidence report and systematic review for the US preventive services task force**. *JAMA* (2021.0) **326**. DOI: 10.1001/JAMA.2021.10403 9. Chan JCN, Lim LL, Wareham NJ, Shaw JE, Orchard TJ, Zhang P. **The lancet commission on diabetes: Using data to transform diabetes care and patient lives**. *Lancet* (2020.0) **396**. DOI: 10.1016/S0140-6736(20)32374-6/ATTACHMENT/FDEC3A23-5EA1-4A0B-925E-D734AEF80FAD/MMC1.PDF 10. Manne-Goehler J, Geldsetzer P, Agoudavi K, Andall-Brereton G, Aryal KK, Bicaba BW. **Health system performance for people with diabetes in 28 low- and middle-income countries: A cross-sectional study of nationally representative surveys**. *PloS Med* (2019.0) **16**. DOI: 10.1371/JOURNAL.PMED.1002751 11. Flood D, Hane J, Dunn M, Brown SJ, Wagenaar BH, Rogers EA. **Health system interventions for adults with type 2 diabetes in low- and middle-income countries: A systematic review and meta-analysis**. *PloS Med* (2020.0) **17** e1003434. DOI: 10.1371/JOURNAL.PMED.1003434 12. Parsaeian M, Mahdavi M, Saadati M, Mehdipour P, Sheidaei A, Khatibzadeh S. **Introducing an efficient sampling method for national surveys with limited sample sizes: Application to a national study to determine quality and cost of healthcare**. *BMC Public Health* (2020.0) **21** 1414 13. Goldenberg R, Punthakee Z. **Definition, classification and diagnosis of diabetes, prediabetes and metabolic syndrome**. *Can J Diabetes* (2013.0) **37**. DOI: 10.1016/J.JCJD.2013.01.011 14. Djalalinia S, Modirian M, Sheidaei A, Yoosefi M, Zokaiee H, Damirchilu B. **Protocol design for Large-scale cross-sectional studies of surveillance of risk factors of non-communicable diseases in Iran: STEPs 2016**. *Arch Iranian Med* (2017.0) **20** 15. Rencher AC, Schimek MG. **Methods of multivariate analysis**. *Comput Statistics* (1997.0) **12** 422 16. 16 NQF: Measures, reports & tools. [cited 23 Mar 2021]. Available at: https://www.qualityforum.org/Measures_Reports_Tools.aspx .. *NQF: Measures, reports & tools. [cited 23 Mar 2021]. Available at:* 17. 17 PPP Conversion factor, GDP (LCU per international $). Iran: Islamic Rep. [cited 13 Jan 2023]. Available at: https://data.worldbank.org/indicator/PA.NUS.PPP?locations=IR.. *PPP Conversion factor, GDP (LCU per international $)* 18. 18Volume 46 Issue Supplement_1 | Diabetes Care | American Diabetes Association. [cited 30 Dec 2022]. Available at: https://diabetesjournals.org/care/issue/46/Supplement_1. 19. Moucheraud C, Lenz C, Latkovic M, Wirtz VJ. **The costs of diabetes treatment in low- and middle-income countries: A systematic review**. *BMJ Global Health* (2019.0) **4** e001258. DOI: 10.1136/BMJGH-2018-001258 20. Khorrami P, Sinha MS, Bhanja A, Allen HL, Kesselheim AS, Sommers BD. **Differences in diabetic prescription drug utilization and costs among patients with diabetes enrolled in Colorado marketplace and Medicaid plans, 2014-2015**. *JAMA Network Open* (2022.0) **5** e2140371-e2140371. DOI: 10.1001/JAMANETWORKOPEN.2021.40371 21. Nguyen DL, DeJesus RS. **Home health care may improve diabetic outcomes among non-English speaking patients in primary care practice: A pilot study**. *J Immigr Minor Health* (2011.0) **13**. DOI: 10.1007/s10903-011-9446-9 22. Siavashi E, Kavosi Z, Zand F, Amini M, Bordbar N. **Inappropriate hospital stays and association with lack of homecare services**. *East Mediterr Health J* (2021.0) **27**. DOI: 10.26719/2021.27.7.656 23. Alwafi H, Alsharif AA, Wei L, Langan D, Naser AY, Mongkhon P. **Incidence and prevalence of hypoglycaemia in type 1 and type 2 diabetes individuals: A systematic review and meta-analysis**. *Diabetes Res Clin Practice* (2020.0) **170**. DOI: 10.1016/J.DIABRES.2020.108522 24. Cryer PE. **Minimizing hypoglycemia in diabetes**. *Diabetes Care* (2015.0) **38**. DOI: 10.2337/DC15-0279 25. Hu Y, Wen X, Wang F, Yang D, Liu S, Li P. **Effect of telemedicine intervention on hypoglycaemia in diabetes patients: A systematic review and meta-analysis of randomised controlled trials**. *J Telemed Telecare* (2019.0) **25**. DOI: 10.1177/1357633X18776823 26. Lean ME, Leslie WS, Barnes AC, Brosnahan N, Thom G, McCombie L. **Primary care-led weight management for remission of type 2 diabetes (DiRECT): an open-label, cluster-randomised trial**. *Lancet* (2018.0) **391**. DOI: 10.1016/S0140-6736(17)33102-1 27. Norris SL, Zhang X, Avenell A, Gregg E, Bowman B, Serdula M. **Long-term effectiveness of lifestyle and behavioral weight loss interventions in adults with type 2 diabetes: A meta-analysis**. *Am J Med* (2004.0) **117**. DOI: 10.1016/J.AMJMED.2004.05.024 28. Hall KD, Kahan S. **Maintenance of lost weight and long-term management of obesity**. *Med Clinics North Am* (2018.0) **102** 183. DOI: 10.1016/J.MCNA.2017.08.012 29. Singh N, Armstrong DG, Lipsky BA. **Preventing foot ulcers in patients with diabetes**. *JAMA* (2005.0) **293**. DOI: 10.1001/JAMA.293.2.217 30. Ye C, Zhu W, Yu J, Li Z, Hu W, Hao L. **Low coverage rate and awareness of influenza vaccine among older people in shanghai, China: A cross-sectional study**. *Hum Vaccines Immunotherapeutics* (2018.0) **14**. DOI: 10.1080/21645515.2018.1491246 31. Wang Y, Cheng M, Wang S, Wu F, Yan Q, Yang Q. **Vaccination coverage with the pneumococcal and influenza vaccine among persons with chronic diseases in shanghai, China, 2017**. *BMC Public Health* (2020.0) **20** 1-9. DOI: 10.1186/s12889-020-8388-3 32. Mohseni M, Shams Ghoreishi T, Houshmandi S, Moosavi A, Azami-Aghdash S, Asgarlou Z. **Challenges of managing diabetes in Iran: Meta-synthesis of qualitative studies**. *BMC Health Serv Res* (2020.0) **20** 1-12. DOI: 10.1186/S12913-020-05130-8/TABLES/2 33. Flood D, Seiglie JA, Dunn M, Tschida S, Theilmann M, Marcus ME. **The state of diabetes treatment coverage in 55 low-income and middle-income countries: A cross-sectional study of nationally representative, individual-level data in 680 102 adults**. *Lancet Healthy Longevity* (2021.0) **2**. DOI: 10.1016/S2666-7568(21)00089-1/ATTACHMENT/F1685BFF-70C4-422D-B5F0-A89DA88C80F2/MMC1.PDF 34. Shahraz S, Pittas AG, Saadati M, Thomas CP, Lundquist CM, Kent DM. **Change in testing, awareness of hemoglobin A1c result, and glycemic control in US adults, 2007-2014**. *JAMA* (2017.0) **318**. DOI: 10.1001/JAMA.2017.11927 35. Shahraz S, Pittas AG, Lundquist CM, Danaei G, Kent DM. **Do patient characteristics impact decisions by clinicians on hemoglobin A1c targets**. *Diabetes Care* (2016.0) **39**. DOI: 10.2337/DC16-0532 36. McGill M, Blonde L, Chan JCN, Khunti K, Lavalle FJ, Bailey CJ. **The interdisciplinary team in type 2 diabetes management: Challenges and best practice solutions from real-world scenarios**. *J Clin Trans Endocrinol* (2017.0) **7** 21. DOI: 10.1016/J.JCTE.2016.12.001 37. Al-Hamarneh YN, Sauriol L, Tsuyuki RT. **After the diabetes care trial ends, now what? A 1-year follow-up of the RxING study**. *BMJ Open* (2015.0) **5** e008152. DOI: 10.1136/BMJOPEN-2015-008152 38. Han L, Ma Y, Wei S, Tian J, Yang X, Shen X. **Are home visits an effective method for diabetes management? A quantitative systematic review and meta-analysis**. *J Diabetes Invest* (2017.0) **8** 701. DOI: 10.1111/JDI.12630 39. Schindler E, Hohmann C, Culmsee C. **Medication review by community pharmacists for type 2 diabetes patients in routine care: Results of the DIATHEM-study**. *Front Pharmacol* (2020.0) **11**. DOI: 10.3389/FPHAR.2020.01176 40. Hood KK, Wong JJ. **Telehealth for people with diabetes: Poised for a new approach**. *Lancet Diabetes Endocrinol* (2022.0) **10** 8-10. DOI: 10.1016/S2213-8587(21)00312-0 41. Guariguata L, Whiting DR, Hambleton I, Beagley J, Linnenkamp U, Shaw JE. **Global estimates of diabetes prevalence for 2013 and projections for 2035**. *Diabetes Res Clin Practice* (2014.0) **103**. DOI: 10.1016/j.diabres.2013.11.002 42. Farzadfar F, Murray CJL, Gakidou E, Bossert T, Namdaritabar H, Alikhani S. **Effectiveness of diabetes and hypertension management by rural primary health-care workers (Behvarz workers) in Iran: A nationally representative observational study**. *Lancet* (2012.0) **379** 47-54. DOI: 10.1016/S0140-6736(11)61349-4 43. Noshad S, Afarideh M, Heidari B, Mechanick JI, Esteghamati A. **Diabetes care in Iran: Where we stand and where we are headed**. *Ann Global Health* (2014.0) **81**. DOI: 10.1016/j.aogh.2015.10.003 44. Esteghamati A, Larijani B, Aghajani MH, Ghaemi F, Kermanchi J, Shahrami A. **Diabetes in Iran: Prospective analysis from first nationwide diabetes report of national program for prevention and control of diabetes (NPPCD-2016).**. *Sci Rep* (2017.0) **7** 13461. DOI: 10.1038/s41598-017-13379-z 45. Sharifi A, Farzi Y, Roshani S, Ghamari A, Tabatabaei-Malazy O, Djalalinia S. **A new model for optimization of diabetes clinics with the case study in Iran**. *J Diabetes Metab Disord* (2022.0) **21**. DOI: 10.1007/s40200-021-00939-4 46. Javanbakht M, Baradaran HR, Mashayekhi A, Haghdoost AA, Khamseh ME, Kharazmi E. **Cost-of-illness analysis of type 2 diabetes mellitus in Iran**. *PloS One* (2011.0) **6**. DOI: 10.1371/JOURNAL.PONE.0026864 47. Javanbakht M, Abolhasani F, Mashayekhi A, Baradaran HR, Jahangiri noudeh Y. **Health related quality of life in patients with type 2 diabetes mellitus in Iran: a national survey**. *PloS One* (2012.0) **7**. DOI: 10.1371/JOURNAL.PONE.0044526 48. Esteghamati A, Larijani B, Aghajani MH, Ghaemi F, Kermanchi J, Shahrami A. **Diabetes in Iran: Prospective analysis from first nationwide diabetes report of national program for prevention and control of diabetes (NPPCD-2016)**. *Sci Rep* (2017.0) **7**. DOI: 10.1038/S41598-017-13379-Z
--- title: The association between maternal HbA1c and adverse outcomes in gestational diabetes authors: - Marie Parfaite Uwimana Muhuza - Lixia Zhang - Qi Wu - Lu Qi - Danqing Chen - Zhaoxia Liang journal: Frontiers in Endocrinology year: 2023 pmcid: PMC10060951 doi: 10.3389/fendo.2023.1105899 license: CC BY 4.0 --- # The association between maternal HbA1c and adverse outcomes in gestational diabetes ## Abstract ### Background The role of HbA1c in women with gestational diabetes mellitus (GDM) is still unclear, particularly in the Asian population. ### Aim To investigate the association between HbA1c levels and adverse outcomes considering maternal age, pre-pregnancy body mass index (BMI), and gestational weight gain (GWG) in women with GDM. ### Method A retrospective study included 2048 women with GDM and singleton live births. Using logistic regression, the associations between HbA1c and adverse pregnancy outcomes were assessed. ### Result Compared to women with HbA1c ≤ $5.0\%$, HbA1c was significantly associated with macrosomia (aOR 2.63,$95\%$CI1.61,4.31), pregnancy-induced hypertension (PIH, aOR 2.56,$95\%$CI1.57,4.19), preterm birth (aOR 1.64,$95\%$CI 1.05,2.55), and primary Cesarean section (primary C-section, aOR1.49,$95\%$CI1.09,2.03) in GDM women with HbA1c ≥$5.5\%$ while significantly associated with PIH (aOR 1.91,$95\%$CI1.24,2.94) in women with HbA1c 5.1-$5.4\%$. The associations between HbA1c and adverse outcomes varied with maternal age, pre-pregnancy BMI, and GWG. In women aged ≤29 years, there’s significant association between HbA1c and primary C-section when HbA1c was 5.1-$5.4\%$ and ≥$5.5\%$. In women aged 29-34 years and HbA1c ≥$5.5\%$, HbA1c was significantly associated with macrosomia. In women aged ≥35 years, there’s significant association between HbA1c and preterm birth when HbA1c was 5.1-$5.4\%$ and macrosomia and PIH when HbA1c ≥$5.5\%$. In pre-pregnant normal-weight women, HbA1c was significantly associated with macrosomia, preterm birth, primary C-section, and PIH when HbA1c ≥$5.5\%$ while HbA1c was significantly associated with PIH when HbA1c was 5.1-$5.4\%$. In pre-pregnant underweight women with HbA1c 5.1-$5.4\%$, HbA1c was significantly associated with primary C-section. HbA1c was significantly associated with macrosomia among women with inadequate GWG or excess GWG and HbA1c≥$5.5\%$. In women with adequate GWG, there’s significant association between HbA1c and PIH when HbA1c was 5.1-$5.4\%$ and ≥$5.5\%$. ### Conclusion Conclusively, HbA1c at the time of diagnosis is significantly associated with macrosomia, preterm birth, PIH, and primary C-section in Chinese women with GDM. ## Introduction Gestational diabetes mellitus (GDM) is carbohydrate intolerance resulting in hyperglycemia during pregnancy without prior history of diabetes (Type 1 or Type 2) [1]. It is screened using fasting plasma glucose (FPG), 1-h postprandial glucose (PG), 2-h PG of 75g oral glucose tolerance test (OGTT) during 24-28 weeks, according to the International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria [2]. The availability of screening for gestational diabetes in the past years has increased the detection rate of GDM [3]. The incidence of GDM in *China is* $14.8\%$, caused by increasing weight gain, maternal age, family history, and many other factors linked with the pregnancy period of women [4]. The increase in gestational diabetes incidence and its association with Type 2 diabetes remains crucial [5]. GDM is associated with both short and long-term pregnancy adverse outcomes, including macrosomia, large for gestational age (LGA), preeclampsia, primary Cesarean section (C-section), shoulder dystocia, preterm birth, postpartum diabetes mellitus and risk of Type 2 diabetes in offspring (6–8). HbA1c is used in diagnosing, treatment, preventing, and detecting progress of diabetes [9]. In women with hyperglycemia, glycated hemoglobin A1c (HbA1c) level has been associated with birthweight, primary C-section, hypoglycemia, cord-serum C-peptide, pre-eclampsia, preterm birth, the sum of skin folds, percent body fat >90th percentile [10]. It has been reported that adverse outcomes in early pregnancy can be predicted by HbA1c (11–13) as well as in GDM pregnant women [14, 15]. But different HbA1c cut-offs have been used in past studies to predict adverse outcomes in GDM pregnancy. HbA1c level ≥$5.0\%$ was used to predict neonatal complications and ≥$6.2\%$ to predict postpartum diabetes mellitus [14, 16]. HbA1c might be useful in predicting adverse outcomes in GDM and studies indicating the association between HbA1c and adverse outcomes have been conducted in Caucasian women with GDM [17]. However, there is a lack of enough evidence in the Asian population. This retrospective study aims to investigate the relationship between HbA1c levels and adverse pregnancy outcomes considering maternal age, pre-pregnancy body mass index (BMI), and gestational weight gain (GWG) among GDM women, which might provide evidence for the prevention of adverse outcomes in GDM pregnant women. ## Study design and population A retrospective study was conducted among women with gestational diabetes who received regular prenatal care and delivered at the Women’s Hospital, School of Medicine, Zhejiang University from 1-July-2017 to 30-June-2018. Women who were diagnosed with GDM by OGTT in the second trimester of pregnancy, delivered a live singleton more than 28 gestational weeks, and had complete medical records were included. Women who had a prior history of diabetes mellitus, chronic diseases (hypertension, liver, kidney, heart, lung, and other major organ diseases), autoimmune diseases (Sjogren’s syndrome, anticardiolipin syndrome, myasthenia gravis), or tumors were excluded. Finally, 2048 GDM women were included in this study. Relevant information about pregnant women, including age, height, weight before pregnancy (within one month before pregnancy), weight gain during pregnancy, gravidity, parity, OGTT value (FPG, 1-h PG, 2-h PG), HbA1c, mode of delivery, gestational week of delivery, neonatal birth weight, pregnancy complications such as macrosomia, pregnancy-induced hypertension (PIH, including gestational hypertension, preeclampsia, eclampsia) was obtained. ## GDM diagnostic criteria GDM was diagnosed according to IADPSG criteria by 75g OGTT in the second trimester of pregnancy by measurement of FPG, 1-h PG, and 2-h PG. OGTT and HbA1c tests were performed in the morning after overnight fasting of at least 8 hours at 24-28 weeks of gestation. G lucose level was measured using a clinical chemistry system (Beckman Coulter AU5800) automatic analyzer. HbA1c was measured by high-performance liquid chromatography (HPLC) on an automated glycosylated hemoglobin analyzer (HLC-723G8), which has been certified by the National Glycohemoglobin Standardization Program (NGSP) to conform to the results of the Diabetes Complications and Control Trial and standardized according to International Federation of Clinical Chemistry (IFCC) reference system. ## BMI BMI was calculated as pre-pregnancy weight in kilograms(kg) divided by the square of height in meters(m). Pre-pregnancy BMI was categorized into underweight (<18.5 kg/m2), normal weight (18.5 kg/m2-23.9 kg/m2), overweight (24.0 kg/m2-27.9 kg/m2), and obese (≥28.0 kg/m2) groups according to Chinese criteria. ( National Health Commission of the People’s Republic of China: Criteria of Weight for Adults. [( accessed on 10 August 2021)];2013 Available online: http://www.nhc.gov.cn/ewebeditor/uploadfile/$\frac{2013}{08}$/20130808135715967). ## GWG GWG was the difference between pre-delivery and pre-pregnancy weight. According to the standard definition of the Institute of Medicine (IOM) guidelines in 2009 [18], appropriate GWG was 12.5-18.0 kg for underweight, 11.5-16.0 kg for normal weight, 7.0-11.5 kg for overweight and 5.0-9.0 kg for obesity respectively. Additionally, falling below the thresholds was defined as inadequate GWG, while exceeding the thresholds was defined as excessive GWG. ## Adverse pregnancy outcomes Neonates were defined as LGA if their birth weight was >90th percentile based on national population references for age and sex. Neonates with gestational age ≥ 28 weeks and < 37 weeks were considered as preterm neonates. Neonates with birth weight ≥4000g were defined as macrosomia. PIH was diagnosed in women with no previous history of hypertension with systolic blood pressure (SBP) ≥140 mmHg and diastolic blood pressure (DBP) ≥90 mmHg on two occasions at least 4 hours apart after 20 gestational weeks with or without proteinuria [19]. ## Statistical analysis Maternal and neonatal demographic and clinical features were reported as frequency (%) or means (± SD). Categorical variables, including maternal age groups, parity, gravidity, pre-pregnancy BMI group, GWG groups, and difference in the incidence of adverse pregnancy outcomes among HbA1c groups, were evaluated by chi-squared test. Continuous data, including birthweight, FPG, 1h-PG, 2h-PG, and maternal age, were evaluated using one-way ANOVA. HbA1c level was divided into three different categories by quartiles, which included ≤25th ($5.0\%$, 31mmol/mol), 25th-75th (5.1-$5.4\%$, 32-36mmol/mol) and ≥75th ($5.5\%$, 37mmol/mol). Logistic regression was used to explore the association between HbA1c level and adverse outcomes in different maternal age groups, pre-pregnancy BMI groups, and GWG groups. Two-sided p-values less than 0.05 were considered significant. All statistical analyses were done with SPSS 26.0 software. ## General clinical characteristics and pregnancy outcomes of three HAb1c groups Our study enrolled 2048 women with GDM of live singleton births without missing data (Figure 1). There were significant differences in maternal age ($p \leq 0.001$), pre-pregnancy BMI ($p \leq 0.001$), GWG ($p \leq 0.001$), parity ($$p \leq 0.001$$), and gravidity ($$p \leq 0.001$$) among three HbA1c groups (Table 1). There were also significant differences in the incidence of macrosomia ($p \leq 0.001$), preterm birth ($$p \leq 0.020$$), primary C-section ($p \leq 0.007$), and PIH ($p \leq 0.001$) among HbA1c groups. Additionally, higher incidences of adverse outcomes (macrosomia, preterm birth, primary C-section, and PIH) were observed in GDM women with HbA1c ≥$5.5\%$ at the time of GDM diagnosis compared to other HbA1c groups. There was no significant difference in the incidence of LGA among HbA1c groups (Table 1). **Figure 1:** *Flow chart of the study population. Demonstrates the inclusion and exclusion criteria of our study population; glycated hemoglobin A1c (HbA1c); gestational diabetes mellitus (GDM); fasting plasma glucose (FPG); 2hPG (2-hour plasma glucose); Oral glucose tolerance test (OGTT); chronic diseases (hypertension, liver, kidney, heart, lung and other major organ diseases, or tumors); autoimmune diseases (Sjogren's syndrome, anticardiolipin syndrome, myasthenia gravis).* TABLE_PLACEHOLDER:Table 1 ## Association between HbA1c and adverse outcomes In GDM women with HbA1c ≥$5.5\%$, HbA1c was significantly associated with preterm birth (aOR 1.64,$95\%$CI1.05,2.55), macrosomia (aOR 2.63,$95\%$CI1.61,4.31), and primary C-section (aOR 1.49,1.09,2.03) compared to their counterparts with HbA1c ≤$5.0\%$. Interestingly, both GDM women with HbA1c $5.1\%$-$5.4\%$ and HbA1c ≥$5.5\%$ had significantly increased risk of PIH (aOR 1.91, $95\%$CI 1.24,2.94; aOR 2.56, $95\%$CI 1.57,4.19), respectively compared to their counterparts with HbA1c ≤$5.0\%$ (Table 2). **Table 2** | Unnamed: 0 | HbA1c% (mmol/mol) | HbA1c% (mmol/mol).1 | HbA1c% (mmol/mol).2 | | --- | --- | --- | --- | | Adverse outcomes | ≤5.0 (31mmol/mol) (n=755) | 5.1≤HbA1c≤ 5.4 (32–36mmol/mol) (n=942) | ≥5.5 (37mmol/mol) (n=351) | | Adverse outcomes | aOR (95% CI) | aOR (95% CI) | aOR (95% CI) | | Preterm birth (n=194) | Ref | 1.39 (0.97,1.97) | 1.64 (1.05,2.55)* | | Macrosomia (n=138) | Ref | 1.26 (0.80,1.97) | 2.63 (1.61,4.31)* | | PIH (n=168) | Ref | 1.91 (1.24,2.94)* | 2.56 (1.57,4.19)* | | Primary C-section (n= 553) | Ref | 1.23 (0.97,1.56) | 1.49 (1.09,2.03)* | ## Association between HbA1c and adverse outcomes in different maternal age groups There w ere significantly positive associations between HbA1c level and primary C-section in women aged ≤29 years with HbA1c 5.1 - $5.4\%$ (aOR 1.51,$95\%$CI1.00,2.29) or HbA1c ≥$5.5\%$ (aOR 2.35, $95\%$CI 1.22,4.53) compared to their counterparts with HbA1c ≤$5.0\%$. Interestingly, young women aged ≤29 years showed an increased risk of PIH when their HbA1c was≥$5.5\%$ (aOR 3.53,$95\%$CI1.34,9.30). Additionally, women aged ≥35 years with HbA1c ≥$5.5\%$ also showed an increased risk of PIH (aOR 2.56,$95\%$CI1.13,5.78) compared to women ≥35 years with HbA1c ≤$5.0\%$. HbA1c ≥$5.5\%$ was significantly associated with macrosomia among women aged 30 -34 years old (aOR2.48,$95\%$CI1.16,5.31) and those aged ≥35 years (aOR 5.52, $95\%$CI 2.00,15.24) compared to HbA1c ≤$5.0\%$ (Table 3). **Table 3** | Unnamed: 0 | HbA1c% (mmol/mol) | HbA1c% (mmol/mol).1 | HbA1c% (mmol/mol).2 | | --- | --- | --- | --- | | Maternal age | ≤5.0 (31mmol/mol) (n=755) | 5.1≤HbA1c≤ 5.4 (32–36mmol/mol) (n=942) | ≥5.5 (37mmol/mol) (n=351) | | Maternal age | aOR (95% CI) | aOR (95% CI) | aOR (95% CI) | | ≤29 years (n=600) | ≤29 years (n=600) | ≤29 years (n=600) | ≤29 years (n=600) | | Preterm birth | Ref | 1.02 (0.52,2.02) | 2.26 (0.93,5.45) | | Macrosomia | Ref | 1.32 (0.62,2.78) | 1.12 (0.38,3.31) | | PIH | Ref | 2.09 (0.95,4.60) | 3.53 (1.34,9.30)* | | Primary C-section | Ref | 1.51 (1.00,2.29)* | 2.35 (1.22,4.53)* | | 30-34 years (n=768) | 30-34 years (n=768) | 30-34 years (n=768) | 30-34 years (n=768) | | Preterm birth | Ref | 1.07 (0.59,1.94) | 1.48 (0.71,3.07) | | Macrosomia | Ref | 0.87 (0.42,1.78) | 2.48 (1.16,5.31)* | | PIH | Ref | 1.89 (0.91,3.91) | 2.04 (0.88,4.69) | | Primary C-section | Ref | 0.98 (0.67,1.44) | 1.18 (0.72,1.96) | | ≥ 35 years (n=680) | ≥ 35 years (n=680) | ≥ 35 years (n=680) | ≥ 35 years (n=680) | | Preterm birth | Ref | 2.11 (1.14,3.90)* | 1.43 (0.67,3.03) | | Macrosomia | Ref | 2.46 (0.89,6.79) | 5.52 (2.00,15.24)* | | PIH | Ref | 1.73 (0.81,3.69) | 2.56 (1.13,5.78)* | | Primary C-section | Ref | 1.23 (0.78,1.94) | 1.44 (0.85,2.43) | ## Association between HbA1c and adverse outcomes in different pre-pregnancy BMI groups Pre-pregnant normal-weight women with HbA1c ≥$5.5\%$ had significantly increased risk of preterm birth (aOR 2.21, $95\%$CI 1.29,3.78), macrosomia (aOR2.92,$95\%$CI1.52,5.61), PIH (aOR 2.72,$95\%$CI1.36,5.45) and primary C-section (aOR 1.51,$95\%$CI1.01,2.25) compared to pre-pregnant normal weight women with HbA1c ≤$5.0\%$. Interestingly, pre-pregnant underweight women with HbA1c 5.1 - $5.4\%$ at the time of GDM diagnosis were significantly associated with a higher risk of primary C-section compared to their counterparts with HbA1c ≤$5.0\%$ (aOR 2.58,1.26,5.26. ( Table 4). **Table 4** | Unnamed: 0 | HbA1c% (mmol/mol) | HbA1c% (mmol/mol).1 | HbA1c% (mmol/mol).2 | | --- | --- | --- | --- | | Pre-pregnancy BMI | ≤5.0 (31mmol/mol) (n=755) | 5.1≤HbA1c≤ 5.4 (32–36mmol/mol) (n=942) | ≥5.5 (37mmol/mol) (n=351) | | Pre-pregnancy BMI | aOR (95% CI) | aOR (95% CI) | aOR (95% CI) | | Normal (n=1371) | Normal (n=1371) | Normal (n=1371) | Normal (n=1371) | | Preterm birth | Ref | 1.31 (0.85,2.01) | 2.21 (1.29,3.78)* | | Macrosomia | Ref | 1.26 (0.72,2.20) | 2.92 (1.52,5.61)* | | PIH | Ref | 1.87 (1.07,3.26)* | 2.72 (1.36,5.45)* | | Primary C-section | Ref | 1.00 (0.75,1.33) | 1.51 (1.01,2.25)* | | Underweight (n=250) | Underweight (n=250) | Underweight (n=250) | Underweight (n=250) | | Preterm birth | Ref | 1.03 (0.33,3.22) | - | | Macrosomia | Ref | 1.55 (0.22,10.72) | - | | PIH | Ref | - | - | | Primary C-section | | 2.58 (1.26,5.26)* | 1.24 (0.27,5.60) | | Overweight and Obese (n=427) | Overweight and Obese (n=427) | Overweight and Obese (n=427) | Overweight and Obese (n=427) | | Preterm birth | Ref | 1.87 (0.75,4.66) | 1.34 (0.51,3.52) | | Macrosomia | Ref | 0.80 (0.33,1.94) | 1.75 (0.75,4.07) | | PIH | Ref | 1.69 (0.78,3.66) | 2.12 (0.97,4.62) | | Primary C-section | Ref | 1.65 (0.88,3.07) | 1.51 (0.80,2.86) | ## Association between HbA1c and adverse outcomes in different GWG groups Interestingly, women with adequate GWG with HbA1c ≥$5.5\%$ at the time of GDM diagnosis were significantly associated with risk of PIH (aOR 3.42,$95\%$CI1.48,7.88) compared to their counterparts with HbA1c ≤$5.0\%$. On the other hand, women with inadequate GWG or excess GWG with HbA1c ≥$5.5\%$ also showed an increased risk of macrosomia compared to women with inadequate GWG or excess GWG who had HbA1c ≤$5.0\%$ (aOR 4.71, $95\%$CI 1.52,14.58; aOR 3.27,$95\%$CI 1.39,7.71) (Table 5). **Table 5** | Unnamed: 0 | HbA1c% (mmol/mol) | HbA1c% (mmol/mol).1 | HbA1c% (mmol/mol).2 | | --- | --- | --- | --- | | GWG | ≤5.0 (31mmol/mol) (n=755) | 5.1≤HbA1c≤ 5.4 (32–36mmol/mol) (n=942) | ≥5.5 (37mmol/mol) (n=351) | | GWG | aOR (95% CI) | aOR (95% CI) | aOR (95% CI) | | Adequate (n=933) | Adequate (n=933) | Adequate (n=933) | Adequate (n=933) | | Preterm birth | Ref | 1.81 (0.96,3.41) | 1.42 (0.59,3.38) | | Macrosomia | Ref | 0.84 (0.44,1.60) | 1.59 (0.72,3.51) | | PIH | Ref | 2.33 (1.11,4.86)* | 3.42 (1.48,7.88)* | | C-section | Ref | 1.38 (0.95,1.99) | 1.13 (0.66,1.92) | | Inadequate (n=752) | Inadequate (n=752) | Inadequate (n=752) | Inadequate (n=752) | | Preterm birth | Ref | 1.19 (0.73,1.95) | 1.70 (0.92,3.14) | | Macrosomia | Ref | 2.44 (0.85,7.00) | 4.71 (1.52,14.58)* | | PIH | Ref | 1.84 (0.86,3.92) | 2.27 (0.91,5.69) | | Primary C-section | Ref | 1.06 (0.72,1.55) | 1.59 (0.96,2.63) | | Excess (n=363) | Excess (n=363) | Excess (n=363) | Excess (n=363) | | Preterm birth | Ref | 1.18 (0.45,3.08) | 1.64 (0.58,4.67) | | Macrosomia | Ref | 1.51 (0.66,3.43) | 3.27 (1.39,7.71)* | | PIH | Ref | 1.64 (0.74,3.62) | 2.28 (0.97,5.37) | | Primary C-section | Ref | 1.22 (0.71,2.12) | 1.76 (0.93,3.33) | ## Discussion This retrospective study demonstrated a strong relationship between HbA1c at the time of GDM diagnosis (24–28 weeks) and adverse pregnancy outcomes (preterm birth, macrosomia, PIH, and primary C-section) in Chinese women with GDM. Chinese women below recommended HbA1c ($6.0\%$) by ADA might be at high risk of adverse outcomes. In our study, women with HbA1c ≥$5.5\%$ had a higher rate of adverse outcomes compared to women with HbA1c $5.1\%$-$5.4\%$ and ≤$5.0\%$. Compared to HbA1c ≤$5.0\%$, HbA1c ≥ $5.5\%$ was significantly associated with an increased risk of macrosomia, preterm birth, PIH, and primary C-section. Our results support the existing evidence that HbA1c might be a biomarker for predicting adverse pregnancy outcomes in GDM women; however, we innovatively demostrated that maternal age, pre-pregnancy BMI, and GWG should be considered when determining the relationship between HbA1c and adverse outcomes. Therefore, our findings may help initiate focused individual prenatal care, health education, and strict counselling to prevent adverse outcomes in high-risk GDM women. HbA1c during mid-pregnancy have been reported to have the risk of adverse outcomes; however, findings are still controversial. This is due to the measurement of HbA1c in different gestational age, different population involved in the study, and different GDM diagnostic criteria. Given this background, there is still lack of optimum HbA1c for identifying adverse outcomes for GDM women. Surprisingly, HbA1c <$5.0\%$ (31mmol/mol) in Asian Indian women with GDM was associated with an increased risk of adverse outcomes [20]. A study conducted in Taiwan that included 1989 GDM high-risk women reported that women with mid-pregnancy HbA1c levels lower than $4.5\%$ (26mmol/mol) and higher or equal to $6\%$ (42mmol/mol) were both at increased risk of gestational hypertension, preterm birth, admission to the neonatal intensive care unit, low birth weight, and macrosomia compared to women with HbA1c $4.5\%$–$4.9\%$ (26mmol/mol–30mmol/mol) [21]. A study showed that Chinese women above the HbA1c cutoff of $6.0\%$ (42mmol/mol) recommended by the American Diabetes Association (ADA) at the time of GDM diagnosis were at increased risk of primary cesarean section, high birth weight, hypertension during pregnancy, placenta abruption, macrosomia, and neonatal asphyxia compared to women with HbA1c<$6.0\%$(42mmol/mol) [22]. In our study, we found that women with HbA1c ≥$5.5\%$might be at increased risk of adverse outcomes, similar to previous studies [17, 23, 24]. Zhang Q et al. divided women into two groups including below and above recommended HbA1c cutoff by ADA; however, the sample size of women with HbA1c ≥$6.0\%$(42mmol/mol) was relatively small (49 women), and the risk of adverse outcomes in women with HbA1c<$6.0\%$(42mmol/mol) was not evaluated [22]. Therefore, this may explain the differences in our findings. The present study evaluated the association between HbA1c at the time of GDM diagnosis with adverse outcomes in the Asian Chinese population, regardless of recommended HbA1c cutoff <$6.0\%$(42mmol/mol) by ADA. It has been suggested that HbA1c <$6.0\%$(42mmol/mol) cutoff might be higher for Asian women with GDM, thus predisposing them to a higher risk of adverse outcomes [25]. It is imperative to note that studies on the association between HbA1c at the time of GDM diagnosis and adverse outcomes were conducted within the Caucasian population, and there is a lack of evidence for the Asian population [17]. Therefore, further studies are needed to evaluate the role of HbA1c at the time of GDM diagnosis and determine optimum cutoff of HbA1c for adverse outcomes in Asian women, particularly Chinese women. Studies have indicated a strong relationship between HbA1c lower than recommended cutoff <$6.0\%$(42mmol/mol) and macrosomia in Asian women with GDM, similar to our findings [20, 21, 25]. Although the mechanism is still unknown, according to Hughes et al., relatively higher HbA1c within the normal range at 24 -28 weeks is associated with adverse pregnancy outcomes due to poor glycemic control in the past 12 weeks before GDM diagnosis [26]. Additionally, both high HbA1c and excess GWG have been strongly related to the risk of macrosomia offspring in accordance with our findings [27, 28]. Pregnant women with excessive GWG have higher levels of amino acids, free fatty acids, and glucose, thus, increasing the risk of high birth weight [29]. On the other hand, hyperglycemia leads to macrosomia by glucose crossing the placenta, increasing the utilization of glucose by the fetus and thus increasing fetal adipose tissue [30]. Zhang, Q et al. found there’s no significant difference of adverse outcomes in women with inadequate GWG between those with HbA1c ≥$6.0\%$(42mmol/mol) and HbA1c<$6.0\%$(42mmol/mol) [22], contrary to our findings. We noted that women with inadequate GWG with HbA1c levels ≥$5.5\%$ (37mmol/mol) had an increased risk of macrosomia compared to women with inadequate GWG women who had HbA1c ≤ $5.0\%$(31mmol/mol) in accordance with the previous study [31]. In the present research, higher HbA1c levels (≥$5.5\%$,37mmol/mol) may contribute to macrosomia in women with insufficient GWG, while a combination of high HbA1c levels and excess GWG might contribute to macrosomia in women with excess GWG. Therefore, strict counselling on lowering HbA1c in women with inadequate GWG and excess GWG might help prevent macrosomia in Chinese women with GDM. Preterm birth is the leading cause of neonatal mortality and morbidity [32]. Contrary to our findings, studies have shown no association between HbA1c and preterm birth [23]. We noted that pre-pregnant normal-weight women with HbA1c ≥$5.5\%$ (37mmol/mol) and those aged ≥35 years had a significantly higher risk of preterm birth compared to normal-weight women with HbA1c ≤$5.0\%$. Women with inappropriate weight during pregnancy are at increased risk of delivering preterm offspring and severe neonatal morbidity [33, 34]. Although the mechanism between weight and preterm birth is still unclear, malnutrition during pregnancy may lead to a lack of essential nutrients, increasing the risk of chronic diseases and inflammation, leading to preterm birth [35]. Malnutrition is less likely to be the cause of preterm birth in Zhejiang province; thus, we assume that higher HbA1c in women with normal pre-pregnant BMI might be the leading cause of preterm birth. There are many risk factors for preterm birth; our findings imply that higher HbA1c levels below the ADA-recommended HbA1c cutoff were also likely to lead to preterm birth in normal-weight Chinese women with GDM. Therefore, it is essential to consider the impact of HbA1c on preterm birth, particularly in women with HbA1c≥$5.5\%$(37mmol/mol). Lowering HbA1c by strict blood glucose monitoring and appropriate GWG can help prevent preterm birth, particularly in normal-weight women. However, research may be required to evaluate the relationship between HbA1c and preterm birth, considering all relevant preterm birth-related factors. Solid conclusions on the relationship between HbA1c and preterm birth may help women with GDM prevent preterm birth. Asian women have lower HbA1c levels compared to other women; thus, the ADA HbA1c cutoff of <$6.0\%$(42mmol/mol) used based on studies that involved only Caucasian women might be higher for Chinese GDM women. An increase in HbA1c is related to the occurrence of microvascular disease, which may play a certain role in the pathogenesis of PIH [36]. Moreover, hyperglycemia promotes increased insulin production leading to vascular stenosis, increased vascular resistance, and high blood pressure. Hyperinsulinemia can stimulate the sympathetic nerve, strengthen its excitability, and thus lead to high blood pressure. In the present study, HbA1c was significantly associated with the risk of PIH in women with HbA1c $5.1\%$-$5.4\%$ (32mmol/mol-36mmol/mol) and HbA1c ≥$5.5\%$ (37mmol/mol), particularly among women with adequate GWG when compared to women with HbA1c ≤$5.0\%$(31mmol/mol). It is still debatable whether GWG using IOM guidelines is suitable for Chinese GDM women. However, studies show that IOM guidelines may not be appropriate for Chinese women based on the fact that the GWG cutoff by IOM guidelines is based on Caucasian women’s characteristics [37], which might not be suitable for Chinese women. Multiple studies found that GDM women who acquired too much weight during pregnancy had a higher risk of PIH, whereas minimal gestational weight gain was related to a lower risk of hypertensive diseases [14]. The possible mechanism is that fat accumulation leads to high estrogen in the body, thus mediating aldosterone secretion, sodium retention caused by the renin-angiotensin system, or directly increasing the recollection of the renal tubules, resulting in hypertension. Another mechanism might be that increased fat accumulation leads to abnormal blood lipid metabolism, which may lead to hypertension. Therefore, using GWG cutoffs based on Chinese women’s characteristics may help Chinese women gain appropriate weight. It is also imperative to note that GWG cutoffs specifically for women with GDM are still lacking. Therefore, more studies on GWG cutoffs in Chinese pregnant women with GDM are warranted. It is imperative to note that gestational weight has been reported as a predictor of glycemic control and adverse pregnancy outcomes in women with GDM [38]. Thus, strict GWG monitoring and lowering HbA1c levels may help reduce the risk of PIH in Chinese women with GDM, particularly those with HbA1c $5.1\%$-$5.4\%$ (32mmol/mol- 36mmol/mol) and HbA1c ≥$5.5\%$ (37mmol/mol). In the present study, the association between HbA1c and the risk of primary C-section varied in different pre-pregnancy BMI groups and maternal age groups. Studies have revealed the utility of HbA1c as a biomarker for predicting C-sections [39]. Meanwhile, our results also indicated that normal-weight women with HbA1c levels≥$5.5\%$ (37mmol/mol) and underweight women with HbA1c $5.1\%$-$5.4\%$ (32mmol/mol – 36mmol/mol) had an increased risk of primary C-section. Antoniou et al. showed that women with pre-pregnancy BMI ≤ 25 kg/m2 and HbA1c ≤$5.5\%$ (37mmol/mol) had a lower risk of C-section [31]. However, women with ≤ 25 kg/m2 and HbA1c ≥$5.5\%$(37mmol/mol) were not evaluated in Antoniou et al. ’s study. Our findings are in accordance with the HAPO study that showed HbA1c ≥$5.8\%$ (at 24 -32 gestational weeks) was significantly associated with an increased risk of primary C- section compared to lower HbA1c levels in pregnant women with hyperglycemia [10]. On the other hand, HbA1c in the early trimester at a mean gestational week of 9.25 was significantly associated with primary C-section in non-diabetic Indian women [40]. Researchers hypothesize that abnormal glycemia in early pregnancy, which may be indicated by comparatively high HbA1c at the time of GDM diagnosis, is the mechanism underlying the relationship between primary C-section and higher mid-pregnancy HbA1c levels [40]. HbA1c reflects glycemia status in the past several weeks; thus, relatively high HbA1c at the time of GDM diagnosis might be associated with poor glycemic control during early pregnancy. It is also important to note that HbA1c at GDM diagnosis that is quite high but still falls within the normal range indicates poor glucose control and is associated with higher odds of adverse outcomes [24, 25]; thus, women with relatively high HbA1c within the normal range should not be ignored instead they should be strictly monitored. HbA1c is an independent risk factor of primary C- section [41]; however, optimum HbA1c and optimum gestational age at which HbA1c might predict primary C-section remain unknown. While HbA1c at term might provide clinical care information for women at high risk of labor induction or a failed induction [41], HbA1c at term does not offer information on earlier primary and preventive care for women at high risk of adverse outcomes. Our findings on the association between HbA1c at 24 -28 weeks with the risk of primary C-section might have an advantage over findings of HbA1c at term and primary C-section [41], as our findings provided information that can lead to preventive care for GDM women at high risk of primary C-section earlier on, in pregnancy. Studies showed that women who receive strict counselling and follow-up during pregnancy have better glycemic control, a lowered HbA1c level, improved health, and better pregnancy outcomes [42, 43]. Therefore, we recommend strict counselling and close follow-up for women with HbA1c $5.1\%$ -$5.4\%$(32mmol/mol-36mmol/mol) and ≥$5.5\%$(37mmol/mol) at 24-28 weeks, particularly those with pre-pregnancy normal weight and underweight BMI for prevention of primary C-section. While prevention care for pregnant women with diabetes with HbA1c ≥ $6.0\%$(42mmol/mol) is well established, there is still a lack of specific guidelines on HbA1c to prevent adverse outcomes in GDM. Our findings indicated that even though the recommended HbA1c cutoff for pregnant women with diabetes is <$6.0\%$(42mmol/mol), it is still crucial to consider HbA1c cutoffs specific for women with GDM in consideration of race. Disregarding relatively higher HbA1c within the normal range in Chinese women with GDM can lead to severe adverse pregnancy outcomes [25]; thus, earlier counselling and follow-up of women with relatively higher HbA1c(below the recommended ADA HbA1c cutoffs) at the time of GDM diagnosis may reduce the risk of adverse pregnancy outcomes. Nevertheless, further studies are needed to determine an optimum HbA1c cutoff based on Chinese women’s characteristics to prevent adverse outcomes. To the best of our knowledge, this study is the first to explore the association between HbA1c levels and adverse outcomes considering maternal age, pre-pregnancy BMI, and GWG. Our findings may help healthcare providers to manage GDM pregnant women personally and reduce the risk of adverse outcomes using HbA1c level, pre-pregnancy weight, maternal age, and GWG. There are several limitations to our study. Firstly, we included a relatively small-size sample. Secondly, there was no further exploration of demographic characteristics, nutrition, and lifestyle, which may influence the results of our study despite the adjustment of confounders. Finally, this was a single-center and retrospective study; further multi-center and future research is required to investigate the utility of HbA1c in predicting adverse outcomes in different ethnicities and gestational age in consideration of pre-pregnant BMI, maternal age, and GWG. Conclusively, HbA1c is significantly associated with macrosomia, preterm birth, PIH, and primary C-section in GDM women, particularly in women with HbA1c≥$5.5\%$. Our findings may help healthcare providers identify women at high risk of adverse outcomes and manage pregnant women with GDM through counselling and health education by their HbA1c, thereby reducing the incidence of adverse outcomes in GDM. Nonetheless, Chinese women with HbA1c below the recommended HbA1c cut-off are also at high risk of adverse outcomes, which should not be disregarded. Thus, further advanced studies are needed to determine optimal HbA1c cut-offs for predicting adverse outcomes in consideration of Chinese population characteristics. Most importantly, maternal age, pre-pregnancy BMI, and GWG should be considered while evaluating the association between HbA1c and adverse outcomes. ## Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. ## Ethics statement This study was approved by the Human Ethics committee at Women’s Hospital, School of Medicine, Zhejiang University (IRB-20210269-R). The patients/participants provided their written informed consent to participate in this study. ## Author contributions Conception and design: ZL, DC, and LQ; Analysis and interpretation of the data: All authors; Drafting of the paper: MM and LZ; Paper revision and editing: ZL; Revising paper critically for intellectual content: All authors; Data collection: QW and LZ; Final approval of the version to be published: All authors. All authors agreed to the final content of the manuscript for submission and accountability for all aspects of this work. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer HM declared a shared affiliation with the author LQ to the handling editor at the time of review. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endored by the publisher. ## References 1. **ACOG practice bulletin no. 190: Gestational diabetes mellitus**. *Obstet Gynecol* (2018) **131**. DOI: 10.1097/AOG.0000000000002501 2. Metzger BE, Gabbe SG, Persson B, Buchanan TA, Catalano PA, Damm P. **International association of diabetes and pregnancy study groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy**. *Diabetes Care* (2010) **33**. DOI: 10.2337/dc10-0719 3. Saeedi M, Cao Y, Fadl H, Gustafson H, Simmons D. **Increasing prevalence of gestational diabetes mellitus when implementing the IADPSG criteria: A systematic review and meta-analysis**. *Diabetes Res Clin Pract* (2021) **172** 108642. DOI: 10.1016/j.diabres.2020.108642 4. Gao C, Sun X, Lu L, Liu F, Yuan J. **Prevalence of gestational diabetes mellitus in mainland China: A systematic review and meta-analysis**. *J Diabetes Investig* (2019) **10**. DOI: 10.1111/jdi.12854 5. Zhu Y, Zhang C. **Prevalence of gestational diabetes and risk of progression to type 2 diabetes: a global perspective**. *Curr Diabetes Rep* (2016) **16** 7. DOI: 10.1007/s11892-015-0699-x 6. Mirghani Dirar A, Doupis J. **Gestational diabetes from a to z**. *World J Diabetes* (2017) **8** 489-511. DOI: 10.4239/wjd.v8.i12.489 7. Vounzoulaki E, Khunti K, Abner SC, Tan BK, Davies MJ, Gillies CL. **Progression to type 2 diabetes in women with a known history of gestational diabetes: systematic review and meta-analysis**. *Bmj* (2020) **369** m1361. DOI: 10.1136/bmj.m1361 8. Ajala O, Chik C. **Ethnic differences in antepartum glucose values that predict postpartum dysglycemia and neonatal macrosomia**. *Diabetes Res Clin Pract* (2018) **140**. DOI: 10.1016/j.diabres.2018.03.025 9. **Standards of medical care in diabetes–2014**. *Diabetes Care* (2014) **37 Suppl 1**. DOI: 10.2337/dc14-S014 10. Lowe LP, Metzger BE, Dyer AR, Lowe J, McCance DR, Lappin TR. **Hyperglycemia and adverse pregnancy outcome (HAPO) study: associations of maternal A1C and glucose with pregnancy outcomes**. *Diabetes Care* (2012) **35**. DOI: 10.2337/dc11-1687 11. Iwama N, Sugiyama T, Metoki H, Saito M, Hoshiai T, Watanabe Z. **Associations between glycosylated hemoglobin level at less than 24 weeks of gestation and adverse pregnancy outcomes in Japan: The Japan environment and children's study (JECS)**. *Diabetes Res Clin Pract* (2020) **169** 108377. DOI: 10.1016/j.diabres.2020.108377 12. Kumar N, Kumar P, Harris N, Monga R, Sampath V. **Impact of maternal HbA1c levels ≤6% and race in nondiabetic pregnancies on birthweight and early neonatal hypoglycemia**. *J Pediatr* (2020) **227** 121-127.e3. DOI: 10.1016/j.jpeds.2020.08.026 13. Yu H, Wang J, Shrestha Y, Hu Y, Ma Y, Ren L. **Importance of early elevated maternal HbA1c levels in identifying adverse fetal and neonatal events**. *Placenta* (2019) **86** 28-34. DOI: 10.1016/j.placenta.2019.07.008 14. Barquiel B, Herranz L, Hillman N, Burgos M, Grande C, Tukia KM. **HbA1c and gestational weight gain are factors that influence neonatal outcome in mothers with gestational diabetes**. *J Womens Health (Larchmt)* (2016) **25**. DOI: 10.1089/jwh.2015.5432 15. Ye M, Liu Y, Cao X, Yao F, Liu B, Li Y. **The utility of HbA1c for screening gestational diabetes mellitus and its relationship with adverse pregnancy outcomes**. *Diabetes Res Clin Pract* (2016) **114**. DOI: 10.1016/j.diabres.2016.02.007 16. Coetzee A, Mason D, Hall DR, Hoffmann M, Conradie M. **Evidence for the utility of antenatal HbA1c to predict early postpartum diabetes after gestational diabetes in south Africa**. *Diabetes Res Clin Pract* (2018) **143**. DOI: 10.1016/j.diabres.2018.06.021 17. Barbry F, Lemaitre M, Ternynck C, Wallet H, Cazaubiel M, Labreuche J. **HbA1c at the time of testing for gestational diabetes identifies women at risk for pregnancy complications**. *Diabetes Metab* (2022) **48** 101313. DOI: 10.1016/j.diabet.2021.101313 18. Institute of M, Rasmussen KM, Yaktine AL. **The National Academies Collection: Reports funded by National Institutes of Health**. *Weight gain during pregnancy: Reexamining the guidelines* (2009) 19. **Gestational hypertension and preeclampsia: ACOG practice bulletin, number 222**. *Obstet Gynecol* (2020) **135**. DOI: 10.1097/aog.0000000000003891 20. Bhavadharini B, Mahalakshmi MM, Deepa M, Harish R, Malanda B, Kayal A. **Elevated glycated hemoglobin predicts macrosomia among Asian Indian pregnant women (WINGS-9)**. *Indian J Endocrinol Metab* (2017) **21**. DOI: 10.4103/2230-8210.196003 21. Ho YR, Wang P, Lu MC, Tseng ST, Yang CP, Yan YH. **Associations of mid-pregnancy HbA1c with gestational diabetes and risk of adverse pregnancy outcomes in high-risk Taiwanese women**. *PloS One* (2017) **12** e0177563. DOI: 10.1371/journal.pone.0177563 22. Zhang Q, Lee CS, Zhang L, Wu Q, Chen Y, Chen D. **The influence of HbA1c and gestational weight gain on pregnancy outcomes in pregnant women with gestational diabetes mellitus**. *Front Med (Lausanne)* (2022) **9**. DOI: 10.3389/fmed.2022.842428 23. Sweeting AN, Ross GP, Hyett J, Molyneaux L, Tan K, Constantino M. **Baseline HbA1c to identify high-risk gestational diabetes: Utility in early vs standard gestational diabetes**. *J Clin Endocrinol Metab* (2017) **102**. DOI: 10.1210/jc.2016-9251 24. Capula C, Mazza T, Vero R, Costante G. **HbA1c levels in patients with gestational diabetes mellitus: Relationship with pre-pregnancy BMI and pregnancy outcome**. *J Endocrinol Invest* (2013) **36**. DOI: 10.3275/9037 25. Yin B, Hu L, Meng X, Wu K, Zhang L, Zhu Y. **Association of higher HbA1c within the normal range with adverse pregnancy outcomes: a cross-sectional study**. *Acta Diabetol* (2021) **58**. DOI: 10.1007/s00592-021-01691-0 26. Nielsen LR, Ekbom P, Damm P, Glümer C, Frandsen MM, Jensen DM. **HbA1c levels are significantly lower in early and late pregnancy**. *Diabetes Care* (2004) **27**. DOI: 10.2337/diacare.27.5.1200 27. Bi J, Ji C, Wu Y, Wu M, Liu Y, Song L. **Association between maternal normal range HbA1c values and adverse birth outcomes**. *J Clin Endocrinol Metab* (2020) **105**. DOI: 10.1210/clinem/dgaa127 28. Li G, Kong L, Li Z, Zhang L, Fan L, Zou L. **Prevalence of macrosomia and its risk factors in china: a multicentre survey based on birth data involving 101,723 singleton term infants**. *Paediatr Perinat Epidemiol* (2014) **28**. DOI: 10.1111/ppe.12133 29. Hull HR, Thornton JC, Ji Y, Paley C, Rosenn B, Mathews P. **Higher infant body fat with excessive gestational weight gain in overweight women**. *Am J Obstet Gynecol* (2011) **205**. DOI: 10.1016/j.ajog.2011.04.004 30. Kc K, Shakya S, Zhang H. **Gestational diabetes mellitus and macrosomia: a literature review**. *Ann Nutr Metab* (2015) **66 Suppl 2** 14-20. DOI: 10.1159/000371628 31. Antoniou MC, Gilbert L, Gross J, Rossel JB, Fischer Fumeaux CJ, Vial Y. **Potentially modifiable predictors of adverse neonatal and maternal outcomes in pregnancies with gestational diabetes mellitus: can they help for future risk stratification and risk-adapted patient care**. *BMC Pregnancy Childbirth* (2019) **19** 469. DOI: 10.1186/s12884-019-2610-2 32. Vogel JP, Chawanpaiboon S, Moller AB, Watananirun K, Bonet M, Lumbiganon P. **The global epidemiology of preterm birth**. *Best Pract Res Clin Obstet Gynaecol* (2018) **52** 3-12. DOI: 10.1016/j.bpobgyn.2018.04.003 33. Eick SM, Welton M, Claridy MD, Velasquez SG, Mallis N, Cordero JF. **Associations between gestational weight gain and preterm birth in Puerto Rico**. *BMC Pregnancy Childbirth* (2020) **20** 599. DOI: 10.1186/s12884-020-03292-1 34. El Rafei R, Abbas HA, Charafeddine L, Nakad P, Al Bizri A, Hamod D. **Association of pre-pregnancy body mass index and gestational weight gain with preterm births and fetal size: an observational study from Lebanon**. *Paediatr Perinat Epidemiol* (2016) **30** 38-45. DOI: 10.1111/ppe.12249 35. Carmichael SL, Abrams B. **A critical review of the relationship between gestational weight gain and preterm delivery**. *Obstet Gynecol* (1997) **89**. DOI: 10.1016/S0029-7844(97)00047-1 36. Guo J, Liu G, Guo G. **Association of insulin resistance and autonomic tone in patients with pregnancy-induced hypertension**. *Clin Exp Hypertens* (2018) **40**. DOI: 10.1080/10641963.2017.1403619 37. Jiang X, Liu M, Song Y, Mao J, Zhou M, Ma Z. **The institute of medicine recommendation for gestational weight gain is probably not optimal among non-American pregnant women: a retrospective study from China**. *J Matern Fetal Neonatal Med* (2019) **32**. DOI: 10.1080/14767058.2017.1405388 38. Komem D, Salman L, Krispin E, Arbib N, Bardin R, Wiznitzer A. **Gestational weight gain and weight loss among women with gestational diabetes mellitus**. *Diabetes Res Clin Pract* (2018) **141** 88-97. DOI: 10.1016/j.diabres.2018.04.034 39. Zhou Z, Chen G, Fan D, Rao J, Li P, Wu S. **Size and shape of associations of OGTT as well as mediating effects on adverse pregnancy outcomes among women with gestational diabetes mellitus: Population-based study from southern han Chinese**. *Front Endocrinol (Lausanne)* (2020) **11**. DOI: 10.3389/fendo.2020.00135 40. Punnose J, Malhotra RK, Sukhija K, Rijhwani RM, Choudhary N, Sharma A. **Is HbA1c in the first trimester associated with adverse outcomes among pregnant Asian Indian women without gestational diabetes**. *J Diabetes Complications* (2022) **36** 108187. DOI: 10.1016/j.jdiacomp.2022.108187 41. Hong JGS, Fadzleeyanna MYN, Omar SZ, Tan PC. **HbA1c at term delivery and adverse pregnancy outcome**. *BMC Pregnancy Childbirth* (2022) **22** 679. DOI: 10.1186/s12884-022-05000-7 42. Ghasemi F, Vakilian K, Khalajinia Z. **Comparing the effect of individual counseling with counseling on social application on self-care and quality of life of women with gestational diabetes**. *Prim Care Diabetes* (2021) **15**. DOI: 10.1016/j.pcd.2021.05.009 43. Kim YS, Kim HS, Kim YL. **Effects of a web-based self-management program on the behavior and blood glucose levels of women with gestational diabetes mellitus**. *Telemed J E Health* (2019) **25**. DOI: 10.1089/tmj.2017.0332
--- title: Clinicopathological features and prognosis of idiopathic membranous nephropathy with thyroid dysfunction authors: - Peiheng Wang - Shulei Wang - Bo Huang - Yiming Liu - Yingchun Liu - Huiming Chen - Junjun Zhang journal: Frontiers in Endocrinology year: 2023 pmcid: PMC10060953 doi: 10.3389/fendo.2023.1133521 license: CC BY 4.0 --- # Clinicopathological features and prognosis of idiopathic membranous nephropathy with thyroid dysfunction ## Abstract ### Background Thyroid dysfunction is common in patients with kidney disease. However, the relationship between thyroid dysfunction and idiopathic membranous nephropathy (IMN) remains unclear. This retrospective study aimed to investigate the clinicopathological characteristics and prognosis of patients with IMN and thyroid dysfunction compared to patients with IMN and without thyroid dysfunction. ### Methods A total of 1052 patients with IMN diagnosed by renal biopsy were enrolled in this study, including 736 ($70\%$) with normal thyroid function and 316 ($30\%$) with abnormal thyroid function. We analyzed the clinicopathological features and prognostic data between the two groups, using propensity score matching (PSM) to reduce the bias. Logistic regression analysis was performed to investigate the risk factors for IMN combined with thyroid dysfunction. Kaplan-Meier curves and Cox regression analysis were used to evaluate the association between thyroid dysfunction and IMN. ### Results Patients with IMN and thyroid dysfunction exhibited more severe clinical features. Female sex, lower albumin level, higher D-dimer level, severe proteinuria, and decreased estimated glomerular filtration rate were predictors of thyroid dysfunction in patients with IMN. After PSM, 282 pairs were successfully matched. Results from the Kaplan-Meier curves indicated that the thyroid dysfunction group had a lower complete remission rate ($$P \leq 0.044$$), higher relapse rate ($P \leq 0.001$), and lower renal survival rate ($$P \leq 0.004$$). The multivariate Cox regression analysis revealed that thyroid dysfunction was an independent risk factor for complete remission [hazard ratio (HR) = 0.810, $$P \leq 0.045$$], relapse (HR = 1.721, $$P \leq 0.001$$), and composite endpoint event (HR = 2.113, $$P \leq 0.014$$) in IMN. ### Conclusions Thyroid dysfunction is relatively common in patients with IMN, and the clinical indicators are more severe in these patients. Thyroid dysfunction is an independent risk factor for poor prognosis in patients with IMN. More attention should be paid to thyroid function in patients with IMN. ## Introduction Idiopathic membranous nephropathy (IMN) is one of the most common types of renal pathology in adults with nephrotic syndrome and one of the leading causes of end-stage renal disease (ESRD) [1]. Massive proteinuria and edema are hallmark clinical characteristics of IMN, and the predominant histopathological alterations include basement membrane thickening and subepithelial immune complex deposition [2]. Sixty percent of patients with untreated IMN experience a gradual decline in renal function, and about $35\%$ progress to ESRD within ten years [3]. M-type phospholipase A2 receptor (PLA2R) is the main antigen expressed on the podocyte surface of IMN, which exists in $70\%$-$80\%$ of IMN cases [4, 5]. Serum PLA2R autoantibody is highly specific for the diagnosis of IMN, and its level is closely related to disease severity, which plays a crucial role in predicting treatment response and disease activity (6–8). There is a close relationship between thyroid hormones and the kidney. Thyroid hormones play an essential role in the renal structure, blood perfusion, glomerular filtration, tubular function, and water-electrolyte balance. Furthermore, the kidney is not only a target organ for thyroid hormones but is also involved in the metabolism and elimination of thyroid hormones [9]. Thyroid dysfunction has been reported in different glomerular diseases and increases the risk of developing chronic kidney disease (CKD) in the elderly [10, 11]. However, the significance of thyroid dysfunction in the development and prognosis of IMN remains unclear. Therefore, this study analyzed the clinicopathological and prognostic data of patients with thyroid dysfunction and IMN in a large cohort to evaluate the relationship between thyroid function and IMN. ## Study participants From January 2015 to December 2019, 1052 patients with biopsy-proven IMN from the First Affiliated Hospital of Zhengzhou University were included in this retrospective cohort analysis. The following inclusion criteria were applied: [1] complete baseline data and follow-up of ≥ 6 months; [2] age ≥ 18 years; [3] no previous history of thyroid disease; and [4] no glucocorticoids or immunosuppressants before the renal biopsy. Patients with other glomerular diseases, such as IgA nephropathy, diabetic nephropathy, minimal change disease, and/or secondary conditions, such as hepatitis, systemic lupus erythematosus, psoriasis, malignancy, serious infectious diseases, or severe cardiopulmonary diseases, were excluded. Blood was collected within 48 hours of admission to assess the free thyroxine (FT4), free triiodothyronine (FT3), and thyroid-stimulating hormone (TSH) levels. The patients were divided into euthyroid and thyroid dysfunction groups according to the thyroid hormone levels. Patients in the thyroid dysfunction group were further divided into three subgroups: hypothyroid, hyperthyroid, and non-thyroid illness syndrome (NTIS) groups. This study followed the standards of the Helsinki Declaration and was supported by the Ethics Review Committee of the First Affiliated Hospital of Zhengzhou University (approval number:2022-KY-1187-002). Informed consent was waived owing to the retrospective nature of this study. ## Data collection Demographic and clinical data were collected at the time of renal biopsy, including age, sex, blood pressure, blood urea nitrogen (BUN), serum creatinine (SCr), uric acid (UA), triglycerides (TG), total cholesterol (TC), blood white blood cells (WBC), hemoglobin, platelets, albumin, estimated glomerular filtration rate (eGFR), M-type phospholipase A2 receptor (PLA2R) antibody, D-dimer, proteinuria, FT3, FT4, and TSH. FT3 and FT4 levels were measured using a commercial radioimmunoassay (RIA) kit (Roche Diagnostics, Mannheim, Germany). TSH levels were measured using a commercial RIA kit (Immunotech, Marseille, France). Two experienced renal pathologists diagnosed the renal biopsy specimens using light microscopy, electron microscopy, and immunofluorescence. Pathological changes were classified into two grades based on the presence or absence of glomerulosclerosis, crescents, mesangial cell proliferation, renal tubular atrophy, renal interstitial fibrosis, inflammatory cell infiltration, and renal arteriolar lesions. Follow-up data included time, proteinuria, SCr, eGFR, albumin, and medications, such as renin-angiotensin-aldosterone system inhibitor (RAASi), corticosteroids, and immunosuppressants. ## Outcomes and definitions The endpoint event was a composite of SCr doubling, eGFR decrease of >$40\%$ from baseline, ESRD, or death from kidney failure (12–14). ESRD was defined as eGFR <15 mL/min/1.73 m2 or the need for renal replacement therapy (dialysis or kidney transplantation). Follow-up time was defined as the interval from renal biopsy to the occurrence of the endpoint event or the last outpatient visit. Treatment response included clinical remission and relapse. Clinical remission included complete remission (CR) and partial remission (PR). CR was defined by proteinuria <0.3 g/d, serum albumin >35 g/L, and SCr stability. PR was defined as proteinuria <3.5 g/d with a >$50\%$ reduction from baseline. After clinical remission, the reappearance of proteinuria >3.5 g/d was defined as relapse. The reference ranges of FT3, FT4, and TSH levels were 3.28–6.47 pmol/L, 7.9–18.4 pmol/L, and 0.34–5.6 μIU/mL, respectively. NTIS, also known as euthyroid sick syndrome, includes low T3, low T4, low T3 and low T4, high T4, and other abnormalities [15]. The time course of kidney disease was defined from discovery to renal biopsy. Hypertension was defined as systolic pressure ≥140 mmHg, diastolic pressure ≥90 mmHg, or the use of antihypertensive drugs. The mean arterial pressure was equal to one-third systolic pressure plus two-thirds diastolic pressure. Nephrotic syndrome was defined as proteinuria >3.5 g/d and serum albumin <30 g/L. The eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation [16]. ## Statistical analysis Continuous variables with normal distribution were expressed as mean ± standard deviation, and the independent samples t-test, one-way analysis of variance, and Bonferroni method were used for comparisons between the groups. Data that did not follow the normal distribution were expressed as median and interquartile ranges ($25\%$, $75\%$), and the differences were compared using the Mann-Whitney U test or Kruskal-Wallis test. Categorical variables were expressed as frequency (percentage) and compared between groups using the χ2 test or Fisher exact test. Logistic regression was used to analyze the risk factors of thyroid dysfunction in patients with IMN. To reduce confounders and balance baseline variables, we applied propensity score matching (PSM) [17]. Matching was performed in a 1:1 ratio using the nearest neighbor approach with no replacement and a matching tolerance of 0.02. The covariates entered into the propensity score model included age, sex, hemoglobin, platelets, albumin, BUN, SCr, UA, TC, TG, eGFR, WBC, PLA2R antibody, D-dimer, proteinuria, and treatment. To compare the rates of CR, relapse, and renal survival between the groups, the Kaplan-Meier curve and log-rank test were used, as well as multiple testing with a Bonferroni correction method. The association between thyroid dysfunction and IMN prognosis was investigated using Cox regression analysis. SPSS version 26.0 (IBM Corp, Armonk, NY, USA) and GraphPad Prism version 8.0.2 (GraphPad Software, San Diego, CA, USA) software were used for statistical analysis and figure creation. All tests were two-sided. A P value <0.05 indicated statistical significance. ## Baseline characteristics Of the 1052 patients with IMN, 736 ($70\%$) had normal thyroid function and 316 ($30.0\%$) had abnormal thyroid function. The most common type of thyroid dysfunction was hypothyroidism ($$n = 226$$; $21.5\%$) including 212 cases of subclinical hypothyroidism ($20.2\%$) and 14 cases of clinical hypothyroidism ($1.3\%$). This was followed by NTIS ($$n = 79$$; $7.5\%$). In contrast, hyperthyroidism occurred less frequently ($$n = 11$$; $1.0\%$) (Figure 1A). When eGFR (unit: mL/min/1.73 m2) was >90, 60–90, and <60, the incidence of thyroid dysfunction was $26.1\%$ ($\frac{224}{857}$), $45.9\%$ ($\frac{78}{170}$), and $56.0\%$ ($\frac{14}{25}$), respectively (Figure 1B). **Figure 1:** *(A) Prevalence of thyroid dysfunction in idiopathic membranous nephropathy. (B) Prevalence of thyroid dysfunction by the level of estimated glomerular filtration rate.* Patients in the thyroid dysfunction group had a higher prevalence of nephrotic syndrome than those in the euthyroid group ($68.4\%$ versus $46.9\%$, $P \leq 0.001$). However, no significant variations in age, sex, course, or blood pressure were seen between the two groups (Table 1). Further subgroup analysis showed that, compared to the euthyroid group, the hypothyroid and NTIS groups had a higher incidence of nephrotic syndrome (Table S1). **Table 1** | Characteristic | Unmatched data | Unmatched data.1 | Unmatched data.2 | Matched data | Matched data.1 | Matched data.2 | | --- | --- | --- | --- | --- | --- | --- | | Characteristic | Euthyroidism (n = 736) | Thyroid dysfunction (n = 316) | P value | Euthyroidism (n = 282) | Thyroid dysfunction (n = 282) | P value | | General Information | General Information | General Information | General Information | General Information | General Information | General Information | | Age (years)* | 46.51 ± 12.63 | 46.43 ± 13.86 | 0.931 | 45.74 ± 12.62 | 46.66 ± 13.76 | 0.411 | | Male, n (%) | 437 (59.4) | 195 (61.7) | 0.479 | 159 (56.4) | 173 (61.3) | 0.231 | | Course of disease (months) | 1 (1, 4) | 1 (1, 3) | 0.317 | 1 (1, 4) | 1 (1, 3) | 0.303 | | Hypertension, n (%) | 335 (45.5) | 140 (44.3) | 0.717 | 138 (48.9) | 118 (41.8) | 0.091 | | Systolic BP (mmHg) | 133.96 ± 15.80 | 134.78 ± 17.59 | 0.456 | 134.74 ± 17.05 | 134.10 ± 17.32 | 0.658 | | Diastolic BP (mmHg) | 85.60 ± 11.53 | 86.33 ± 11.57 | 0.348 | 86.40 ± 12.22 | 85.75 ± 11.33 | 0.513 | | Mean arterial pressure (mmHg) | 101.72 ± 11.70 | 102.48 ± 12.21 | 0.341 | 102.51 ± 12.54 | 101.87 ± 11.88 | 0.530 | | Nephrotic syndrome, n (%) | 345 (46.9) | 216 (68.4) | < 0.001 | 184 (65.2) | 184 (65.2) | 1.000 | | Laboratory Result | Laboratory Result | Laboratory Result | Laboratory Result | Laboratory Result | Laboratory Result | Laboratory Result | | Blood urea nitrogen (mmol/L) | 4.90 ± 1.64 | 5.23 ± 2.31 | 0.021 | 4.97 ± 1.89 | 5.12 ± 1.86 | 0.338 | | Serum creatinine (μmol/L) | 67.70 ± 17.49 | 74.87 ± 25.13 | < 0.001 | 70.32 ± 21.07 | 72.03 ± 18.85 | 0.311 | | Uric acid (μmol/L) | 330.62 ± 88.85 | 320.81 ± 94.12 | 0.107 | 312.41 ± 79.67 | 324.86 ± 87.12 | 0.077 | | Albumin (g/L) | 27.48 ± 6.35 | 23.36 ± 5.77 | < 0.001 | 23.87 ± 5.16 | 24.07 ± 5.61 | 0.661 | | Total cholesterol (mmol/L) | 6.79 (5.50, 8.25) | 7.37 (6.09, 9.31) | < 0.001 | 7.38 (6.12, 9.31) | 7.29 (6.05, 9.05) | 0.465 | | Triglycerides (mmol/L) | 1.99 (1.41, 2.99) | 2.19 (1.52, 3.32) | 0.017 | 2.02 (1.48, 3.12) | 2.12 (1.43, 3.21) | 0.613 | | eGFR (mL/min/1.73 m2) | 104.13 ± 16.12 | 98.53 ± 20.24 | < 0.001 | 101.93 ± 18.73 | 100.41 ± 18.18 | 0.328 | | White blood cell (×109/L) | 6.51 ± 1.88 | 6.65 ± 1.96 | 0.271 | 6.59 ± 1.96 | 6.61 ± 1.94 | 0.891 | | Hemoglobin (g/L) | 134.71 ± 16.85 | 132.34 ± 18.05 | 0.041 | 131.47 ± 17.18 | 133.19 ± 17.63 | 0.241 | | Platelet (×109/L) | 239.03 ± 62.47 | 248.33 ± 75.70 | 0.055 | 239.07 ± 60.09 | 242.41 ± 67.56 | 0.535 | | Serum anti-PLA2R titer (RU/mL) | 41.85 (10.95, 122.90) | 86.25 (30.33, 215.05) | < 0.001 | 75.45 (21.75, 195.88) | 79.05 (28.28, 203.23) | 0.475 | | Serum anti-PLA2R titer > 50 RU/mL, n (%) | 350 (47.6) | 207 (65.5) | < 0.001 | 168 (59.6) | 180 (63.8) | 0.299 | | D-dimer (mg/L) | 0.21 (0.11, 0.36) | 0.29 (0.17, 0.58) | < 0.001 | 0.27 (0.16, 0.46) | 0.26 (0.16, 0.51) | 0.950 | | Proteinuria (g/d) | 4.14 (2.25, 6.36) | 5.56 (3.66, 8.25) | < 0.001 | 5.28 (3.17, 8.02) | 5.25 (3.47, 7.89) | 0.623 | | Renal Pathology | Renal Pathology | Renal Pathology | Renal Pathology | Renal Pathology | Renal Pathology | Renal Pathology | | Glomerulosclerosis, n (%) | 440 (59.8) | 197 (62.3) | 0.436 | 178 (63.1) | 175 (62.1) | 0.794 | | Crescents, n (%) | 47 (6.4) | 26 (8.2) | 0.281 | 23 (8.2) | 22 (7.8) | 0.877 | | Mesangial cell proliferation, n (%) | 10 (1.4) | 8 (2.5) | 0.179 | 8 (2.8) | 7 (2.5) | 0.794 | | Tubular atrophy, n (%) | 376 (51.1) | 161 (50.9) | 0.967 | 152 (53.9) | 142 (50.4) | 0.399 | | Interstitial fibrosis, n (%) | 367 (49.9) | 170 (53.8) | 0.242 | 143 (50.7) | 150 (53.2) | 0.555 | | Inflammatory cell infiltration, n (%) | 453 (61.5) | 216 (68.4) | 0.035 | 187 (66.3) | 188 (66.7) | 0.929 | | Arteriolar lesions, n (%) | 488 (66.3) | 222 (70.3) | 0.210 | 175 (62.1) | 196 (69.5) | 0.062 | | Treatment | Treatment | Treatment | Treatment | Treatment | Treatment | Treatment | | RAASi, n (%) | 488 (66.3) | 186 (58.9) | 0.021 | 168 (59.6) | 167 (59.2) | 0.932 | | Glucocorticoid or immunosuppressants alone, n (%) | 341 (46.3) | 133 (42.1) | 0.205 | 122 (43.3) | 120 (42.6) | 0.865 | | Glucocorticoid and immunosuppressants, n (%) | 259 (35.2) | 148 (46.8) | < 0.001 | 134 (47.5) | 131 (46.5) | 0.800 | | Outcome | Outcome | Outcome | Outcome | Outcome | Outcome | Outcome | | Complete remission, n (%) | 526 (71.5) | 196 (62.0) | 0.002 | 205 (72.7) | 180 (63.8) | 0.024 | | Relapse, n (%) | 196 (28.2) | 101 (34.6) | 0.046 | 90 (33.8) | 90 (34.5) | 0.875 | | Composite endpoint, n (%) | 55 (7.5) | 35 (11.1) | 0.055 | 19 (6.7) | 33 (11.7) | 0.042 | ## Clinicopathological features and treatment before and after PSM Patients in the thyroid dysfunction group exhibited higher levels of BUN, SCr, TG, TC, PLA2R antibody titer, D-dimer, and proteinuria and a greater percentage of PLA2R antibody titer > 50 RU/mL than those in the euthyroid group, but lower eGFR, albumin, and hemoglobin levels (all $P \leq 0.05$). Moreover, more patients in the thyroid dysfunction group had pathological changes in renal interstitial inflammatory cell infiltration ($68.4\%$ versus $61.5\%$, $$P \leq 0.035$$). Furthermore, the thyroid dysfunction group used a lower proportion of RAASi ($58.9\%$ versus $66.3\%$, $$P \leq 0.021$$) but a higher proportion of glucocorticoids combined with immunosuppressants ($46.8\%$ versus $35.2\%$, $P \leq 0.001$) (Table 1). Further subgroup analysis revealed that compared to the euthyroid group, the hypothyroid and NTIS groups had higher levels of SCr, TC, D-dimer, and PLA2R antibody titer, proteinuria, and a higher proportion of PLA2R antibody titer >50 RU/mL, but lower albumin and eGFR levels. In addition, the NTIS group had higher BUN and lower hemoglobin levels, while the hypothyroid group had higher levels of TG, platelets, and a higher proportion of glucocorticoids combined with immunosuppressants (Tables S1, S2). After PSM, 282 pairs were successfully matched, including 282 cases in the euthyroid group and 282 cases in the thyroid dysfunction group. The baseline data of the two groups reached a balance (Table 1). ## Risk factors of thyroid dysfunction in IMN The univariate logistic regression analysis showed that BUN, SCr, albumin, TC, TG, eGFR, hemoglobin, platelets, D-dimer, PLA2R antibody, proteinuria, and renal interstitial inflammatory cell infiltration were predictors of thyroid dysfunction in patients with IMN. Age, sex, and significant variables in the univariate analysis were included in the multivariate logistic regression. The results of the multivariate logistic regression revealed that female sex, lower albumin and eGFR levels, higher D-dimer level, and more proteinuria were independent risk factors for thyroid dysfunction in patients with IMN (Table 2). **Table 2** | Characteristic | Univariate logistic | Univariate logistic.1 | Multivariate logistic* | Multivariate logistic*.1 | | --- | --- | --- | --- | --- | | Characteristic | OR (95% CI) | P value | OR (95% CI) | P value | | Sex (male vs female) | 1.103 (0.841–1.455) | 0.479 | 0.729 (0.540–0.985) | 0.039 | | Age (years) | 1.000 (0.989–1.010) | 0.928 | | | | Hypertension (yes vs no) | 0.952 (0.730–1.241) | 0.717 | | | | Blood urea nitrogen (mmol/L) | 1.095 (1.021–1.175) | 0.011 | | | | Serum creatinine (μmol/L) | 1.017 (1.010–1.024) | 0.000 | | | | Uric acid (μmol/L) | 0.999 (0.997–1.000) | 0.108 | | | | Albumin (g/L) | 0.892 (0.870–0.914) | < 0.001 | 0.910 (0.885–0.935) | < 0.001 | | Total cholesterol (mmol/L) | 1.148 (1.085–1.215) | < 0.001 | | | | Triglycerides (mmol/L) | 1.090 (1.013–1.173) | 0.021 | | | | eGFR (mL/min/1.73 m2) | 0.982 (0.975–0.990) | < 0.001 | 0.990 (0.982–0.998) | 0.017 | | White blood cell (×109/L) | 1.039 (0.971–1.112) | 0.271 | | | | Hemoglobin (g/L) | 0.992 (0.984–1.000) | 0.041 | | | | Platelet (×109/L) | 1.002 (1.000–1.004) | 0.039 | | | | D-dimer (mg/L) | 1.917 (1.474–2.493) | < 0.001 | 1.347 (1.044–1.738) | 0.022 | | Serum anti-PLA2R titer (RU/mL) | 1.001 (1.001–1.002) | < 0.001 | | | | Proteinuria (g/d) | 1.133 (1.092–1.176) | < 0.001 | 1.064 (1.021–1.108) | 0.003 | | Glomerulosclerosis (yes vs no) | 1.114 (0.849–1.460) | 0.436 | | | | Crescents (yes vs no) | 1.314 (0.799–2.163) | 0.282 | | | | Mesangial cell proliferation (yes vs no) | 1.886 (0.737–4.824) | 0.186 | | | | Tubular atrophy (yes vs no) | 0.995 (0.764–1.295) | 0.967 | | | | Interstitial fibrosis (yes vs no) | 1.171 (0.899–1.525) | 0.242 | | | | Inflammatory cell infiltration (yes vs no) | 1.349 (1.020–1.785) | 0.036 | | | | Arteriolar lesions (yes vs no) | 1.200 (0.902–1.597) | 0.210 | | | ## Follow-up and prognosis analysis The median follow-up durations in the euthyroid and thyroid dysfunction groups were 28 (range =17-40) months and 24 (range = 14-34) months, respectively. The incidence rates of treatment response and composite events in the two groups are shown in Table 1. Results from the Kaplan-Meier curves revealed that the thyroid dysfunction group had a lower cumulative CR probability ($$P \leq 0.002$$, Figure 2A), higher cumulative relapse probability ($P \leq 0.001$, Figure 2B), and lower cumulative renal survival probability ($$P \leq 0.013$$, Figure 2C) than the euthyroid group. In the subgroup comparisons (Figures 3, S1), the thyroid dysfunction subgroups were separately compared with the euthyroid group. Kaplan-Meier curves identified a lower cumulative CR probability in the hypothyroid ($$P \leq 0.023$$) and NTIS groups ($$P \leq 0.017$$), but the differences were not statistically significant. Additionally, the cumulative relapse probabilities were higher in the hypothyroid ($P \leq 0.001$) and NTIS groups ($$P \leq 0.002$$), while the cumulative renal survival probability was lower in the hypothyroid group ($$P \leq 0.011$$). In the unadjusted model, Cox regression analysis revealed that thyroid dysfunction was a significant predictor of CR [hazard ratio (HR) = 0.781, $$P \leq 0.003$$], relapse (HR = 1.699, $$P \leq 0.001$$), and composite endpoint event (HR = 1.703, $$P \leq 0.014$$) in patients with IMN. After adjusting for age, sex, hypertension, proteinuria, albumin, eGFR, PLA2R antibody, and treatment, thyroid dysfunction remained an independent risk factor for IMN relapse (HR = 1.726, $P \leq 0.001$) and composite endpoint event (HR = 1.576, $$P \leq 0.043$$), but not for CR (HR = 0.941, $$P \leq 0.486$$) (Table 3). **Figure 2:** *Kaplan-Meier curves between the euthyroid group and thyroid dysfunction group. In the complete dataset: (A) Complete remission rate; (B) Relapse rate; (C) Renal survival rate. In the propensity matched dataset: (D) Complete remission rate; (E) Relapse rate; (F) Renal survival rate.* **Figure 3:** *Kaplan-Meier curves between the euthyroid group and thyroid dysfunction subgroups. In the complete dataset: (A) Complete remission rate; (B) Relapse rate; (C) Renal survival rate. In the propensity matched dataset: (D) Complete remission rate; (E) Relapse rate; (F) Renal survival rate. NTIS, non-thyroid illness syndrome.* TABLE_PLACEHOLDER:Table 3 In the matched cohort, the median follow-up time in the euthyroid and thyroid dysfunction groups was 34 [20, 53] months and 24 [14, 34] months, respectively. The incidence rates of the different outcomes are also presented in Table 1. The thyroid dysfunction group had a lower cumulative CR probability ($$P \leq 0.044$$, Figure 2D), a higher cumulative relapse probability ($P \leq 0.001$, Figure 2E), and a poorer cumulative renal survival probability ($$P \leq 0.004$$, Figure 2F) than the euthyroid group. Subgroup analysis revealed that compared with the euthyroid group, the hypothyroid group ($$P \leq 0.003$$) and NTIS group ($$P \leq 0.004$$) had a higher cumulative relapse probability; the hypothyroid group ($$P \leq 0.006$$) had a lower cumulative renal survival probability, but differences in cumulative CR probability between the groups were not significant (Figures 3, S2). Results from the multivariate Cox regression analysis showed that after adjusting for the confounding factors, thyroid dysfunction remained an independent risk factor for CR (HR = 0.810, $$P \leq 0.045$$), relapse (HR = 1.721, $$P \leq 0.001$$), and composite endpoint event (HR = 2.113, $$P \leq 0.014$$) in patients with IMN (Table 4). **Table 4** | Unnamed: 0 | Unadjusted | Unadjusted.1 | Model 1 a | Model 1 a.1 | Model 2 b | Model 2 b.1 | Model 3 c | Model 3 c.1 | | --- | --- | --- | --- | --- | --- | --- | --- | --- | | | HR (95% CI) | P value | HR (95% CI) | P value | HR (95% CI) | P value | HR (95% CI) | P value | | Complete remission | 0.819 (0.671–1.001) | 0.051 | 0.831 (0.679–1.016) | 0.071 | 0.791 (0.644–0.970) | 0.024 | 0.810 (0.659–0.996) | 0.045 | | Relapse | 1.699 (1.259–2.292) | 0.001 | 1.657 (1.228–2.236) | 0.001 | 1.726 (1.266–2.355) | 0.001 | 1.721 (1.261–2.348) | 0.001 | | Composite endpoint event | 2.292 (1.292–4.066) | 0.005 | 2.127 (1.194–3.787) | 0.010 | 2.153 (1.192–3.888) | 0.011 | 2.113 (1.165–3.831) | 0.014 | ## Discussion In non-iodine deficient areas, the prevalence of hyperthyroidism, clinical hypothyroidism, and subclinical hypothyroidism is approximately $0.2\%$–$1.3\%$, $1\%$–$2\%$, and $4\%$–$10\%$, respectively [18, 19]. In our study ($$n = 1052$$), the prevalence of thyroid dysfunction was approximately $30\%$. Of this $30\%$, hypothyroidism accounted for $21.5\%$ (subclinical hypothyroidism was about $20.2\%$ and clinical hypothyroidism was about $1.3\%$), followed by NTIS (approximately $7.5\%$), while hyperthyroidism was low at about $1\%$. The prevalence of thyroid dysfunction was significantly higher in patients with IMN than in the general population, and the incidence increased with decreasing eGFR levels, as reported in the CKD cohorts [20]. This may be related to several issues. First, massive proteinuria increases the excretion of carrier proteins, such as thyroid-binding globulin, transthyretin, and albumin. Second, the conversion of T4 to T3 is inhibited, and iodine clearance is also impaired. Finally, metabolic acidosis, inflammatory status, and diet are factors to consider [21, 22]. The epidemiology of MN shows that it is more common in middle-aged and elderly people, with a 2:1 male predominance [3]. This is reflected in our study sample of 1052 patients with IMN, in which the female-to-male ratio was 0.66:1. This study also found that females, lower albumin and eGFR levels, higher D-dimer level, and severe proteinuria, were independent predictor variables for thyroid dysfunction in patients with IMN. Li et al. [ 10], in their cohort of 317 patients with nephrotic syndrome, found that SCr, TC, platelets, hemoglobin, albumin, and proteinuria were predictors of thyroid dysfunction. Li et al. ’s findings are consistent with part of our results. The discrepancy may be related to different study samples and sizes between the two studies. Therefore, the thyroid functions of patients with IMN who have one or more of the risk factors should be monitored to avoid missed diagnoses and delayed treatment. We found that patients in the thyroid dysfunction group had higher levels of SCr and proteinuria, and lower levels of albumin and eGFR. These factors have been identified as risk predictors of kidney disease progression, indicating that patients with IMN and thyroid dysfunction have more severe clinical manifestations [2]. Thyroid hormones play an essential role in lipid metabolism, and thyroid dysfunction, particularly hypothyroidism, increases the likelihood of hyperlipidemia [23]. Hyperlipidemia has been linked to an acceleration of renal function decline in patients with CKD [24]. In this study, we discovered that patients with IMN and thyroid dysfunction tended to have higher levels of TC and TG, suggesting that thyroid dysfunction may induce more severe renal disorders by altering lipid levels. Research has also demonstrated that abnormal thyroid function increases the risk of anemia by affecting erythrocyte production and survival, together with iron metabolism and utilization [25]. Moreover, anemia has been associated with poor prognosis in CKD [26]. Furthermore, we observed that patients in the thyroid dysfunction group had a lower hemoglobin level, implying that thyroid dysfunction may negatively impact patients with IMN by reducing hemoglobin levels. Therefore, correcting the levels of blood lipids and hemoglobin in patients with IMN may be helpful. PLA2R is the primary target antigen of IMN ($70\%$–$80\%$), and a PLA2R antibody titer greater than 50RU/mL is defined as a high risk for IMN according to the Kidney Disease Improving Global Outcomes (KDIGO) 2021 guideline. Additionally, a high level of PLA2R antibodies is an independent risk factor for persistent deterioration of renal function [2, 7, 27]. Our study found that both the PLA2R antibody titer and the proportion of titers greater than 50 RU/mL were higher in the thyroid dysfunction group, inferring that thyroid dysfunction may have adverse effects on the development of IMN through an immune mechanism. Further investigations on this aspect are warranted. Venous thromboembolism (VTE) is a potentially fatal complication of nephrotic syndrome, with the highest incidence in membranous nephropathy ($7\%$–$60\%$), and D-dimer is a vital biomarker for assessing VTE [28]. Recent evidence suggests that thyroid hormones can affect the coagulation and fibrinolytic systems, increasing the risk of bleeding or thrombosis [29]. Our study revealed that patients in the thyroid dysfunction group had a higher D-dimer level, implying that abnormal thyroid function may increase the risk of VTE in patients with IMN, which should be taken seriously in clinical practice. Regarding renal pathology, we found that patients in the thyroid dysfunction group demonstrated increased renal interstitial inflammatory cell infiltration relative to those in the euthyroid group, but there was no significant difference in glomerulopathy and arteriolar lesions. The investigation of thyroid dysfunction on pathological changes in patients with IMN is limited and requires further exploration. Previous studies have reported that thyroid dysfunction increases the risk of cardiorenal injury and all-cause death in CKD (30–34). To date, there have been no large-scale clinical studies on thyroid dysfunction and the prognosis of IMN. In our large study cohort, patients with IMN combined with thyroid dysfunction demonstrated a poor prognosis, as well as severe clinical manifestations. Patients with thyroid dysfunction had a lower CR rate, a greater relapse rate, and a poorer kidney survival rate even after applying the PSM approach to minimize bias. The subgroup analysis further indicated that patients in the hypothyroid group had a higher relapse rate and a lower renal survival rate, whereas those in the NTIS group only had a higher relapse rate. There was no significant difference in the prognosis between the hyperthyroid and euthyroid groups, which may be due to the limited number of cases in our study. Notably, the multivariate *Cox analysis* confirmed that thyroid dysfunction was an independent risk factor for CR, relapse, and composite endpoint event in patients with IMN. Thyroid dysfunction affects kidney function in several ways [21, 22]. First, it can damage the renal structure, resulting in decreased kidney volume, thickened glomerular basement membrane, increased mesangial matrix, and capillary permeability. Second, thyroid hormone disorders alter eGFR through water-sodium metabolism, renal tubular ion transporters, and tubular-glomerular feedback. Thyroid dysfunction can also disrupt the autonomic regulation of renal blood perfusion via the renin-angiotensin-aldosterone system. Moreover, thyroid dysfunction influences cardiac output and blood volume by changing myocardial contractility, peripheral vascular resistance, and erythropoietin production, thereby altering renal blood flow. Consequently, patients with IMN and thyroid disorders should be regularly monitored. According to previous studies, thyroid hormone supplementation improves renal function in patients with thyroid deficiency, which may be related to thyroid hormone-enhanced circulating blood volume, renal blood flow, and endothelial function [35, 36]. For patients with IMN and thyroid dysfunction, correcting abnormal thyroid function as soon as possible may be beneficial. Nevertheless, studies on the effectiveness and safety of thyroid hormone replacement therapy in patients with IMN are scarce. Hence, more basic research and multicenter cohort studies are required. This study has some limitations. As a single-center retrospective study, the causal relationship between thyroid dysfunction and the prognosis of IMN could not be determined. The follow-up duration was also insufficient. Moreover, thyroid hormone levels were not dynamically observed during the follow-up period and patients with thyroid dysfunction were not treated or monitored regularly. We will continue to investigate this in future research. In conclusion, using the PSM method in a large cohort of patients with IMN, this study found that patients with IMN and thyroid dysfunction have more severe clinical characteristics and worse prognoses, especially those with hypothyroidism. Moreover, thyroid dysfunction is an independent risk factor for poor prognosis in patients with IMN. Therefore, the thyroid function of patients with IMN should be monitored in clinical practice. ## Data availability statement The original contributions presented in the study are included in the article/ Supplementary Material. Further inquiries can be directed to the corresponding author. ## Ethics statement The studies involving human participants were reviewed and approved by The Ethics Review Committee of the First Affiliated Hospital of Zhengzhou University (approval number: 2022-KY-1187-002). Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements. ## Author contributions PW and JZ designed the study. YML, YCL, and HC collected the data. SW and BH analyzed the data. PW drafted the manuscript. All authors critically reviewed the article. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2023.1133521/full#supplementary-material ## References 1. Cattran DC, Brenchley PE. **Membranous nephropathy: Integrating basic science into improved clinical management**. *Kidney Int* (2017) **91**. DOI: 10.1016/j.kint.2016.09.048 2. Ronco P, Beck L, Debiec H, Fervenza FC, Hou FF, Jha V. **Membranous nephropathy**. *Nat Rev Dis Primers* (2021) **7** 69. DOI: 10.1038/s41572-021-00303-z 3. Couser WG. **Primary membranous nephropathy**. *Clin J Am Soc Nephrol* (2017) **12**. DOI: 10.2215/CJN.11761116 4. Beck LH, Bonegio RG, Lambeau G, Beck DM, Powell DW, Cummins TD. **M-type phospholipase A2 receptor as target antigen in idiopathic membranous nephropathy**. *N Engl J Med* (2009) **361** 11-21. DOI: 10.1056/NEJMoa0810457 5. Hoxha E, Reinhard L, Stahl RAK. **Membranous nephropathy: New pathogenic mechanisms and their clinical implications**. *Nat Rev Nephrol* (2022) **18**. DOI: 10.1038/s41581-022-00564-1 6. Beck LH, Fervenza FC, Beck DM, Bonegio RG, Malik FA, Erickson SB. **Rituximab-induced depletion of anti-Pla2r autoantibodies predicts response in membranous nephropathy**. *J Am Soc Nephrol* (2011) **22**. DOI: 10.1681/ASN.2010111125 7. Hoxha E, Harendza S, Pinnschmidt H, Panzer U, Stahl RA. **M-type phospholipase A2 receptor autoantibodies and renal function in patients with primary membranous nephropathy**. *Clin J Am Soc Nephrol* (2014) **9**. DOI: 10.2215/CJN.03850414 8. Tesar V, Hruskova Z. **Autoantibodies in the diagnosis, monitoring, and treatment of membranous nephropathy**. *Front Immunol* (2021) **12**. DOI: 10.3389/fimmu.2021.593288 9. Iglesias P, Bajo MA, Selgas R, Diez JJ. **Thyroid dysfunction and kidney disease: An update**. *Rev Endocr Metab Disord* (2017) **18**. DOI: 10.1007/s11154-016-9395-7 10. Li LZ, Hu Y, Ai SL, Cheng L, Liu J, Morris E. **The relationship between thyroid dysfunction and nephrotic syndrome: A clinicopathological study**. *Sci Rep* (2019) **9** 6421. DOI: 10.1038/s41598-019-42905-4 11. Chuang MH, Liao KM, Hung YM, Wang PY, Chou YC, Chou P. **Abnormal thyroid-stimulating hormone and chronic kidney disease in elderly adults in Taipei city**. *J Am Geriatr Soc* (2016) **64**. DOI: 10.1111/jgs.14102 12. Maschio G, Alberti D, Janin G, Locatelli F, Mann JF, Motolese M. **Effect of the angiotensin-Converting-Enzyme inhibitor benazepril on the progression of chronic renal insufficiency. the angiotensin-Converting-Enzyme inhibition in progressive renal insufficiency study group**. *N Engl J Med* (1996) **334**. DOI: 10.1056/NEJM199604113341502 13. Brenner BM, Cooper ME, de Zeeuw D, Keane WF, Mitch WE, Parving HH. **Effects of losartan on renal and cardiovascular outcomes in patients with type 2 diabetes and nephropathy**. *N Engl J Med* (2001) **345**. DOI: 10.1056/NEJMoa011161 14. Coresh J, Turin TC, Matsushita K, Sang Y, Ballew SH, Appel LJ. **Decline in estimated glomerular filtration rate and subsequent risk of end-stage renal disease and mortality**. *JAMA* (2014) **311**. DOI: 10.1001/jama.2014.6634 15. Lee S, Farwell AP. **Euthyroid sick syndrome**. *Compr Physiol* (2016) **6**. DOI: 10.1002/cphy.c150017 16. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF, Feldman HI. **A new equation to estimate glomerular filtration rate**. *Ann Intern Med* (2009) **150**. DOI: 10.7326/0003-4819-150-9-200905050-00006 17. Austin PC. **An introduction to propensity score methods for reducing the effects of confounding in observational studies**. *Multivariate Behav Res* (2011) **46** 399-424. DOI: 10.1080/00273171.2011.568786 18. Biondi B, Cooper DS. **The clinical significance of subclinical thyroid dysfunction**. *Endocr Rev* (2008) **29** 76-131. DOI: 10.1210/er.2006-0043 19. Taylor PN, Albrecht D, Scholz A, Gutierrez-Buey G, Lazarus JH, Dayan CM. **Global epidemiology of hyperthyroidism and hypothyroidism**. *Nat Rev Endocrinol* (2018) **14**. DOI: 10.1038/nrendo.2018.18 20. Lo JC, Chertow GM, Go AS, Hsu CY. **Increased prevalence of subclinical and clinical hypothyroidism in persons with chronic kidney disease**. *Kidney Int* (2005) **67**. DOI: 10.1111/j.1523-1755.2005.00169.x 21. Narasaki Y, Sohn P, Rhee CM. **The interplay between thyroid dysfunction and kidney disease**. *Semin Nephrol* (2021) **41**. DOI: 10.1016/j.semnephrol.2021.03.008 22. Echterdiek F, Ranke MB, Schwenger V, Heemann U, Latus J. **Kidney disease and thyroid dysfunction: The chicken or egg problem**. *Pediatr Nephrol* (2022) **37**. DOI: 10.1007/s00467-022-05640-z 23. Su X, Peng H, Chen X, Wu X, Wang B. **Hyperlipidemia and hypothyroidism**. *Clin Chim Acta* (2022) **527** 61-70. DOI: 10.1016/j.cca.2022.01.006 24. Tsai CW, Huang HC, Chiang HY, Chung CW, Chang SN, Chu PL. **Longitudinal lipid trends and adverse outcomes in patients with ckd: A 13-year observational cohort study**. *J Lipid Res* (2019) **60**. DOI: 10.1194/jlr.P084590 25. Wopereis DM, Du Puy RS, van Heemst D, Walsh JP, Bremner A, Bakker SJL. **The relation between thyroid function and anemia: A pooled analysis of individual participant data**. *J Clin Endocrinol Metab* (2018) **103**. DOI: 10.1210/jc.2018-00481 26. Sato Y, Fujimoto S, Konta T, Iseki K, Moriyama T, Yamagata K. **Anemia as a risk factor for all-cause mortality: Obscure synergic effect of chronic kidney disease**. *Clin Exp Nephrol* (2018) **22**. DOI: 10.1007/s10157-017-1468-8 27. **Kdigo 2021 clinical practice guideline for the management of glomerular diseases**. *Kidney Int* (2021) **100** S1-S276. DOI: 10.1016/j.kint.2021.05.021 28. Li SJ, Guo JZ, Zuo K, Zhang J, Wu Y, Zhou CS. **Thromboembolic complications in membranous nephropathy patients with nephrotic syndrome-a prospective study**. *Thromb Res* (2012) **130**. DOI: 10.1016/j.thromres.2012.04.015 29. Elbers LPB, Squizzato A, Gerdes VEA. **Thyroid disorders and hemostasis**. *Semin Thromb Hemost* (2018) **44**. DOI: 10.1055/s-0038-1666825 30. You AS, Sim JJ, Kovesdy CP, Streja E, Nguyen DV, Brent GA. **Association of thyroid status prior to transition to end-stage renal disease with early dialysis mortality**. *Nephrol Dial Transplant* (2019) **34**. DOI: 10.1093/ndt/gfy289 31. Rhee CM, Kalantar-Zadeh K, Ravel V, Streja E, You AS, Brunelli SM. **Thyroid status and death risk in us veterans with chronic kidney disease**. *Mayo Clin Proc* (2018) **93**. DOI: 10.1016/j.mayocp.2018.01.024 32. Rhee CM, You AS, Nguyen DV, Brunelli SM, Budoff MJ, Streja E. **Thyroid status and mortality in a prospective hemodialysis cohort**. *J Clin Endocrinol Metab* (2017) **102**. DOI: 10.1210/jc.2016-3616 33. Rhee CM, Ravel VA, Streja E, Mehrotra R, Kim S, Wang J. **Thyroid functional disease and mortality in a national peritoneal dialysis cohort**. *J Clin Endocrinol Metab* (2016) **101**. DOI: 10.1210/jc.2016-1691 34. Rhee CM, Kim S, Gillen DL, Oztan T, Wang J, Mehrotra R. **Association of thyroid functional disease with mortality in a national cohort of incident hemodialysis patients**. *J Clin Endocrinol Metab* (2015) **100**. DOI: 10.1210/jc.2014-4311 35. Blackaller GN, Chavez-Iniguez JS, Carreon-Bautista EE, Gonzalez-Torres FJ, Villareal-Contreras M, Barrientos Avalos JR. **A pilot trial on the effect of levothyroxine on proteinuria in patients with advanced ckd**. *Kidney Int Rep* (2021) **6**. DOI: 10.1016/j.ekir.2020.10.016 36. Gondil VS, Chandrasekaran A, Rastogi A, Yadav AK, Sood A, Ramachandran R. **Proteinuria in severe hypothyroidism: A prospective study**. *J Clin Endocrinol Metab* (2021) **106**. DOI: 10.1210/clinem/dgaa871
--- title: Loss of ganglioglomerular nerve input to the carotid body impacts the hypoxic ventilatory response in freely-moving rats authors: - Paulina M. Getsy - Gregory A. Coffee - Stephen J. Lewis journal: Frontiers in Physiology year: 2023 pmcid: PMC10060956 doi: 10.3389/fphys.2023.1007043 license: CC BY 4.0 --- # Loss of ganglioglomerular nerve input to the carotid body impacts the hypoxic ventilatory response in freely-moving rats ## Abstract The carotid bodies are the primary sensors of blood pH, pO2 and pCO2. The ganglioglomerular nerve (GGN) provides post-ganglionic sympathetic nerve input to the carotid bodies, however the physiological relevance of this innervation is still unclear. The main objective of this study was to determine how the absence of the GGN influences the hypoxic ventilatory response in juvenile rats. As such, we determined the ventilatory responses that occur during and following five successive episodes of hypoxic gas challenge (HXC, $10\%$ O2, $90\%$ N2), each separated by 15 min of room-air, in juvenile (P25) sham-operated (SHAM) male Sprague Dawley rats and in those with bilateral transection of the ganglioglomerular nerves (GGNX). The key findings were that 1) resting ventilatory parameters were similar in SHAM and GGNX rats, 2) the initial changes in frequency of breathing, tidal volume, minute ventilation, inspiratory time, peak inspiratory and expiratory flows, and inspiratory and expiratory drives were markedly different in GGNX rats, 3) the initial changes in expiratory time, relaxation time, end inspiratory or expiratory pauses, apneic pause and non-eupneic breathing index (NEBI) were similar in SHAM and GGNX rats, 4) the plateau phases obtained during each HXC were similar in SHAM and GGNX rats, and 5) the ventilatory responses that occurred upon return to room-air were similar in SHAM and GGNX rats. Overall, these changes in ventilation during and following HXC in GGNX rats raises the possibility the loss of GGN input to the carotid bodies effects how primary glomus cells respond to hypoxia and the return to room-air. ## Highlights • Bilateral GGNX blunts the initial increases in minute ventilation in response to consecutive hypoxic challenges in juvenile Sprague Dawley rats.• Bilateral GGNX blunts the initial increases in inspiratory and expiratory drives in response to consecutive hypoxic challenges.• Bilateral GGNX blunts the initial increases in non-eupneic breathing index that occur upon return to room-air following consecutive hypoxic challenges. ## Introduction The superior cervical ganglion (SCG) contains cell bodies of post-ganglionic sympathetic nerves (Rando et al., 1981; Tang et al., 1995a; Tang et al., 1995b; Tang et al., 1995c; Llewellyn-Smith et al., 1998) and small intensely fluorescent (SIF) cells (McDonald, 1983a; McDonald, 1983b; Zaidi and Matthews, 2013; Takaki et al., 2015). These post-ganglionic sympathetic nerves and SIF cells receive their pre-ganglionic innervation from thoracic spinal cord (T1-T4) nerves that course through the ipsilateral cervical sympathetic chain (CSC) (Rando et al., 1981; McDonald, 1983a; McDonald, 1983b; Tang et al., 1995a; Tang et al., 1995b; Tang et al., 1995c; Llewellyn-Smith et al., 1998). Previously we reported that freely-moving mice with bilateral CSC transection (CSCX) (Getsy et al., 2021a) or bilateral superior cervical ganglionectomy (SCGX) (Getsy et al., 2021b) display substantially different ventilatory responses during and after a hypoxic gas challenge (HXC) than those of their sham-operated counterparts. While providing evidence that the CSC-SCG complex has a role in modulating the hypoxic ventilatory response (HVR), the exact pathways and target structures by which this complex regulates the HVR were not elucidated in these mouse studies. It is well-known that, post-ganglionic nerves in the SCG project to target structures in the head and neck via the external (ECN) and the internal (ICN) carotid nerves (Bowers and Zigmond, 1979; Buller and Bolter, 1997; Asamoto, 2004; Savastano et al., 2010) including, the upper airway and tongue (Flett and Bell, 1991; Kummer et al., 1992; O'Halloran et al., 1996; O'Halloran et al., 1998; Hisa et al., 1999; Wang and Chiou, 2004; Oh et al., 2006), vascular structures within the brain including the Circle of Willis and cerebral arteries (Sadoshima et al., 1981; Sadoshima et al., 1983a; Sadoshima et al., 1983b; Werber and Heistad, 1984), and nuclei in the hypothalamus and brainstem (Cardinali et al., 1981a; Cardinali et al., 1981b; Cardinali et al., 1982; Gallardo et al., 1984; Saavedra, 1985; Wiberg and Widenfalk, 1993; Westerhaus and Loewy, 1999; Esquifino et al., 2004; Hughes-Davis et al., 2005; Mathew, 2007). SCG post-ganglionic neurons in the ECN branch into the ganglioglomerular nerve (GGN) to innervate type 1 glomus (chemoresponsive) cells, chemoreceptor afferent nerve terminals and vasculature in the carotid bodies (Biscoe and Purves, 1967; Zapata et al., 1969; Bowers and Zigmond, 1979; Brattström, 1981a; McDonald and Mitchell, 1981; McDonald, 1983a; McDonald, 1983b; Verna et al., 1984; Torrealba and Claps, 1988; Ichikawa, 2002; Asamoto, 2004; Savastano et al., 2010). Fibers in the GGN also modulate responsiveness of baroreceptor afferent nerve terminals within the carotid sinus (Floyd and Neil, 1952; Rees, 1967; Bolter and Ledsome, 1976; Brattström, 1981b; Felder et al., 1983; Buller and Bolter, 1993). It is clear that the ability of the GGN input to elicit vasoconstriction within the carotid body indirectly activates primary glomus cells via ensuing tissue hypoxemia (Majcherczyk et al., 1974; Llados and Zapata, 1978; Majcherczyk et al., 1980; Matsumoto et al., 1981; Yokoyama et al., 2015). However, studies examining the direct effects of GGN input to the carotid body on resting activity of glomus cells and chemoafferents in the carotid sinus nerve (CSN), and the responses of these structures during HXC have yielded controversial findings. For instance, GGN (and CSC) activity increases during HXC raising the likelihood that excitatory neurotransmitters, such as norepinephrine, dopamine and neuropeptide Y are released during the challenge (Lahiri et al., 1986; Matsumoto et al., 1986; Matsumoto et al., 1987; Yokoyama et al., 2015), although direct activation of the GGN decreases the chemosensory responsiveness to HXC in the carotid bodies of the cat (McQueen et al., 1989). Disparate responses also occur upon application of the above neurotransmitters to carotid body preparations (in vivo and in vitro), including 1) biphasic responses that consisted of initial brief bursts in CSN activity followed by a more prolonged phase of depressed activity (Bisgard et al., 1979), 2) a biphasic pattern of responses consisting of initial brief decrease in CSN activity followed by long-lasting excitation (Matsumoto et al., 1981), 3) direct activation of primary glomus cells and/or chemoafferents (Lahiri et al., 1981; Matsumoto et al., 1981; Milsom and Sadig, 1983; Heinert et al., 1995; Pang et al., 1999), 4) direct inhibition of primary glomus cells and/or chemoafferents (Zapata et al., 1969; Zapata, 1975; Llados and Zapata, 1978; Mills, et al., 1978; Folgering et al., 1982; Kou et al., 1991; Pizarro et al., 1992; Bisgard et al., 1993; Prabhakar et al., 1993; Ryan et al., 1995; Almaraz, et al., 1997; Overholt and Prabhakar, 1999), and 5) constriction of arteriolar blood flow in carotid bodies leading to indirect excitation of carotid body glomus cells (Potter et al., 1987; Yokoyama et al., 2015). As mentioned above, our studies in mice with bilateral CSCX or SCGX could not discriminate between the roles of SCG projections to the brain from those to the carotid bodies with respect to the ventilatory responses to HXC (Getsy et al., 2021a; Getsy et al., 2021b). We have not been able to confidently transect the GGN (GGNX) bilaterally in mice to date, but have successfully performed this surgery in juvenile P21 (21 days post-natal age) male Sprague Dawley rats to allow for direct comparison of potential findings to those we obtained following bilateral CSN transection (CSNX) in such rats (Getsy et al., 2020). We described in detail why we use juvenile rats and the reasons included that this is a pivotal age in their development and that P25 (day of actual testing) is the optimal day that we take the rats for electrophysiological experiments (Getsy et al., 2020). To directly address how the absence of GGN input to the carotid bodies affects the ventilatory responses that occur during and after episodes of HXC, the present study determined the responses that occurred during five successive episodes of HXC ($10\%$ O2, $90\%$ N2) each separated by 15 min of room-air in 25 day-old (P25) male Sprague Dawley rats that had undergone sham-operation (SHAM rats) or bilateral GGNX (GGNX rats) 4 days earlier (P21). ## Animals and surgeries All experiments described here were carried out in strict accordance with the National Institute of Health Guide for the Care and Use of Laboratory Animals (NIH Publication No. 80–23) revised in 1996 (https://www.nap.edu/catalog/5140/guide-for-the-care-and-use-of-laboratory-animals). The protocols were approved by the Institutional Animal Care and Use Committee of Case Western Reserve University (Cleveland, OH). Ninety male Sprague Dawley (SD) rats (postnatal age 21, P21) from ENVIGO (Indianapolis, IN) were used in these studies. All of the rats were anesthetized with an intraperitoneal injection of ketamine (80 mg/kg, Ketaset, Zoetis, Parsippany, NJ) and xylazine (10 mg/kg, Akorn Animal Health, Lake Forest, IL), and placed on a surgical station to maintain body temperature at 37°C via a heating pad (SurgiSuite, Kent Scientific Corporation, Torrington, CT). The surgical plane of anesthesia was checked every 15 min by a toe pinch. Bilateral GGNX and sham-operations (SHAM) were performed. For bilateral (i.e., left and right side) GGNX, a midline incision of approximately 2 cm was made in the neck. Dual forceps were used to tease away connective tissue and expose the SCG behind the carotid artery bifurcation. Figure 1A shows the dissection to expose the GGN immediately before being transected and Figure 1B shows the GGN immediately after being transected. Briefly, in the anesthetized rat, the ECN was exposed as it exited the SCG and was followed until the GGN was located branching off the ECN. The GGN was then transected using micro-scissors at the point where it branched from the ipsilateral ECN. This procedure was performed on both the left and ride side. For SHAM procedures, the left and right GGN was identified but not transected. Figure 1C shows the anatomy in a paraformaldehyde P25 male rat to show the proximity of the CSN and other nerve structures. **FIGURE 1:** *(A) Photograph in a male P25 juvenile Sprague Dawley rat under anesthesia showing the ganglioglomerular nerve (GGN), a branch off the external carotid nerve (ECN), entering the carotid body (CB). The ECN branches from the superior cervical ganglion (SCG) as shown. The carotid artery (CA) is also depicted. (B) Photograph in a male P25 juvenile Sprague Dawley rat under anesthesia showing the ganglioglomerular nerve (GGN), a branch off the external carotid nerve (ECN), transected. The ECN branches from the superior cervical ganglion (SCG) as shown. The carotid body (CB) is also depicted. (C) Photograph in a male P25 juvenile Sprague Dawley rat perfused with 4% paraformaldehyde showing the ganglioglomerular nerve (GGN), a branch off the external carotid nerve (ECN), entering the carotid body (CB), and the carotid sinus nerve (CSN) branching off the glossopharyngeal nerve (IX) and entering the CB. The ECN branches from the superior cervical ganglion (SCG) as shown. The pharyngeal nerve (PN), hypoglossal nerve (XII), and internal carotid artery (ICA) are also depicted. Dissections for panels (A–C) were done on the left side of the rat. Scale bar for all photos is 500 μm.* The rats were allowed 4 days to recover from surgery and were P25 on the day of the study. All rats were monitored for pain and distress every day following surgery. Rats were given an injection of the non-steroidal anti-inflammatory drug, carprofen (2 mg/kg, Rimadyl, Zoetis, Parsippany, NJ), 24 and 48 h post-surgery to reduce any pain or inflammation at the incision site. None of the rats showed any signs of pain or inflammation from the surgeries and began moving about the cages and eating and drinking approximately 1 h after surgery. Rats were weighed daily to ensure proper weight gain. We have determined that these injections of carprofen do not affect resting ventilation or the responses to HXC on day 4 post-surgery (data not shown). ## Protocols for whole body plethysmography measurement of ventilatory parameters Ventilatory parameters were continuously recorded in the unanesthetized unrestrained SHAM or GGNX rats via whole body plethysmography (Buxco Small Animal Whole Body Plethysmography, DSI a division of Harvard Biosciences, Inc., St. Paul, MN, USA) as detailed previously (May et al., 2013a; May et al., 2013b; Young et al., 2013; Getsy et al., 2014; Henderson et al., 2014; Baby et al., 2018; Gaston et al., 2020; Getsy et al., 2020; Baby S. et al., 2021; Getsy et al., 2021a; Baby S. M. et al., 2021; Getsy et al., 2021b; Gaston et al., 2021; Seckler et al., 2022). The directly recorded and calculated (derived) parameters are defined in Supplementary Table S1 (Hamelmann et al., 1997; Lomask, 2006; Tsumuro et al., 2006; Quindry et al., 2016; Gaston et al., 2021). Directly recorded and derived ventilatory parameters and the abbreviations used in this manuscript are: frequency of breathing (Freq), tidal volume (TV), minute ventilation (MV), inspiratory time (Ti), expiratory time (Te), Ti/Te, end inspiratory pause (EIP), end expiratory pause (EEP), peak inspiratory flow (PIF), peak expiratory flow (PEF), PIF/PEF, expiratory flow at $50\%$ expired TV (EF50), relaxation time (RT), inspiratory drive (TV/Ti), expiratory drive (TV/Te), apneic pause [(Te/RT)-1], non-eupneic breathing index (NEBI) and NEBI corrected for Freq (NEBI/Freq). A diagram of relationships between some directly recorded parameters and apneic pause (adapted from Lomask, 2006) are shown in Supplementary Figure S1. All studies were done in a quiet laboratory with atmospheric pressure of 760 mmHg (sea-level). The chamber volumes were 1.5 L and the room-air or gas flowing through each of the chambers was set at 1.5 L/min. The chamber temperatures during the acclimatization period were: 26.6 ± 0.1°C for the SHAM rats and 26.6 ± 0.1°C for GGNX rats ($p \leq 0.05$, GGNX versus SHAM). The chamber humidities during acclimatization were $52.1\%$ ± $2.4\%$ for SHAM rats and $50.8\%$ ± $2.0\%$ for GGNX rats ($p \leq 0.05$, GGNX versus SHAM). The FinePointe (DSI) software constantly corrected digitized ventilatory values originating from the actual waveforms for alterations in chamber humidity and chamber temperature. Pressure changes associated with the respiratory waveforms were converted to volumes (e.g., TV, PIF, PEF, EF50) employing the algorithms of Epstein and colleagues (Epstein and Epstein, 1978; Epstein et al., 1980). Specifically, factoring in chamber humidity and temperature, cycle analyzers filtered the acquired signals, and FinePointe algorithms generated an array of box flow data that identified a waveform segment as an acceptable breath. From that data vector, minimum and maximum box flow values were determined and multiplied by a compensation factor provided by selected algorithms (Epstein and Epstein, 1978; Epstein et al., 1980) thus producing TV, PIF and PEF values that were used to determine non-eupneic breathing events expressed as the non-eupneic breathing index (NEBI), reported as the percentage of non-eupneic breathing events per epoch (Getsy et al., 2014). ## Protocols and data recording including maximal attainable responses The rats were placed in plethysmography chambers to continuously record breath by breath ventilatory parameters. The rats were allowed to acclimatize for at least 60 min to allow stable baseline values to be recorded over a 15 min period prior to exposing the rats to HXC. After the rat was placed in the chamber, it usually explores the new environment for about 15–20 min and then would usually lay still, periodically grooming and occasionally sniffing the air. As such, at the time the hypoxic gas was delivered to the chambers, the rats were awake and resting quietly. The behavior of the rats did not change appreciably upon delivery of the HXC. The occasional rat explored the chamber for 5–10 s or groomed for 2–5 s. The rats were exposed to five 5-min episodes of a poikilocapnic hypoxic ($10\%$ O2, $90\%$ N2) gas challenge each separated by 15 min of room-air. For each ventilatory parameter, data points collected during every 15 s epoch were averaged for each rat for graphing and analyses. The maximal values during each HXC did not necessarily occur during the same 15 s epoch in each rat, and so the maximal values obtained by each rat were also collected. ## Data analysis The directly recorded and arithmetically-derived parameters (1 min bins) were taken for statistical analyses. The Pre hypoxic gas challenge 1 min bins excluded occasional marked deviations from resting values due to abrupt movements by the rats, such as grooming or sniffing. The exclusions ensured accurate determination of baseline parameters. All data are presented as mean ± SEM and were evaluated using one-way and two-way ANOVA followed by Bonferroni corrections for multiple comparisons between means using the error mean square terms from each ANOVA analysis (Wallenstein et al., 1980; Ludbrook, 1998; McHugh, 2011) as detailed previously (Getsy et al., 2021a; Getsy et al., 2021b). A $p \leq 0.05$ value denoted the initial level of statistical significance that was modified according to the number of comparisons between means as described by Wallenstein et al. [ 1980]. The modified t-statistic is t = (mean group 1—mean group 2)/[s x (1/n1 + 1/n2)$\frac{1}{2}$] where s2 = the mean square within groups term from the ANOVA (the square root of this value is used in the modified t-statistic formula) and n1 and n2 are the number of rats in each group under comparison. Based on an elementary inequality called Bonferroni’s inequality, a conservative critical value for modified t-statistics obtained from tables of t-distribution, using a significance level of P/m, where m is the number of comparisons between groups to be performed (Winer, 1971). The degrees of freedom are those for the mean square for within group variation from the ANOVA table. In the majority of situations, the critical Bonferroni value cannot be found in conventional tables of the t-distribution, but can be approximated from tables of the normal curve by t* = z + (z + z3)/4n, with n being the degrees of freedom and z being the critical normal curve value for P/m (Wallenstein et al., 1980; Ludbrook, 1998; McHugh, 2011). Wallenstein et al. [ 1980] first demonstrated that the Bonferroni procedure is preferable for general use since it is easy to apply, has the widest range of applications, and provides critical values that are lower than those of other procedures when the investigator can limit the number of comparisons and will be slightly larger than those of other procedures if many comparisons are made. As mentioned, a value of $p \leq 0.05$ was taken as the initial level of statistical significance (Wallenstein et al., 1980; Ludbrook, 1998; McHugh, 2011 and statistical analyses were performed with the aid of GraphPad Prism software (GraphPad Software, Inc., La Jolla, CA). ## Resting ventilatory values A summary of the numbers of rats in the SHAM and GGNX groups, and their ages and body weights are provided in Supplementary Table S2. There were no between-group differences in the ages or body weights ($p \leq 0.05$ for both comparisons). The baseline (Pre-HX challenge) values recorded in the SHAM and GGNX rats are also summarized in Supplementary Table S2. There were no between-group differences for most of the parameters ($p \leq 0.05$, for all comparisons). However, resting Freq was lower in GGNX rats than in SHAM rats and this was associated with higher Te and EEP values in GGNX rats than in SHAM rats. In addition, the PEF/PIF quotient was higher in the GGNX rats than in SHAM rats. Finally, although NEBI was lower in the GGNX rats due to their lower Freq values, the NEBI/Freq quotient was similar in GGNX and SHAM rats. ## Freq, TV and MV responses Freq values in SHAM and GGNX rats before and during five HXC ($10\%$ O2, $90\%$ N2) each separated by 15 min of room-air are shown in Figure 2A. Each HXC elicited robust increases in Freq that rapidly declined upon return to room-air in the SHAM and GGNX rats. The initial (1–90 s) responses to the first HXC (HXC 1) were similar in both groups Figure 2B whereas the initial responses to HXC 2-5 were markedly smaller in GGNX rats. The total responses recorded over the first 90 s Figure 2C and the entire 5 min Figure 2D of HXC 2-5 were smaller in GGNX rats than SHAM rats. TV responses: TV values in SHAM and GGNX rats before and during five HXC ($10\%$ O2, $90\%$ N2) each separated by 15 min of room-air are shown in of Figure 3A. Each HXC elicited robust increases in TV that rapidly declined to below baseline levels upon return to room-air in SHAM and GGNX rats. It was evident that the increases in TV in GGNX rats were higher than SHAM rats at the later phases of HXC 1-5. The initial responses (1–90 s) during HXC 1-5 were smaller in GGNX rats than SHAM rats Figure 3B. The total responses recorded over the first 90 s of HXC 1-5 were smaller in GGNX rats Figure 3C whereas the responses recorded over the entire 5 min of HXC 2-5 were similar in the GGNX and SHAM rats Figure 3D. MV responses: MV values in SHAM and GGNX rats before and during five HXC ($10\%$ O2, $90\%$ N2) each separated by 15 min of room-air are shown in of Figure 4A. Each HXC elicited robust increases in MV that rapidly declined to and often below baseline levels upon return to room-air in the SHAM and GGNX rats. The initial (1–90 s) responses during HXC 1-5 were smaller in GGNX rats than in SHAM rats Figure 4B. The total responses recorded over the first 90 s of HXC 2-5 were markedly smaller in GGNX rats than in SHAM rats Figure 4C. The total responses recorded over the entire 5 min of HXC 1-5 were similar in the GGNX and SHAM rats Figure 4D. **FIGURE 2:** *(A) Frequency of breathing in sham-operated (SHAM) rats and in rats with bilateral ganglioglomerular nerve transection (GGNX) before (Pre) and during five hypoxic (HX, 10% O2, 90% N2) gas challenges, each separated by 15 min of room-air (RA). (B) Arithmetic changes in frequency during the first 90 s of HX gas challenge. (C) Total changes in frequency during the first 90 s of HX gas challenge. (D) Total changes in frequency during the entire 5 min of HX gas challenge. The SHAM group had 10 rats. The GGNX group had 12 rats. The data are presented as mean ± SEM. *p < 0.05, significant response. † p < 0.05, GGNX rats versus SHAM rats.* **FIGURE 3:** *(A) Tidal volume in sham-operated (SHAM) rats and in rats with bilateral ganglioglomerular nerve transection (GGNX) before (Pre) and during five hypoxic (HX, 10% O2, 90% N2) gas challenges, each separated by 15 min of room-air (RA). (B) Arithmetic changes in tidal volume during the first 90 s of HX gas challenge. (C) Total changes in tidal volume during the first 90 s of HX gas challenge. (D) Total changes in tidal volume during the entire 5 min of HX gas challenge. The SHAM group had 10 rats. The GGNX group had 12 rats. The data are presented as mean ± SEM. *p < 0.05, significant response. † p < 0.05, GGNX rats versus SHAM rats.* **FIGURE 4:** *(A) Minute ventilation in sham-operated (SHAM) rats and in rats with bilateral ganglioglomerular nerve transection (GGNX) before (Pre) and during five hypoxic (HX, 10% O2, 90% N2) gas challenges, each separated by 15 min of room-air (RA). (B) Arithmetic changes in minute ventilation during the first 90 s of HX gas challenge. (C) Total changes in minute ventilation during the first 90 s of HX gas challenge. (D) Total changes in minute ventilation during the entire 5 min of HX gas challenge. The SHAM group had 10 rats. The GGNX group had 12 rats. The data are presented as mean ± SEM. *p < 0.05, significant response. † p < 0.05, GGNX rats versus SHAM rats.* The arithmetic changes Freq, TV and MV that occurred in the first 90 s following return to room-air after each HXC (i.e., RA1–RA5) are summarized in each of Supplementary Figures S8A, S9A, S10A, respectively. The return to room-air elicited generally transient increases in Freq, TV and MV. The increases in Freq tended to be smaller in GGNX rats, whereas the increases in TV were larger in the GGNX rats than the SHAM rats, which together resulted in similar increases in MV in the two groups. As shown in each of Supplementary Figures S8B, S9B, S10B, the total increases in Freq that occurred over the first 90 s of RA1 and RA2 were smaller in GGNX rats, whereas the total increases in TV in GGNX rats for RA1-RA5 were markedly different from the decreased responses seen in SHAM rats. As a result, the increases in MV were similar between SHAM and GGNX rats except for RA4 in which the increase in MV was greater in GGNX rats. ## Freq, TV and MV responses in the later period of HXC 1-5 The arithmetic changes in Freq, TV and MV during the later period of HXC 1-5 are presented in Figures 5A–C, respectively). The data confirm that the initial increases in Freq, TV and MV were smaller in GGNX rats than in SHAM rats and that the increases in TV (but not Freq or MV) were greater at the later stages of HXC 1-5 in GGNX rats than in SHAM rats. As such, the total increases in Freq between 2.5 and 5 min of HXC 1-2 were smaller in GGNX rats than in SHAM rats Figure 5D, whereas the increases in TV recorded between 2.5 and 5 min of HXC 1-5 were greater in GGNX rats than in SHAM rats Figure 5E. As a result, the total increases in MV recorded between 2.5 and 5 min of HXC 1-5 were similar in GGNX and SHAM rats Figure 5F. **FIGURE 5:** *Arithmetic changes in the frequency of breathing (A), tidal volume (B) and minute ventilation (C) in sham-operated (SHAM) rats and in rats with bilateral ganglioglomerular nerve transection (GGNX) during the five hypoxic (HX, 10% O2, 90% N2) gas challenges. Total changes in frequency of breathing (D), tidal volume (E) and minute ventilation (F) during the final 2.5 min of HX gas challenge. The SHAM group had 10 rats. The GGNX group had 12 rats. The data are presented as mean ± SEM. *p < 0.05, significant response. † p < 0.05, GGNX rats versus SHAM rats.* ## Ti responses Ti values in SHAM and GGNX rats before and during five HXC ($10\%$ O2, $90\%$ N2) each separated by 15 min of room-air are shown in Figure 6A. Each HXC elicited robust decreases in Ti that rapidly returned to baseline levels upon return to room-air in SHAM and GGNX rats. The initial (1–90 s) responses during HXC 2-5 were smaller in GGNX rats than in SHAM rats Figure 6B. The total responses recorded over the first 90 s of HXC 2-3 were markedly smaller in the GGNX rats than SHAM rats Figure 6C, whereas the total responses recorded over the entire 5 min of HXC 1-5 were smaller in GGNX rats than SHAM rats for HXC 2 only Figure 6D. **FIGURE 6:** *(A) Inspiratory time in sham-operated (SHAM) rats and in rats with bilateral ganglioglomerular nerve transection (GGNX) before (Pre) and during five hypoxic (HX, 10% O2, 90% N2) gas challenges, each separated by 15 min of room-air (RA). (B) Arithmetic changes in inspiratory time during the first 90 s of HX gas challenge. (C) Total changes in inspiratory time during the first 90 s of HX gas challenge. (D) Total changes in inspiratory time during the entire 5 min of HX gas challenge. The SHAM group had 10 rats. The GGNX group had 12 rats. The data are presented as mean ± SEM. *p < 0.05, significant response. † p < 0.05, GGNX rats versus SHAM rats.* ## Te responses Te values in SHAM and GGNX rats before and during five HXC ($10\%$ O2, $90\%$ N2) each separated by 15 min of room-air are shown in of Figure 7A. Each HXC elicited robust decreases in Te Figure 7A that rapidly returned to and greatly exceeded baseline levels upon return to room-air in SHAM and GGNX rats. The initial (1–90 s) responses during HXC 1-5 were similar in SHAM and GGNX rats. The total responses recorded over the first 90 s Figure 7C or the entire 5 min Figure 7D of HXC 1-5 were similar in SHAM and GGNX rats. **FIGURE 7:** *(A) Expiratory time in sham-operated (SHAM) rats and in rats with bilateral ganglioglomerular nerve transection (GGNX) before (Pre) and during five hypoxic (HX, 10% O2, 90% N2) gas challenges, each separated by 15 min of room-air (RA). (B) Arithmetic changes in expiratory time during the first 90 s of HX gas challenge. (C) Total changes in expiratory time during the first 90 s of HX gas challenge. (D) Total changes in expiratory time during the entire 5 min of HX gas challenge. The SHAM group had 10 rats. The GGNX group had 12 rats. The data are presented as mean ± SEM. *p < 0.05, significant response. † p < 0.05, GGNX rats versus SHAM rats.* ## Te/Ti responses Te/Ti values in SHAM and GGNX rats before and during five HXC ($10\%$ O2, $90\%$ N2) each separated by 15 min of room-air are shown in of Supplementary Figure S2A. Each HXC elicited robust decreases in Te/Ti that rapidly returned to and greatly exceeded baseline upon return to room-air in SHAM and in GGNX rats. The initial (1–90 s) responses during HXC 1-5 were similar in SHAM and GGNX rats Supplementary Figure S2B. The total decrease in Te/Ti over the first 90 s Supplementary Figure S2C was markedly greater in GGNX rats compared to SHAM rats in HXC 2 and similar in HXC 1, 3, 4 and 5. The total responses over the entire 5 min Supplementary Figure S2D of HXC 1-5 were similar in SHAM and GGNX rats. ## EIP responses EIP values in SHAM and GGNX rats before and during five HXC ($10\%$ O2, $90\%$ N2) each separated by 15 min of room-air are shown in Supplementary Figure S3A. Each HXC elicited robust decreases in EIP in the SHAM and GGNX rats that rapidly returned to baseline levels upon return to room-air. The initial (1–90 s) responses during HXC 1-5 were similar in SHAM and GGNX rats (Supplementary Figure S3B). The total responses recorded over the first 90 s of HXC 1-5 were similar in SHAM and GGNX rats Supplementary Figure S3C. The total decreases in EIP recorded over the entire 5 min of HXC 1, 3 and 4 were greater in GGNX than SHAM rats Supplementary Figure S3D. ## EEP responses EEP values in SHAM and GGNX rats before and during five HXC ($10\%$ O2, $90\%$ N2) each separated by 15 min of room-air are shown in of Figure 8A. Each HXC elicited minimal changes in EEP in SHAM rats, but sustained increases in GGNX rats. These responses were followed by rapid increases in EEP upon return to room-air in SHAM and GGNX rats. The initial (1–90 s) responses during HXC 1-5 were similar in SHAM and GGNX rats Figure 8B. The total increases recorded over the first 90 s of HXC 1-2 were greater in GGNX than SHAM rats Figure 8C. The total increases recorded over the entire 5 min of HXC 1-5 were greater in GGNX rats than SHAM rats Figure 8D. **FIGURE 8:** *(A) End Expiratory Pause (EEP) values in sham-operated (SHAM) rats and in rats with bilateral ganglioglomerular nerve transection (GGNX) before (Pre) and during five hypoxic (HX, 10% O2, 90% N2) gas challenges, each separated by 15 min of room-air (RA). (B) Arithmetic changes in EEP during the first 90 s of HX gas challenge. (C) Total changes in EEP during the first 90 s of HX gas challenge. (D) Total changes in EEP during the entire 5 min of HX gas challenge. The SHAM group had 10 rats. The GGNX group had 12 rats. The data are presented as mean ± SEM. *p < 0.05, significant response. † p < 0.05, GGNX rats versus SHAM rats.* ## PIF responses PIF values in SHAM and GGNX rats before and during five HXC ($10\%$ O2, $90\%$ N2) each separated by 15 min of room-air are shown in of Figure 9A. Each HXC elicited pronounced increases in PIF in the SHAM and GGNX rats that rapidly returned to baseline levels upon return to room-air. The initial (1–90 s) responses during HXC 2-5 were similar markedly smaller in GGNX rats as compared to SHAM rats Figure 9B. The total responses recorded over the first 90 s of HXC 2-5 were smaller in SHAM rats compared to GGNX rats Figure 9C, whereas the total increases recorded over the entire 5 min of HXC 1-5 were similar in SHAM and GGNX Figure 9D. **FIGURE 9:** *(A) Peak Inspiratory Flow (Peak Insp Flow; PIF) values in sham-operated (SHAM) rats and in rats with bilateral ganglioglomerular nerve transection (GGNX) before (Pre) and during five hypoxic (HX, 10% O2, 90% N2) gas challenges, each separated by 15 min of room-air (RA). (B) Arithmetic changes in PIF during the first 90 s of HX gas challenge. (C) Total changes in PIF during the first 90 s of HX gas challenge. (D) Total changes in PIF during the entire 5 min of HX gas challenge. The SHAM group had 10 rats. The GGNX group had 12 rats. The data are presented as mean ± SEM. *p < 0.05, significant response. † p < 0.05, GGNX rats versus SHAM rats.* ## PEF responses PEF values in SHAM and GGNX rats before and during five HXC ($10\%$ O2, $90\%$ N2) each separated by 15 min of room-air are shown in of Figure 10A. Each HXC elicited pronounced increases in PEF in the SHAM and GGNX rats that rapidly returned to baseline levels upon return to room-air. The initial (1–90 s) responses during HXC 1-5 were smaller in the GGNX rats than in the SHAM rats Figure 10B. The total responses recorded over the first 90 s of HXC 2-5 were smaller in GGNX than SHAM rats Figure 10C. The total increases recorded over the entire 5 min of HXC 3-5 were larger in GGNX and SHAM rats Figure 10D, with the differences being particularly pronounced during the second half of each 5 min HXC as seen in Figure 10A. **FIGURE 10:** *(A) Peak Expiratory Flow (Peak Exp Flow, PEF) values in sham-operated (SHAM) rats and in rats with bilateral ganglioglomerular nerve transection (GGNX) before (Pre) and during five hypoxic (HX, 10% O2, 90% N2) gas challenges, each separated by 15 min of room-air (RA). (B) Arithmetic changes in PEF during the first 90 s of HX gas challenge. (C) Total changes in PEF during the first 90 s of HX gas challenge. (D) Total changes in PEF during the entire 5 min of HX gas challenge. The SHAM group had 10 rats. The GGNX group had 12 rats. The data are presented as mean ± SEM. *p < 0.05, significant response. † p < 0.05, GGNX rats versus SHAM rats.* ## PEF/PIF responses PEF/PIF values in the SHAM and GGNX rats before and during five HXC ($10\%$ O2, $90\%$ N2) each separated by 15 min of room-air are shown in Supplementary Figure S4A. Each HXC elicited pronounced increases in PEF/PIF in SHAM and GGNX rats that were far larger in GGNX rats from about 2 min onward, and which rapidly returned to baseline levels upon return to room-air. PEF/PIF changed minimally over the initial (1–90 s) phase of HXC 1 in the SHAM rats, whereas it trended below baseline in the GGNX rats Supplementary Figure S4B. The initial increases in PEF/PIF during HXC 2 tended to be greater in the GGNX rats, whereas the PEF/PIF ratio tended to be smaller in in the GGNX rats for HXC 3–5. The total responses recorded over the first 90 s of HXC 1 were smaller in the GGNX rats than the SHAM rats, whereas the responses were greater in the GGNX rats than in the SHAM rats for HXC 2 Supplementary Figure S4C. The total increases recorded over the entire 5 min of HXC 2-5 were substantially larger in the GGNX rats as compared to the SHAM rats Supplementary Figure S4D. ## EF50 responses EF50 values in SHAM and GGNX rats before and during five HXC ($10\%$ O2, $90\%$ N2) each separated by 15 min of room-air are shown in of Figure 11A. Each HXC elicited pronounced increases in EF50 in the SHAM and GGNX rats that rapidly declined toward and often below baseline levels upon return to room-air in the SHAM and GGNX rats. The initial (1–90 s) responses during HXC 1-5 were smaller in GGNX rats than in SHAM rats Figure 11B. The total responses recorded over the first 90 s of HXC 2-5 were smaller in GGNX than SHAM rats Figure 11C. The total increases recorded over the entire 5 min of HXC 2-5 were smaller in GGNX rats than in SHAM rats Figure 11D. **FIGURE 11:** *(A) EF50 values in sham-operated (SHAM) rats and in rats with bilateral ganglioglomerular nerve transection (GGNX) before (Pre) and during five hypoxic (HX, 10% O2, 90% N2) gas challenges, each separated by 15 min of room-air (RA). (B) Arithmetic changes in EF50 during the first 90 s of HX gas challenge. Total changes in EF50 during the first 90 s (C) and over the entire 5 min (D) of HX gas challenge. The SHAM group had 10 rats. The GGNX group had 12 rats. The data are presented as mean ± SEM. *p < 0.05, significant response. † p < 0.05, GGNX rats versus SHAM rats.* The arithmetic changes in EF50 that occurred over the first 90 s following return to room-air after each HXC (RA1-RA5) are shown in Supplementary Figure S19. Upon return to room-air, EF50 values rose initially and then fell below baseline values in a similar trend in both the SHAM and GGNX groups Supplementary Figure S19A such that the total changes during the first 90 s Supplementary Figure S19B and 5 min Supplementary Figure S19C were in general similar in both groups. ## Relaxation Time responses Relaxation time values in the SHAM and GGNX rats before and during five HXC ($10\%$ O2, $90\%$ N2) each separated by 15 min of room-air are shown in of Supplementary Figure S5A. Each HXC elicited transient decreases in relaxation time in the SHAM and GGNX rats, whereas the return to room-air resulted in dramatic increases in relaxation time in SHAM and GGNX rats. The initial (1–90 s) decreases in relaxation times during HXC 1-5 were similar in the SHAM and GGNX rats with a few time-points at which the responses were greater in the GGNX rats Supplementary Figure S5B. The total responses recorded over the first 90 s Supplementary Figure S5C and during the entire 5 min of HXC 1-5 were similar in the SHAM and GGNX rats Supplementary Figure S5D. ## Apneic pause responses Apneic pause values in SHAM and GGNX rats before and during five HXC ($10\%$ O2, $90\%$ N2) each separated by 15 min of room-air are shown in Supplementary Figure S6A. Each HXC elicited minimal changes in apneic pause in the SHAM and GGNX rats, whereas the return to room-air caused transient increases in apneic pause in SHAM and GGNX rats that were especially evident following HXC 4 and HXC 5. The initial (1–90 s) changes in apneic pause during HXC 1-5 were similar in the SHAM and GGNX rats with a few time-points at which the responses were smaller in the GGNX rats Supplementary Figure S6B. The total responses recorded over the first 90 s Supplementary Figure S6C and during the entire 5 min of HXC 1-5 were similar in the SHAM and GGNX rats Supplementary Figure S6D. The arithmetic changes in apneic pause values that occurred over the first 90 s following return to room-air after each HXC (RA1-RA5) are shown in Supplementary Figure S21. Upon return to room-air, apneic pause values gradually rose above baseline values in a similar trend in the SHAM and GGNX rats Supplementary Figure S21A such that the total changes during the first 90 s Supplementary Figure S21B and the first 5 min Supplementary Figure S21C were in general similar in the two groups. ## Inspiratory Drive responses Inspiratory Drive values in SHAM and GGNX rats before and during five HXC ($10\%$ O2, $90\%$ N2) each separated by 15 min of room-air are shown in Figure 12A. Each HXC elicited pronounced increases in Inspiratory Drive in SHAM and GGNX rats that rapidly returned to baseline values upon return to room-air. The initial (1–90 s) increases in Inspiratory Drive during HXC 1-5 were markedly smaller in the GGNX rats than in the SHAM rats Figure 12B. The total responses recorded over the first 90 s of HXC 1-5 were smaller in the GGNX than SHAM rats Figure 12C. The total responses recorded during the entire 5 min of HXC 1-5 were similar in the SHAM and GGNX rats Figure 12D. **FIGURE 12:** *(A) Inspiratory Drive (Insp Drive) values in sham-operated (SHAM) rats and in rats with bilateral ganglioglomerular nerve transection (GGNX) before (Pre) and during five hypoxic (HX, 10% O2, 90% N2) gas challenges, each separated by 15 min of room-air (RA). (B) Arithmetic changes in Inspiratory Drive during the first 90 s of HX gas challenge. Total changes in Inspiratory Drive during the first 90 s (C) and over the entire 5 min (D) of HX gas challenge. The SHAM group had 10 rats. The GGNX group had 12 rats. The data are presented as mean ± SEM. *p < 0.05, significant response. † p < 0.05, GGNX rats versus SHAM rats.* ## Expiratory drive responses Expiratory Drive values in SHAM and GGNX rats before and during five HXC ($10\%$ O2, $90\%$ N2) each separated by 15 min of room-air are shown in Figure 13A. Each HXC elicited pronounced increases in Expiratory Drive in SHAM and GGNX rats that rapidly fell at or below baseline values upon return to room-air. The initial (1–90 s) increases in Expiratory Drive during HXC 1-5 were markedly smaller in GGNX rats than in SHAM rats Figure 13B. The total responses recorded over the first 90 s of HXC 1-5 were smaller in GGNX than SHAM rats Figure 13C. The total responses recorded during the entire 5 min of HXC 1-5 were similar in the SHAM and GGNX rats Figure 13D. **FIGURE 13:** *(A) Expiratory Drive (Exp Drive) values in sham-operated (SHAM) rats and in rats with bilateral ganglioglomerular nerve transection (GGNX) before (Pre) and during five hypoxic (HX, 10% O2, 90% N2) gas challenges, each separated by 15 min of room-air (RA). (B) Arithmetic changes in Expiratory Drive during the first 90 s of HX gas challenge. Total changes in Expiratory Drive during the first 90 s (C) and over the entire 5 min (D) of HX gas challenge. The SHAM group had 10 rats. The GGNX group had 12 rats. The data are presented as mean ± SEM. *p < 0.05, significant response. † p < 0.05, GGNX rats versus SHAM rats.* The arithmetic changes in expiratory drive values that occurred over the first 90 s following return to room-air after each HXC (RA1-RA5) are shown in Supplementary Figure S23. Upon return to room-air, expiratory drive values initially rose above baseline values and then returned toward and sometimes below baseline values in a similar trend in the SHAM and GGNX rats Supplementary Figure S23A such that the total changes during the first 90 s Supplementary Figure S23B and the first 5 min Supplementary Figure S23C were in general similar in the two groups, except for RA1 during which the increases were significantly smaller in GGNX rats. ## NEBI responses NEBI values in SHAM and GGNX rats before and during five HXC ($10\%$ O2, $90\%$ N2) each separated by 15 min of room-air are shown in Figure 14A. Each HXC elicited relatively minor increases in NEBI in SHAM and GGNX rats that transiently increased upon return to room-air in the SHAM rats, but not in the GGNX rats. The initial (1–90 s) increases in NEBI during HXC 1-5 were in general similar in SHAM and GGNX rats with a few time-points in which the responses were smaller in the GGNX rats Figure 14B. The total increases recorded over the first 90 s of HXC 1-5 were similar in the SHAM and GGNX rats Figure 14C. The total increases recorded during the entire 5 min of HXC 1-5 were greater in the GGNX rats than SHAM rats Figure 14D. **FIGURE 14:** *(A) Non-eupneic breathing index (NEBI) values in sham-operated (SHAM) rats and in rats with bilateral ganglioglomerular nerve transection (GGNX) before (Pre) and during five hypoxic (HX, 10% O2, 90% N2) gas challenges, each separated by 15 min of room-air (RA). (B) Arithmetic changes in NEBI during the first 90 s of HX gas challenge. Total changes in NEBI during the first 90 s (C) and over the entire 5 min (D) of HX gas challenge. The SHAM group had 10 rats. The GGNX group had 12 rats. Data are presented as mean ± SEM. *p < 0.05, significant response. † p < 0.05, GGNX rats versus SHAM rats.* ## NEBI/Freq responses NEBI/Freq values in SHAM and GGNX rats before and during five HXC ($10\%$ O2, $90\%$ N2) each separated by 15 min of room-air are presented in Supplementary Figure S7A. Each HXC elicited minor increases in NEBI/Freq in SHAM and GGNX rats that rose upon return to room-air in SHAM rats, but not in GGNX rats. The initial (1–90 s) increase in NEBI/Freq during HXC 2-5 were greater in GGNX than in SHAM rats Supplementary Figure S7B, C. The total increase in NEBI/Freq recorded during the entire 5 min of HXC 1-5 in GGNX rats contrasted to the decrease in NEBI/Freq (HXC 1 to HXC 5) seen in the SHAM rats Supplementary Figure S7D. The ventilatory responses that occurred upon return to room-air will be detailed in the next section, but special mention is made of the changes in NEBI. The initial (1–90 s) arithmetic increases in NEBI upon return to room-air following HXC 1–5 (RA1-RA5) are shown in Figure 15A. The rapid and substantial increase in NEBI observed in SHAM rats was largely absent in the GGNX rats. The total increase in NEBI that occurred during the initial 90 s Figure 15B and first 5 min Figure 15C of RA-1-RA5 were markedly smaller in GGNX rats than in SHAM rats. **FIGURE 15:** *(A) Arithmetic changes in non-eupneic breathing index (NEBI) from Pre-values in sham-operated (SHAM) rats and in rats with bilateral ganglioglomerular nerve transection (GGNX) during the first 90 s upon return to room-air (RA1-RA5), following the 5 hypoxic (HX, 10% O2, 90% N2) gas challenges. (B) Total arithmetic changes in NEBI during the first 90 s of the return to room-air phases (RA1-RA5). (C) Total arithmetic changes in NEBI during the first 5 min of the return to room-air phases (RA1-RA5). The SHAM group had 10 rats. The GGNX group had 12 rats. The data are presented as mean ± SEM. *p < 0.05, significant response. † p < 0.05, GGNX rats versus SHAM rats.* The arithmetic changes in NEBI/Freq values that occurred over the first 90 s following return to room-air after each HXC (RA1-RA5) are shown in Supplementary Figure S24. Upon return to room-air, NEBI/Freq values gradually rose to levels substantially above baseline values in SHAM rats, whereas these increases were in general smaller in GGNX rats Supplementary Figure S24A. As such, the total changes during the first 90 s Supplementary Figure S24B and the first 5 min Supplementary Figure S24C were smaller in GGNX than in SHAM rats. ## Ti, Te and Te/Ti responses The arithmetic changes Ti, Te and Te/Ti that occurred over the first 90 s upon return to room air after each HXC (RA1–RA5) are summarized in each of Supplementary Figures S11A, S12A, S13A, respectively. The return to room-air elicited decreases in Ti, initial decreases followed by increases in Te, and increases in Te/Ti. The changes in Ti, Te and Te/Ti were most often similar in the SHAM and GGNX rats and this was reflected in the total changes in Ti, Te and Te/Ti during the first 90 s Supplementary Figures S11B, S12B, S13B or first 5 min of return to room-air Supplementary Figures S11C, S12C, S13C. ## EIP and EEP responses The arithmetic changes in EIP and EEP that occurred over the first 90 s following return to room-air after each HXC (RA1-RA5) are summarized in each of Supplementary Figures S14A and S15A, respectively. The return to room-air elicited decreases in EIP that tended to be greater in the GGNX rats, and increases in EEP that were substantially greater in the GGNX rats. The total decreases in EIP during the first 90 s Supplementary Figure S14B or first 5 min Supplementary Figure S14C were substantially greater in the GGNX rats than in the SHAM rats. The total increases in EEP during the first 90 s Supplementary Figures S15B or first 5 min Supplementary Figures S15C were substantially greater in the GGNX rats than in the SHAM rats. ## PIF, PEF and PEF/PIF responses The arithmetic changes in PIF, PEF and PEF/PIF ratio that occurred over the first 90 s following return to room-air after each HXC (RA1-RA5) are shown in each Supplementary Figures S16, S17, S18, respectively. As can be seen in Supplementary Figure S16, the return to room-air elicited increases in PIF that were similar in magnitude in SHAM and GGNX rats Supplementary Figure S16A, and the total responses recorded over 90 s and 5 min were also overall similar between the groups Supplementary Figure S16B, C. As seen in Supplementary Figure S17, the return to room-air elicited increases in PEF that were greater in GGNX rats compared to SHAM rats recorded over 90 s and 5 min Supplementary Figures S17A–C. As a result, the increases in PEF/PIF (Supplementary Figure S18) were larger in the GGNX rats compared to the SHAM rats for RA2-RA5 recorded over 90 s and 5 min (Supplementary Figures S18B, C). ## Relaxation time responses The arithmetic changes in relaxation time that occurred over the first 90 s after return to room-air after each HXC (RA1-RA5) are shown in Supplementary Figure S20. Upon return to room-air, relaxation time values fell initially and then often rose above baseline values in a similar trend in the SHAM and GGNX groups Supplementary Figure S20A such that the total changes during the first 90 s Supplementary Figure S20B and 5 min Supplementary Figure S20C were in general similar in the two groups, except for those of RA1, in which the total decreases in relaxation time seen in the SHAM rats were markedly less over the first 90 s and converted to a net increase over the first 5 min in the GGNX rats. ## Inspiratory drive responses Arithmetic changes in inspiratory drive values that occurred over the first 90 s upon return to room-air after each HXC (RA1-RA5) are shown in Supplementary Figure S22. Upon return to room-air, inspiratory drive values initially rose above baseline values and then mostly returned toward baseline values in a similar fashion in the SHAM and GGNX rats Supplementary Figure S22A such that the total changes during the first 90 s Supplementary Figure S22B and the first 5 min Supplementary Figure S22C were in general similar in the two groups, except for RA3 and RA4 during which the increases were greater in the GGNX rats. It is noted the the increases from baseline in inspiratory drive observed during the 5 min period for RA5 being significantly smaller in the GGNX rats compared to the SHAM rats (Supplementary Figure S22C). ## Discussion There is wide-spread agreement that post-ganglionic sympathetic nerves provide extensive innervation of the vasculature within the carotid body (McDonald and Mitchell, 1975; Ichikawa, 2002). The majority of evidence suggests that there are 2 subtypes of primary glomus cells in rats, known as type 1A and type 1B. Type 1A and type 1B primary glomus cells do not receive post-ganglionic sympathetic innervation, however, type 1A, but not type 1B glomus cells, receive innervation from petrosal ganglion chemoafferents (McDonald and Mitchell, 1975; McDonald, 1983a; McDonald, 1983b). There is no evidence that sustentacular (type 2) glomus cells receive afferent or efferent innervation (McDonald and Mitchell, 1975; McDonald, 1983a; McDonald, 1983b; Ichikawa, 2002). On the basis of the substantial evidence that GGNs innervate their ipsilateral carotid bodies (Biscoe and Purves, 1967; Zapata et al., 1969; Bowers and Zigmond, 1979; Brattström, 1981a; McDonald and Mitchell, 1981; McDonald, 1983a; McDonald, 1983b; Verna et al., 1984; Torrealba and Claps, 1988; Ichikawa, 2002; Asamoto, 2004; Savastano et al., 2010), we are working on the key assumption that bilateral transection of the GGN (GGNX) leads to alterations in the functions of primary (type 1) glomus cells and vasculature, and perhaps sustenacular (type 2) glomus cells and/or chemoafferent nerve terminals. A decrease in GGN input to the carotid bodies may result from a loss of function of pre-ganglion neurons and/or post-ganglionic cell bodies in the SCG as a result of physical insults and/or disease processes including inflammatory diseases (Rudik, 1969; Camargos and Machado, 1988; Laudanna et al., 1998; Hanani et al., 2010), prion diseases (Liberski, 2019), herpes simplex virus infection (Price and Schmitz, 1979), acquired immunodeficiency syndrome (Chimelli et al., 2002), metastatic states (Moubayed et al., 2017), hyperthyroidism (Matano et al., 2014); amyotrophic lateral sclerosis (Kandinov et al., 2013), neuro-endocrine disorders in females (Pirard, 1954); myocardial ischemia (Liu et al., 2013; Cheng et al., 2018), obstructive jaundice (Chen et al., 2021); Duchenne muscular dystrophy (De Stefano et al., 2005), Alzheimer’s disease (Jengeleski et al., 1989; Alzoubi et al., 2011), amyloid precursor protein deficiency (Cai et al., 2016), Lewy body disease and Parkinson’s disease (Del Tredici et al., 2010), Mecp2 deficiency (Roux et al., 2008), lead exposure (Zhu et al., 2019), hypercholesterolemia Chumasov et al., 1994), hypertension (Tang et al., 1995a; Tang et al., 1995b; Tang et al., 1995c), direct ischemic challenge (Kilic et al., 2019), multiple systems neuropathy (McGorum et al., 2015), diabetic neuropathy (Minker et al., 1978; Bitar et al., 1997; Cameron and Cotter, 2001; Li et al., 2017), small fiber neuropathies (Han et al., 2012) and damage to peripheral axons in the CSC (Shin et al., 2014; Niemi et al., 2017). Moreover, a loss of activity of post-ganglionic SCG neurons could results from damage to T1-T4 regions of the spinal cord which contain pre-ganglionic cell bodies that innervate the SCG (Rando et al., 1981; McDonald, 1983a; McDonald, 1983b; Tang et al., 1995a; Tang et al., 1995b; Tang et al., 1995c; Llewellyn-Smith et al., 1998). Such thoracic spinal cord damage causes an array of ventilatory impairments (Takeda et al., 1977; Sugarman, 1985; Oku et al., 1997; Roth et al., 1997; Ayas et al., 1999; Forster, 2003; Bolser et al., 2009; Schilero et al., 2009; Bascom et al., 2016; Berlowitz, et al., 2016; Hachmann et al., 2017), as well as sleep disordered breathing (Braun et al., 1982; Sankari et al., 2014a; Sankari et al., 2014b; Bascom et al., 2015; Sankari et al., 2019). It may be expected that removal of the GGNX input to the carotid bodies will lead to changes in glomus cell-chemoafferent nerve activity that result in altered baseline ventilatory status. Previous evidence has shown that an increase in activity of the carotid body-carotid sinus nerve complex leads to increases in Freq, TV and MV (Eldridge, 1972; Eldridge, 1976; Eldridge, 1978; Marek et al., 1985; Engwall et al., 1991; Katayama et al., 2019). In contrast, silencing of the carotid body complex, such as under hyperoxic challenge, has a relatively minor but distinct decrease in Freq, although variable effects on TV and therefore MV have been reported (Palecek and Chválová, 1976; Cardenas and Zapata, 1983; Arieli, 1994; Strohl et al., 1997; Nakano et al., 2002; Souza et al., 2018). As such, key findings of the present study were that although most resting ventilatory values was similar in SHAM and GGNX rats, resting Freq was lower in GGNX rats than in the SHAM rats and this was associated with higher Te and EEP values in GGNX rats than in SHAM rats. These findings certainly suggest that the loss of GGNX input to the carotid body complex diminished carotid body activity. Since this decrease in Freq was expressed under room-air in quietly resting rats, it is unlikely that the decrease in Freq was caused indirectly by increases in blood flow to the carotid body microvasculature since an increase in blood flow as a result of vasodilation due to the loss of GGN vasoconstrictor input is unlikely to change oxygenation status under resting/normoxic states. Contrarily, a decrease in blood flow would indirectly activate carotid body glomus cells (Llados and Zapata, 1978; Majcherczyk et al., 1980; Matsumoto et al., 1981; Yokoyama et al., 2015) to promote Freq. As such, it would be tempting to assume that the loss of GGNX input to carotid body has altered signaling processes in primary glomus cells that leads to diminished neurotransmitter release and less activation of chemoafferent nerve terminals. Thus, the presumed decrease in carotid body function should mimic the changes in resting ventilatory parameters seen in rats with bilateral carotid sinus nerve (CSN) transection (CSNX). Supplementary Table S3 provides a qualitative assessment of the status of resting (Pre-challenge) ventilatory parameters in GGNX and CSNX rats compared to their respective sham-operated (SHAM) controls with the original Freq, TV and MV data for the CSNX rats coming from Getsy et al. [ 2020] and the remaining original unpublished data for the variables described for the CSNX rats. As can be seen, resting Freq was lower than the SHAM in GGNX and CSNX rats. Although resting TV was not diminished in GGNX or CSNX rats, the combined changes in Freq and TV resulted in a decrease in resting MV in CSNX, but not GGNX rats. The decreases in resting Freq in GGNX and CSNX rats were associated with increases in Te, but not changes in Ti. Other notable differences between GGNX and CSNX rats were that resting EEP, PEF/PIF were elevated, whereas NEBI was decreased in GGNX but not CSNX rats, and PEF and inspiratory drive were decreased, whereas NEBI/Freq was elevated in CSNX but not GGNX rats. It should be noted that resting values for Te, Te/Ti, EF50, relaxation time, apneic pause and expiratory drive were similar in GGNX and CSNX rats to those of their respective SHAM controls. Overall, it appears that GGNX elicits changes in carotid body function that mimic some but not all aspects of CSNX on resting ventilatory parameters. We are currently performing RNAseq and other studies to uncover the molecular changes that may result from GGNX. It is important to note that whereas the increases in Freq, TV and MV elicited by a 5 min HX challenge were certainly diminished in juvenile (P25) with bilateral CSNX, substantial responses still remained. Accordingly, other yet to be determined HX-sensitive, but carotid body-independent processes, that would not necessarily be under the control of the GGN input to the carotid bodies must exist (Getsy et al., 2020; Getsy et al., 2021a; Getsy et al., 2021b). This is important information that will hopefully help to frame our understanding of how GGNX modulates the HX-induced changes in ventilation. Despite being separated by 15 min of room-air, there were reactively modest, but clear, gradual diminutions in the total changes in many of the ventilatory parameters during the later HX challenges compared to the initial HX challenges. Whether this diminution in the later HX response reflects a true adaptation of carotid body-central signaling pathways or is due to some other internal (e.g., behavioral) adaptation remains to be determined. The ventilatory responses elicited by the five HX challenges in juvenile (P25) Sprague-Dawley SHAM rats included increases in Freq in which a substantial roll-off during the 5 min challenges only occurred during HX1. These increases in Freq were associated with decreases in Ti, Te and EIP, but minor changes in EEP. Why HX challenges were able to decrease the pause at the end of inspiration so that expiration started more rapidly instead of decreasing the pause at the end of expiration so that inspiration began as it would under normoxic states, is an unresolved question, but suggests that carotid body chemoafferent input to the brain preferentially controls the processes that regulate EIP. The HX1-HX5 challenges were associated with substantial increases in TV each of which displayed distinct roll-off during the 5 min HX challenges. Taken together, these changes in Freq and TV resulted in robust increases in MV. The HX challenges elicited robust increases in PIF, PEF, PEF/PIF and EF50 of which all except PIF displayed roll-off. The HX challenges also elicited robust increases in both inspiratory drive and expiratory drive with the increases in expiratory drive only displaying substantial roll-off. The above responses were associated with decreases in relaxation time that did not show roll-off and minor decreases in apneic pause that did not display roll-off. Importantly, we observed a substantial increase in NEBI during HX1, but minor changes in NEBI during HX2-HX5 and a minor increase in NEBI/Freq during HX1, but substantial decreases in NEBI/Freq during HX2-HX5. The later observations related to NEBI and Freq clearly suggest that the rats processed the first HX challenge differently from other challenges. Again, whether this represents adaptations in ventilatory signaling, changes in internal (behavioral) response to the HX challenges or interactions between these processes is an interesting unresolved issue. Moreover, we have no explanations as to why some of the ventilatory responses show roll-off, whereas others do not. However, it would seem that adaptations within the carotid bodies and resulting chemoafferent signals to the brain would result in the presence of or the lack of roll-off in every parameter. It would therefore seem that the presence or absence of roll-off is due to central processing unique to each parameter. With respect to the ventilatory responses elicited by the five HX challenges, it is clear that the differences in responses between the GGNX and SHAM rats were much more evident with the second through fifth HX challenges (HX2-HX5) than with the first HX challenge (HX1). As summarized in Supplementary Table S4, this group difference was most evident for Freq, MV, Ti, PIF, PEF, PEF/PIF, EF50 and inspiratory drive whereas it was not as noticeable for TV, Te, Te/Ti, EIP, EEP, relaxation time, expiratory drive, apneic pause, NEBI or NEBI/Freq. We have no definitive explanation for these findings, but believe it possible that the first HX challenge may be able to release and deplete residual catecholamine stores in sympathetic terminals of transected GGN nerves such that the ventilatory responses elicited by HX2-HX5 in GGNX rats represent the actual status of the carotid body complex. Such residual stores have been found in a variety of adult animals (Edvinsson et al., 1975; Dowell, 1976; Priola et al., 1981; Eisenach et al., 2002; Imrich et al., 2009) and in juvenile (P32) rats (Smith, 1986). Why the differences in HX-induced responses between SHAM and GGNX, such as the increases in Freq, are clearly more evident for episodes HX2-HX5 than HX1, whereas the differences in other responses (e.g., TV) between GGNX and SHAM rats during HX1-HX5 challenges are similar to one another is another perplexing question. A consistent observation and most noticeable during HX2-HX5 was that the initial rates of response during the HX challenges were often slower in GGNX rats than in SHAM rats (e.g., Freq, TV, MV, PIF, PEF, EF50, and inspiratory and expiratory drives), although similar plateau levels were usually obtained in both groups. This key finding tentatively suggests that the mechanisms responsible for the initial ventilatory responses to HX challenge are different to those that maintain these responses and that the mechanisms responsible for the initial responses are substantially downregulated in the absence of GGN input. Obviously a plethora of known and suspected functional proteins/signaling pathways within the carotid bodies and brain could be involved (Lahiri et al., 2006; Teppema and Dahan, 2010; Prabhakar, 2013; Semenza and Prabhakar, 2015). A rather unique observation was that EEP rose gradually and substantially during each HX challenge in the GGNX rats, whereas EEP did not change during HX1-HX5 in the SHAM rats. As such, we can conclude that under normal circumstances, input to the brainstem from the carotid body chemofferents prevents the delay in switching from expiration to inspiration. ## Room-air responses The return to room-air following the HX challenges resulted in a series of ventilatory responses in the juvenile rats. The substantial changes in breathing that occurs upon return to room-air is consistent with our findings that returning to room-air following HX, hypercapnic or hypoxic hypercapnic-gas challenges results in a series of ventilatory responses in juvenile Sprague-Dawly rats (Getsy et al., 2020), adult Sprague-Dawley rats (May et al., 2013a; May et al., 2013b; Gaston et al., 2020) and adult C57BL6 mice (Palmer et al., 2013a; Palmer et al., 2013b; Gaston et al., 2014; Getsy et al., 2014; Getsy et al., 2021a; Getsy et al., 2021b; Getsy et al., 2021c; Getsy et al., 2021d; Getsy et al., 2021e). Moreover, the return to room-air responses were virtually absent in juvenile rats (Getsy et al., 2020), adult rats (Gaston et al., 2020) and in adult mice (Getsy et al., 2021c) with bilateral CSN transection and were markedly smaller in adult mice lacking endothelial nitric oxide synthase (Getsy et al., 2021d; Getsy et al., 2021e) or hemoglobin beta-93-cysteine (Gaston et al., 2014), whereas the ventilatory responses were greater in adult mice lacking S-nitrosoglutathione reductase (Palmer et al., 2013b). Our working hypotheses are that the return to room-air responses are vitally dependent upon the generation of S-nitrosothiols in red blood cells that act in the carotid body complex, and that activation of chemosensory afferents is the vital signaling mechanism in the expression of the return to room-air ventilatory responses (Gaston et al., 2014, 2020; Getsy et al., 2020; Getsy et al., 2021c). Since the qualitative and/or quantitative nature of many of the return to room-air responses after HX challenges were markedly different in mice with bilateral CSCX (Getsy et al., 2021a) or bilateral SCGX (Getsy et al., 2021b) compared to their SHAM controls, we hypothesize that GGN input to the carotid body is essential for maintaining signaling mechanisms responsible for the return to room-air responses. In SHAM rats, Freq rose rapidly upon return to room-air and returned to baseline levels within 2 min during RA1. The elevations in Freq upon return to room-air were progressively smaller for RA2-RA5 and returned much more quickly to baseline levels during RA2-RA5. TV rose immediately upon return to room-air during RA1-RA5 and then dropped rapidly toward baseline. As such MV rose initially upon return to room-air and returned to baseline levels within 2–3 min during RA1 and more rapidly during RA2-RA5. Ti fell during RA1 and gradually returned to baseline levels within 2 min. However, after a brief fall, Te rose to well above baseline before recovering by 5 min. Te/Ti rose remarkably during RA1-RA5 before returning to baseline within 3–4 min. PIF and PEF rose during RA1-RA5 and returned toward baseline within 90–120 s, such that PEF/PIF values did not change appreciably overall although there were several instances when PEF/PIF rose to about the 60–90 s time-points. EF50 rose noticeably during RA1, but rose progressively less during RA2-RA5 before returning to or falling below baseline levels. Relaxation time fell substantially during RA1 before recovering to baseline by 120 s. Relaxation time during RA2-RA5 fell only minimally before rising above baseline levels. Apneic pause values changed minimally during RA1-RA5. Inspiratory drive values increased during RA1-RA5 before returning to baseline values within 90–120 s. Expiratory drive values increased during RA1-RA5 before returning to or falling baseline (RA2-RA5) within 30–60 s. NEBI and NEBI/Freq rose markedly during RA1-RA5 and fell gradually toward baseline within 90–150 s. This remarkable set of responses was changed substantially in GGNX rats. The qualitative changes in ventilatory responses during RA1-RA5 in GGNX rats compared to SHAM rats are summarized in Supplementary Table S5. The increase in Freq seen during RA1 and RA2 in SHAM rats was diminished in GGNX rats. The decreases in TV observed during the first 90–120 s of RA1-RA5 in SHAM rats were converted to increases in TV in the GGNX rats. As such, the increase in MV during RA1 was smaller in GGNX rats compared to SHAM rats, whereas the increase in MV during RA4 was greater in GGNX rats. The decreases in Ti in GGNX rats were largely similar to those in SHAM rats, however the decreases in Te in RA1 and RA2 in SHAM rats were absent or converted to an increase in Te, respectively, in the GGNX rats. As such, the increase in Te/Ti during RA1 was substantially greater in the GGNX rats. The substantial falls in EIP seen during RA1-RA5 in SHAM rats were greater in GGNX rats, whereas the substantial rises in EEP seen during RA1-RA5 in SHAM rats were greater in GGNX rats. The increases in PIF seen in SHAM rats were somewhat augmented in GGNX rats, whereas the increases in PEF observed in SHAM rats were substantially augmented in GGNX rats, such that the changes in PEF/PIF were greatly enhanced in the GGNX rats. The changes in EF50 observed in the SHAM rats during episodes RA1-RA3 were dramatically altered in GGNX rats, whereas the pronounced decrease in relaxation time seen in the SHAM rats during HX1 was reversed to an increase in GGNX rats. The increases in apneic pause, inspiratory drive and expiratory drive observed in the SHAM rats were similar in the GGNX rats except for a notably smaller increase in expiratory drive in the GGNX rats during RA1. Of major interest with respect to the status of breathing patterns were the findings that the RA1-RA5 increases in NEBI and NEBI/Freq were remarkably reduced in the GGNX rats. Taken together, these data strongly suggest that GGNX induces functional changes within the carotid body complex. The precise nature of these changes and the subtypes of structures within the carotid body in which these changes occur remain to be determined. We have reported that return to room-air following HX challenge elicits pronounced changes in ventilatory parameters in naïve C57BL6 mice that are associated with only minor behavioral responses (Getsy et al., 2014). Similarly, the SHAM and GGNX rats did not display any overt changes in behavior (e.g., movement about the chamber, grooming, rearing, paw licking) upon return to room-air. As such, it is possible that the differences in ventilatory responses seen upon return to room-air in the present study reflects true differences in ventilatory signaling between the SHAM and GGNX rats. ## Study limitations There are several important limitations of this study. The first was that this study was performed in juvenile (P25) male rats only and studies in juvenile (P25) female rats and in adult (e.g., P100) male and female rats are now certainly warranted. Juvenile rats at P21 were chosen for these studies because the survival surgeries can be successfully performed and then after full recovery, the rats at age P25 can be studied. Additionally P25 is the optimal age that allows electrophysiological studies to be done in brainstems from these rats (Getsy et al., 2019). Another important limitation is that the testing/recording sessions were performed 4 days post-surgery and obviously it will be important to test the SHAM and GGNX rats at later time-points to see whether the presumed changes in signaling mechanisms within the carotid bodies changes over time. Moreover, we need to gather information as to the precise nature of the structural, biochemical and cell-signaling changes that may occur in the subtypes of structures (e.g., primary glomus cells, sustenacular cells, nerve terminals of chemoafferents and vasculature) in the carotid bodies. A final limitation is the lack of evidence as to whether the changes to hypercapnic or hypoxic-hypercapnic gas challenges that we have established to elicit robust ventilatory responses in juvenile P25 rats (Getsy et al., 2020) are modulated in male and female GGNX rats. Since resting ventilatory parameters were similar in the SHAM and GGNX mice, we expect that resting arterial blood chemistry (ABG) values (e.g., pH, pCO2, pO2 and sO2) would have been similar in the two groups. We are preparing to address the vital question as to how the ABG chemistry values change during HX challenge in these rats to gain a better understanding of how GGNX affects ventilatory performance. Moreover, the key issue as to whether ventilatory responses to hypercapnic gas challenges will be different in GGNX rats will add greatly to our understanding of how the loss of GGN input to the carotid bodies affects ventilatory signaling. Additionally, a more in-depth evaluation of the true effects of GGNX on ventilatory signaling must await studies in which the rats are challenged with progressively greater HX challenges (e.g., $18\%$–$15\%$ to $12\%$–$10\%$ O2), and the data pertinent to carotid body function analyzed by exponential curve analyses. Furthermore, the precise effects of GGNX on carotid body function would be further clarified in studies in which the changes in magnitude and gain of the carotid body-mediated chemoreflex during HX challenge were evaluated. ## Conclusion Our data demonstrate that bilateral removal of the GGN post-ganglionic sympathetic input to the carotid bodies has a dramatic effect on the changes in ventilatory function that occur during and following HX gas challenges in juvenile rats. There is substantial evidence that the GGN project to various structures in the carotid bodies and to the carotid sinus to regulate the functions of primary glomus cells, chemoafferents, vasculature (Biscoe and Purves, 1967; Zapata et al., 1969; Bowers and Zigmond, 1979; Brattström, 1981a; McDonald and Mitchell, 1981; McDonald, 1983a; McDonald, 1983b; Verna et al., 1984; Torrealba and Claps, 1988; Ichikawa, 2002; Asamoto, 2004; Savastano et al., 2010) and baroreceptor afferents (Floyd and Neil, 1952; Rees, 1967; Bolter and Ledsome, 1976; Brattström, 1981b; Felder et al., 1983; Buller and Bolter, 1993). It is tempting to assume that most of the changes in ventilatory function in the GGNX rats are therefore due to adaptive changes in the carotid bodies, although changes in baroreceptor afferent input to the brainstem may also play a role. The rather startling changes in the responses of particular ventilatory parameters in GGNX rats, such as EEP and NEBI, both during the HX challenges and upon return to room-air, provide us with deeper insights into the possible mechanisms by which carotid body chemoafferent input regulates ventilatory parameters, and how aberrant changes in this input affects breathing. Additionally, it is important to note that the SCG contains small intensely fluorescent (SIF) cells that are innervated by spinal pre-ganglionic sympathetic nerves and glossopharyngeal sensory nerves endings whose cell bodies reside in the petrosal ganglia (Takaki et al., 2015). Neurotransmitters/neuromodulators released from SIF cells modulate the activity of pre-and post-ganglionic neurons within the SCG (Tanaka and Chiba, 1996) and appear to play a role in the upregulation of norepinephrine synthesis in SCG post-ganglionic neurons in response to hypoxia (Brokaw and Hansen, 1987). This raises the intriguing possibility that SIF cells play an important role in regulating the activity of SCG cells that project through the GGN to innervate the carotid bodies (Verna et al., 1984; Brognara et al., 2021). Our data provides a basis for planning studies in which implantation of stimulating devices in the spinal cord or upon the CSC-SCG complex may be of therapeutic benefit. The potential challenges of spinal cord and autonomic nerve stimulation approaches to restore ventilatory function has been addressed and many hurdles still remain (Hachmann et al., 2017). The fundamental question arising from this study pertains to how the loss of GGN input to CB structures, such as primary glomus cells, satellite cells, chemoafferent nerve terminals and vasculature (Brognara et al., 2021), results in altered functional responses of the carotid body to HX challenges. We hypothesize that the loss of GGN input alters the expression of functional proteins (Mulligan et al., 1981; Prabhakar, 1999; Lahiri et al., 2006; Prabhakar, 2013; Prabhakar and Semenza, 2015) including those that generate catecholamines (Mir et al., 1982; Pequignot et al., 1991), which play vital roles in mediating the ventilatory responses to HX challenges. While it is evident the early ventilatory responses to HX challenge were markedly different in GGNX rats than in SHAM rats, it was obvious that the maximal responses of the two groups were similar after about 90–120 s. As such, it is evident that compensatory mechanisms within the carotid body chemosensitive glomus cells, and perhaps efferent and afferent neurons associated with the carotid bodies, come into play as the HX challenge progresses. We can only speculate as to what these mechanisms are, but it is intriguing to consider that they may involve alterations in the expression of functional plasma membrane proteins and/or gradual rises in the influence of mitochondrial-dependent mechanisms as ATP levels are gradually depleted during hypoxia exposure (Mulligan et al., 1981; Prabhakar, 1999; Lahiri et al., 2006; Prabhakar, 2013; Prabhakar and Semenza, 2015). ## Data availability statement The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation. ## Ethics statement The animal study was reviewed and approved by the Institutional Animal Care and Use Committee of Case Western Reserve University (Cleveland, OH). ## Author contributions The study was originated and designed by PMG and SJL. All experiments were performed by PMG and GAC. The data were collated and statistically analyzed by PMG and SJL. The figures and tables were prepared by PMG and SJL. All authors contributed to the writing of the original version of the manuscript and the revision of the final document that was submitted for publication. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphys.2023.1007043/full#supplementary-material ## Abbreviations CSC, cervical sympathetic chain; CSCX, cervical sympathetic chain transection; CSN, carotid sinus nerve; CSNX, carotid sinus nerve transection; ECN, external carotid nerve; EF50, expiratory flow at $50\%$ expired tidal volume; GGN, ganglioglomerular nerve; GGNX, ganglioglomerular nerve transection; HXC, hypoxic gas challenge; ICN, internal carotid nerve; SCG, superior cervical ganglion; SCGX, superior cervical ganglionectomy. ## References 1. Almaraz L., Pérez-García M. T., Gómez-Nino A., González C.. **Mechanisms of alpha2-adrenoceptor-mediated inhibition in rabbit carotid body**. *Am. J. Physiol.* (1997) **272** C628-C637. DOI: 10.1152/ajpcell.1997.272.2.C628 2. Alzoubi K. H., Alhaider I. A., Tran T. T., Mosely A., Alkadhi K. K.. **Impaired neural transmission and synaptic plasticity in superior cervical ganglia from beta-amyloid rat model of Alzheimer's disease**. *Curr. Alzheimer Res.* (2011) **8** 377-384. DOI: 10.2174/156720511795745311 3. Arieli R.. **Normoxic, hyperoxic, and hypoxic ventilation in rats continuously exposed for 60 h to 1 ATA O2**. *Aviat. Space Environ. Med.* (1994) **65** 1122-1127. PMID: 7872914 4. Asamoto K.. **Neural circuit of the cervical sympathetic nervous system with special reference to input and output of the cervical sympathetic ganglia: Relationship between spinal cord and cervical sympathetic ganglia and that between cervical sympathetic ganglia and their target organs**. *Kaib. Zasshi* (2004) **79** 5-14 5. Ayas N. T., Garshick E., Lieberman S. L., Wien M. F., Tun C., Brown R.. **Breathlessness in spinal cord injury depends on injury level**. *J. Spinal Cord. Med.* (1999) **1999** 2297-3101. DOI: 10.1080/10790268.1999.11719553 6. Baby S., Gruber R., Discala J., Puskovic V., Jose N., Cheng F.. **Systemic administration of tempol attenuates the cardiorespiratory depressant effects of fentanyl**. *Front. Pharmacol.* (2021a) **12** 690407. DOI: 10.3389/fphar.2021.690407 7. Baby S. M., Discala J. F., Gruber R., Getsy P. M., Cheng F., Damron D. S.. **Tempol reverses the negative effects of morphine on arterial blood-gas chemistry and tissue oxygen saturation in freely-moving rats**. *Front. Pharmacol.* (2021b) **12** 749084. DOI: 10.3389/fphar.2021.749084 8. Baby S. M., Gruber R. B., Young A. P., MacFarlane P. M., Teppema L. J., Lewis S. J.. **Bilateral carotid sinus nerve transection exacerbates morphine-induced respiratory depression**. *Eur. J. Pharmacol.* (2018) **834** 17-29. DOI: 10.1016/j.ejphar.2018.07.018 9. Bascom A. T., Sankari A., Badr M. S.. **Spinal cord injury is associated with enhanced peripheral chemoreflex sensitivity**. *Physiol. Rep.* (2016) **4** e12948. DOI: 10.14814/phy2.12948 10. Bascom A. T., Sankari A., Goshgarian H. G., Badr M. S.. **Sleep onset hypoventilation in chronic spinal cord injury**. *Physiol. Rep.* (2015) **3** e12490. DOI: 10.14814/phy2.12490 11. Berlowitz D. J., Wadsworth B., Ross J.. **Respiratory problems and management in people with spinal cord injury**. *Breathe (Sheff).* (2016) **12** 328-340. DOI: 10.1183/20734735.012616 12. Biscoe T. J., Purves M. J.. **Observations on carotid body chemoreceptor activity and cervical sympathetic discharge in the cat**. *J. Physiol.* (1967) **190** 413-424. DOI: 10.1113/jphysiol.1967.sp008218 13. Bisgard G. E., Mitchell R. A., Herbert D. A.. **Effects of dopamine, norepinephrine and 5-hydroxytryptamine on the carotid body of the dog**. *Respir. Physiol.* (1979) **37** 61-80. DOI: 10.1016/0034-5687(79)90092-6 14. Bisgard G., Warner M., Pizarro J., Niu W., Mitchell G.. **Noradrenergic inhibition of the goat carotid body**. *Adv. Exp. Med. Biol.* (1993) **337** 259-263. DOI: 10.1007/978-1-4615-2966-8_36 15. Bitar M. S., Pilcher C. W., Khan I., Waldbillig R. J.. **Diabetes-induced suppression of IGF-1 and its receptor mRNA levels in rat superior cervical ganglia**. *Diabetes Res. Clin. Pract.* (1997) **38** 73-80. DOI: 10.1016/s0168-8227(97)00077-6 16. Bolser D. C., Jefferson S. C., Rose M. J., Tester N. J., Reier P. J., Fuller D. D.. **Recovery of airway protective behaviors after spinal cord injury**. *Respir. Physiol. Neurobiol.* (2009) **169** 150-156. DOI: 10.1016/j.resp.2009.07.018 17. Bolter C. P., Ledsome J. R.. **Effect of cervical sympathetic nerve stimulation on canine carotid sinus reflex**. *Am. J. Physiol.* (1976) **230** 1026-1030. DOI: 10.1152/ajplegacy.1976.230.4.1026 18. Bowers C. W., Zigmond R. E.. **Localization of neurons in the rat superior cervical ganglion that project into different postganglionic trunks**. *J. Comp. Neurol.* (1979) **185** 381-391. DOI: 10.1002/cne.901850211 19. Brattström A.. **Coincidental relationship of activity in the sympathetic ganglioglomerular nerve innervating the carotid bifurcation with the intracarotid systolic pulses**. *Brain Res.* (1981b) **204** 13-19. DOI: 10.1016/0006-8993(81)90647-8 20. Brattström A.. **Modification of carotid baroreceptor function by electrical stimulation of the ganglioglomerular nerve**. *J. Auton. Nerv. Syst.* (1981a) **4** 81-92. DOI: 10.1016/0165-1838(81)90008-4 21. Braun S. R., Giovannoni R., Levin A. B., Harvey R. F.. **Oxygen saturation during sleep in patients with spinal cord injury**. *Am. J. Phys. Med.* (1982) **61** 302-309. DOI: 10.1097/00002060-198212000-00003 22. Brognara F., Felippe I. S. A., Salgado H. C., Paton J. F. R.. **Autonomic innervation of the carotid body as a determinant of its sensitivity: Implications for cardiovascular physiology and pathology**. *Cardiovasc. Res.* (2021) **117** 1015-1032. DOI: 10.1093/cvr/cvaa250 23. Brokaw J. J., Hansen J. T.. **Evidence that dopamine regulates norepinephrine synthesis in the rat superior cervical ganglion during hypoxic stress**. *J. Auton. Nerv. Syst.* (1987) **18** 185-193. DOI: 10.1016/0165-1838(87)90117-2 24. Buller K. M., Bolter C. P.. **Carotid bifurcation pressure modulation of spontaneous activity in external and internal carotid nerves can occur in the superior cervical ganglion**. *J. Auton. Nerv. Syst.* (1997) **67** 24-30. DOI: 10.1016/s0165-1838(97)00088-x 25. Buller K. M., Bolter C. P.. **The localization of sympathetic and vagal neurones innervating the carotid sinus in the rabbit**. *J. Auton. Nerv. Syst.* (1993) **44** 225-231. DOI: 10.1016/0165-1838(93)90035-s 26. Cai Z. L., Zhang J. J., Chen M., Wang J. Z., Xiao P., Yang L.. **Both pre- and post-synaptic alterations contribute to aberrant cholinergic transmission in superior cervical ganglia of APP(-/-) mice**. *Neuropharmacology* (2016) **110** 493-502. DOI: 10.1016/j.neuropharm.2016.08.021 27. Camargos E. R., Machado C. R.. **Morphometric and histological analysis of the superior cervical ganglion in experimental Chagas' disease in rats**. *Am. J. Trop. Med. Hyg.* (1988) **39** 456-462. DOI: 10.4269/ajtmh.1988.39.456 28. Cameron N. E., Cotter M. A.. **Diabetes causes an early reduction in autonomic ganglion blood flow in rats**. *J. Diabetes Complicat.* (2001) **15** 198-202. DOI: 10.1016/s1056-8727(01)00149-0 29. Cardenas H., Zapata P.. **Ventilatory reflexes originated from carotid and extracarotid chemoreceptors in rats**. *Am. J. Physiol.* (1983) **244** R119-R125. DOI: 10.1152/ajpregu.1983.244.1.R119 30. Cardinali D. P., Pisarev M. A., Barontini M., Juvenal G. J., Boado R. J., Vacas M. I.. **Efferent neuroendocrine pathways of sympathetic superior cervical ganglia. Early depression of the pituitary-thyroid axis after ganglionectomy**. *Neuroendocrinology* (1982) **35** 248-254. DOI: 10.1159/000123390 31. Cardinali D. P., Vacas M. I., Gejman P. V.. **The sympathetic superior cervical ganglia as peripheral neuroendocrine centers**. *J. Neural. Transm.* (1981a) **52** 1-21. DOI: 10.1007/BF01253092 32. Cardinali D. P., Vacas M. I., Luchelli de Fortis A., Stefano F. J.. **Superior cervical ganglionectomy depresses norepinephrine uptake, increases the density of alpha-adrenoceptor sites, and induces supersensitivity to adrenergic drugs in rat medial basal hypothalamus**. *Neuroendocrinology* (1981b) **33** 199-206. DOI: 10.1159/000123229 33. Chen M., Zhang Y., Wang H., Yang H., Yin W., Xu S.. **Inhibition of the norepinephrine transporter rescues vascular hyporeactivity to catecholamine in obstructive jaundice**. *Eur. J. Pharmacol.* (2021) **900** 174055. DOI: 10.1016/j.ejphar.2021.174055 34. Cheng L., Wang X., Liu T., Tse G., Fu H., Li G.. **Modulation of ion channels in the superior cervical ganglion neurons by myocardial ischemia and fluvastatin treatment**. *Front. Physiol.* (2018) **9** 1157. DOI: 10.3389/fphys.2018.01157 35. Chimelli L., Martins A. R.. **Degenerative and inflammatory lesions in sympathetic ganglia: Further morphological evidence for an autonomic neuropathy in AIDS**. *J. Neuro AIDS.* (2002) **2** 67-82. DOI: 10.1300/j128v02n03_05 36. Chumasov E. I., Seliverstova V. G., Svetikova K. M.. **The ultrastructural changes in the sympatheic ganglion in experimental hypercholesterolemia**. *Morfologiia* (1994) **106** 92-100. PMID: 8718640 37. De Stefano M. E., Leone L., Lombardi L., Paggi P.. **Lack of dystrophin leads to the selective loss of superior cervical ganglion neurons projecting to muscular targets in genetically dystrophic mdx mice**. *Neurobiol. Dis.* (2005) **20** 929-942. DOI: 10.1016/j.nbd.2005.06.006 38. Del Tredici K., Hawkes C. H., Ghebremedhin E., Braak H.. **Lewy pathology in the submandibular gland of individuals with incidental Lewy body disease and sporadic Parkinson's disease**. *Acta Neuropathol.* (2010) **119** 703-713. DOI: 10.1007/s00401-010-0665-2 39. Dowell R. T.. **Myocardial contractile function and myofibrillar adenosine triphosphatase activity in chemically sympathectomized rats**. *Circ. Res.* (1976) **39** 683-689. DOI: 10.1161/01.res.39.5.683 40. Edvinsson L., Aubineau P., Owman C., Sercombe R., Seylaz J.. **Sympathetic innervation of cerebral arteries: Prejunctional supersensitivity to norepinephrine after sympathectomy or cocaine treatment**. *Stroke* (1975) **6** 525-530. DOI: 10.1161/01.str.6.5.525 41. Eisenach J. H., Clark E. S., Charkoudian N., Dinenno F. A., Atkinson J. L., Fealey R. D.. **Effects of chronic sympathectomy on vascular function in the human forearm**. *J. Appl. Physiol. (1985)* (2002) **92** 2019-2025. DOI: 10.1152/japplphysiol.01025.2001 42. Eldridge F. L.. **Expiratory effects of brief carotid sinus nerve and carotid body stimulations**. *Respir. Physiol.* (1976) **26** 395-410. DOI: 10.1016/0034-5687(76)90009-8 43. Eldridge F. L.. **The different respiratory effects of inspiratory and expiratory stimulations of the carotid sinus nerve and carotid body**. *Adv. Exp. Med. Biol.* (1978) **99** 325-333. DOI: 10.1007/978-1-4613-4009-6_35 44. Eldridge F. L.. **The importance of timing on the respiratory effects of intermittent carotid sinus nerve stimulation**. *J. Physiol.* (1972) **222** 297-318. DOI: 10.1113/jphysiol.1972.sp009798 45. Engwall M. J., Daristotle L., Niu W. Z., Dempsey J. A., Bisgard G. E.. **Ventilatory afterdischarge in the awake goat**. *J. Appl. Physiol. (1985)* (1991) **71** 1511-1517. DOI: 10.1152/jappl.1991.71.4.1511 46. Epstein M. A., Epstein R. A.. **A theoretical analysis of the barometric method for measurement of tidal volume**. *Respir. Physiol.* (1978) **32** 105-120. DOI: 10.1016/0034-5687(78)90103-2 47. Epstein R. A., Epstein M. A., Haddad G. G., Mellins R. B.. **Practical implementation of the barometric method for measurement of tidal volume**. *J. Appl. Physiol.* (1980) **49** 1107-1115. DOI: 10.1152/jappl.1980.49.6.1107 48. Esquifino A. I., Alvarez M. P., Cano P., Jiménez V., Duvilanski B.. **Superior cervical ganglionectomy differentially modifies median eminence and anterior and mediobasal hypothalamic GABA content in male rats: Effects of hyperprolactinemia**. *Exp. Brain. Res.* (2004) **157** 296-302. DOI: 10.1007/s00221-004-1843-z 49. Felder R. B., Heesch C. M., Thames M. D.. **Reflex modulation of carotid sinus baroreceptor activity in the dog**. *Am. J. Physiol.* (1983) **244** H437-H443. DOI: 10.1152/ajpheart.1983.244.3.H437 50. Flett D. L., Bell C.. **Topography of functional subpopulations of neurons in the superior cervical ganglion of the rat**. *J. Anat.* (1991) **177** 55-66. PMID: 1769899 51. Floyd W. F., Neil E.. **The influence of the sympathetic innervation of the carotid bifurcation on chemoceptor and baroceptor activity in the cat**. *Arch. Int. Pharmacodyn. Ther.* (1952) **91** 230-239. PMID: 13008503 52. Folgering H., Ponte J., Sadig T.. **Adrenergic mechanisms and chemoreception in the carotid body of the cat and rabbit**. *J. Physiol.* (1982) **325** 1-21. DOI: 10.1113/jphysiol.1982.sp014131 53. Forster H. V.. **Plasticity in the control of breathing following sensory denervation**. *J. Appl. Physiol. (1985)* (2003) **94** 784-794. DOI: 10.1152/japplphysiol.00602.2002 54. Gallardo E., Chiocchio S. R., Tramezzani J. H.. **Sympathetic innervation of the median eminence**. *Brain Res.* (1984) **290** 333-335. DOI: 10.1016/0006-8993(84)90951-x 55. Gaston B., Baby S. M., May W. J., Young A. P., Grossfield A., Bates J. N.. **D-Cystine di(m)ethyl ester reverses the deleterious effects of morphine on ventilation and arterial blood gas chemistry while promoting antinociception**. *Sci. Rep.* (2021) **11** 10038. DOI: 10.1038/s41598-021-89455-2 56. Gaston B., May W. J., Sullivan S., Yemen S., Marozkina N. V., Palmer L. A.. **Essential role of hemoglobin beta-93-cysteine in posthypoxia facilitation of breathing in conscious mice**. *J. Appl. Physiol. (1985)* (2014) **116** 1290-1299. DOI: 10.1152/japplphysiol.01050.2013 57. Gaston B., Smith L., Bosch J., Seckler J., Kunze D., Kiselar J.. **Voltage-gated potassium channel proteins and stereoselective S-nitroso-l-cysteine signaling**. *JCI Insight* (2020) **5** e134174. DOI: 10.1172/jci.insight.134174 58. Getsy P. M., Coffee G. A., Hsieh Y. H., Lewis S. J.. **Loss of cervical sympathetic chain input to the superior cervical ganglia affects the ventilatory responses to hypoxic challenge in freely-moving C57BL6 mice**. *Front. Physiol.* (2021a) **12** 619688. DOI: 10.3389/fphys.2021.619688 59. Getsy P. M., Coffee G. A., Hsieh Y. H., Lewis S. J.. **The superior cervical ganglia modulate ventilatory responses to hypoxia independently of preganglionic drive from the cervical sympathetic chain**. *J. Appl. Physiol. (1985)* (2021b) **131** 836-857. DOI: 10.1152/japplphysiol.00216.2021 60. Getsy P. M., Coffee G. A., Lewis S. J.. **The role of carotid sinus nerve input in the hypoxic-hypercapnic ventilatory response in juvenile rats**. *Front. Physiol.* (2020) **11** 613786. DOI: 10.3389/fphys.2020.613786 61. Getsy P. M., Davis J., Coffee G. A., May W. J., Palmer L. A., Strohl K. P.. **Enhanced non-eupneic breathing following hypoxic, hypercapnic or hypoxic-hypercapnic gas challenges in conscious mice**. *Respir. Physiol. Neurobiol.* (2014) **204** 147-159. DOI: 10.1016/j.resp.2014.09.006 62. Getsy P. M., Mayer C. A., MacFarlane P. M., Jacono F. J., Wilson C. G.. **Acute lung injury in neonatal rats causes postsynaptic depression in nucleus tractus solitarii second-order neurons**. *Respir. Physiol. Neurobiol.* (2019) **269** 103250. DOI: 10.1016/j.resp.2019.103250 63. Getsy P. M., Sundararajan S., Lewis S. J.. **Carotid sinus nerve transection abolishes the facilitation of breathing that occurs upon cessation of a hypercapnic gas challenge in male mice**. *J. Appl. Physiol. (1985)* (2021c) **131** 821-835. DOI: 10.1152/japplphysiol.01031.2020 64. Getsy P. M., Sundararajan S., May W. J., von Schill G. C., McLaughlin D. K., Palmer L. A.. **Short-term facilitation of breathing upon cessation of hypoxic challenge is impaired in male but not female endothelial NOS knock-out mice**. *Sci. Rep.* (2021d) **11** 18346. DOI: 10.1038/s41598-021-97322-3 65. Getsy P. M., Sundararajan S., May W. J., von Schill G. C., McLaughlin D. K., Palmer L. A.. **Ventilatory responses during and following hypercapnic gas challenge are impaired in male but not female endothelial NOS knock-out mice**. *Sci. Rep.* (2021e) **11** 20557. DOI: 10.1038/s41598-021-99922-5 66. Hachmann J. T., Grahn P. J., Calvert J. S., Drubach D. I., Lee K. H., Lavrov I. A.. **Electrical neuromodulation of the respiratory system after spinal cord injury**. *Mayo Clin. Proc.* (2017) **92** 1401-1414. DOI: 10.1016/j.mayocp.2017.04.011 67. Hamelmann E., Schwarze J., Takeda K., Oshiba A., Larsen G. L., Irvin C. G.. **Noninvasive measurement of airway responsiveness in allergic mice using barometric plethysmography**. *Am. J. Respir. Crit. Care Med.* (1997) **156** 766-775. DOI: 10.1164/ajrccm.156.3.9606031 68. Han C., Hoeijmakers J. G., Liu S., Gerrits M. M., te Morsche R. H., Lauria G.. **Functional profiles of SCN9A variants in dorsal root ganglion neurons and superior cervical ganglion neurons correlate with autonomic symptoms in small fibre neuropathy**. *Brain* (2012) **135** 2613-2628. DOI: 10.1093/brain/aws187 69. Hanani M., Caspi A., Belzer V.. **Peripheral inflammation augments gap junction-mediated coupling among satellite glial cells in mouse sympathetic ganglia**. *Neuron Glia Biol.* (2010) **6** 85-89. DOI: 10.1017/S1740925X10000025 70. Heinert G., Paterson D. J., Bisgard G. E., Xia N., Painter R., Nye P. C.. **The excitation of carotid body chemoreceptors of the cat by potassium and noradrenaline**. *Adv. Exp. Med. Biol.* (1995) **393** 323-330. DOI: 10.1007/978-1-4615-1933-1_61 71. Henderson F., May W. J., Gruber R. B., Discala J. F., Puskovic V., Young A. P.. **Role of central and peripheral opiate receptors in the effects of fentanyl on analgesia, ventilation and arterial blood-gas chemistry in conscious rats**. *Respir. Physiol. Neurobiol.* (2014) **191** 95-105. DOI: 10.1016/j.resp.2013.11.005 72. Hisa Y., Koike S., Tadaki N., Bamba H., Shogaki K., Uno T.. **Neurotransmitters and neuromodulators involved in laryngeal innervation**. *Ann. Otol. Rhinol. Laryngol. Suppl.* (1999) **178** 3-14. DOI: 10.1177/00034894991080s702 73. Hughes-Davis E. J., Cogen J. P., Jakowec M. W., Cheng H. W., Grenningloh G., Meshul C. K.. **Differential regulation of the growth-associated proteins GAP-43 and superior cervical ganglion 10 in response to lesions of the cortex and substantia nigra in the adult rat**. *Neuroscience* (2005) **135** 1231-1239. DOI: 10.1016/j.neuroscience.2005.07.017 74. Ichikawa H.. **Innervation of the carotid body: Immunohistochemical, denervation, and retrograde tracing studies**. *Microsc. Res. Tech.* (2002) **59** 188-195. DOI: 10.1002/jemt.10193 75. Imrich R., Eldadah B. A., Bentho O., Pechnik S., Sharabi Y., Holmes C.. **Functional effects of cardiac sympathetic denervation in neurogenic orthostatic hypotension**. *Park. Relat. Disord.* (2009) **15** 122-127. DOI: 10.1016/j.parkreldis.2008.04.002 76. Jengeleski C. A., Powers R. E., O'Connor D. T., Price D. L.. **Noradrenergic innervation of human pineal gland: Abnormalities in aging and Alzheimer's disease**. *Brain Res.* (1989) **481** 378-382. DOI: 10.1016/0006-8993(89)90818-4 77. Kandinov B., Grigoriadis N. C., Touloumi O., Drory V. E., Offen D., Korczyn A. D.. **Immunohistochemical analysis of sympathetic involvement in the SOD1-G93A transgenic mouse model of amyotrophic lateral sclerosis**. *Amyotroph. Lateral Scler. Front. Degener.* (2013) **14** 424-433. DOI: 10.3109/21678421.2013.780622 78. Katayama P. L., Castania J. A., Fazan R., Salgado H. C.. **Interaction between baroreflex and chemoreflex in the cardiorespiratory responses to stimulation of the carotid sinus/nerve in conscious rats**. *Auton. Neurosci.* (2019) **216** 17-24. DOI: 10.1016/j.autneu.2018.12.001 79. Kilic M., Kilic B., Aydin M. D., Kanat A., Yilmaz I., Eseoglu M.. **Paradoxic relations between basilar artery reconfiguration and superior cervical ganglia ischemia after bilateral common carotid artery ligation**. *World Neurosurg.* (2019) **125** e658-e664. DOI: 10.1016/j.wneu.2019.01.144 80. Kou Y. R., Ernsberger P., Cragg P. A., Cherniack N. S., Prabhakar N. R.. **Role of alpha 2-adrenergic receptors in the carotid body response to isocapnic hypoxia**. *Respir. Physiol.* (1991) **83** 353-364. DOI: 10.1016/0034-5687(91)90054-m 81. Kummer W., Fischer A., Kurkowski R., Heym C.. **The sensory and sympathetic innervation of Guinea-pig lung and trachea as studied by retrograde neuronal tracing and double-labelling immunohistochemistry**. *Neuroscience* (1992) **49** 715-737. DOI: 10.1016/0306-4522(92)90239-x 82. Lahiri S., Matsumoto S., Mokashi A.. **Responses of ganglioglomerular nerve activity to respiratory stimuli in the cat**. *J. Appl. Physiol. (1985)* (1986) **60** 391-397. DOI: 10.1152/jappl.1986.60.2.391 83. Lahiri S., Pokorski M., Davies R. O.. **Augmentation of carotid body chemoreceptor responses by isoproterenol in the cat**. *Respir. Physiol.* (1981) **44** 351-364. DOI: 10.1016/0034-5687(81)90029-3 84. Lahiri S., Roy A., Baby S. M., Hoshi T., Semenza G. L., Prabhakar N. R.. **Oxygen sensing in the body**. *Prog. Biophys. Mol. Biol.* (2006) **91** 249-286. DOI: 10.1016/j.pbiomolbio.2005.07.001 85. Laudanna A., Nogueira M. I., Mariano M.. **Expression of fos protein in the rat central nervous system in response to noxious stimulation: Effects of chronic inflammation of the superior cervical ganglion**. *Braz. J. Med. Biol. Res.* (1998) **31** 847-850. DOI: 10.1590/s0100-879x1998000600019 86. Li G., Sheng X., Xu Y., Jiang H., Zheng C., Guo J.. **Co-expression changes of lncRNAs and mRNAs in the cervical sympathetic ganglia in diabetic cardiac autonomic neuropathic rats**. *J. Neurosci. Res.* (2017) **95** 1690-1699. DOI: 10.1002/jnr.24000 87. Liberski P. P.. **Axonal changes in experimental prion diseases recapitulate those following constriction of postganglionic branches of the superior cervical ganglion: A comparison 40 years later**. *Prion* (2019) **13** 83-93. DOI: 10.1080/19336896.2019.1595315 88. Liu J., Li G., Peng H., Tu G., Kong F., Liu S.. **Sensory-sympathetic coupling in superior cervical ganglia after myocardial ischemic injury facilitates sympathoexcitatory action via P2X7 receptor**. *Purinergic Signal* (2013) **9** 463-479. DOI: 10.1007/s11302-013-9367-2 89. Llados F., Zapata P.. **Effects of adrenoceptor stimulating and blocking agents on carotid body chemosensory inhibition**. *J. Physiol.* (1978) **274** 501-509. DOI: 10.1113/jphysiol.1978.sp012163 90. Llewellyn-Smith I. J., Arnolda L. F., Pilowsky P. M., Chalmers J. P., Minson J. B.. **GABA- and glutamate-immunoreactive synapses on sympathetic preganglionic neurons projecting to the superior cervical ganglion**. *J. Auton. Nerv. Syst.* (1998) **71** 96-110. DOI: 10.1016/s0165-1838(98)00069-1 91. Lomask M.. **Further exploration of the Penh parameter**. *Exp. Toxicol. Pathol.* (2006) **57** 13-20. DOI: 10.1016/j.etp.2006.02.014 92. Ludbrook J.. **Multiple comparison procedures updated**. *Clin. Exp. Pharmacol. Physiol.* (1998) **25** 1032-1037. DOI: 10.1111/j.1440-1681.1998.tb02179.x 93. Majcherczyk S., Chruścielewski L., Trzebski A.. **Effect of stimulation of carotid body chemoreceptors upon ganglioglomerular nerve activity and on chemoreceptor discharges in contralateral sinus nerve**. *Brain Res.* (1974) **76** 167-170. DOI: 10.1016/0006-8993(74)90524-1 94. Majcherczyk S., Coleridge J. C., Coleridge H. M., Kaufman M. P., Baker D. G.. **Carotid sinus nerve efferents: Properties and physiological significance**. *Fed. Proc.* (1980) **39** 2662-2667. PMID: 7398895 95. Marek W., Prabhakar N. R., Loeschcke H. H.. **Electrical stimulation of arterial and central chemosensory afferents at different times in the respiratory cycle of the cat: I. Ventilatory responses**. *Pflugers Arch.* (1985) **403** 415-421. DOI: 10.1007/BF00589255 96. Matano F., Murai Y., Adachi K., Kitamura T., Teramoto A.. **Pathophysiology and management of intracranial arterial stenosis around the circle of Willis associated with hyperthyroidism: Case reports and literature review**. *Neurosurg. Rev.* (2014) **37** 347-356. DOI: 10.1007/s10143-013-0511-9 97. Mathew T. C.. **Scanning electron microscopic observations on the third ventricular floor of the rat following cervical sympathectomy**. *Folia Morphol. Warsz.* (2007) **66** 94-99. PMID: 17594665 98. Matsumoto S., Ibi A., Nagao T., Nakajima T.. **Effects of carotid body chemoreceptor stimulation by norepinephrine, epinephrine and tyramine on ventilation in the rabbit**. *Arch. Int. Pharmacodyn. Ther.* (1981) **252** 152-161. PMID: 7305548 99. Matsumoto S., Mokashi A., Lahiri S.. **Cervical preganglionic sympathetic nerve activity and chemoreflexes in the cat**. *J. Appl. Physiol. (1985)* (1987) **62** 1713-1720. DOI: 10.1152/jappl.1987.62.4.1713 100. Matsumoto S., Mokashi A., Lahiri S.. **Influence of ganglioglomerular nerve on carotid chemoreceptor activity in the cat**. *J. Auton. Nerv. Syst.* (1986) **15** 7-20. DOI: 10.1016/0165-1838(86)90075-5 101. May W. J., Gruber R. B., Discala J. F., Puskovic V., Henderson F., Palmer L. A.. **Morphine has latent deleterious effects on the ventilatory responses to a hypoxic challenge**. *Open J. Mol. Integr. Physiol.* (2013a) **3** 166-180. DOI: 10.4236/ojmip.2013.34022 102. May W. J., Henderson F., Gruber R. B., Discala J. F., Young A. P., Bates J. N.. **Morphine has latent deleterious effects on the ventilatory responses to a hypoxic-hypercapnic challenge**. *Open J. Mol. Integr. Physiol.* (2013b) **3** 134-145. DOI: 10.4236/ojmip.2013.33019 103. McDonald D. M.. **A morphometric analysis of blood vessels and perivascular nerves in the rat carotid body**. *J. Neurocytol.* (1983a) **12** 155-199. DOI: 10.1007/BF01148091 104. McDonald D. M., Mitchell R. A.. **The innervation of glomus cells, ganglion cells and blood vessels in the rat carotid body: A quantitative ultrastructural analysis**. *J. Neurocytol.* (1975) **4** 177-230. DOI: 10.1007/BF01098781 105. McDonald D. M., Mitchell R. A.. **The neural pathway involved in "efferent inhibition" of chemoreceptors in the cat carotid body**. *J. Comp. Neurol.* (1981) **201** 457-476. DOI: 10.1002/cne.902010310 106. McDonald D. M.. **Morphology of the rat carotid sinus nerve. I. Course, connections, dimensions and ultrastructure**. *J. Neurocytol.* (1983b) **12** 345-372. DOI: 10.1007/BF01159380 107. McGorum B. C., Pirie R. S., Eaton S. L., Keen J. A., Cumyn E. M., Arnott D. M.. **Proteomic profiling of cranial (superior) cervical ganglia reveals beta-amyloid and ubiquitin proteasome system perturbations in an equine multiple system neuropathy**. *Mol. Cell. Proteomics* (2015) **14** 3072-3086. DOI: 10.1074/mcp.M115.054635 108. McHugh M. L.. **Multiple comparison analysis testing in ANOVA**. *Biochem. Med. Zagreb.* (2011) **21** 203-209. DOI: 10.11613/bm.2011.029 109. McQueen D. S., Evrard Y., Gordon B. H., Campbell D. B.. **Ganglioglomerular nerves influence responsiveness of cat carotid body chemoreceptors to almitrine**. *J. Auton. Nerv. Syst.* (1989) **27** 57-66. DOI: 10.1016/0165-1838(89)90129-x 110. Mills E., Smith P. G., Slotkin T. A., Breese G.. **Role of carotid body catecholamines in chemoreceptor function**. *Neuroscience* (1978) **3** 1137-1146. DOI: 10.1016/0306-4522(78)90134-3 111. Milsom W. K., Sadig T.. **Interaction between norepinephrine and hypoxia on carotid body chemoreception in rabbits**. *J. Appl. Physiol. Respir. Environ. Exerc. Physiol.* (1983) **55** 1893-1898. DOI: 10.1152/jappl.1983.55.6.1893 112. Minker E., Koltai M., Blazsó G.. **Diabetes-induced alterations of autonomic nerve function in the cat**. *Acta Physiol. Acad. Sci. hung.* (1978) **51** 413-419. PMID: 38632 113. Mir A. K., Al-Neamy K., Pallot D. J., Nahorski S. R.. **Catecholamines in the carotid body of several mammalian species: Effects of surgical and chemical sympathectomy**. *Brain Res.* (1982) **252** 335-342. DOI: 10.1016/0006-8993(82)90401-2 114. Moubayed S. P., Machado R., Osorio M., Khorsandi A., Hernandez-Prera J., Urken M. L.. **Metastatic squamous cell carcinoma to the superior cervical ganglion mimicking a retropharyngeal lymph node**. *Am. J. Otolaryngol.* (2017) **38** 720-723. DOI: 10.1016/j.amjoto.2017.07.001 115. Mulligan E., Lahiri S., Storey B. T.. **Carotid body O**. *J. Appl. Physiol. Respir. Environ. Exerc. Physiol.* (1981) **51** 438-446. DOI: 10.1152/jappl.1981.51.2.438 116. Nakano H., Lee S. D., Farkas G. A.. **Dopaminergic modulation of ventilation in obese Zucker rats**. *J. Appl. Physiol. (1985)* (2002) **92** 25-32. DOI: 10.1152/jappl.2002.92.1.25 117. Niemi J. P., Filous A. R., DeFrancesco A., Lindborg J. A., Malhotra N. A., Wilson G. N.. **Injury-induced gp130 cytokine signaling in peripheral ganglia is reduced in diabetes mellitus**. *Exp. Neurol.* (2017) **296** 1-15. DOI: 10.1016/j.expneurol.2017.06.020 118. O'Halloran K. D., Curran A. K., Bradford A.. **Influence of cervical sympathetic nerves on ventilation and upper airway resistance in the rat**. *Eur. Respir. J.* (1998) **12** 177-184. DOI: 10.1183/09031936.98.12010177 119. O'Halloran K. D., Curran A. K., Bradford A.. **The effect of sympathetic nerve stimulation on ventilation and upper airway resistance in the anaesthetized rat**. *Adv. Exp. Med. Biol.* (1996) **410** 443-447. DOI: 10.1007/978-1-4615-5891-0_68 120. Oh E. J., Mazzone S. B., Canning B. J., Weinreich D.. **Reflex regulation of airway sympathetic nerves in Guinea-pigs**. *J. Physiol.* (2006) **573** 549-564. DOI: 10.1113/jphysiol.2005.104661 121. Oku Y., Kurusu M., Hara Y., Sugita M., Muro S., Chin K.. **Ventilatory responses and subjective sensations during arm exercise and hypercapnia in patients with lower-cervical and upper-thoracic spinal cord injuries**. *Intern Med.* (1997) **36** 776-780. DOI: 10.2169/internalmedicine.36.776 122. Overholt J. L., Prabhakar N. R.. **Norepinephrine inhibits a toxin resistant Ca**. *J. Neurophysiol.* (1999) **81** 225-233. DOI: 10.1152/jn.1999.81.1.225 123. Palecek F., Chválová M.. **Pattern of breathing in the rat**. *Physiol. Bohemoslov.* (1976) **25** 159-166. PMID: 131347 124. Palmer L. A., May W. J., deRonde K., Brown-Steinke K., Bates J. N., Gaston B.. **Ventilatory responses during and following exposure to a hypoxic challenge in conscious mice deficient or null in S-nitrosoglutathione reductase**. *Respir. Physiol. Neurobiol.* (2013b) **185** 571-581. DOI: 10.1016/j.resp.2012.11.009 125. Palmer L. A., May W. J., deRonde K., Brown-Steinke K., Gaston B., Lewis S. J.. **Hypoxia-induced ventilatory responses in conscious mice: Gender differences in ventilatory roll-off and facilitation**. *Respir. Physiol. Neurobiol.* (2013a) **185** 497-505. DOI: 10.1016/j.resp.2012.11.010 126. Pang L., Miao Z. H., Dong L., Wang Y. L.. **Hypoxia-induced increase in nerve activity of rabbit carotid body mediated by noradrenaline**. *Sheng Li Xue Bao* (1999) **51** 407-412. PMID: 11498968 127. Pequignot J. M., Dalmaz Y., Claustre J., Cottet-Emard J. M., Borghini N., Peyrin L.. **Preganglionic sympathetic fibres modulate dopamine turnover in rat carotid body during long-term hypoxia**. *J. Auton. Nerv. Syst.* (1991) **32** 243-249. DOI: 10.1016/0165-1838(91)90118-m 128. Pirard P.. **Infiltration of the superior cervical ganglion in functional neuro-endocrine disorders in the female**. *Sem. Hop.* (1954) **30** 3249-3252. PMID: 13216238 129. Pizarro J., Warner M. M., Ryan M., Mitchell G. S., Bisgard G. E.. **Intracarotid norepinephrine infusions inhibit ventilation in goats**. *Respir. Physiol.* (1992) **90** 299-310. DOI: 10.1016/0034-5687(92)90110-i 130. Potter E. K., McCloskey D. I.. **Excitation of carotid body chemoreceptors by neuropeptide-Y**. *Respir. Physiol.* (1987) **67** 357-365. DOI: 10.1016/0034-5687(87)90065-x 131. Prabhakar N. R., Kou Y. R., Cragg P. A., Cherniack N. S.. **Effect of arterial chemoreceptor stimulation: Role of norepinephrine in hypoxic chemotransmission**. *Adv. Exp. Med. Biol.* (1993) **337** 301-306. DOI: 10.1007/978-1-4615-2966-8_42 132. Prabhakar N. R.. **NO and CO as second messengers in oxygen sensing in the carotid body**. *Respir. Physiol.* (1999) **115** 161-168. DOI: 10.1016/s0034-5687(99)00019-5 133. Prabhakar N. R., Semenza G. L.. **Oxygen sensing and homeostasis**. *Physiol. (Bethesda)* (2015) **30** 340-348. DOI: 10.1152/physiol.00022.2015 134. Prabhakar N. R.. **Sensing hypoxia: Physiology, genetics and epigenetics**. *J. Physiol.* (2013) **591** 2245-2257. DOI: 10.1113/jphysiol.2012.247759 135. Price R. W., Schmitz J.. **Route of infection, systemic host resistance, and integrity of ganglionic axons influence acute and latent herpes simplex virus infection of the superior cervical ganglion**. *Infect. Immun.* (1979) **23** 373-383. DOI: 10.1128/iai.23.2.373-383.1979 136. Priola D. V., O'Brien W. J., Dail W. G., Simpson W. W.. **Cardiac catecholamine stores after cardiac sympathectomy, 6-OHDA, and cardiac denervation**. *Am. J. Physiol.* (1981) **240** H889-H895. DOI: 10.1152/ajpheart.1981.240.6.H889 137. Quindry J. C., Ballmann C. G., Epstein E. E., Selsby J. T.. **Plethysmography measurements of respiratory function in conscious unrestrained mice**. *J. Physiol. Sci.* (2016) **66** 157-164. DOI: 10.1007/s12576-015-0408-1 138. Rando T. A., Bowers C. W., Zigmond R. E.. **Localization of neurons in the rat spinal cord which project to the superior cervical ganglion**. *J. Comp. Neurol.* (1981) **196** 73-83. DOI: 10.1002/cne.901960107 139. Rees P. M.. **Observations on the fine structure and distribution of presumptive baroreceptor nerves at the carotid sinus**. *J. Comp. Neurol.* (1967) **131** 517-548. DOI: 10.1002/cne.901310409 140. Roth E. J., Lu A., Primack S., Oken J., Nusshaum S., Berkowitz M.. **Ventilatory function in cervical and high thoracic spinal cord injury. Relationship to level of injury and tone**. *Am. J. Phys. Med. Rehabil.* (1997) **76** 262-267. DOI: 10.1097/00002060-199707000-00002 141. Roux J. C., Dura E., Villard L.. **Tyrosine hydroxylase deficit in the chemoafferent and the sympathoadrenergic pathways of the Mecp2 deficient mouse**. *Neurosci. Lett.* (2008) **447** 82-86. DOI: 10.1016/j.neulet.2008.09.045 142. Rudik V. P.. **Clinical aspects, diagnosis and therapy of ganglionitis of the superior cervical vegetative ganglion**. *Vrach. Delo.* (1969) **6** 94-98. PMID: 5821675 143. Ryan M. L., Hedrick M. S., Pizarro J., Bisgard G. E.. **Effects of carotid body sympathetic denervation on ventilatory acclimatization to hypoxia in the goat**. *Respir. Physiol.* (1995) **99** 215-224. DOI: 10.1016/0034-5687(94)00096-i 144. Saavedra J. M.. **Central and peripheral catecholamine innervation of the rat intermediate and posterior pituitary lobes**. *Neuroendocrinology* (1985) **40** 281-284. DOI: 10.1159/000124087 145. Sadoshima S., Busija D., Brody M., Heistad D.. **Sympathetic nerves protect against stroke in stroke-prone hypertensive rats. A preliminary report**. *Hypertension* (1981) **3** I124-I127. DOI: 10.1161/01.hyp.3.3_pt_2.i124 146. Sadoshima S., Busija D. W., Heistad D. D.. **Mechanisms of protection against stroke in stroke-prone spontaneously hypertensive rats**. *Am. J. Physiol.* (1983a) **244** H406-H412. DOI: 10.1152/ajpheart.1983.244.3.H406 147. Sadoshima S., Heistad D. D.. **Regional cerebral blood flow during hypotension in normotensive and stroke-prone spontaneously hypertensive rats: Effect of sympathetic denervation**. *Stroke* (1983b) **14** 575-579. DOI: 10.1161/01.str.14.4.575 148. Sankari A., Badr M. S., Martin J. L., Ayas N. T., Berlowitz D. J.. **Impact of spinal cord injury on sleep: Current perspectives**. *Nat. Sci. Sleep.* (2019) **11** 219-229. DOI: 10.2147/NSS.S197375 149. Sankari A., Bascom A., Oomman S., Badr M. S.. **Sleep disordered breathing in chronic spinal cord injury**. *J. Clin. Sleep. Med.* (2014a) **10** 65-72. DOI: 10.5664/jcsm.3362 150. Sankari A., Bascom A. T., Badr M. S.. **Upper airway mechanics in chronic spinal cord injury during sleep**. *J. Appl. Physiol. (1985)* (2014b) **116** 1390-1395. DOI: 10.1152/japplphysiol.00139.2014 151. Savastano L. E., Castro A. E., Fitt M. R., Rath M. F., Romeo H. E., Muñoz E. M.. **A standardized surgical technique for rat superior cervical ganglionectomy**. *J. Neurosci. Methods* (2010) **192** 22-33. DOI: 10.1016/j.jneumeth.2010.07.007 152. Schilero G. J., Spungen A. M., Bauman W. A., Radulovic M., Lesser M.. **Pulmonary function and spinal cord injury**. *Respir. Physiol. Neurobiol.* (2009) **166** 129-141. DOI: 10.1016/j.resp.2009.04.002 153. Seckler J. M., Grossfield A., May W. J., Getsy P. M., Lewis S. J.. **Nitrosyl factors play a vital role in the ventilatory depressant effects of fentanyl in unanesthetized rats**. *Biomed. Pharmacother.* (2022) **146** 112571. DOI: 10.1016/j.biopha.2021.112571 154. Semenza G. L., Prabhakar N. R.. **Neural regulation of hypoxia-inducible factors and redox state drives the pathogenesis of hypertension in a rodent model of sleep apnea**. *J. Appl. Physiol. (1985)* (2015) **119** 1152-1156. DOI: 10.1152/japplphysiol.00162.2015 155. Shin J. E., Geisler S., DiAntonio A.. **Dynamic regulation of SCG10 in regenerating axons after injury**. *Exp. Neurol.* (2014) **252** 1-11. DOI: 10.1016/j.expneurol.2013.11.007 156. Smith P. G.. **Functional plasticity in the sympathetic nervous system of the neonatal rat**. *Exp. Neurol.* (1986) **91** 136-146. DOI: 10.1016/0014-4886(86)90031-2 157. Souza G. M. P. R., Kanbar R., Stornetta D. S., Abbott S. B. G., Stornetta R. L., Guyenet P. G.. **Breathing regulation and blood gas homeostasis after near complete lesions of the retrotrapezoid nucleus in adult rats**. *J. Physiol.* (2018) **596** 2521-2545. DOI: 10.1113/JP275866 158. Strohl K. P., Thomas A. J., St Jean P., Schlenker E. H., Koletsky R. J., Schork N. J.. **Ventilation and metabolism among rat strains**. *J. Appl. Physiol. (1985)* (1997) **82** 317-323. DOI: 10.1152/jappl.1997.82.1.317 159. Sugarman B.. **Atelectasis in spinal cord injured people after initial medical stabilization**. *J. Am. Paraplegia Soc.* (1985) **8** 47-50. PMID: 3842981 160. Takaki F., Nakamuta N., Kusakabe T., Yamamoto Y.. **Sympathetic and sensory innervation of small intensely fluorescent (SIF) cells in rat superior cervical ganglion**. *Cell. Tissue Res.* (2015) **359** 441-451. DOI: 10.1007/s00441-014-2051-1 161. Takeda K., Kanno K., Minami T., Katsurada K.. **Hemodynamic and respiratory function following acute spinal cord injury**. *No To Shinkei* (1977) **29** 639-645. PMID: 911549 162. Tanaka K., Chiba T.. **Microvascular organization of sympathetic ganglia, with special reference to small intensely-fluorescent cells**. *Microsc. Res. Tech.* (1996) **35** 137-145. DOI: 10.1002/(SICI)1097-0029(19961001)35:2<137::AID-JEMT4>3.0.CO;2-N 163. Tang F. R., Tan C. K., Ling E. A.. **A comparative study by retrograde neuronal tracing and substance P immunohistochemistry of sympathetic preganglionic neurons in spontaneously hypertensive rats and Wistar-Kyoto rats**. *J. Anat.* (1995b) **186** 197-207. PMID: 7544334 164. Tang F. R., Tan C. K., Ling E. A.. **A comparative study of NADPH-diaphorase in the sympathetic preganglionic neurons of the upper thoracic cord between spontaneously hypertensive rats and Wistar-Kyoto rats**. *Brain Res.* (1995c) **691** 153-159. DOI: 10.1016/0006-8993(95)00658-d 165. Tang F. R., Tan C. K., Ling E. A.. **An ultrastructural study of the sympathetic preganglionic neurons that innervate the superior cervical ganglion in spontaneously hypertensive rats and Wistar-Kyoto rats**. *J. Hirnforsch.* (1995a) **36** 411-420. PMID: 7560913 166. Teppema L. J., Dahan A.. **The ventilatory response to hypoxia in mammals: Mechanisms, measurement, and analysis**. *Physiol. Rev.* (2010) **90** 675-754. DOI: 10.1152/physrev.00012.2009 167. Torrealba F., Claps A.. **The carotid sinus connections: A WGA-HRP study in the cat**. *Brain Res.* (1988) **455** 134-143. DOI: 10.1016/0006-8993(88)90122-9 168. Tsumuro T., Alejandra Hossen M., Kishi Y., Fujii Y., Kamei C.. **Nasal congestion model in Brown Norway rats and the effects of some H1-antagonists**. *Int. Immunopharmacol.* (2006) **6** 759-763. DOI: 10.1016/j.intimp.2005.11.009 169. Verna A., Barets A., Salat C.. **Distribution of sympathetic nerve endings within the rabbit carotid body: A histochemical and ultrastructural study**. *J. Neurocytol.* (1984) **13** 849-865. DOI: 10.1007/BF01148589 170. Wallenstein S., Zucker C. L., Fleiss J. L.. **Some statistical methods useful in circulation research**. *Circ. Res.* (1980) **47** 1-9. DOI: 10.1161/01.res.47.1.1 171. Wang H. W., Chiou W. Y.. **Sympathetic innervation of the tongue in rats**. *ORL J. Otorhinolaryngol. Relat. Spec.* (2004) **66** 16-20. DOI: 10.1159/000077228 172. Werber A. H., Heistad D. D.. **Effects of chronic hypertension and sympathetic nerves on the cerebral microvasculature of stroke-prone spontaneously hypertensive rats**. *Circ. Res.* (1984) **55** 286-294. DOI: 10.1161/01.res.55.3.286 173. Westerhaus M. J., Loewy A. D.. **Sympathetic-related neurons in the preoptic region of the rat identified by viral transneuronal labeling**. *J. Comp. Neurol.* (1999) **414** 361-378. DOI: 10.1002/(sici)1096-9861(19991122)414:3<361::aid-cne6>3.0.co;2-x 174. Wiberg M., Widenfalk B.. **Involvement of connections between the brainstem and the sympathetic ganglia in the pathogenesis of rheumatoid arthritis. An anatomical study in rats**. *Scand. J. Plast. Reconstr. Surg. Hand Surg.* (1993) **27** 269-276. DOI: 10.1080/02844311.1993.12005640 175. Winer B. J.. *Statistical principles of experimental design* (1971) 752-809 176. Yokoyama T., Nakamuta N., Kusakabe T., Yamamoto Y.. **Sympathetic regulation of vascular tone via noradrenaline and serotonin in the rat carotid body as revealed by intracellular calcium imaging**. *Brain Res.* (2015) **1596** 126-135. DOI: 10.1016/j.brainres.2014.11.037 177. Young A. P., Gruber R. B., Discala J. F., May W. J., McLaughlin D., Palmer L. A.. **Co-activation of μ- and δ-opioid receptors elicits tolerance to morphine-induced ventilatory depression via generation of peroxynitrite**. *Respir. Physiol. Neurobiol.* (2013) **186** 255-264. DOI: 10.1016/j.resp.2013.02.028 178. Zaidi Z. F., Matthews M. R.. **Source and origin of nerve fibres immunoreactive for substance P and calcitonin gene-related peptide in the normal and chronically denervated superior cervical sympathetic ganglion of the rat**. *Auton. Neurosci.* (2013) **173** 28-38. DOI: 10.1016/j.autneu.2012.11.002 179. Zapata P.. **Effects of dopamine on carotid chemo- and baroreceptors**. *J. Physiol.* (1975) **244** 235-251. DOI: 10.1113/jphysiol.1975.sp010794 180. Zapata P., Hess A., Bliss E. L., Eyzaguirre C.. **Chemical, electron microscopic and physiological observations on the role of catecholamines in the carotid body**. *Brain Res.* (1969) **14** 473-496. DOI: 10.1016/0006-8993(69)90123-1 181. Zhu G., Dai B., Chen Z., He L., Guo J., Dan Y.. **Effects of chronic lead exposure on the sympathoexcitatory response associated with the P2X7 receptor in rat superior cervical ganglia**. *Auton. Neurosci.* (2019) **219** 33-41. DOI: 10.1016/j.autneu.2019.03.005
--- title: Faecalibacterium prausnitzii prevents hepatic damage in a mouse model of NASH induced by a high-fructose high-fat diet authors: - Ji-Hee Shin - Yoonmi Lee - Eun-Ji Song - Dokyung Lee - Seo-Yul Jang - Hye Rim Byeon - Moon-Gi Hong - Sang-Nam Lee - Hyun-Jin Kim - Jae-Gu Seo - Dae Won Jun - Young-Do Nam journal: Frontiers in Microbiology year: 2023 pmcid: PMC10060964 doi: 10.3389/fmicb.2023.1123547 license: CC BY 4.0 --- # Faecalibacterium prausnitzii prevents hepatic damage in a mouse model of NASH induced by a high-fructose high-fat diet ## Abstract ### Introduction Nonalcoholic steatohepatitis (NASH) is an advanced nonalcoholic fatty liver disease characterized by chronic inflammation and fibrosis. A dysbiosis of the gut microbiota has been associated with the pathophysiology of NASH, and probiotics have proven helpful in its treatment and prevention. Although both traditional and next-generation probiotics have the potential to alleviate various diseases, studies that observe the therapeutic effect of next-generation probiotics on NASH are lacking. Therefore, we investigated whether a next-generation probiotic candidate, Faecalibacterium prausnitzii, contributed to the mitigation of NASH. ### Methods In this study, we conducted 16S rRNA sequencing analyses in patients with NASH and healthy controls. To test F. prausnitzii could alleviate NASH symptoms, we isolated four F. prausnitzii strains (EB-FPDK3, EB-FPDK9, EB-FPDK11, and EB-FPYYK1) from fecal samples collected from four healthy individuals. Mice were maintained on a high-fructose high-fat diet for 16 weeks to induce a NASH model and received oral administration of the bacterial strains. Changes in characteristic NASH phenotypes were assessed via oral glucose tolerance tests, biochemical assays, and histological analyses. ### Results 16S rRNA sequencing analyses confirmed that the relative abundance of F. prausnitzii reduced significantly in patients with NASH compared to healthy controls ($p \leq 0.05$). In the NASH mice, F. prausnitzii supplementation improved glucose homeostasis, prevented hepatic lipid accumulation, curbed liver damage and fibrosis, restored damaged gut barrier functions, and alleviated hepatic steatosis and liver inflammation. Furthermore, real-time PCR assays documented that the four F. prausnitzii strains regulated the expression of genes related to hepatic steatosis in these mice. ### Discussion Our study, therefore, confirms that the administration of F. prausnitzii bacteria can alleviate NASH symptoms. We propose that F. prausnitzii has the potential to contribute to the next-generation probiotic treatment of NASH. ## Introduction Nonalcoholic fatty liver disease (NAFLD) refers to a group of diseases, including simple steatosis (in which fat is excessively accumulated in hepatocytes), nonalcoholic steatohepatitis (NASH, with hepatocellular necrosis, inflammation, and fibrosis), and further progressive cirrhosis (Brunt, 2001). Although the pathogenesis of NASH and cirrhosis is not fully understood, the double-hit hypothesis is widely accepted. The first hit comprises fat accumulation in the liver due to insulin resistance, and the second hit consists of lipid peroxidation and inflammatory processes caused by oxidative stress, thereby causing hepatocellular damage and an inflammatory response (Day and James, 1998). However, it is now understood that NAFLD and particularly NASH progression is caused by more complex and diversely parallel metabolic stimuli (known as the multiple parallel hit theory), such as insulin resistance, hormones secreted from the adipose tissue, nutritional factors, gut microbiota, and genetic and epigenetic factors (Arab et al., 2017; Tilg et al., 2021). Moreover, a recent study demonstrated that an imbalance in the intestinal microbiome is associated with liver disease (Goel et al., 2014). The human intestinal microbiome consists of 100 trillion microorganisms, which is 10 times the number of human somatic and reproductive cells (Backhed et al., 2005; Hooper and Macpherson, 2010). The commensal microbiome metabolizes indigestible compounds, produces vitamins, defends against opportunistic pathogens, and contributes to the development and regulation of mammalian immune systems (Carding et al., 2015; Jandhyala et al., 2015). A balance in the microbial composition is crucial for maintaining the host health. When this balance is disrupted, the host may experience dysbiosis, a decreased resistance to pathogens, the collapse of pathological immune responses, or the onset of severe diseases (Kamada et al., 2013; Carding et al., 2015; Das and Nair, 2019). Several studies have implicated gut microbial dysbiosis in NAFLD and NASH (Zhu et al., 2013; Michail et al., 2015; Boursier et al., 2016). Patients with obesity and NASH exhibited decreased microbial diversity compared to healthy controls (Zhu et al., 2013); the proportion of Bacteroides and Prevotella species increased significantly in these patients, whereas those of Blautia and Faecalibacterium decreased (Zhu et al., 2013; Michail et al., 2015; Boursier et al., 2016). The Food and Agriculture Organization and World Health Organization defined probiotics as “live microorganisms which, when administered in adequate amounts, confer a health benefit on the host” in 2001. Probiotics have the potential to prevent or treat various health problems such as inflammatory bowel disease, obesity, diabetes, and cardiovascular disease by controlling host-gut microbial interactions (Kim et al., 2019). Commercialized Streptococcus, Lactobacillus, and Bifidobacterium are well-known probiotics that promote an anti-inflammatory environment and assist with gut barrier function (Paolella et al., 2014). Recently, microorganism-based studies have investigated their potential to alleviate chronic liver disease (Li et al., 2003; Ma et al., 2008; Xu et al., 2012; Xin et al., 2014). The administration of VSL#3, a multi-strain formulation that contains a mixture of the aforementioned three bacterial genera, improved the serum alanine aminotransferase (ALT) levels and histological spectrum of liver damage in Lep ob/ob mice and rats (Li et al., 2003; Ma et al., 2008). In addition, the administration of *Bifidobacterium longum* reduced hepatic fat accumulation irrespective of gut permeability restoration in a rat model (Xu et al., 2012). Further investigations using a high-fat diet mouse model indicated that the administration of *Lactobacillus johnsonii* BS15 also protects against hepatic steatosis and hepatocyte apoptosis (Xin et al., 2014). Increasing knowledge of the human gut microbiome has changed the paradigm of probiotics and leads to a natural shift to novel therapeutics such as next-generation probiotics (NPGs) and pharmaceuticals using dominant gut microbes such as Akkermansia muciniphila, Faecalibacterium prausnitzii, and Prevotella copri (Chang et al., 2019; Cheng and Xie, 2021; He et al., 2021). Unlike traditional probiotics that are derived from fermented foods, NGPs have been explored in commensal gut microbiota that supports human health (Martín and Langella, 2019). NGPs have been primarily identified through comparisons of microbiota compositions between healthy and unhealthy individuals, and they comprise various genera (Martín and Langella, 2019). Therefore, many NGP candidates have been reported to alleviate various diseases, such as obesity and type 2 diabetes (Munukka et al., 2017; Depommier et al., 2019; López-Moreno et al., 2021). Although these microorganisms are often referred to as novel next-generation therapeutics, studies on the preventative mechanism of these microbes on NASH symptoms are still lacking. In this study, F. prausnitzii was selected as a next-generation probiotic candidate in an exploration of the gut microbiota of 45 patients with NASH and 99 healthy controls. We investigated the effect of four F. prausnitzii isolates on NASH using high-fat and high-fructose diet mouse models that most closely recapitulate the human phenotype of NASH. NASH symptoms, such as glucose homeostasis, hepatic lipid accumulation, and liver damage, were evaluated. Furthermore, we analyzed gut barrier function and mRNA levels of genes related to liver hepatic steatosis and liver inflammation to explore mechanistic insights into the anti-NASH effect of F. prausnitzii. ## Human participants Patients diagnosed with NASH based on liver histology were recruited from the Hanyang University College of Medicine (Seoul, Republic of Korea). The diagnostic criteria for NASH were satisfied if patients met the following three conditions: an alcohol consumption of less than 20 g per day, biopsy-proven steatohepatitis, and the absence of other chronic liver diseases. The exclusion criteria comprised any consumption of probiotics or prebiotics within 3 months of the study, pregnancy and/or lactation, or any history of major gastrointestinal surgery. All participants provided written informed consent, and the study was approved by the institutional review board of Hanyang University College of Medicine (IRB number: 2014–03–008-005). Age-and sex-matched healthy subjects recruited in our previous study (Lim M. Y. et al., 2021) were used as the control group in this study. ## 16S ribosomal RNA sequencing of stool samples Fresh stool samples were collected from the participants using OMR-200 fecal sampling kits (OMNIgene GUT; DNA Genotek, Kanata, ON, Canada) and stored at-80°C prior to DNA extraction. Bacterial genomic DNA was extracted from fecal samples according to the instructions of the QIAamp DNA Stool Mini Kit (Qiagen, Hilden, Germany). The V3/V4 hypervariable region of 16S rRNA genes was amplified and sequenced using the Illumina MiSeq 2 × 300 System (Illumina, San Diego, CA, United States) according to the manufacturer’s instructions. Raw sequencing reads were analyzed using the QIIME 2 pipeline (Bolyen et al., 2019). Briefly, raw sequence data were demultiplexed and filtered for quality using the DADA2 plugin (Callahan et al., 2016). Only features belonging to the bacterial domain and in the range of 380–450 base pairs were assessed. De novo chimera filtration was performed using the “vsearch uchime-denovo” program (Rognes et al., 2016). Following data filtration, taxonomy was assigned using a pre-trained naive Bayes classifier against Silva-138 reference sequences. Species assignment was conducted with the “vsearch usearch_global” tool and was based on a $99\%$ identity threshold. The dataset was rarefied to the smallest sample before beta diversity analysis. All raw sequencing data presented in this study were deposited in the Sequence Read Archive database under accession number PRJNA901628 1. ## Bacterial strains and culture Faecalibacterium prausnitzii bacteria were isolated from human feces according to the method described by Martín et al. [ 2017], with some modifications. Human feces were collected from healthy Koreans aged 7–60 years as approved by the Institutional Review Board of Dongguk University Ilsan Hospital in the Republic of Korea (2018–06–001-012). As detailed in a previous study (Lee et al., 2022), we performed polymerase chain reaction (PCR) tests using species-specific primers for F. prausnitzii (forward primer: 5’-ACTCAACAAGGAAGTGA-3′; reverse primer: 5’-AATTCCGCCTACCTCTG-3′) to identify the isolates, producing a single band of the available product size (192 bp). Following PCR confirmation, we conducted 16S rRNA gene sequencing using 27F primer (5’-AGAGTTTGATCCTGGCTCAG-3′) and 1492R primer (5’-GGTTACCTTGTTACGACTT-3′). For our bacterial studies, F. prausnitzii strains were cultured in soy peptone-based medium containing (per liter): 20 g soy peptone; 10 g yeast extract; 2.5 g K2HPO4; 0.5 g l-cysteine hydrochloride, and some supplements. The bacteria were cultured at 37°C in an anaerobic chamber containing $90\%$ N2, $5\%$ CO2, and $5\%$ H2. Cells were harvested via centrifugation at 12,000 × g for 5 min at 4°C. Thereafter, the pellets were resuspended in pre-reduced and sterile phosphate-buffered saline (anaerobic PBS), aliquoted, and stored at-80°C in $20\%$ glycerol. ## Antibiotic susceptibility and hemolytic activity The minimum inhibitory concentrations (MIC) were determined for the isolates of seven antibiotic classes (piperacillin-tazobactam, ceftizoxime, chloramphenicol, clindamycin, meropenem, moxifloxacin, metronidazole, and ciprofloxacin) which are effective against anaerobic bacteria using the Wilkins–*Chalgren medium* according to Clinical and Laboratory Standards Institute [2017]. All MICs were interpreted using the CLSI breakpoints for anaerobes. The hemolytic activity of the isolates was determined using tryptic soy agar containing $5\%$ (v/v) defibrinated sheep blood, with the plates being incubated at 37°C for 24 h under the anaerobic conditions listed in Section 2.3. After incubation, hemolytic activity was evaluated and classified based on red blood cell lysis in the medium surrounding the colonies. Strains with no zones around the colonies (γ-hemolysis) were considered safe. ## Study animals and treatments Six-week-old female C57BL/6 mice were purchased from Daehan Biolink Co., Ltd. (Chungbuk, Korea). The use and care of animals were reviewed and approved by the Institutional Animal Care and Use Committee (IACUC) at Dongguk University (approval number: IACUC-2019-041-1) and conformed with the guidelines of the International Association for the Study of Pain policies on the use of laboratory animals. After 1 week of acclimation, the animals were randomly assigned and housed in standard plastic cages (three mice per cage). A total of 96 mice were randomized into the following eight groups (each group $$n = 12$$): a normal control (CON), NASH, NASH with silymarin, A2-165, EB-FPDK3, EB-FPDK9, EB-FPDK11, or EB-FPYYK1 group. All groups were maintained for 16 weeks under different regimens. The normal group was fed a low-fat diet (10 kcal% fat; Research Diets, Inc., NJ, United States) and had free access to plain tap water. NASH model groups were fed a high-fat diet (60 kcal% fat; Research Diets, Inc.) and had free access to water enriched with $30\%$ fructose (high-fructose, HF). Mice in the remaining groups started receiving oral administrations at 8 weeks, consisting of silymarin (NASH with silymarin group) or 1 × 108 CFU of F. prausnitzii strains (A2-165, EB-FPDK3, EB-FPDK9, EB-FPDK11, and EB-FPYYK1 groups). The weight and calorific intake of all mice were measured weekly. After the experiment, the mice were anesthetized to collect blood samples (see Sections 2.6 and 2.7) and euthanized before removal of the spleen, liver, and large intestine. Fresh spleens and livers were weighed. The livers and large intestine were partially sectioned and fixed for histological analysis (Section 2.8), with the remaining tissues stored at-80°C for RNA analysis (Section 2.9). ## Oral glucose tolerance test An OGTT was performed during the last week of the study. After being subjected to 14 h of fasting, the mice were administered oral glucose (2 g/kg), and blood was obtained from the tail vein 0, 30, 60, and 120 min after glucose treatment. Glucose levels (mg/dL) were measured using Accu-Chek test strips on an Accu-Chek Active blood glucose meter (Roche Diagnostics, Rotkreuz, Switzerland). The glucose area under the curve was calculated by plotting the glucose concentration as a function of time (min). ## Biochemical analysis Whole blood samples were centrifuged (2000 × g for 15 min at 4°C) to separate the serum. The serum triglyceride (TG), total cholesterol (TC), aspartate aminotransferase (AST), and ALT levels were measured using assay kits (Asan Pharmaceutical, Seoul, Korea). Lipids were extracted from the liver tissue according to the Folch protocol (Folch et al., 1957). ## Histological analysis of liver and large intestine tissues The liver and large intestine were fixed in neutral-buffered $10\%$ formalin solution, embedded in paraffin wax, and sectioned at a thickness of 4 μm using a microtome. Hematoxylin and eosin (H&E) staining, Sirius Red staining, and α-SMA (ab5694; Abcam, Cambridge, MA, United States) immunohistochemistry (IHC) were performed on the liver sections. To assess the degree of steatosis, lobular inflammation, and hepatocyte ballooning, a NAFLD Activity Score (NAS) was assigned to each group. The NAS system was proposed by the National Institute of Diabetes and Digestive and Kidney Diseases–NASH Clinical Research Network, and the score range is described in Table 1. The thickness of the mucosa and muscularis externa in the large intestine tissue sections was measured with a Nikon Eclipse Ni microscope (Nikon Corporation, Tokyo, Japan). Large intestine sections were stained with anti-zonular-1 antibody (67–7,300; Invitrogen, Waltham, MA, United States) and anti-occludin antibody (71–1,500; Invitrogen) for the analysis of tight junctions. The area of liver fibrosis was quantified using ImageJ software (NIH, Bethesda, MD, United States). **Table 1** | NAS components | NAS components.1 | NAS components.2 | | --- | --- | --- | | Item | Score | Extent | | Steatosis | 0 | <5% | | Steatosis | 1 | 5–33% | | Steatosis | 2 | >33–66% | | Steatosis | 3 | >66% | | Lobular inflammation | 0 | No Foci | | Lobular inflammation | 1 | <2 foci at ×200 | | Lobular inflammation | 2 | 2–4 foci at ×200 | | Lobular inflammation | 3 | >4 foci at ×200 | | Hepatocyte ballooning | 0 | | | Hepatocyte ballooning | 1 | Few balloon cells | | Hepatocyte ballooning | 2 | Many cells/prominent ballooning | ## Real-time PCR for assessing mRNA expression Total RNA was extracted from homogenized liver and large intestine tissues using TRIzol Reagent (Life Technologies, Carlsbad, CA, United States) and purified using RNA PureLink RNA Mini Kits (Thermo Fisher Scientific, Waltham, MA, United States) according to the manufacturer’s instructions. Complementary DNA (cDNA) was synthesized with a reaction micture of volume 20 μl, containing 2 μg of pure RNA, oligo dT primer (M-MLV cDNA Synthesis Kit, Enzynomics), and an M-MLV reverse transcriptase (M-MLV cDNA Synthesis Kit, Enzynomics, Daejeon, Korea) according to the manufacturer’s instructions. For quantitative real-time polymerase chain reaction (qRT–PCR), the cDNA (2 μl) was mixed with primer pairs (250 nM each) and 10 μl of qPCR 2× SYBR Green Premix (Enzynomics, Daejeon, Korea) in reaction mixture of volume 20 μl. After initial denaturation at 95°C for 10 min, cDNA was amplified for 40 cycles of denaturation (95°C, 15 s) and annealing (60°C, 1 min) using QuantStudio3 (Applied Biosystems). The results were normalized to glyceraldehyde-3-phosphatase dehydrogenase (GAPDH). All primer sequences are listed in Table 2. **Table 2** | Target | NCBI Gene accession number | Primer sequence (5′ to 3′) | | --- | --- | --- | | GAPDH | NM_001411843 | F: GAC ATC AAG AAG GTG GTG AAG CAG | | GAPDH | NM_001411843 | R: ATA CCA GGA AAT GAG CTT GAC AAA | | PPAR-γ | NM_011146 | F: CAA GAA TAC CAA AGT GCG ATC AA | | PPAR-γ | NM_011146 | R: GAG CTG GGT CTT TTC AGA ATA ATA AG | | FAS | NM_007987 | F: TAT CAA GGA GGC CCA TTT TGC | | FAS | NM_007987 | R: TGT TTC CAC TTC TAA ACC ATG CT | | LPL | NM_008509 | F: CTC TGT ATG GCA CAG TGG CT | | LPL | NM_008509 | R: TCC ACC TCC GTG TAA ATC AA | | SREBP-1c | NM_001358315 | F: GCT ACC GGT CTT CTA TCA ATG | | SREBP-1c | NM_001358315 | R: GCA AGA AGC GGA TGT AGT C | | CD36 | NM_001159558 | F: GCT TGC AAC TGT CAG CAC AT | | CD36 | NM_001159558 | R: GCC TTG CTG TAG CCA AGA AC | | FATP 5 | NM_009512 | F: GAC TTT TGA TGG GCA GAA GC | | FATP 5 | NM_009512 | R: GGG CCT TGT TGT CCA GTA TG | | L-FABP | NM_017399 | F: ACC TCA TCC AGA AAG GGA AGG | | L-FABP | NM_017399 | R: ACA ATG TCG CCC AAT GTC ATG | | TNF-α | NM_001278601 | F: CCG ATG GGT TGT ACC TTG TC | | TNF-α | NM_001278601 | R: GGGCTGGGTAGAGAATGGAT | | TLR4 | NM_021297 | F: CCT CTG CCT TCA CTA CAG AGA CTT T | | TLR4 | NM_021297 | R: TGT GGA AGC CTT CCT GGA TG | | MCP1 | NM_011333 | F: TTA AAA ACC TGG ATC GGA ACC A | | MCP1 | NM_011333 | R: GCA TTA GCT TCA GAT TTA CGG G | | IL-6 | NM_031168 | F: TCC TAC CCC AAT TTC CAA TGC | | IL-6 | NM_031168 | R: CAT AAC GCA CTA GGT TTG CCG | | Col1a1 | NM_007742 | F: GCT CCT CTT AGG GGC CAC T | | Col1a1 | NM_007742 | R: CCA CGT CTC ACC ATT GGG G | | TIMP-1 | NM_011593 | F: CGA GAC CAC CTT ATA CCA GCG | | TIMP-1 | NM_011593 | R: GGC GTA CCG GAT ATC TGC G | | ZO-1 | NM_00163574 | F: GCC GCT AAG AGC ACA GCA A | | ZO-1 | NM_00163574 | R: TCC CCA CTC TGA AAA TGA GGA | | Occludin | NM_001360538 | F: ATG TCC GGC CGA TGC TCT C | | Occludin | NM_001360538 | R: TTT GGC TGC TCT TGG GTC TGT AT | | GPR43 | NM_001168512 | F: GGC TTC TAC AGC AGC ATC TA | | GPR43 | NM_001168512 | R: AAG CAC ACC AGG AAA TTA AG | | GLP-1 | NM_008100 | F: GGC ACA TTC ACC AGC GAC TAC | | GLP-1 | NM_008100 | R: CAA TGG CGA CTT CTT CTG GG | ## Analysis of gut microbial populations from the cecum Using the QIAamp® DNA Stool Mini Kit, DNA was extracted from cecal samples of control, NASH-induced, EB-FPDK9, and EB-FPDK11-treated mice ($$n = 7$$ per group). 16S rRNA hypervariable regions in V1–V2 were amplified by using primers containing unique 10-base barcodes and sequenced by using the Ion Torrent PGM system according to manufacturer’s instructions. Quality-filtered raw sequence reads were clustered into amplicon sequence varients (ASVs) using the SILVA rRNA gene database with a $99\%$ sequence identity threshold. The software Quantitative Insights into Microbial Ecology 2 (QIIME2) was used to select representative reads and calculate alpha-and beta-diversity. The linear discriminant analysis (LDA) effect size (LEfSe) was used to identify taxa with varying abundances between groups, with a logarithmic LDA score threshold of 2.0 and an alpha value of 0.05 for the factorial Kruskal-Wallis test. ## Analysis of short chain fatty acids from the cecum To extract short chain fatty acids (SCFAs), the cecum (50 mg) was mixed with 800 μl of distilled water and 10 μl of 5 M HCl and after adding 400 μl of ether, the mixture was shaken at 4°C for 5 min. After spin down, 200 μl of ether layer was derivatized by adding 20 μl of N, O-bis (trimethylsilyl) trifluoroacetamide (BSTFA) at 70°C for 20 min and then incubated at 37°C for 2 h. The derivatized SCFAs were analyzed using a GC/MS system (Shimadzu Corp., Kyoto, Japan) equipped with a DB-5MS column (30 m × 0.25 mm, 0.25 μm film thickness, Agilent Technologies, Santa Clara, CA, United States) at a split ratio of 1: 50. The injector temperature was set at 200°C, and helium was used as the carrier gas at a flow rate of 0.89 ml/min. The oven temperature program was set as holding at 40°C for 2 min, increasing from 40 to 70°C at a rate of 10°C/min, increasing from 70 to 85°C at a rate of 4°C/min, increasing from 85 to 110°C at a rate of 6°C/min, increasing from 110 to 290°C at a rate of 90°C/min, and holding 290°C for 5 min. The effluent was detected using a GCMS-TQ 8030 MS (Shimadzu Corp.) system with selected ion monitoring (SIM). The ion source and interface temperatures were 200 and 250°C, respectively, and detector voltage was 0.1 kV. SCFAs were detected by SIM mode with m/z 117, 131, and 145 of acetic acid, propionic acid, and butyric acid, respectively. Authentic standard SCFAs were used to quantitative analysis of SCFAs. ## Identification of the microbial anti-inflammatory molecule protein MAM sequences were identified from four F. prausnitzii genomes (EB-FPDK3, EB-FPDK9, EB-FPDK11, and EB-FPYYK1) by BLAST search with low e-values against the MAM sequence from A2-165 as the query (Quévrain et al., 2016; Auger et al., 2022; Hong and Nam, n.d.). Translated MAM sequences were aligned by using Clustal Omega (Sievers et al., 2011) to investigate MAM-derived peptides which were previously identified as pep1–5 (Quévrain et al., 2016). Further, homology models of MAM proteins derived from four F. prausnitzii strains were provisionally built using the Modeler program (Webb and Sali, 2016). ## Statistical analyzes All data are expressed as the arithmetic mean ± standard error of the mean (SEM). We used GraphPad Prism 7 (GraphPad, San Diego, CA, United States) for all statistical analyzes, comprised of Mann–Whitney U tests (two-tailed) or Kruskal–Wallis one-way ANOVAs, with Dunn’s test to correct for multiple comparisons (unless indicated otherwise). We considered a p value <0.05 to indicate statistical significance. ## Gut microbiota composition in patients with NASH and healthy individuals To explore the dysbiosis of gut microbiota in NASH, we performed 16S rRNA gene amplicon sequencing on fecal samples from patients with NASH and compared the results with those obtained for healthy individuals. We used our previously published data (Lim M. Y. et al., 2021) on the gut microbiota composition of healthy Korean subjects ($$n = 99$$), who were age-and sex-matched with patients with NASH ($$n = 45$$). Gut microbiota compositional discrimination was observed via unweighted UniFrac principal component analysis between the NASH and healthy control groups (Figure 1A). Bacterial diversity (according to the Shannon index, Faith’s PD index, observed features, and Chao1 index) in NASH patients was significantly lower compared to the healthy controls (Figure 1B). We also observed dysbiosis in the gut microbiota profile, particularly deficient F. prausnitzii and abundant *Fusobacterium mortiferum* (Figure 1C). As a next step, we found that two ASVs belonging to F. prausnitzii, 5fdd92ad3225b67f02453b5c4590b968 and 692ed0000e9f6e5d47f92a7c59d88434, showed significant differences between the groups (Supplementary Figure S1A). Using these ASVs, strains EB-FPDK3 and EB-FPDK9 were matched to ASV 692ed0000e9f6e5d47f92a7c59d88434, whereas strains EB-FPDK11 and EB-FPYYK1 were matched to ASV 5fdd92ad3225b67f02453b5c685 with only minor variations (Supplementary Figure S1B). Therefore, we hypothesized that restoration of these strains in the gut could alleviate NASH symptoms. To test this hypothesis, we isolated four F. prausnitzii strains (EB-FPDK3, EB-FPDK9, EB-FPDK11, and EB-FPYYK1) from fecal samples collected from four healthy individuals (Supplementary Figure S2). Before administering these strains to mice, we confirmed their basic safety via laboratory tests for hemolytic activity and antibiotic susceptibility. The hemolytic activities of five strains (the four isolates plus the type strain, A2-165) were evaluated on blood agar plates. None of the tested strains showed α-hemolytic or β-hemolytic activities when grown on blood agar plates. All five strains showed γ-hemolytic activity, that is, negative or no hemolytic activity. Susceptibility to antimicrobial agents was assessed according to the CLSI guidelines. All strains, including the type strain A2-165, were susceptible to clindamycin (MICs <0.125) and metronidazole (MICs ranging from ≤0.125 to 4 μg/ml). However, all strains were resistant to meropenem (MICs >64 μg/ml) and fluoroquinolones (moxifloxacin and ciprofloxacin, MICs ranging from 8 to >32 μg/ml; Supplementary Table S1). **Figure 1:** *A comparison of gut microbiota in patients with NASH (n = 45) and sex- and age-matched healthy individuals (n = 99). (A) Principal component analysis plot comparing the microbiota of patients with NASH and healthy individuals using unweighted and weighted UniFrac distances. Adonis tests confirmed significant differences between the two groups. (B) A comparison of the alpha diversity index between fecal samples of patients with NASH and healthy individuals (Mann–Whitney U test; *p ≤ 0.05, ∗∗p < 0.01). (C) A species-level comparison of the relative abundance of gut microbiota between participants with NASH and healthy individuals. NASH: nonalcoholic steatohepatitis.* ## Faecalibacterium prausnitzii supplementation improves glucose homeostasis in NASH mice A high-fructose and high-fat (HFHF) diet is widely used to induce NASH in animal models to achieve a state that most closely resembles the human NAFLD (Rafiq et al., 2009; Im et al., 2021). To examine the effect of F. prausnitzii on NASH, five F. prausnitzii strains (comprising the reference strain A2-165 and four F. prausnitzii isolates) were orally administered to HFHF-fed mice for 9 weeks. Silymarin was used as a positive control; it contains a mixture of flavonolignans extracted from milk thistle and has been used as a natural drug against liver disease for centuries (Kim et al., 2012; Ni and Wang, 2016). HFHF feeding of mice in the NASH group led to a significant increase in body weight and calorie intake compared to mice following a normal diet in the CON group (Figures 2A,B). There were no significant differences in body weight or calorie intake in any of the treatment groups (referring to the groups in which either silymarin or a bacterial strain was administered) compared to the NASH group. To confirm the effect of F. prausnitzii on glucose homeostasis, an OGTT was performed after 16 weeks using blood from mice that had been subjected to a fasting state for 14 h. The blood glucose level reached its highest level 30 min after glucose administration and gradually decreased (Figure 2C). The area under the receiver operating characteristic curve was significantly higher in the NASH group than in the CON group (Figure 2D). Silymarin, A2-165, EB-FPDK9, and EB-FPDK11 supplementation significantly reduced the blood glucose levels compared to those in the NASH group ($p \leq 0.05$). This indicates that A2-165, EB-FPDK9, and EB-FPDK11 bacterial strains were effective at improving glucose tolerance. **Figure 2:** *Faecalibacterium prausnitzii improves glucose levels in mouse models of NASH induced with a high-fructose and high-fat diet. (A) Body weight changes across 12 weeks. (B) Weekly calorie intake. (C) Oral glucose tolerance test results. (D) Glucose area under the curve with glucose concentration as a function of time. The data are presented as mean ± SEM (n = 12). Statistical analyzes were performed using Mann–Whitney U test (two tailed) (B,D). ∗∗p < 0.01 versus the CON group; ## p < 0.01 versus the NASH group. CON: control, NASH: nonalcoholic steatohepatitis.* ## Faecalibacterium prausnitzii prevents hepatic lipid accumulation in NASH mice NASH mouse models are characterized by the accumulation of hepatic lipids (Saito et al., 2015). To investigate lipid accumulation in the livers of our study animals, we measured the concentrations of TG and TC. Their levels in both the liver and serum were significantly increased in the NASH group compared to the respective levels in the CON group (Figures 3A–D). All treatments significantly decreased TG and TC levels in the liver compared to those in the NASH group (Figures 3A,B). Serum TG levels were significantly decreased in the groups administered with silymarin and all F. prausnitzii strains except EB-FPYYK1; the largest decreases were recorded in the EB-FPDK3 and EB-FPDK11 groups. Serum TC levels were significantly decreased by silymarin, EB-FPDK9, and EB-FPDK11 treatment (Figures 3C,D). Serum AST and ALT levels were significantly higher in the NASH group than in the CON group (Figures 3E,F). Compared to those in the NASH group, the serum AST levels were significantly lower in all experimental groups, while the serum ALT levels were significantly decreased in all experimental groups except EB-FPYYK1. **Figure 3:** *Effects of Faecalibacterium prausnitzii in preventing lipid accumulation in and damage to the liver. (A) TG and (B) TC concentrations in the liver. (C) TG and (D) TC concentrations in serum. (E) Serum AST and (F) serum ALT levels. The data are presented as the mean ± SEM (n = 12). Statistical analyzes were performed using Kruskal–Wallis one-way ANOVA with Dunn’s test (A,B) and the Mann–Whitney U test (two-tailed) (C–F). ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001 versus the CON group; #p < 0.05, ##p < 0.01, ###p < 0.001 versus the NASH group; $p < 0.05 versus the silymarin group; &p < 0.05 versus the A2-165 group. TG, triglycerides; TC, total cholesterol; AST, aspartate aminotransferase; ALT, alanine aminotransferase.* ## Faecalibacterium prausnitzii prevents liver damage and fibrosis in NASH mice Liver images were captured to investigate the extent of liver damage. The livers of mice in the NASH group displayed an enlarged volume of lipid accumulation characterized by a yellow color and hard texture compared to the CON group, in which hepatic lipids exhibited a soft texture with a smooth, red-brown surface (Figures 4A,C). For histopathological analysis, microscope images were obtained of H&E-stained liver tissues (Figure 4B). In the NASH group, we observed microvascular steatosis by lipid deposition and abnormal hepatocyte morphology, such as lobular inflammation and ballooning. When each group was scored according to the NAS criteria listed in Table 1, no abnormal morphology was observed in the CON group, whereas the NAS scores of the EB-FPDK3, EB-FPDK9, and EB-FPDK11 groups were significantly lower than that of the NASH group (Figure 4D). These results show that the oral administration of F. prausnitzii strains reduces the amount of lipid accumulation in and subsequent damage to the liver, as induced by NASH. **Figure 4:** *Effects of Faecalibacterium prausnitzii in preventing lipid accumulation in and damage to the liver. (A) Photographs of mice liver. (B) Microscopic images of liver tissue after H&E staining. Scale bar: 0.1 mm. (C) Weights of mice liver samples. (D) NAFLD Activity Score (NAS). The data are presented as the mean ± SEM (n = 12). The black arrow indicates the lipid deposition site in the liver tissue Statistical analyzes were performed using Kruskal–Wallis one-way ANOVA with Dunn’s test, ∗∗p < 0.01, ∗∗∗p < 0.001 versus the CON group; #p < 0.05, ##p < 0.01 versus the NASH group. NAFLD: nonalcoholic fatty liver disease, NASH: nonalcoholic steatohepatitis.* The progression to fibrosis and cirrhosis is a key challenge in human NAFLD. To assess the degree of fibrosis that NASH can induce in the liver, we performed Sirius Red assays (Figures 5A–C). By measuring the Sirius Red-positive areas of livers with ImageJ software, hepatic fibrosis progression was mainly observed in the periportal region of all mouse groups. The livers of NASH group mice showed a significantly larger Sirius red-positive area than those of the EB-FPDK3, EB-FPDK9, EB-FPDK11, EB-FPYYK1, and silymarin groups (Figures 5A,B). Our IHC assay used α-SMA as a marker for evaluating stellate cell activation and fibrosis progression (Akpolat et al., 2005). It identified α-SMA-positive areas predominantly in lipid-accumulated foci. These regions were significantly larger in the NASH group than in the CON group, while they were significantly smaller in all treatment groups compared to the NASH group (Figures 5A,C). We also found that the expression levels of collagen type 1 a 1 and metallopeptidase inhibitor 1 were significantly decreased by F. prausnitzii infection (Figures 5D,E). These results indicate a reduction in fibrosis and collagen deposition in the liver after the oral administration of F. prausnitzii. **Figure 5:** *Faecalibacterium prausnitzii prevents the development of fibrosis in the liver. (A) Microscope images of Sirius Red- and α-SMA-stained liver tissues for immunohistochemistry assays. Scale bar: 0.1 mm. (B,C) Areas stained positive for Sirius Red and α-SMA in the liver. mRNA levels of hepatic fibrosis markers (D) Col1a1 and (E) TIMP-1. The data are presented as the mean ± SEM (n = 12). Statistical analyzes were performed using the Mann–Whitney U test (two-tailed) ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001 versus the CON group; #p < 0.05, ##p < 0.01, ###p < 0.001 versus the NASH group; $p < 0.05 versus the silymarin group, &p < 0.05, &&&p < 0.001 versus the A2-165 group. α-SMA, alpha-smooth muscle actin; Col1a1, collagen type 1 a 1; TIMP-1, metallopeptidase inhibitor 1; CON, control; NASH, nonalcoholic steatohepatitis.* ## Faecalibacterium prausnitzii improves the damaged gut barrier functions of NASH mice The consumption of an HFHF diet can shift the intestinal luminal composition and increase the risk of gut leakiness (Moreira et al., 2012). Using microscope photographs of H&E-stained large intestine tissues, we measured the thickness of the mucosa and muscularis externa where the damaged colonic barrier appeared thinner (Figures 6A–C). In the NASH group, the mucosa and muscularis externa were significantly thinner compared to those in the CON group. The silymarin group showed significantly increased mucosal and muscularis externa thicknesses compared to the NASH group. Among the F. prausnitzii strains, EB-FPDK9, EB-FPDK11, and EB-FPYYK1 showed significant efficacy in improving both the mucosa and muscularis externa thickness. **Figure 6:** *Faecalibacterium prausnitzii improves impaired gut barrier functions by modulating the tight junctions in the large intestine. (A) Microscope images of large intestine after H&E staining. Scale bar: 0.1 mm. Yellow arrows indicate the thickness of mucosa tissue, and black arrows indicate the thickness of muscularis externa tissue. (B) The thickness of the mucosa and (C) muscularis externa of the large intestine. (D) Microscope images of large intestine tissue after ZO-1 and occludin immunohistochemical staining. Scale bar: 0.1 mm. (E) The ZO-1-positive and (F) occludin-positive regions. The data are presented as the mean ± SEM (n = 12). Statistical analyzes were performed using the Mann–Whitney U test (two-tailed) ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001 versus the CON group; #p < 0.05, ##p < 0.01, ###p < 0.001 versus the NASH group; $p < 0.05, $$p < 0.01 versus the silymarin group; &p < 0.05, &&p < 0.01, &&&p < 0.001 versus the A2-165 group. ZO-1, zonula occludens-1; CON, control.* We investigated the efficacy of barrier function in the large intestine via IHC assay, using the expression of zonula occludens-1 (ZO-1) and occludin (OCLN) proteins as indicators (Figures 6D–F). The tight junction protein ZO-1 is essential for barrier function and plays a critical role in the effective mucosal repair (Kuo et al., 2021). In the NASH group, the ZO-1-positive area was significantly smaller compared with that in the CON group, suggesting barrier damage (Figure 6E). Among the F. prausnitzii treatments, only that of the EB-FPDK11 strain resulted in a significant increase in ZO-1-positive area compared to the NASH group. OCLN is another tight junction protein that is crucial for maintaining the epithelial barrier (Chelakkot et al., 2018). The OCLN-positive area was significantly smaller in the NASH group than in the CON group, but the area was recovered in mice receiving EB-FPDK9 and EB-FPDK11 treatments (Figure 6F). ## Faecalibacterium prausnitzii alleviates hepatic steatosis in NASH mice To understand the mechanisms underlying the observed effects of F. prausnitzii treatment in a NASH mouse model, we evaluated the key signaling pathways involved in the modulation of lipid metabolism, as quantified by real-time reverse-transcriptase PCR. The levels of mRNA expressing the transport proteins CD36 and fatty acid transport protein 5 (FATP5) were approximately 1.6- and 1.99-fold higher in the NASH group than in the CON group, respectively (Figures 7A,B). Additionally, the levels of mRNA expressing peroxisome proliferator-activated receptor gamma (PPAR-γ), sterol regulatory element-binding protein-1c (SREBP-1c), fatty acid synthase (FAS), and lipoprotein lipase (LPL) were also significantly higher in the NASH group. Our PCR results revealed that the expression level of genes involved in lipid metabolism was significantly reduced by oral administration of F. prausnitzii (Figures 7C–F). This indicates that F. prausnitzii bacteria can regulate the genetic mechanisms underlying lipid metabolism as related to the alleviation of hepatic steatosis in NASH mice. **Figure 7:** *Faecalibacterium prausnitzii reduces hepatic steatosis and alleviates liver fat disease. mRNA levels of hepatic steatosis markers (A) CD36, (B) FATP5, (C) PPAR-𝛾, (D) SREBP-1c, (E) FAS, and (F) LPL. Statistical analyzes were performed using Kruskal–Wallis one-way ANOVA with Dunn’s test (A,D,E) or Mann–Whitney U test (two tailed) (B,C,F). ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001 versus the CON group; #p < 0.05, ##p < 0.01, ###p < 0.001 versus the NASH group. FATP5, fatty acid transport protein-5; PPAR-𝛾, peroxisome proliferator-activated receptor gamma; SREBP-1c, sterol regulatory element-binding protein-1c; FAS, fatty acid synthase; LPL, lipoprotein lipase; CON, control; NASH, nonalcoholic steatohepatitis.* ## Faecalibacterium prausnitzii alleviates liver inflammation in NASH mice We also investigated the expression levels of mRNA that encode key inflammatory cytokines, namely, tumor necrosis factor-α, monocyte chemoattractant protein-1, and interleukin-6. The levels of these cytokines in the NASH group were significantly higher than those in the CON group (Figures 8A–C). However, oral administration of F. prausnitzii strains attenuated the severity of the hepatic inflammatory state by preventing an increase in inflammatory gene expression levels induced by NASH (Figures 8A–C). In particular, F. prausnitzii EB-FPDK9, EB-FPDK11, and EB-FPYYK1 showed a significant reduction in gene expression for all three cytokines compared with the NASH group. The mRNA expression of Toll-like receptor 4 was also significantly higher in the NASH group than in the CON group. However, the oral administration of F. prausnitzii significantly reduced the mRNA expression of Toll-like receptor 4 as induced by NASH (Figure 8D). This receptor is responsible for pathogen detection and the initiation of cytokine production. Our results, therefore, show that F. prausnitzii alleviated liver inflammation by regulating the expression of related genes in NASH-induced mice. **Figure 8:** *Faeclibacterium prausnitzii reduces the expression of inflammatory cytokines in the liver. mRNA levels of the inflammatory cytokines (A) TNF-α, (B) TLR4, (C) MCP-1, and (D) IL-6. Statistical analyzes were performed using Mann–Whitney U-test (two-tailed) (A,B) and Kruskal–Wallis one-way ANOVA with Dunn’s test (C,D). ∗p < 0.05, ∗∗p < 0.01 versus the CON group; #p < 0.05, ##p < 0.01, ###p < 0.001 versus the NASH group. TNF-α, tumor necrosis factor-α; TLR4, Toll-like receptor 4; MCP-1, monocyte chemoattractant protein-1; IL-6, interleukin-6; CON, control; NASH, nonalcoholic steatohepatitis.* ## Discussion This study establishes that healthy individuals and patients with NASH display distinct gut microbial structures. F. prausnitzii was among the microorganisms that were significantly lower in patients with NASH compared to healthy individuals, and we, therefore, selected this bacterium to isolate as a next-generation probiotic candidate. Indeed, treatment with F. prausnitzii strains in a NASH mouse model relieved NASH symptoms such as glucose homeostasis, hepatic lipid accumulation, liver damage, and liver fibrosis. Additionally, treatment with F. prausnitzii strains restored NASH-induced gut inflammation and altered gut permeability. The current focus of treatment for patients with NAFLD is on dietary and lifestyle modifications, and no approved pharmacological therapies or surgical procedures exist to combat symptoms. Probiotics, prebiotics, and fecal microbiota transplants targeting the gut-liver axis are emerging as new strategies in precision medicine for both alcoholic and non-alcoholic fatty liver diseases (Bluemel et al., 2016). Probiotics are widely applied in the management of various diseases involving host-gut microbial interactions (Kim et al., 2019), and the development of effective probiotics is critical for treating liver disease (Meroni et al., 2019; Xu et al., 2021). Li et al. [ 2003] first reported the use of probiotics in a NASH animal model. They found that VSL#3 improved the liver histology, hepatic lipid accumulation, and serum ALT levels of mice. Since then, various studies have investigated the mechanisms underlying the effects of VSL#3 supplementation in NAFLD models (Loguercio et al., 2005; Esposito et al., 2009; Velayudham et al., 2009). Ahn et al. [ 2019] reported that 12 weeks of treatment with a multi-species probiotic mixture (Lactobacillus acidophilus, Lactobacillus rhamnosus, Lactobacillus paracasei, Pediococcus pentosaceus, Bifidobacterium lactis, and Bifidobacterium breve) significantly reduced the intrahepatic fat fraction and body weight of patients with obesity and NAFLD. Administration of B. longum reduced hepatic fat accumulation irrespective of gut permeability restoration in a NAFLD rat model (Xu et al., 2012). However, a single-strain or probiotic treatment targeting NASH, an extremely advanced form of NAFLD, is still lacking. We selected F. prausnitzii as a next-generation probiotic candidate through an analysis of the gut microbiota in NASH patients, which indicated that the abundance of F. prausnitzii was significantly lower in patients with NASH than in healthy controls. This finding is consistent with previous reports (Da Silva et al., 2018; Iino et al., 2019; Zhong et al., 2021; Hu et al., 2022b) and suggests that F. prausnitzii may serve a potential role in the treatment of NASH. This approach increases the efficiency of probiotic development by targeting only the relevant microorganisms affected by NASH. Lim E. Y. et al. [ 2021] similarly developed an effective probiotic for alleviating menopausal symptoms by selecting probiotic candidates based on the gut microbiota dysbiosis. In this study, we found that F. prausnitzii strains had anti-NASH effects in a mouse model where NASH was induced via an HFHF diet. Treatment with F. prausnitzii may offer a useful therapeutic option for human patients with NASH. However, there is still a lack of information regarding the mechanisms underlying the NASH improvements observed. In our study, the F. prausnitzii treatment group displayed an improvement in hepatic lipid accumulation compared to the NASH group. There is a direct relationship between liver inflammation in NASH and TG stored in the liver (Kawano and Cohen, 2013; Simental-Mendia et al., 2016). TG has been used as a marker for screening simple steatosis and NASH (Kawano and Cohen, 2013; Simental-Mendia et al., 2016). Excess ingested cholesterol is removed from the body through hepatic excretion, resulting in the liver exhibiting a lower cholesterol concentration than other tissues. The exact contribution of cholesterol consumption to NASH has not yet been determined, but it has shown a relation to NASH risk and severity (Puri et al., 2007). In addition, the induction of NASH results in a dysregulation of the hepatic cholesterol homeostasis (Ioannou, 2016). Circulating free fatty acids are a major source of hepatic lipids in NASH models (Bradbury, 2006). The mechanisms promoting liver injury are not fully understood, but the involvement of substrates derived from adipose tissues, such as free fatty acids, leptin, and adiponectin, have been suggested (McPherson et al., 2015). Free fatty acid uptake by hepatic fatty acid transporters such as CD36 and FATP5 promotes hepatic steatosis by increasing PPAR-γ (Inoue et al., 2005). SREBP also increases the liver steatosis (Moslehi and Hamidi-Zad, 2018); its activation induces the expression of FAS and LPL, which regulate the fatty acid metabolism (Kim and Spiegelman, 1996). In this study, we found that F. prausnitzii strains regulated the expression of genes related to hepatic steatosis in NASH mice. This suggests that the anti-NASH mechanism of F. prausnitzii may inhibit lipid accumulation by regulating the genetic source of hepatic steatosis. Disruption of the gut barrier results in a leaky gut that allows harmful substances to pass through mucosal tissues and leads to several diseases, including inflammatory bowel disease, celiac disease, and type 1 diabetes (Groschwitz and Hogan, 2009). Gut permeability is increased in patients with NAFLD patients compared to healthy controls and is associated with hepatic steatosis (De Munck et al., 2020). NASH is characterized by varying degrees of steatosis and aggressive inflammation (Hansen et al., 2017). The inflammatory response is a critical component leading to subsequent liver damage (Budick-Harmelin et al., 2008; Henao-Mejia et al., 2012). Changes in gut microbiota composition can alter gut barrier function, complementing the progress and advancement of liver disease (Plaza-Díaz et al., 2020). Our further analysis of cecum microbiota composition revealed that the intestinal microbial community was distinct between the normal and NASH groups, and EB-FPDK11 exhibited a distinct difference between the NASH and EB-FPDK9 group (Supplementary Figure S3A). In the alpha-diversity analysis, both Shannon index and Chao1 index were significantly lower in the NASH group than in the normal group. However, no significant difference was observed in the group treated with the two strains (Supplementary Figure S3B–C). The linear discriminant analysis (LDA) effect size algorithm (LEfSe) identified 11 biomarkers at the genus level in the gut microbiome of EB-FPDK9-treated group, such as Parabacteroides and Odoribacter (Supplementary Figure S3D), which have been reported to be positively correlated with the mRNA expression of tight junction proteins such as ZO1 and occludin (Koh et al., 2020; Zhao et al., 2020; Jiang et al., 2023). The abundance of Lachnospiraceae_NK4A136_group was significantly higher in the EB-FPDK11-treated group than in the NASH group (Supplementary Figure S3E), and this genus has been reported to play a role in maintaining the integrity of the intestinal barrier (Ma et al., 2020). In addition to Lachnospiraceae_NK4A136_group, Gemella, the proportion of which was increased in the EB-FPDK11-treated group, has reported to be positively correlated with the expression of tight junction proteins (Hu et al., 2023). The altered microbiota in the F. prausnitzii-treated group may have influenced the strength of the gut barrier, indicating a potential connection between the two. Several studies have reported that F. prausnitzii shows a strong anti-inflammatory activity (Sokol et al., 2008; Miquel et al., 2015; Lopez-Siles et al., 2017). This study recorded an improvement in inflammation and gut barrier function in the F. prausnitzii treatment groups when compared to conditions in the NASH group. Considering these results in combination, we can speculate that F. prausnitzii may improve liver histology via the restoration of gut barrier function that alleviates liver inflammation. However, the mechanisms underlying such gut barrier function modulation remain unexplored and deserve further research. Another possible scenario is that F. prausnitzii can produce short-chain fatty acids (SCFAs) by breaking down undigested dietary fibers (Zhou et al., 2021). SCFAs include acetate, propionate, and butyrate, and their ability to alleviate the symptoms of hepatic diseases has been demonstrated in vivo and in vitro. In a mouse model of NAFLD, butyrate supplementation via gut microbiota attenuated hepatic steatosis via AMPK (adenosine 5′-monophosphate-activated protein kinase)-dependent SREBP-1c transcriptional inactivation; this effect reduced the expression of lipogenesis-related genes such as FAS and SCD1 (Zhao et al., 2021). In addition, Deng et al. [ 2020] reported that acetate similarly reduced liver steatosis and inflammation via AMPK activation in a mouse model of NASH and in vitro (Deng et al., 2020). Therefore, we expected the SCFA levels to decrease in the NASH model and increase in the strain-treated group. However, contrary to our expectations, in the two groups inoculated with the strain, it was observed that the levels of the three SCFAs in the cecum increased in the NASH group compared with those in the normal group, and then recovered to levels similar to those in the normal group (Supplementary Figure S4). Generally, studies in the field of liver diseases suggest that SCFAs are metabolically beneficial (Mattace Raso et al., 2013; Jin et al., 2015) and protect against gut inflammation (Kles and Chang, 2006). On the contrary, Rau et al. [ 2018] reported that patients with NASH had higher levels of SCFAs and more number of SCFA-producing bacteria in their fecal samples than the control group. This study established an association between increased levels of SCFAs and the progression of disease, along with immune characteristics. In patients with NASH, the increased levels of SCFAs has been linked to a decrease in the count of resting regulatory T cells (CD4 + CD45RA + CD25+) and an increase in the ratio of T helper 17 cells to resting regulatory T cells in peripheral blood (Rau et al., 2018). The study suggests that the higher prevalence of SCFA-producing bacteria in the feces of patients with NAFLD could contribute to disease progression by sustaining low-grade inflammatory processes that influence immune cells in circulation, thereby affecting peripheral target organs such as the liver or gut barrier (Rau et al., 2018). They also hinder the activity of adenosine monophosphate-activated protein kinase, leading to the buildup of hepatic free fatty acids. Similarly, the increased levels of SCFAs observed in NASH model in our study may have contributed to the persistence of an inflammatory state affecting the liver or gut barrier. The administration of F. prausnitzii may have played a role in restoring these processes to a normal state. The findings obtained in our study were from the caecum and thus may have limitations in terms of evaluating systemic physiological phenomena. Nevertheless, we opine that our findings are sufficient to support the application of the strains studied in clinical trials in humans. F. prausnitzii possesses exceptional anti-inflammatory properties, some of which can be attributed to the generation of the MAM protein (Quévrain et al., 2016). Past phylogenetic investigations have revealed diverse phylogroups of F. prausnitzii strains (Fitzgerald et al., 2018; Hu et al., 2022a). Interestingly, signature MAMs derived from different F. prausnitzii phylogroups exhibit varied anti-inflammatory properties (Auger et al., 2022). These findings suggest that the MAM protein can be used as a distinctive marker for characterizing F. prausnitzii strains as probiotics. The MAM proteins identified from the four strains showed genetic variations in their sequences (Supplementary Figure S5A). Homology modeling of the MAM proteins showed structural differences among F. prausnitzii strains (Supplementary Figure S5B). Although we could not shed light on all the differences in the probiotic efficacy of each F. prausnitzii strain from this study, MAM protein may explain the differences in the efficacy of F. prausnitzii as probiotics. Moreover, Xu et al. demonstrated that MAM proteins derived from F. prausnitzii can restore the structure and function of the intestinal barrier by regulating the tight junction pathway and expression of ZO-1 (Xu et al., 2020). MAMs have the ability to suppress Th1 and Th17 immune responses and NF-kB activation (Sokol et al., 2008; Breyner et al., 2017). The reduction in serum LPS levels that result from the anti-inflammatory effects of MAMs (Xu et al., 2020) could also enhance gut barrier integrity and potentially decrease the likelihood of hepatic injury in NASH, given that inflammation is a contributing factor to the disease. Further research is required to examine the therapeutic effects of signature MAMs derived from tested strains in NASH. In summary, our results demonstrated that F. prausnitzii treatment significantly ameliorated the symptoms associated with NASH in a mouse model, restoring gut barrier function. Furthermore, we investigated the potential mechanisms underlying the observed effects of F. prausnitzii treatment on lipid metabolism and inflammation. Despite the significance of our results, there were some limitations to our study. The findings of this study indicate the potential of F. prausnitzii as a next-generation probiotic agent for NASH prevention. ## Data availability statement The data presented in this study are deposited in the NCBI Sequence Read Archive (SRA) repository (https://www.ncbi.nlm.nih.gov), accession numbers PRJNA901628 and PRJNA938166. ## Ethics statement The studies involving human participants were reviewed and approved by the institutional review board of Hanyang University College of Medicine (IRB number: 2014–03–008-005). The patients/participants provided their written informed consent to participate in this study. The animal study was reviewed and approved by the Institutional Animal Care and Use Committee (IACUC) at Dongguk University (approval number: IACUC-2019-041-1). ## Author contributions J-GS, DWJ, and Y-DN conceived and designed the project. DL, S-YJ, HRB, M-GH, S-NL, and H-JK provided an experimental supplement and technical support and managed the animal study. J-HS and YL conducted data analysis. J-HS, YL, and E-JS wrote the manuscript. All authors read and approved the final version of the manuscript. ## Funding This work was supported by the Main Research Program (grant number E0170600-07) of the Korea Food Research Institute, funded by the Korean Ministry of Science and Information & Communication Technology. ## Conflict of interest Authors YL, DL, S-YJ, HRB, M-GH, S-NL, and J-GS were employed by Enterobiome Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2023.1123547/full#supplementary-material ## References 1. Ahn S. B., Jun D. W., Kang B.-K., Lim J. H., Lim S., Chung M.-J.. **Randomized, double-blind, placebo-controlled study of a multispecies probiotic mixture in nonalcoholic fatty liver disease**. *Sci. Rep.* (2019) **9** 5688. DOI: 10.1038/s41598-019-42059-3 2. Akpolat N., Yahsi S., Godekmerdan A., Yalniz M., Demirbag K.. **The value of alpha-SMA in the evaluation of hepatic fibrosis severity in hepatitis B infection and cirrhosis development: a histopathological and immunohistochemical study**. *Histopathology* (2005) **47** 276-280. DOI: 10.1111/j.1365-2559.2005.02226.x 3. Arab J. P., Karpen S. J., Dawson P. A., Arrese M., Trauner M.. **Bile acids and nonalcoholic fatty liver disease: molecular insights and therapeutic perspectives**. *Hepatology* (2017) **65** 350-362. DOI: 10.1002/hep.28709 4. Auger S., Kropp C., Borras-Nogues E., Chanput W., Andre-Leroux G., Gitton-Quent O.. **Intraspecific diversity of microbial anti-inflammatory molecule (MAM) from**. *Int. J. Mol. Sci.* (2022) **23** 1705. DOI: 10.3390/ijms23031705 5. Backhed F., Ley R. E., Sonnenburg J. L., Peterson D. A., Gordon J. I.. **Host-bacterial mutualism in the human intestine**. *Science* (2005) **307** 1915-1920. DOI: 10.1126/science.1104816 6. Bluemel S., Williams B., Knight R., Schnabl B.. **Precision medicine in alcoholic and nonalcoholic fatty liver disease via modulating the gut microbiota**. *Am. J. Physiol. Gastrointest. Liver Physiol.* (2016) **311** G1018-g1036. DOI: 10.1152/ajpgi.00245.2016 7. Bolyen E., Rideout J. R., Dillon M. R., Bokulich N. A., Abnet C. C., Al-Ghalith G. A.. **Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2**. *Nat. Biotechnol.* (2019) **37** 852-857. DOI: 10.1038/s41587-019-0209-9 8. Boursier J., Mueller O., Barret M., Machado M., Fizanne L., Araujo-Perez F.. **The severity of nonalcoholic fatty liver disease is associated with gut dysbiosis and shift in the metabolic function of the gut microbiota**. *Hepatology* (2016) **63** 764-775. DOI: 10.1002/hep.28356 9. Bradbury M. W.. **Lipid metabolism and liver inflammation. I. Hepatic fatty acid uptake: possible role in steatosis**. *Am. J. Physiol. Gastrointest. Liver Physiol.* (2006) **290** G194-G198. DOI: 10.1152/ajpgi.00413.2005 10. Breyner N. M., Michon C., de Sousa C. S., Vilas Boas P. B., Chain F., Azevedo V. A.. **Microbial anti-inflammatory molecule (MAM) from**. *Front. Microbiol.* (2017) **8** 114. DOI: 10.3389/fmicb.2017.00114 11. Brunt E. M.. **Nonalcoholic steatohepatitis: definition and pathology**. *Semin. Liver Dis.* (2001) **21** 003-016. DOI: 10.1055/s-2001-12925 12. Budick-Harmelin N., Dudas J., Demuth J., Madar Z., Ramadori G., Tirosh O.. **Triglycerides potentiate the inflammatory response in rat Kupffer cells**. *Antioxid. Redox Signal.* (2008) **10** 2009-2022. DOI: 10.1089/ars.2007.1876 13. Callahan B. J., McMurdie P. J., Rosen M. J., Han A. W., Johnson A. J., Holmes S. P.. **DADA2: high-resolution sample inference from Illumina amplicon data**. *Nat. Methods* (2016) **13** 581-583. DOI: 10.1038/nmeth.3869 14. Carding S., Verbeke K., Vipond D. T., Corfe B. M., Owen L. J.. **Dysbiosis of the gut microbiota in disease**. *Microb. Ecol. Health Dis.* (2015) **26** 26191. DOI: 10.3402/mehd.v26.26191 15. Chang C.-J., Lin T.-L., Tsai Y.-L., Wu T.-R., Lai W.-F., Lu C.-C.. **Next generation probiotics in disease amelioration**. *J. Food Drug Anal.* (2019) **27** 615-622. DOI: 10.1016/j.jfda.2018.12.011 16. Chelakkot C., Ghim J., Ryu S. H.. **Mechanisms regulating intestinal barrier integrity and its pathological implications**. *Exp. Mol. Med.* (2018) **50** 1-9. DOI: 10.1038/s12276-018-0126-x 17. Cheng D., Xie M.. **A review of a potential and promising probiotic candidate–**. *J. Appl. Microbiol.* (2021) **130** 1813-1822. DOI: 10.1111/jam.14911 18. Clinical and Laboratory Standards Institute (2017). Performance Standards for Antimicrobial Susceptibility Testing. Clinical and Laboratory Standards Institute, Wayne, PA.. *Performance Standards for Antimicrobial Susceptibility Testing* (2017) 19. Da Silva H. E., Teterina A., Comelli E. M., Taibi A., Arendt B. M., Fischer S. E.. **Nonalcoholic fatty liver disease is associated with dysbiosis independent of body mass index and insulin resistance**. *Sci. Rep.* (2018) **8** 1-12. DOI: 10.1038/s41598-018-19753-9 20. Das B., Nair G. B.. **Homeostasis and dysbiosis of the gut microbiome in health and disease**. *J. Biosci.* (2019) **44** 117. DOI: 10.1007/s12038-019-9926-y 21. Day C. P., James O. F.. **Steatohepatitis: a tale of two hits?**. *Gastroenterology* (1998) **114** 842-845. DOI: 10.1016/s0016-5085(98)70599-2 22. De Munck T. J. I., Xu P., Verwijs H. J. A., Masclee A. A. M., Jonkers D., Verbeek J.. **Intestinal permeability in human nonalcoholic fatty liver disease: a systematic review and meta-analysis**. *Liver Int.* (2020) **40** 2906-2916. DOI: 10.1111/liv.14696 23. Deng M., Qu F., Chen L., Liu C., Zhang M.. **SCFAs alleviated steatosis and inflammation in mice with NASH induced by MCD**. *J. Endocrinol.* (2020) **245** 425-437. DOI: 10.1530/JOE-20-0018 24. Depommier C., Everard A., Druart C., Plovier H., Hul M., Silva S.. **Supplementation with**. *Nat. Med.* (2019) **25** 1096-1103. DOI: 10.1038/s41591-019-0495-2 25. Esposito E., Iacono A., Bianco G., Autore G., Cuzzocrea S., Vajro P.. **Probiotics reduce the inflammatory response induced by a high-fat diet in the liver of young rats**. *J. Nutr.* (2009) **139** 905-911. DOI: 10.3945/jn.108.101808 26. Fitzgerald C. B., Shkoporov A. N., Sutton T. D., Chaplin A. V., Velayudhan V., Ross R. P.. **Comparative analysis of**. *BMC Genomics* (2018) **19** 931-920. DOI: 10.1186/s12864-018-5313-6 27. Folch J., Lees M., Sloane Stanley G. H.. **A simple method for the isolation and purification of total lipides from animal tissues**. *J. Biol. Chem.* (1957) **226** 497-509. DOI: 10.1016/S0021-9258(18)64849-5 28. Goel A., Gupta M., Aggarwal R.. **Gut microbiota and liver disease**. *J. Gastroenterol. Hepatol.* (2014) **29** 1139-1148. DOI: 10.1111/jgh.12556 29. Groschwitz K. R., Hogan S. P.. **Intestinal barrier function: molecular regulation and disease pathogenesis**. *J. Allergy Clin. Immunol.* (2009) **124** 3-20. DOI: 10.1016/j.jaci.2009.05.038 30. Hansen H. H., Feigh M., Veidal S. S., Rigbolt K. T., Vrang N., Fosgerau K.. **Mouse models of nonalcoholic steatohepatitis in preclinical drug development**. *Drug Discov. Today* (2017) **22** 1707-1718. DOI: 10.1016/j.drudis.2017.06.007 31. He X., Zhao S., Li Y.. *Can. J. Infect. Dis. Med. Microbiol.* (2021) **2021** 1-10. DOI: 10.1155/2021/6666114 32. Henao-Mejia J., Elinav E., Jin C., Hao L., Mehal W. Z., Strowig T.. **Inflammasome-mediated dysbiosis regulates progression of NAFLD and obesity**. *Nature* (2012) **482** 179-185. DOI: 10.1038/nature10809 33. Hong M. -D., Nam Y. -D.. (n.d.) 34. Hooper L. V., Macpherson A. J.. **Immune adaptations that maintain homeostasis with the intestinal microbiota**. *Nat. Rev. Immunol.* (2010) **10** 159-169. DOI: 10.1038/nri2710 35. Hu W., Gao W., Liu Z., Fang Z., Wang H., Zhao J.. **Specific strains of**. *Nutrients* (2022b) **14** 2945. DOI: 10.3390/nu14142945 36. Hu W., Gao W., Liu Z., Fang Z., Zhao J., Zhang H.. **Biodiversity and physiological characteristics of novel**. *Microorganisms* (2022a) **10** 297. DOI: 10.3390/microorganisms10020297 37. Hu S., Ma Y., Xiong K., Wang Y., Liu Y., Sun Y.. **Ameliorating effects of vitamin K2 on dextran sulfate sodium-induced ulcerative colitis in mice**. *Int. J. Mol. Sci.* (2023) **24** 2986. DOI: 10.3390/ijms24032986 38. Iino C., Endo T., Mikami K., Hasegawa T., Kimura M., Sawada N.. **Significant decrease in**. *Hepatol. Int.* (2019) **13** 748-756. DOI: 10.1007/s12072-019-09987-8 39. Im Y. R., Hunter H., de Gracia Hahn D., Duret A., Cheah Q., Dong J.. **A systematic review of animal models of NAFLD finds high-fat, high-fructose diets most closely resemble human NAFLD**. *Hepatology* (2021) **74** 1884-1901. DOI: 10.1002/hep.31897 40. Inoue M., Ohtake T., Motomura W., Takahashi N., Hosoki Y., Miyoshi S.. **Increased expression of PPARgamma in high fat diet-induced liver steatosis in mice**. *Biochem. Biophys. Res. Commun.* (2005) **336** 215-222. DOI: 10.1016/j.bbrc.2005.08.070 41. Ioannou G. N.. **The role of cholesterol in the pathogenesis of NASH**. *Trends Endocrinol. Metab.* (2016) **27** 84-95. DOI: 10.1016/j.tem.2015.11.008 42. Jandhyala S. M., Talukdar R., Subramanyam C., Vuyyuru H., Sasikala M., Nageshwar Reddy D.. **Role of the normal gut microbiota**. *World J. Gastroenterol.* (2015) **21** 8787-8803. DOI: 10.3748/wjg.v21.i29.8787 43. Jiang J., Fu Y., Tang A., Gao X., Zhang D., Shen Y.. **Sex difference in prebiotics on gut and blood–brain barrier dysfunction underlying stress-induced anxiety and depression**. *CNS Neurosci. Ther.* (2023) **2023** 14091. DOI: 10.1111/cns.14091 44. Jin C. J., Sellmann C., Engstler A. J., Ziegenhardt D., Bergheim I.. **Supplementation of sodium butyrate protects mice from the development of non-alcoholic steatohepatitis (NASH)**. *Br. J. Nutr.* (2015) **114** 1745-1755. DOI: 10.1017/S0007114515003621 45. Kamada N., Seo S. U., Chen G. Y., Nunez G.. **Role of the gut microbiota in immunity and inflammatory disease**. *Nat. Rev. Immunol.* (2013) **13** 321-335. DOI: 10.1038/nri3430 46. Kawano Y., Cohen D. E.. **Mechanisms of hepatic triglyceride accumulation in non-alcoholic fatty liver disease**. *J. Gastroenterol.* (2013) **48** 434-441. DOI: 10.1007/s00535-013-0758-5 47. Kim S. K., Guevarra R. B., Kim Y. T., Kwon J., Kim H., Cho J. H.. **Role of probiotics in human gut microbiome-associated diseases**. *J. Microbiol. Biotechnol.* (2019) **29** 1335-1340. DOI: 10.4014/jmb.1906.06064 48. Kim J. B., Spiegelman B. M.. **ADD1/SREBP1 promotes adipocyte differentiation and gene expression linked to fatty acid metabolism**. *Genes Dev.* (1996) **10** 1096-1107. DOI: 10.1101/gad.10.9.1096 49. Kim M., Yang S. G., Kim J. M., Lee J. W., Kim Y. S., Lee J. I.. **Silymarin suppresses hepatic stellate cell activation in a dietary rat model of non-alcoholic steatohepatitis: analysis of isolated hepatic stellate cells**. *Int. J. Mol. Med.* (2012) **30** 473-479. DOI: 10.3892/ijmm.2012.1029 50. Kles K. A., Chang E. B.. **Short-chain fatty acids impact on intestinal adaptation, inflammation, carcinoma, and failure**. *Gastroenterology* (2006) **130** S100-S105. DOI: 10.1053/j.gastro.2005.11.048 51. Koh G. Y., Kane A. V., Wu X., Crott J. W.. **Parabacteroides distasonis attenuates tumorigenesis, modulates inflammatory markers and promotes intestinal barrier integrity in azoxymethane-treated a/J mice**. *Carcinogenesis* (2020) **41** 909-917. DOI: 10.1093/carcin/bgaa018 52. Kuo W. T., Zuo L., Odenwald M. A., Madha S., Singh G., Gurniak C. B.. **The tight junction protein ZO-1 is dispensable for barrier function but critical for effective mucosal repair**. *Gastroenterology* (2021) **161** 1924-1939. DOI: 10.1053/j.gastro.2021.08.047 53. Lee Y., Byeon H. R., Jang S.-Y., Hong M.-G., Kim D., Lee D.. **Oral administration of**. *Sci. Rep.* (2022) **12** 1-15. DOI: 10.1038/s41598-022-11048-4 54. Li Z., Yang S., Lin H., Huang J., Watkins P. A., Moser A. B.. **Probiotics and antibodies to TNF inhibit inflammatory activity and improve nonalcoholic fatty liver disease**. *Hepatology* (2003) **37** 343-350. DOI: 10.1053/jhep.2003.50048 55. Lim M. Y., Hong S., Bang S.-J., Chung W.-H., Shin J.-H., Kim J.-H.. **Gut microbiome structure and association with host factors in a Korean population**. *Msystems* (2021) **6** e00179-e00121. DOI: 10.1128/mSystems.00179-21 56. Lim E. Y., Song E. J., Kim J. G., Jung S. Y., Lee S. Y., Shin H. S.. *Benef Microbes* (2021) **12** 503-516. DOI: 10.3920/bm2020.0217 57. Loguercio C., Federico A., Tuccillo C., Terracciano F., D'Auria M. V., De Simone C.. **Beneficial effects of a probiotic VSL#3 on parameters of liver dysfunction in chronic liver diseases**. *J. Clin. Gastroenterol.* (2005) **39** 540-543. DOI: 10.1097/01.mcg.0000165671.25272.0f 58. López-Moreno A., Acuña I., Torres-Sánchez A., Ruiz-Moreno Á., Cerk K., Rivas A.. **Next generation probiotics for neutralizing obesogenic effects: taxa culturing searching strategies**. *Nutrients* (2021) **13** 1617. DOI: 10.3390/nu13051617 59. Lopez-Siles M., Duncan S. H., Garcia-Gil L. J., Martinez-Medina M.. *ISME J.* (2017) **11** 841-852. DOI: 10.1038/ismej.2016.176 60. Ma X., Hua J., Li Z.. **Probiotics improve high fat diet-induced hepatic steatosis and insulin resistance by increasing hepatic NKT cells**. *J. Hepatol.* (2008) **49** 821-830. DOI: 10.1016/j.jhep.2008.05.025 61. Ma L., Ni Y., Wang Z., Tu W., Ni L., Zhuge F.. **Spermidine improves gut barrier integrity and gut microbiota function in diet-induced obese mice**. *Gut Microbes* (2020) **12** 1832857-1832819. DOI: 10.1080/19490976.2020.1832857 62. Martín R., Langella P.. **Emerging health concepts in the probiotics field: streamlining the definitions**. *Front. Microbiol.* (2019) **10** 1047. DOI: 10.3389/fmicb.2019.01047 63. Martín R., Miquel S., Benevides L., Bridonneau C., Robert V., Hudault S.. **Functional characterization of novel**. *Front. Microbiol.* (2017) **8** 1226. DOI: 10.3389/fmicb.2017.01226 64. Mattace Raso G., Simeoli R., Russo R., Iacono A., Santoro A., Paciello O.. **Effects of sodium butyrate and its synthetic amide derivative on liver inflammation and glucose tolerance in an animal model of steatosis induced by high fat diet**. *PLoS One* (2013) **8** e68626. DOI: 10.1371/journal.pone.0068626 65. McPherson S., Hardy T., Henderson E., Burt A. D., Day C. P., Anstee Q. M.. **Evidence of NAFLD progression from steatosis to fibrosing-steatohepatitis using paired biopsies: implications for prognosis and clinical management**. *J. Hepatol.* (2015) **62** 1148-1155. DOI: 10.1016/j.jhep.2014.11.034 66. Meroni M., Longo M., Dongiovanni P.. **The role of probiotics in nonalcoholic fatty liver disease: a new insight into therapeutic strategies**. *Nutrients* (2019) **11** 2642. DOI: 10.3390/nu11112642 67. Michail S., Lin M., Frey M. R., Fanter R., Paliy O., Hilbush B.. **Altered gut microbial energy and metabolism in children with non-alcoholic fatty liver disease**. *FEMS Microbiol. Ecol.* (2015) **91** 1-9. DOI: 10.1093/femsec/fiu002 68. Miquel S., Leclerc M., Martin R., Chain F., Lenoir M., Raguideau S.. **Identification of metabolic signatures linked to anti-inflammatory effects of**. *MBio* (2015) **6** e00300. DOI: 10.1128/mBio.00300-15 69. Moreira A. P., Texeira T. F., Ferreira A. B., Peluzio Mdo C., Alfenas Rde C.. **Influence of a high-fat diet on gut microbiota, intestinal permeability and metabolic endotoxaemia**. *Br. J. Nutr.* (2012) **108** 801-809. DOI: 10.1017/S0007114512001213 70. Moslehi A., Hamidi-Zad Z.. **Role of SREBPs in liver diseases: a mini-review**. *J. Clin. Transl. Hepatol.* (2018) **6** 1-7. DOI: 10.14218/jcth.2017.00061 71. Munukka E., Rintala A., Toivonen R., Nylund M., Yang B., Takanen A.. *ISME J.* (2017) **11** 1667-1679. DOI: 10.1038/ismej.2017.24 72. Ni X., Wang H.. **Silymarin attenuated hepatic steatosis through regulation of lipid metabolism and oxidative stress in a mouse model of nonalcoholic fatty liver disease (NAFLD)**. *Am. J. Transl. Res.* (2016) **8** 1073-1081. PMID: 27158393 73. Paolella G., Mandato C., Pierri L., Poeta M., Di Stasi M., Vajro P.. **Gut-liver axis and probiotics: their role in non-alcoholic fatty liver disease**. *World J. Gastroenterol.* (2014) **20** 15518-15531. DOI: 10.3748/wjg.v20.i42.15518 74. Plaza-Díaz J., Solís-Urra P., Rodríguez-Rodríguez F., Olivares-Arancibia J., Navarro-Oliveros M., Abadía-Molina F.. **The gut barrier, intestinal microbiota, and liver disease: molecular mechanisms and strategies to manage**. *Int. J. Mol. Sci.* (2020) **21** 8351. DOI: 10.3390/ijms21218351 75. Puri P., Baillie R. A., Wiest M. M., Mirshahi F., Choudhury J., Cheung O.. **A lipidomic analysis of nonalcoholic fatty liver disease**. *Hepatology* (2007) **46** 1081-1090. DOI: 10.1002/hep.21763 76. Quévrain E., Maubert M. A., Michon C., Chain F., Marquant R., Tailhades J.. **Identification of an anti-inflammatory protein from**. *Gut* (2016) **65** 415-425. DOI: 10.1136/gutjnl-2014-307649 77. Rafiq N., Bai C., Fang Y., Srishord M., McCullough A., Gramlich T.. **Long-term follow-up of patients with nonalcoholic fatty liver**. *Clin. Gastroenterol. Hepatol.* (2009) **7** 234-238. DOI: 10.1016/j.cgh.2008.11.005 78. Rau M., Rehman A., Dittrich M., Groen A. K., Hermanns H. M., Seyfried F.. **Fecal SCFAs and SCFA-producing bacteria in gut microbiome of human NAFLD as a putative link to systemic T-cell activation and advanced disease**. *United Eur. Gastroenterol. J.* (2018) **6** 1496-1507. DOI: 10.1177/2050640618804444 79. Rognes T., Flouri T., Nichols B., Quince C., Mahé F.. **VSEARCH: a versatile open source tool for metagenomics**. *PeerJ* (2016) **4** e2584. DOI: 10.7717/peerj.2584 80. Saito K., Uebanso T., Maekawa K., Ishikawa M., Taguchi R., Nammo T.. **Characterization of hepatic lipid profiles in a mouse model with nonalcoholic steatohepatitis and subsequent fibrosis**. *Sci. Rep.* (2015) **5** 12466. DOI: 10.1038/srep12466 81. Sievers F., Wilm A., Dineen D., Gibson T. J., Karplus K., Li W.. **Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal omega**. *Mol. Syst. Biol.* (2011) **7** 539. DOI: 10.1038/msb.2011.75 82. Simental-Mendia L. E., Simental-Mendia E., Rodriguez-Hernandez H., Rodriguez-Moran M., Guerrero-Romero F.. **The product of triglycerides and glucose as biomarker for screening simple steatosis and NASH in asymptomatic women**. *Ann. Hepatol.* (2016) **15** 715-720. DOI: 10.5604/16652681.1212431 83. Sokol H., Pigneur B., Watterlot L., Lakhdari O., Bermudez-Humaran L. G., Gratadoux J. J.. *Proc. Natl. Acad. Sci. U. S. A.* (2008) **105** 16731-16736. DOI: 10.1073/pnas.0804812105 84. Tilg H., Adolph T. E., Moschen A. R.. **Multiple parallel hits hypothesis in nonalcoholic fatty liver disease: revisited after a decade**. *Hepatology* (2021) **73** 833-842. DOI: 10.1002/hep.31518 85. Velayudham A., Dolganiuc A., Ellis M., Petrasek J., Kodys K., Mandrekar P.. **VSL#3 probiotic treatment attenuates fibrosis without changes in steatohepatitis in a diet-induced nonalcoholic steatohepatitis model in mice**. *Hepatology* (2009) **49** 989-997. DOI: 10.1002/hep.22711 86. Webb B., Sali A.. **Comparative protein structure modeling using MODELLER**. *Curr. Protoc. Bioinform.* (2016) **54** 5.6.1-5.6.37. DOI: 10.1002/cpbi.3 87. Xin J., Zeng D., Wang H., Ni X., Yi D., Pan K.. **Preventing non-alcoholic fatty liver disease through**. *Appl. Microbiol. Biotechnol.* (2014) **98** 6817-6829. DOI: 10.1007/s00253-014-5752-1 88. Xu J., Liang R., Zhang W., Tian K., Li J., Chen X.. *J. Diabetes* (2020) **12** 224-236. DOI: 10.1111/1753-0407.12986 89. Xu R. Y., Wan Y. P., Fang Q. Y., Lu W., Cai W.. **Supplementation with probiotics modifies gut flora and attenuates liver fat accumulation in rat nonalcoholic fatty liver disease model**. *J. Clin. Biochem. Nutr.* (2012) **50** 72-77. DOI: 10.3164/jcbn.11-38 90. Xu S., Zhao M., Wang Q., Xu Z., Pan B., Xue Y.. **Effectiveness of probiotics and prebiotics against acute liver injury: a meta-analysis**. *Front. Med. (Lausanne)* (2021) **8** 739337. DOI: 10.3389/fmed.2021.739337 91. Zhao Z.-H., Wang Z.-X., Zhou D., Han Y., Ma F., Hu Z.. **Sodium butyrate supplementation inhibits hepatic steatosis by stimulating liver kinase B1 and insulin-induced gene**. *Cell. Mol. Gastroenterol. Hepatol.* (2021) **12** 857-871. DOI: 10.1016/j.jcmgh.2021.05.006 92. Zhao B., Xia B., Li X., Zhang L., Liu X., Shi R.. **Sesamol supplementation attenuates DSS-induced colitis via mediating gut barrier integrity, inflammatory responses, and reshaping gut microbiome**. *J. Agric. Food Chem.* (2020) **68** 10697-10708. DOI: 10.1021/acs.jafc.0c04370 93. Zhong X., Cui P., Jiang J., Ning C., Liang B., Zhou J.. *Front. Cell. Infect. Microbiol.* (2021) **11** 649060. DOI: 10.3389/fcimb.2021.649060 94. Zhou Y., Xu H., Xu J., Guo X., Zhao H., Chen Y.. *AMB Express* (2021) **11** 1-10. DOI: 10.1186/s13568-021-01197-6 95. Zhu L., Baker S. S., Gill C., Liu W., Alkhouri R., Baker R. D.. **Characterization of gut microbiomes in nonalcoholic steatohepatitis (NASH) patients: a connection between endogenous alcohol and NASH**. *Hepatology* (2013) **57** 601-609. DOI: 10.1002/hep.26093
--- title: Characteristics and patients’ portrayals of Norwegian social media memes. A mixed methods analysis authors: - Anders Hagen Jarmund - Sofie Eline Tollefsen - Mariell Ryssdal - Audun Bakke Jensen - Baard Cristoffer Sakshaug - Eirik Unneland - Berge Solberg - Bente Prytz Mjølstad journal: Frontiers in Medicine year: 2023 pmcid: PMC10060973 doi: 10.3389/fmed.2023.1069945 license: CC BY 4.0 --- # Characteristics and patients’ portrayals of Norwegian social media memes. A mixed methods analysis ## Abstract ### Background Despite reports on troublesome contents created and shared online by healthcare professionals, a systematic inquiry of this potential problem has been missing. Our objective was to characterize the content of healthcare-associated social media memes in terms of common themes and how patients were portrayed. ### Materials and methods This study applied a mixed methods approach to characterize the contents of Instagram memes from popular medicine- or nursing-associated accounts in Norway. In total, 2,269 posts from 18 Instagram accounts were included and coded for thematic contents. In addition, we conducted a comprehensive thematic analysis of 30 selected posts directly related to patients. ### Results A fifth of all posts ($21\%$) were related to patients, including 139 posts ($6\%$) related to vulnerable patients. Work was, however, the most common theme overall ($59\%$). Nursing-associated accounts posted more patient-related contents than medicine-associated accounts ($p \leq 0.01$), but the difference may be partly explained by the former focusing on work life rather than student life. Patient-related posts often thematized [1] trust and breach of trust, [2] difficulties and discomfort at work, and [3] comical aspects of everyday life as a healthcare professional. ### Discussion We found that a considerable number of Instagram posts from healthcare-associated accounts included patients and that these posts were diverse in terms of contents and offensiveness. Awareness that professional values also apply online is important for both healthcare students and healthcare providers. Social media memes can act as an educational resource to facilitate discussions about (e-)professionalism, the challenges and coping of everyday life, and ethical conflicts arising in healthcare settings. ## 1. Introduction The arrival and spread of online social media have introduced possibilities and challenges for society all around the world. For healthcare students and professionals, the implications of online presence and behavior are still emerging and e-professionalism is a construct comprising “the attitudes and behaviors (some of which may occur in private settings) reflecting traditional professionalism paradigms that are manifested through digital media” [1]. Unfortunately, studies have revealed that e-professionalism is difficult, especially for students (2–6), and concerns have recently been raised across countries that certain forms of online humor published by healthcare workers conflict with professional values (7–10). These concerns have, however, been anecdotal in nature and systematic characterization of such material is lacking. Humor is a complicated matter in terms of professionalism and serves multiple functions for healthcare professionals. It can facilitate communication, support therapeutic processes or act as a strategy to cope with demanding situations and difficult emotions [11, 12]. By sharing challenging experiences through jokes, healthcare workers remind each other that struggling and making mistakes are common, without afflicting shame or guilt [7]. However, not every form of humor aligns with the professional norms in healthcare. Stigmatized groups seem to be especially vulnerable to ridicule [13]. Dark humor, ridiculing tragic events and suffering can be a useful tool in the face of distress, but may appear uncanny, hostile or offensive from the outside [14]. In some cases, humor can become abusive or degrading in respect of vulnerable patients [15, 16]. Thus, there has been a call for the education of healthcare professionals to also address the use of humor [17] as part of the wider “hidden curriculum” [18]. Memes constitute a genre of humor that has gained attention in relation to troublesome online contents (7–9). A meme is typically an image or short video annotated with text shared in social media. Examples are not reproduced here for legal reasons, but illustrative examples have been published by Berre and Peveri [9], Harvey [7], and Song and Crowder [10]. The social media platform Instagram, which is intended for image and video sharing, has about 2.8 million users in Norway, corresponding to $67\%$ of the adult population, and more than half of those between 18 and 50 years of age report daily use [19]. The use is highest among young women (18–29 years) where $89\%$ has an Instagram account. The potential for wide outreach is thus considerable and problematic contents produced by healthcare students have already caused concerns among educators [9]. The lack of systematic knowledge regarding the contents of these images and videos makes it impossible to assess the prevalence of problematic material and restricts how educators can thematize this phenomenon in terms of e-professionalism. To address the need for systematic descriptions of social media memes, this paper employs a mixed methods approach to characterize Norwegian healthcare-associated memes posted on Instagram. The aim of this study is to provide systematic knowledge to guide and support public discussions regarding healthcare professionalism and humor in social media, and to identify areas where social media memes can be used as a resource for professional identity formation in healthcare education. ## 2.1. Data collection Google was used to search for an initial list of relevant accounts (search queries: “medisin memes site:instagram.com” and “sykepleie memes site:instagram.com”). The search was conducted on June 16th, 2021. For each account with less than 500 followers, the lists of followers and followings were manually reviewed, and relevant accounts noted. The process was repeated until no more relevant accounts could be identified. Accounts were included in the study according to the inclusion and exclusion criteria in Table 1 and categorized as related to nursing or medicine, and the number of followers and followings was recorded. From the selected accounts, all posts published prior to June 1st, 2021 were assessed for eligibility. The delay between June 1st and 16th was assumed enough for the posts to receive representative reactions in form of likes and comments. Images, videos, date, caption, and the number of likes and comments were extracted for each post. The publication date of the first post from each account was used to calculate account age. **TABLE 1** | Inclusion criteria | Exclusion criteria | | --- | --- | | Name or description refers to medicine or nursing | Mentions specific persons in name or description | | Primarily publishing memes | | | Public | | | Has more than 100 followers | | The study was approved by the Norwegian centre for research data (NSD, reference number 128255) and the included accounts were notified and received written information about the study in line with privacy regulations. ## 2.2. Quantitative analysis The quantitative analysis aimed to [1] characterize the popularity of various themes and [2] explore whether specific themes affect the response to the posts. Codes were developed by two authors in collaboration from a set of 100 randomly selected posts and independently validated by three coders. The inter-rater reliability of each code was assessed by Gwet’s Agreement Coefficient 1 (AC1). AC1 is robust to the Kappa Paradox where Cohen’s Kappa underestimates agreement in the case of skewed data, i.e., when the prevalence of some codes is small [20]. The codes were refined and independently tested until satisfactory inter-rater reliability was reached (AC1 > 0.40), except for codes that were expected to show large inter-rater variability (i.e., Vulnerable patient and Offensive). Next, each post was randomly assigned to three independent coders. The coders had an option to flag posts for review if they were difficult to code and posts were excluded if all three coders found it difficult to assign suiting themes. To improve validity, only codes applied by ≥ 2 coders were kept for analysis. All posts marked for review were evaluated by two authors in collaboration and recoded. The proportion of posts belonging to each theme was calculated by account to compensate for the varying number of published posts. These proportions were used to calculate correlation between themes (Spearman’s correlation coefficient) and compare prevalence between professions (Kruskal–Wallis test). Linear mixed models were used to assess the effect of specific themes on the number of reactions (likes and comments). The number of reactions to a post depended on the number of followers of the account at the time of posting. To account for this, we devised a case-control comparison by selecting four control posts for each theme-related post (case). For each post related to a specific theme (e.g., student life), the two previous and next posts not related to that theme were selected from the same account (Supplementary Figure 1). Thus, multiple case-control groups of 3–5 posts were created for each theme. Next, the number of reactions was standardized by dividing on the standard deviation of the corresponding control posts. The regression coefficient can then be interpreted in terms of how many standard deviations a specific theme will increase or decrease the number of reactions. Nested clustering (case-control group nested within account) was included in the model as a random intercept. Profession and theme were included as fixed effects, as well as their interaction. Independent models were fitted for the number of likes and comments. Bootstrapping was used to estimate confidence intervals for the regression coefficients. The case-control groups were stratified by account and resampled with replacement. Next, new linear mixed model regression coefficients were estimated from the bootstrapped samples. Finally, the 2.5 and $97.5\%$ percentiles were extracted and regarded as $95\%$ confidence intervals for the coefficients. All calculations were conducted in R version 4.0.2 [21] and p-values were adjusted within test with the Benjamini-Hochberg procedure. Visualizations were made with the UpSetR [22], corrplot, and ggplot2 [23] packages for R. ## 2.3. Qualitative analysis Qualitative analysis aimed to provide rich descriptions of how the memes portrayed patients and their relatives and to explore characteristics of professionally problematic posts. To this end, 15 problematic posts and 15 unproblematic posts were systematically selected for focused discussions. To identify problematic and unproblematic posts, the posts ($$n = 491$$) including patients/relatives were scored by offensiveness on a numerical rating scale from 0 (not offensive) to 10 (highly offensive) by at least three authors. The 15 posts with highest and lowest mean score were considered most and least offensive, respectively, and selected for comprehensive qualitative analysis. The qualitative analysis was conducted using a methodology originally designed for analysis of press photograph story [24] and later adapted for social media analysis [25]. Four authors jointly reviewed all selected posts through focused discussions and the following features were detailed for each selected post (Supplementary Table 2): [1] Uninterpreted content, [2] Text, [3] Interpreted content, [4] Humor, [5] Caption, [6] Offensiveness, and [7] Theme. Two experienced qualitative researchers (BPM and BS) reviewed the selected posts independently to cross-check that identified themes corresponded to the overall impression. A consensus on the most prominent message in each meme was achieved through thorough discussion. ## 3.1. Accounts and posts After the initial Google search and review of lists of followers and followings, 51 accounts were assessed for eligibility. Of them, 18 accounts were included and categorized as related to medicine ($$n = 5$$) or nursing ($$n = 13$$). Account characteristics are shown in Table 2. In total, 2,319 posts had been published prior to June 1st, 2021. The median (range) number of posts per account was 96 (6–596). Not all accounts were actively publishing posts at the time of the study. The median (range) time span from the first to the latest post was 284 (14–979) days, and the median (range) number of posts per month was 11.9 (2.2–86.8). **TABLE 2** | Unnamed: 0 | Nursing | Medicine | | --- | --- | --- | | Accounts, sum, n | 13 | 5 | | Followers, median (range), n | 1,131 (254–44,631) | 1,161 (954–2,987) | | Following, median (range), n | 69 (10–1,811) | 121 (43–225) | | Age, days | 172 (14–979) | 284 (205–637) | | Published posts, sum, n | 1879 | 440 | | Included posts, sum (% of published), n | 1,835 (98%) | 434 (99%) | In total, 16 posts were marked for review by all three coders and excluded from further analysis, whereas 227 were flagged for review by one or two coders and recoded by two authors in collaboration. Thirty-four posts were excluded during recoding, leaving 2,269 posts for further analysis. Of these, 14 posts did not reach majority on any codes but have been kept in Table 2 as they were not flagged for review during coding. A flow chart of post inclusions and exclusion can be found in Supplementary Figure 2. ## 3.2. Quantitative analysis *Eleven* general themes were identified and are described in Table 3. The posts were coded by three authors, resulting in high inter-rater reliability (AC1 ranging 0.77–1.00, adjusted $p \leq 0.001$, Supplementary Table 1). **TABLE 3** | Theme | Example | Blikes [CI] | Bcomments [CI] | | --- | --- | --- | --- | | Advertisement | A specific product is mentioned as the solution when the electronics are failing, with a lifeguard running to “save the day” | –0.57 [–1.45, 0.26] | 14.36 [–0.01, 31.71] | | Academic concept | Two children captioned Bax and Bcl–2 (proteins involved in apoptosis) are playing with Bax hitting Bcl–2 in the head with a bottle captioned “apoptosis” | –0.44 [–0.82, –0.12] | –0.12 [–0.40, 0.15] | | Corona | A man running with the caption indicating that there is focus on COVID–19 and that someone has coughed | –0.16 [–0.43, 0.10] | –0.13 [–0.41, 0.20] | | Exams/Tests | A child gradually disappearing with the caption indicating that a subject is not relevant for the exams | –0.17 [–0.55, 0.22] | 1.18 [0.39, 2.10] | | In-jokes | Picture of a possible lecturer with a celebrity in a panel next by | 0.59 [0.24, 0.95] | 1.85 [0.20, 3.61] | | Internship | Mr. Bean proudly displaying a card, with the caption indicating that a student has received his first hospital identification card | 0.32 [0.07, 0.58] | 0.81 [0.22, 1.38] | | Offensive | A man offering olanzapine (antipsychotic medication) to the sad relatives of a patient | 0.05 [–0.52, 0.61] | 0.40 [–0.19, 1.09] | | Patient | An older person is told he has a wife and a family, to which he responds “Well shit” | 0.38 [0.19, 0.56] | 0.37 [0.15, 0.60] | | Private life | A man pointing with the caption indicating he is pointing out medical errors in television shows | 0.12 [–0.14, 0.40] | 0.44 [0.09, 0.81] | | Student life | A crying woman with the caption indicating that she is trying to catch up with the curriculum | 0.41 [0.07, 0.75] | 0.36 [–0.01, 0.78] | | Vulnerable patient | A nude man hanging from the roof, with a caption indicating that he is a patient who is refusing hospitalization despite his apparent need for it | 0.59 [0.19, 0.94] | 0.98 [0.27, 1.76] | | Work | Leaving a chaotic scene with the caption indicating shift change | 0.07 [–0.10, 0.36] | 0.90 [0.67, 1.12] | The occurrence of various themes is illustrated in Figure 1 for all accounts and in Supplementary Figure 3 for medicine- and nursing-associated accounts separately. Most posts were related to work, either alone ($$n = 699$$) or in combination with patients ($$n = 422$$) or private life ($$n = 183$$). In total, 491 posts were patient-related (Figure 2). Of these, 116 posts were regarded as offensive ($24\%$), 148 posts ($30\%$) as depicting vulnerable patients, and 67 posts ($14\%$) as an offensive depiction of a vulnerable patient. There were significant correlations between some themes (Supplementary Figure 4): Accounts posting about work tended to post about vulnerable patients and patients in general, but not in-jokes or about student life or exams. Accounts posting mostly about student life, on the other hand, tended to post in-jokes and about exams, but less about patients or work. **FIGURE 1:** *Number of posts related to common themes. The total number of posts related to each theme is shown to the left, whereas the upper bar plot shows the intersections between various themes (e.g., 343 posts were related to both work and patient). Only intersections with ≥5 posts are shown.* **FIGURE 2:** *Number of patient-related posts coded as vulnerable or offensive. The total number of posts related to each theme is shown to the left, whereas the upper bar plot shows the intersections between various themes (e.g., 67 posts were relating to vulnerable patients and considered offensive).* Accounts related to medicine or nursing showed significant differences in the number of posts related to several themes. The relative occurrence of various themes is shown in Figure 3. Posts from medicine-associated accounts were more often about exams ($p \leq 0.05$), student life ($p \leq 0.05$) or in-jokes ($p \leq 0.01$). In contrast, posts from nursing-associated accounts were more frequently related to work ($p \leq 0.05$), private life ($p \leq 0.01$) or patients, both vulnerable ($p \leq 0.05$) and in general ($p \leq 0.01$). **FIGURE 3:** *Proportion of posts related to each theme, by the assumed professional belonging of the accounts. Adjusted p-values from the Kruskal–Wallis test.* Overall, theme had only minor effect on the number of reactions as shown by the regression coefficients given in Table 3 and shown by profession in Figure 4, with some exceptions. The strongest effect was seen for advertisements that had a clear tendency to have more comments than other posts. However, due to a low number of such posts, the effect was not robust to bootstrapping and was only estimable for nursing-associated accounts (Figure 4). Posts containing in-jokes or relating to exams/tests also tended to have more comments, but the effect was much weaker than for advertisements (Table 3). Although the effect of theme on number of likes was overall weak (<1 standard deviation compared to control posts), posts with in-jokes or relating to vulnerable patients tended to have more likes than other posts. In contrast, posts depicting academic concepts tended to receive fewer likes. When assessed by profession, a similar pattern emerged (Figure 4). In medicine-associated accounts, posts about work or coded as offensive tended to receive fewer likes. In contrast, posts about work received more comments in both medicine- and nursing associated accounts and some more likes in nursing-related accounts. There was a tendency for patient-related posts to receive more likes and comments in nursing-associated accounts, whereas this effect was absent for medicine-associated accounts. **FIGURE 4:** *Effect of theme on the number of (A) likes and (B,C) comments. The regression coefficients are from linear mixed models and represent the deviation from the mean in terms of standard deviations of nearby posts without the specified theme. The distribution shows the robustness of the estimates as calculated by bootstrap validation. The dotted lines indicate no effect. Some themes were separated (C) to avoid skewing of scale, as indicated by arrows in panel (B). For medicine-associated accounts, the number of posts related to advertisements (4 posts) and vulnerable patients (1 post) were too low to yield interpretable estimates.* ## 3.3.1. The depiction of patients: How and who The 30 selected posts showed a rich variation in graphical techniques and the use of symbols. The largest portion of posts contained cartoons or snapshots, either pictures or videoclips, from popular culture (e.g., scenes from TV series) with added explanatory text and captions. Few posts depicted actual situations involving healthcare. Instead, patients and healthcare workers were typically represented by other characters, using text captions to convey the setting and the roles. Both patients and healthcare workers were sometimes depicted as animals. There were, however, examples of what may have been authentic patients, in ambulance or in hospital, and a photo taken inside a Norwegian healthcare institution (the photo did not show any patients or sensitive information). Some posts made use of more advanced symbolism, such as the trojan horse. Some groups of patients were repeatedly depicted in the 30 selected posts. These were typically vulnerable patients such as confused or fragile elderly patients, patients suffering from psychosis or delirium, and drug-affected or agitated patients. The healthcare worker was often anonymous, and profession and position were typically not stated explicitly. The point of view varied between posts. Often, the character representing a healthcare worker was marked with personal pronouns such as “me”, “I”, or “you”. In others, we observed the situation as an unnamed third party. Another common configuration was a photo or video representing the patient’s response to an action, captioned “every time you [do something to the patient]”. The patient was referred to as “me” in only one of the 30 selected posts. ## 3.3.2. Thematic analysis: Main themes Three overarching and recurring themes emerged during the analysis of posts considered the most or least offensive. Below we present main themes and related subthemes from the thematic analysis with illustrative examples demonstrating how the themes manifest themselves in distinct ways in posts considered offensive when compared to posts considered innocent. ## 3.3.2.1. Trust and the breach thereof Many posts involved some form of breach of trust. This was thematized in various and diverse ways, often in the shape of deception: healthcare workers lying, omitting, pretending. Among the most offensive posts, this theme was frequently connected to administrating medications, typically antipsychotics or sedatives. An illustrative example: a healthcare worker saying “I am just flushing your venous catheter” whereas the syringes are clearly marked with antipsychotics. In one such post, the healthcare worker additionally calms the patient by, falsely, saying “Yes, it is only salt water”. Another form of pretending was demonstrated by a slow code scenario where an elderly patient receives incomplete and superficial chest compression from a healthcare worker while the relatives are crying in the background. Some of the more innocent posts also touched upon forms of deception, such as concealing feelings in front of the patient or pretending to be working while hiding from tiresome patients or relatives. Dealing with unprofessional thoughts, fantasies and feelings related to patients was considered a distinct aspect of managing trust as a healthcare worker. This spanned from expressed desire to hurt and punish patients for being difficult and enjoying that they struggle to frustration over patients, not prioritizing what is best for the patient, and looking at patients’ bodies with un-caring eyes. ## 3.3.2.2. Difficulties and discomfort at work Almost all the discussed posts depicted situations at work that involved some form of difficulty or discomfort. In contrast to posts considered offensive, innocent posts typically revolved around challenges encountered at work as a healthcare professional and with patients having passive roles such as observers or extras or were just referred to. Examples include doing heavy lifting alone, hiding from and avoiding patients, feeling incompetent or as an imposter, and struggling with a task in front of a patient. One post stood out as more confession-like than a meme: a healthcare worker described being sexually assaulted by a patient and, subsequently, laughed at by colleagues when searching support. In posts considered offensive, on the other hand, the patients were often portrayed as the direct cause to the discomfort or challenge. A post considered offensive depicted a healthcare worker entering a patient’s room where the patient is lying exhausted on the floor with hands covered by feces, which have also been smeared onto the walls. A text caption informs that the patient had previously refused to receive assistance. Many posts thematized how difficulties at work were solved in less-than-optimal ways, often involving breach of trust as described above. Uncooperative patients and patients using long time to perform basic tasks – delaying or creating “difficulties” for the healthcare worker – tended to be met with frustration, anger, force, and deceit. ## 3.3.2.3. The comedy of everyday life as healthcare professionals Another distinct theme emerged from work-situated posts that did not involve discomfort or difficulties but rather focusing on absurdity or surprise. A subgroup of the posts that were considered innocent which depicted small, everyday incidents such as a patient being wakened by the alarm of an infusion pump, a healthcare worker telling the same joke to multiple patients, or a healthcare worker accidently making noises when checking up on a sleeping patient. These posts often implied deep compassion for the patient or an unspoken alliance between patient and healthcare worker. For example, several posts showed the administration of medicine where the dosage is far too low to sufficiently help the patient. This was, however, framed as the fault of an absent doctor, leaving both the depicted healthcare worker and patient in shared helplessness. In the offensive group there were posts where the comedy was entirely on the patient’s behalf, such as psychotic patients doing or saying allegedly strange, ridiculous, or stupid things or patient’s angry responses to naloxone (an antidote to opioids). These posts were considered more malign. An interesting contrast was the depiction of an elderly patient happily and eagerly folding hospital towels. Despite this being humor on the patient’s behalf, it was perceived as more compassionate than ridicule and was part of the group of posts considered innocent. ## 3.3.3. Humor based on whose pain? Systematic differences emerged between posts considered as offensive or not, regarding whose expense the post’s humor was based. In many of the offensive posts the patients were subject to an action by a healthcare worker. Consequently, the humor was at the expense of the patient and the patients’ vulnerability was an important part of the humorous element of the post. This is exemplified by the repeated theme of deceitful administration of medication to patients, often depicted as either psychotic or demented. In the innocent posts, on the other hand, the patients were not negatively affected by the actions of the healthcare worker, and the patients were mostly supporting characters in the situations depicted. Here, the “pain” was clearly at the expense of the healthcare worker. However, the focused discussions revealed that these differences were not always obvious. For example, some of the posts considered to be offensive and involving pain on the patient’s expense could be interpreted as displays of the power- and helplessness healthcare workers may experience when facing specific patients. ## 4. Discussion Despite growing concerns regarding e-professionalism among healthcare students and professionals, the contents of social media humor from these groups have evaded systematic characterization. To fill this gap, we employed a mixed methods approach to map important themes both quantitatively and qualitatively. The examined memes showed diverse, yet characteristic, forms of humorous contents and clear differences were found between professions. While nursing-associated accounts had large audiences and focused on themes related to work-life, the medicine-associated accounts had smaller outreach and focused on student-life. Theme had only minor effects on the number of reactions and comments. The most offensive posts included vulnerable patients such as elderly patients and people with mental disorders or drug-addictions, whereas the least offensive posts thematized challenges as a health-care professional and the comedy of everyday life. Although the patient-related content comprised only a minor subset of the material, many problematic examples were found, and those regarded as most offensive were found to jeopardize the trust between patients and healthcare professionals. It should, however, be noted that none of the included posts broke the duty of patient confidentiality or were found so problematic that further steps were considered. The accounts belonging to the different professions (medicine and nursing) were clearly targeting distinct audiences: the nursing-associated accounts targeted mainly nurses in working positions whereas the medicine-associated accounts targeted student populations. This notion is supported by the medicine-associated account names often referring to universities. It is possible that the shorter duration of the nursing education, with frequent separation into internships at various places, leaves less room for a meme culture to form. The relatively small subset of student-targeted nursing accounts have, however, caused ethical concerns [9]. Another possible explanation is that the number of working nurses (about 50,000, excluding midwives and specialist nurses [26]) is larger than the number of nursing student (about 5,000 students [27]). The relative lack of medicine-associated memes from working physicians may reflect professional maturation during the study or that other platforms or private accounts are used. Shedding light on the “hidden curriculum” has been recognized as an important step to fully integrate professional identity formation as part of healthcare educations [18] and our findings suggest that refining e-professionalism cannot be a process isolated to educational institutions but must include professional bodies reaching healthcare practitioners as well. The professional tension accompanying social media has manifested itself during the last decade, and along with it the discussion of how healthcare professionals should conduct themselves on such platforms, so-called e-professionalism. One extreme approach to this may be to conclude that all public online depictions of patients produced by healthcare professionals are dubious. Being or feeling seen, exposed, looked at, or deprecated by others are central components of shame [28, 29], and reminding the patient that one is constantly observed, evaluated, thought about, and discussed may induce self-consciousness and perhaps evoke both shame and a sense of betrayal or alienation – especially if one is negatively portrayed or the perspective conflicts with one’s own experiences. We found several examples of this, such as healthcare professionals experiencing discomfort when meeting or observing a patient or finding a patient laughable in appearance or behavior. Depriving patients the control over how they are imagined, portrayed, and spoken about may add to their powerlessness in face of a healthcare system where their social and bodily control has, often, already been weakened. Trust is one of the pillars of professionality [18] and healthcare professionals are obliged to guard patient integrity in all situations and this commitment conflicts with the creation of humorous memes. This view invites students of healthcare professions to reflect upon reasons to why collapses in (e-) professionalism may occur and why one might be tempted to expose or ridicule a patient. In addition, one of the expressed concerns relating to the social media memes has been the possible normalization of problematic attitudes among students. The memes can become memorable and influential parts of the so called “hidden curriculum” of healthcare education [7] which is now recognized as an integral part of how professionalism develops [18]. The repeated exposure of vulnerable patient groups, such as patients suffering from dementia or psychiatric or addiction disorders, that was identified in the current study may contribute to an “othering process” similar to what have been seen during the COVID-19 pandemic [30]. Another possible route of harm is that “these memes can distort our senses, blunting our abilities to detect human vulnerability and, in so doing, poison the relational ethics of our practice” [8]. These concerns are, however, not unique to medical memes and pertain to all use of humor in healthcare settings [16]. A contrasting view may be that the production of humorous memes are important forms of self-expression that, if they manage to maintain patient confidentiality, are creative ways to identify, communicate, and cope with problems and challenges arising in professional life. Creative artmaking is an effective way to explore issues related to professional development and visual arts offer distinct benefits compared to verbal reflection [31]. Patients are not to be infantilized but should be respectfully treated as ordinary people, which may include that unflattering behavior is commented and pointed out – not as an act of humiliation but to help refine patients’ ability to mentalize and know how they appear to others. Thus, the memes can possibly serve honorable causes, including as educational tool or as a way to cope or vent [7, 8, 10]. The empowering and positive potential of healthcare-associated memes is illustrated by memes produced by or for patients [e.g., (32–37)]. This view invites students of healthcare professions to explore how humor and social media can be used in constructive ways to raise awareness about challenges encountered at work and as an alternative and casual way of communicating with (specific groups of) patients. The fact that most of the memes analyzed by us relate to work or student life – and often frustrating sides of these, such as work-spare time conflicts or exams – suggests that the memes are primarily a way to vent. Especially, the memes can be used as vehicle to communicate experiences that are not easily shared otherwise, such as shame [38], the embarrassment from making mistakes [7] or being disempowered [10]. These are common yet painful and vulnerable experiences among healthcare workers that may be eased by establishing them as shared experiences that can be joked about. Thus, educators may seek to “help students and trainees to find an authentic voice, based at least in part on the profession’s ideals, that works in both medical and non-medical life-worlds” [39, 40] so that the memes can remain useful while adhering to professional standards. The Medical Education e-Professionalism (MEeP) framework is a research-based attempt to define core competencies for healthcare professionals in relation to digital space [41]. Here, developing professionality involves recognizing the mission and social contract of the medical profession, and specific competencies are described along the axes of professional values, behaviors, and identity formation. The framework has been shown useful to guide implementation of e-professionalism education [42]. The qualitative analysis revealed that problematic posts often depict conflicts between normative and descriptive ways of providing healthcare services. Although all healthcare professionals are trained to know the importance of patient respect, confidentiality, and trust, one might find oneself in situations where the highest professional standards cannot be met due to organizational (e.g., high workload or understaffing) or personal (e.g., inexperience, anger, or frustration) reasons and where techniques such as deceit are found necessary. These illustrations may have educational value that can enlighten healthcare professionals and administrators about unpleasant pragmatism arising from how the services are organized. From a patient perspective, however, the unpleasant pragmatism may lower the public’s trust in the healthcare services. Nevertheless, healthcare professionals must reconcile human imperfections and organizational limitations with the demands of professionalism, and keeping patient-directed humor at spatial and temporal distance from patients – such as between colleagues in the lunch room – has being suggested as an acceptable but controversial solution [16, 43]. With online social media, however, spatial and temporal distance collapses and the borders between private and public are blurred [1]. All the Instagram accounts included in this study were public accounts, accessible for everyone. For some, deciding to create a public rather than a private profile (where access must be granted manually) may have been a rushed decision not given much thought. For others, however, the meme accounts provide a platform to reach tens of thousands every day. Although we found few advertisements in our material, the potential for economic gain adds yet another ethical dimension to the online presence of healthcare professionals. In contrast to the collapse of temporal and spatial distance, the memes commonly preserve a social distance by using medical terminology, requiring detailed medical knowledge to “get it” or by referring to situations unique to healthcare professionals. It is likely that this exclusiveness makes the memes able to strengthen the sense of group identity among healthcare professionals [7]. One may also argue that this social distance mitigates the potential for harm as it makes the contents of the memes less accessible and understandable for people outside healthcare professions. The official presence of governmental bodies and healthcare institutions on the same platform – possibly serving contents side-by-side the anonymous accounts – is yet another example of unclear borders that may give the memes unwarranted legitimacy. Overall, this study has demonstrated that patients play a peripheral role in the healthcare-associated social media memes but, unfortunately, close to $5\%$ of the included memes were regarded as offensive. The characteristic features of these offensive memes were intentionally deceptive practices, which may have been deemed necessary at the time, mainly in the form of administering medications, as well as unflattering depictions of often vulnerable patient populations. Future studies are, however, necessary to investigate the concordance between the opinions of fourth year medical students, as in this study, actual patients, and experienced heath care professionals. The rapid development of new social media platforms where the borders between private and public are progressively dissolved and where algorithms select for increasingly shocking or eyebrow-raising contents, urges for further research to enable educational institutions to deal with these aspects of e-professionalism. The diversity revealed by the current study makes an open-minded approach necessary rather than abrupt condemnation. We hope that our findings can support nuanced reflections regarding positive and negative sides of healthcare-associated memes through empiric knowledge and guide the continuous refinement of e-professionalism in healthcare so that space can be found for the human sides of both patients and professionals. ## 4.1. Strengths and limitations This study is, to our knowledge, the first broad and systematic characterization of social media memes produced by healthcare students and professionals. Norway is a country with a population who possess excellent digital skills and have wide access to social media [19, 44, 45], suggesting that both creators and the audience of the included memes are likely to be diverse and representative for a wider population. The combination of quantitative and qualitative methods enabled both broad and deep characterization of the memes. However, the approach involves important limitations. Although the study aimed to characterize the content of medical memes in an objective manner, the group of coders was small and homogenous (all medical students, both genders were represented) which could have influenced the results. To ensure consistency and trustworthiness of our results, the supervision and active participation of two senior researchers, both with experience from qualitative research and either clinical work or medical ethics, was necessary. Nevertheless, both the quantitative coding of posts and the thematic analysis involved subjective judgment. For example, the classification of posts as offensive or not revealed significant differences between coders. However, interrater agreement was found to be satisfactory, and the subjectivity of the general coding was further mitigated by removing codes lacking majority support. Humor is inherently subjective and individual, and shaped by factors such as culture, age, sex, and experience. It is therefore likely that medical students’ view on what is offensive or not that may differ from other groups, and it would have been interesting to include coders with other backgrounds, such as patients or experienced clinicians, to get a more diverse point of view. This is also the case in the thematic analysis, where it would have been interesting to involve a more heterogenous group in the discussion of the selected memes. Finally, the focused discussion only involved a selection of the posts and may thus have missed themes that were present in the larger material. ## Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. ## Ethics statement The study was approved by the Norwegian Centre for Research Data (NSD, reference number 128255) and the included accounts were notified and received written information about the study in line with privacy regulations. ## Author contributions BM and BS provided supervision. ST and BCS developed the codes to thematically classify posts and MR, ABJ, and AHJ validated it. MR, AHJ, ST, ABJ, and BCS conducted the coding of the posts. AHJ and MR re-coded posts marked for review. ST, BCS, MR, EU, and AHJ rated posts for offensiveness. ST, BCS, AHJ, MR, ABJ, BM, and BS participated in focused discussions to qualitatively assess selected posts. AHJ conducted statistical analyses and wrote the first draft of the manuscript. All authors contributed to the design and conceptualization of the study, contributed significantly to the submitted work, and reviewed and approved the manuscript. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmed.2023.1069945/full#supplementary-material ## References 1. Cain J, Romanelli F. **E-professionalism: a new paradigm for a digital age.**. (2009) **1** 66-70. DOI: 10.1016/j.cptl.2009.10.001 2. Neville P, Waylen A. **Social media and dentistry: some reflections on e-professionalism.**. (2015) **218** 475-8. DOI: 10.1038/sj.bdj.2015.294 3. Barlow C, Morrison S, Stephens H, Jenkins E, Bailey M, Pilcher D. **Unprofessional behaviour on social media by medical students.**. (2015) **203** 439-439. DOI: 10.5694/mja15.00272 4. Nyangeni T, Du Rand S, Van Rooyen D. **Perceptions of nursing students regarding responsible use of social media in the Eastern Cape.**. (2015) **38**. DOI: 10.4102/curationis.v38i2.1496 5. Guraya S, Guraya S, Yusoff M. **Preserving professional identities, behaviors, and values in digital professionalism using social networking sites; a systematic review.**. (2021) **21**. DOI: 10.1186/s12909-021-02802-9 6. Chretien K, Azar J, Kind T. **Physicians on Twitter.**. (2011) **305**. DOI: 10.1001/jama.2011.68 7. Harvey A. **Medical memes.**. (2020) **368**. DOI: 10.1136/bmj.m531 8. Wright D. **Nursing memes at odds with our values.**. (2017) **113**. PMID: 29235827 9. Berre V, Peveri A.. (2020) 10. Song Y, Crowder J. **Memes in medical education.**. (2019) **6** 102-19. DOI: 10.17157/mat.6.2.714 11. Wanzer M, Booth-Butterfield M, Booth-Butterfield S. **“If we didn’t use humor, We’d Cry”: humorous coping communication in health care settings.**. (2005) **10** 105-25. DOI: 10.1080/10810730590915092 12. Penson R, Partridge R, Rudd P, Seiden M, Nelson J, Chabner B. **Laughter: the best medicine?**. (2005) **10** 651-60. DOI: 10.1634/theoncologist.10-8-651 13. 13.MSU Bioethics. Humor in Medicine: Nasty, Dark, and Shades of Grey. (2015). Available online at: https://msubioethics.com/2015/09/29/humor-in-medicine/ (accessed June 27, 2020).. (2015) 14. Kim J.. (2015) 15. **Our family secrets.**. (2015) **163**. DOI: 10.7326/M14-2168 16. Aultman J. **When humor in the hospital is no laughing matter.**. (2009) **20** 227-34. DOI: 10.1086/JCE200920304 17. Oczkowski S. **Virtuous laughter: we should teach medical learners the art of humor.**. (2015) **19**. DOI: 10.1186/s13054-015-0927-4 18. O’Sullivan H, van Mook W, Fewtrell R, Wass V. **Integrating professionalism into the curriculum: AMEE Guide No. 61.**. (2012) **34** e64-77. DOI: 10.3109/0142159X.2012.655610 19. Ipsos N.. (2022) 20. Wongpakaran N, Wongpakaran T, Wedding D, Gwet KL. **A comparison of Cohen’s Kappa and Gwet’s AC1 when calculating inter-rater reliability coefficients: a study conducted with personality disorder samples.**. (2013) **13**. DOI: 10.1186/1471-2288-13-61 21. 21.R Core Team. R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing (2020).. (2020) 22. Gehlenborg N.. (2019) 23. Wickham H.. (2016) 24. Kêdra J. **To see more: a model for press photograph story analysis.**. (2013) **32** 27-50. DOI: 10.1080/23796529.2013.11674704 25. Shanahan N, Brennan C, House A. **Self-harm and social media: thematic analysis of images posted on three social media sites.**. (2019) **9**. DOI: 10.1136/bmjopen-2018-027006 26. 26.Norsk Sykepleierforbund. Lønn og Tariff: Statistikk [Salary and Collective Agreements: Statistics. (2020). Available online at: https://www.nsf.no/lonn-og-tariff/statistikk (accessed June 18, 2022). (2020) 27. Dolonen K.. (2020) 28. Loughead T. **Freudian repression revisited: the power and pain of shame.**. (1992) **15** 127-36. DOI: 10.1007/BF00116484 29. Weiss H. **Introduction: the role of shame in psychoanalytic theory and practice.**. (2015) **96** 1585-8. DOI: 10.1111/1745-8315.12418 30. Labbé F, Pelletier C, Bettinger J, Curran J, Graham J, Greyson D. **Stigma and blame related to COVID-19 pandemic: a case-study of editorial cartoons in Canada.**. (2022) **296**. DOI: 10.1016/j.socscimed.2022.114803 31. Shapiro J, McMullin J, Miotto G, Nguyen T, Hurria A, Nguyen M. **Medical students’ creation of original poetry, comics, and masks to explore professional identity formation.**. (2021) **42** 603-25. DOI: 10.1007/s10912-021-09713-2 32. Headley S, Jones T, Kanekar A, Vogelzang J. **Using memes to increase health literacy in vulnerable populations.**. (2022) **53** 11-5. DOI: 10.1080/19325037.2021.2001777 33. Kelleher E, Giampietro P, Moreno M. **Marfan syndrome patient experiences as ascertained through postings on social media sites.**. (2015) **167** 2629-34. DOI: 10.1002/ajmg.a.37255 34. Baxendale S. **Epilepsy: lessons for clinicians from popular memes on social media.**. (2021) **118**. DOI: 10.1016/j.yebeh.2021.107899 35. Yi-Frazier J, Cochrane K, Mitrovich C, Pascual M, Buscaino E, Eaton L. **Using instagram as a modified application of photovoice for storytelling and sharing in adolescents with type 1 diabetes.**. (2015) **25** 1372-82. DOI: 10.1177/1049732315583282 36. Short H. **The role of social media in menopausal healthcare.**. (2017) **23** 4-5. DOI: 10.1177/2053369116680895 37. Reynolds S, Boyd S. **Healthcare worker’s perspectives on use of memes as an implementation strategy in infection prevention: an exploratory descriptive analysis.**. (2021) **49** 969-71. DOI: 10.1016/j.ajic.2020.11.019 38. Whelan B, Hjörleifsson S, Schei E. **Shame in medical clerkship: “You just feel like dirt under someone’s shoe.”.**. (2021) **10** 265-71. DOI: 10.1007/s40037-021-00665-w 39. Haidet P. **Where we’re headed: a new wave of scholarship on educating medical professionalism.**. (2008) **23** 1118-9. DOI: 10.1007/s11606-008-0670-5 40. Haidet P. **Jazz and the “Art” of medicine: improvisation in the medical encounter.**. (2007) **5** 164-9. DOI: 10.1370/afm.624 41. Guraya S, Guraya S, Harkin D, Ryan Á, Mat Nor M, Yusoff M. **Medical education e-professionalism (MEeP) framework; from conception to development.**. (2021) **26**. DOI: 10.1080/10872981.2021.1983926 42. Guraya S, Yusoff M, Rashid-Doubell F, Harkin D, Al-Amad S, Fredericks S. **Changing professional behaviors in the digital world using the medical education e-professionalism (MEeP) framework—a mixed methods multicentre study.**. (2022) **9**. DOI: 10.3389/fmed.2022.846971 43. Sobel R. **Does laughter make good medicine?**. (2006) **354** 1114-5. DOI: 10.1056/NEJMp058089 44. Statistics Norway, Fjørtoft T.. (2022) 45. 45.Eurostat. Individuals’ Level of Digital Skills (from 2021 Onwards). (2022). Available online at: https://ec.europa.eu/eurostat/databrowser/view/isoc_sk_dskl_i21/default/table?lang=en (accessed June 16, 2022).. (2022)
--- title: Deletion of Cryab increases the vulnerability of mice to the addiction-like effects of the cannabinoid JWH-018 via upregulation of striatal NF-κB expression authors: - Leandro Val Sayson - Darlene Mae Ortiz - Hyun Jun Lee - Mikyung Kim - Raly James Perez Custodio - Jaesuk Yun - Chae Hyeon Lee - Yong Sup Lee - Hye Jin Cha - Jae Hoon Cheong - Hee Jin Kim journal: Frontiers in Pharmacology year: 2023 pmcid: PMC10060981 doi: 10.3389/fphar.2023.1135929 license: CC BY 4.0 --- # Deletion of Cryab increases the vulnerability of mice to the addiction-like effects of the cannabinoid JWH-018 via upregulation of striatal NF-κB expression ## Abstract Synthetic cannabinoids have exhibited unpredictable abuse liabilities, especially self-administration (SA) responses in normal rodent models, despite seemingly inducing addiction-like effects in humans. Thus, an efficient pre-clinical model must be developed to determine cannabinoid abuse potential in animals and describe the mechanism that may mediate cannabinoid sensitivity. The Cryab knockout (KO) mice were recently discovered to be potentially sensitive to the addictive effects of psychoactive drugs. Herein, we examined the responses of Cryab KO mice to JWH-018 using SA, conditioned place preference, and electroencephalography. Additionally, the effects of repeated JWH-018 exposure on endocannabinoid- and dopamine-related genes in various addiction-associated brain regions were examined, along with protein expressions involving neuroinflammation and synaptic plasticity. Cryab KO mice exhibited greater cannabinoid-induced SA responses and place preference, along with divergent gamma wave alterations, compared to wild-type (WT) mice, implying their higher sensitivity to cannabinoids. Endocannabinoid- or dopamine-related mRNA expressions and accumbal dopamine concentrations after repeated JWH-018 exposure were not significantly different between the WT and Cryab KO mice. Further analyses revealed that repeated JWH-018 administration led to possibly greater neuroinflammation in Cryab KO mice, which may arise from upregulated NF-κB, accompanied by higher expressions of synaptic plasticity markers, which might have contributed to the development of cannabinoid addiction-related behavior in Cryab KO mice. These findings signify that increased neuroinflammation via NF-κB may mediate the enhanced addiction-like responses of Cryab KO mice to cannabinoids. Altogether, Cryab KO mice may be a potential model for cannabinoid abuse susceptibility. ## 1 Introduction The development of synthetic cannabinoids was initially aimed at understanding the endocannabinoid system and developing potential pharmacotherapies for cannabinoid-induced disorders (de Luca and Fattore, 2018; Diao and Huestis, 2019). However, the growing number of novel synthetic cannabinoids has become a major public health concern as the identification of specific synthetic cannabinoids consumed by abusers became difficult. Additionally, novel cannabinoids are classified under the highest drug schedule due to their adverse effects and lack of medical use (Drug Enforcement Administration, 2015). Prevalence of their abuse might be due to their stronger effects compared to trans-Δ⁹-tetrahydrocannabinol (THC), the main psychoactive component of marijuana (Weinstein et al., 2017), and being “legally” available online. Despite inducing adverse effects (MacDonald and Pappas, 2016; Schmitz and Richert, 2020; National Institute on Drug Abuse, 2021), synthetic cannabinoids have persistently circumvented legal regulations and initial detection owing to their varying chemical composition, resulting from the constant modification of functional groups. Thus, evaluating the potential dangers of these substances has piqued the interest of the scientific community. The pharmacological properties of synthetic cannabinoids are similar to those of exogenous phytocannabinoids, with synthetic cannabinoids being more potent due to their higher affinity for cannabinoid receptors (Tai and Fantegrossi, 2014). Recent studies have suggested that CB1 receptors are ubiquitously expressed throughout the central nervous system, forming part of the endocannabinoid system (Fagundo et al., 2013), which is interconnected with the mesolimbic dopaminergic pathway, a signaling network implicated in mediating drug addiction (Adinoff, 2004; Vlachou and Panagis, 2014). The endocannabinoid system may mediate addiction-like behaviors by regulating γ-Aminobutyric acid (GABA)/glutamate neurotransmission via retrograde signaling of CB1 receptors expressed on GABAergic/glutamatergic neurons, thus influencing dopaminergic neuronal activity (Parsons and Hurd, 2015). This may provide the basis for cannabinoid-induced addiction in humans, leading to cannabinoid use disorders. However, some studies have demonstrated that not all synthetic cannabinoids can induce significant addiction-like behaviors in animal models (Polissidis et al., 2009; Hyatt and Fantegrossi, 2014; Tampus et al., 2015; Rodríguez-Arias et al., 2016; Bilel et al., 2019), especially in self-administration (SA) paradigms, contradictory to clinical reports and personal accounts (Le Boisselier et al., 2017; de Luca and Fattore, 2018; Leung et al., 2020). This presents several challenges in screening the abuse potential of novel cannabinoids and the involvement of complex neural mechanisms in the pathophysiology of cannabinoid abuse. Thus, there is a need to develop an efficient tool for evaluating the abuse potential of synthetic cannabinoids. Accumulating evidence suggest the involvement of neuroinflammation in cannabinoid addiction, potentially entailing both pro- and anti-inflammatory activities (Kozela et al., 2010; Kinsey et al., 2011; Javed et al., 2016; Bayazit et al., 2017). Some cannabinoids also seem to mitigate the addictive effects of abused drugs (Xi et al., 2011; Delis et al., 2017; Lin et al., 2018). Increased neuroinflammation induced by drugs of abuse may modulate glutamate synapses via increased astroglia glutamate reuptake (Boycott et al., 2008; Ramos et al., 2010), thus affecting glutamate-dependent synaptic plasticity (Park et al., 2015), which may reinforce drug-seeking behavior (Kauer and Malenka, 2007). Since some cannabinoids may reduce cytokine production (Xu et al., 2007) and deactivate glial cells (Rodrigues et al., 2014), their potential effects on neuroinflammation may provide another possible mechanism that may also mediate cannabinoid addiction development. This further suggests their inconsistent addiction-inducing effects in rodent models to probably involve inflammatory processes. Transgenic mice models have been widely used to elucidate the pathophysiology of substance abuse disorder (Spanagel and Sanchis-Segura, 2003; Sora et al., 2010). These models are commonly developed based on genes that are differentially expressed in response to drugs of abuse. *These* genes are then modified in mice to generate transgenic rodent models (Yazdani et al., 2015; Szumlinski et al., 2017). Preliminary findings have suggested that treatment with methamphetamine or cocaine modulates the expression of Cryab in mice (Ministry of Food and Drug Safety, 2021). Cryab codes a small heat shock protein (CRYAB) with anti-inflammatory functions, such as reducing apoptosis through enhancing PI3K and activating AKT signaling (Ren et al., 2018), and inhibiting inflammatory responses in glial cells by downregulating pro-inflammatory mediators (Kuipers et al., 2017; Guo et al., 2019). Although initially, CRYAB functions as a molecular chaperone and aggregates misfolded proteins (Dai et al., 2022), thus preventing detrimental protein accumulation during stress (Calderwood et al., 2019). Furthermore, it may also participate in various types of cancer (Zhang et al., 2019), providing it a potential anti-inflammatory and anti-apoptotic role. Although the administration of addictive drugs may alter the expression of Cryab, the role of Cryab in cannabinoid-induced addiction has not yet been investigated. Therefore, a Cryab transgenic mouse model may be possibly used to determine the abuse potential of novel substances and identify susceptibility to potentially addictive drugs, such as synthetic cannabinoids. Thus, this study aimed to provide an animal model that would be susceptible to cannabinoid abuse by determining the responses of Cryab knockout (KO) mice to 1-naphthalenyl(1-pentyl-1H-indol-3-yl)-methanone (JWH-018; a representative cannabinoid) in addiction-associated behavioral paradigms. Given the effect of Cryab on inflammation and the involvement of inflammation in addiction development, Cryab KO mice might exhibit increased sensitivity to JWH-018 abuse. As neural electrical activity indicates physiological responses to drugs of abuse, the electroencephalogram of Cryab KO mice may perhaps also differ from those of wild-type (WT) mice after repeated JWH-018 exposure. Since cannabinoid addiction may also involve endocannabinoid, dopaminergic, and neuroinflammatory pathways, repeated exposure to JWH-018 might differentially modulate endocannabinoid-related, dopamine-related, and neuroinflammation-related genes and/or proteins in Cryab KO mice, contributing to their divergent cannabinoid-induced behaviors when compared with WT mice. ## 2.1 Animals The Department of Pharmacy, Chungbuk National University (Cheongju City, Korea) provided two male and two female Cryab KO mice (aged 8 weeks). Transgenic mice were bred with C57BL/6N mice (aged 8 weeks, 22–25 g) obtained from Hanlim Animal Laboratory Co. (Hwasung, Korea), to obtain heterozygous Cryab (Cryab Het) mice. All procured mice were acclimatized in the animal room for 1 week prior to being used. Male and female Cryab Het mice (aged 8 weeks, F1) were subsequently bred to obtain male Cryab KO mice (F2), which were used for all experiments in this study (aged 8–12 weeks, 25–30 g). Newborn pups were genotyped at 3–4 weeks old using DNA from the tail. Only male mice were used for behavioral and molecular experiments. Mice for experiments were housed together in cages (4–6 mice per cage). To obtain next generation mice (F3), male and female Cryab Het mice (F2) were bred. All mice were housed in a room under controlled conditions (circadian cycle, 12-h light/dark cycle (7 AM–7 PM); temperature, 22°C ± 2°C) and had access to food ad libitum. The schedule of experiments is presented in Figure 1. Animal treatment and maintenance procedures were performed according to the Principles of Laboratory Animal Care (NIH Publication No. 85–23, revised 1985) and the Animal Care and Use Guidelines of Sahmyook University, South Korea (SYUIACUC 2021-020 and SYUIACUC 2022-001). **FIGURE 1:** *Schedule of behavioral experiments. Mice are genotyped between 3 and 5 weeks of age using tail samples. Mice are then segregated according to genotype. Starting at 8 weeks of age, only male mice are housed according to the experiment. Brains may be collected at the end of each behavioral experiment.* ## 2.2 Drugs JWH-018 was synthesized according to reported procedures (Huffman et al., 2005). In brief, indole was reacted with pentyl bromide and potassium hydroxide in DMF. The obtained N-pentylindole was acylated with 1-naphthoyl chloride in the presence of Me2AlCl in dichloromethane to produce JWH-018. The structure was confirmed by the following spectroscopic analyses: 1H-NMR (500 MHz, CDCl3) δ 8.52-8.48 (m, 1H), 8.20 (d, $J = 8.6$ Hz, 1H), 7.95 (t, $J = 8.3$ Hz, 1H), 7.90 (d, $J = 8.0$ Hz, 1H), 7.65 (dd, $J = 6.9$, 1.1 Hz, 1H), 7.54-7.45 (m, 3H), 7.40-7.35 (m, 4H), 4.05 (t, $J = 7.4$ Hz, 2H), 1.82-1.76 (m, 2H), 1.32-1.22 (m, 4H), 0.85 (t, $J = 7.2$ Hz, 3H); 13C-NMR (125 MHz, CDCl3) δ 192.1, 139.2, 138.1, 137.1, 133.8, 130.9, 130.1, 128.3, 127.1, 126.8, 126.4, 126.1, 125.9, 124.7, 123.7, 123.0, 122.9, 117.6, 110.1, 47.3, 29.6, 29.0, 22.3, 14.0; HR-MS calculated for C24H23NO [M + H]+ 342.1852, found 342.1852; HPLC purity $99.46\%$. In all experiments, JWH-018 was dissolved in a solution comprising Tween 80, absolute ethanol, and $0.9\%$ (w/v) saline (SAL) in the ratio 1:1:48 to obtain the desired dosage (0.3 mg/kg body weight). JWH-018 was selected as it has been used in several early cannabinoid-based studies, and the dose was selected based on reports that demonstrated significant addiction-related effects of JWH-018 and other cannabinoids in rodents (de Luca et al., 2015; Ossato et al., 2017). ## 2.3.1 Open-field test (OFT) Baseline locomotor activity was evaluated using a square Plexiglas open-field arena (42 cm × 42 cm × 42 cm), as per the methodology described in previous studies (Custodio et al., 2018; dela Peña et al., 2019; Kim et al., 2019). Mice were placed at the center of the arena and allowed to explore for 12 min for three consecutive days. The recorded data of the first 2 min were excluded from the analysis, as this period was considered a habituation period. The distance moved (cm) and the movement duration (s) of each mouse was measured using an automated system (EthoVision, Noldus, Netherlands). ## 2.3.2 Y-maze test Basic cognition in mice was assessed using a “Y”-shaped maze comprising three identical arms (each arm spaced at an angle of 120°) with each arm measuring 5 cm × 35 cm × 10 cm. Each mouse was placed at the end of an arm and allowed to explore the maze freely for 8 min. Spontaneous alternating behavior and total entry in each arm were obtained following a previous study (Custodio et al., 2018). The alternation behavior (actual alternation) was described as the consecutive entry into three arms (continuous exploration of three different arms (e.g., ABC, BCA, or CAB) with stepwise sequence combinations. The maximum number of alternations was defined as the total number of arms entered minus two. The percentage spontaneous alternation behavior was calculated as follows: spontaneous alternation behavior (%) = (actual alternations/maximum alternations) × 100. ## 2.3.3 Rota-rod test General motor balance and coordination of mice were measured using a rotating rod (Ugo Basile, Varese, Italy) at a fixed speed of 36 rpm, as per the methodology described in previous studies (Custodio et al., 2018; dela Peña et al., 2019). Two consecutive days before the experiment, the mice were allowed to habituate and were trained to run on the rotating rod for 3 min (T1 and T2). On the day of the experiment, “challenge day” (Ch), the mice were placed on the rotating rod for 10 min. Mice that fell during the experiment were gently placed back onto the rota-rod. The falling latency and frequency were recorded. ## 2.3.4 Cliff avoidance test The impulsive behavior of the mice was assessed using a 50 cm long cylindrical Plexiglass rod support with a round Plexiglass platform (diameter = 20 cm; thickness = 1 cm) on both ends. Each mouse was placed at the center of the platform, and its behavior was observed for 10 min. The first fall latency and falling frequency were recorded. ## 2.3.5 Elevated plus-maze test (EPM) Inherent anxiety-like behavior in mice was determined using a plus-maze comprising four arms, two open and two closed arms measuring 30 cm × 6 cm (closed arms were enclosed by 20 cm high walls). The center of the maze contained a 6 cm × 6 cm delimited area. At the start of each test, individual mice were placed at the center, facing one of the open arms. The percentage of entries (100 × open/total entries) and time spent in the open arms were calculated after each 5 min test, similar to a previous study (dela Peña et al., 2019). The EthoVision system was used for automated recording and analysis. ## 2.3.6 Electroencephalography (EEG) The mice were anesthetized with 0.02 mL Zoletil® (50 mg/mL) and Rompun® (xylazine 23.32 mg/mL) prepared in SAL. A three-channel tethered head mount (8200K3-iS/iSE) was placed and fixed with stainless screws positioned in the frontal region (A/P: −1.0 mm, M/L −1.5 mm) and posterior brain (A/P: −1.0 mm, M/L ±1.5 mm), conductive epoxy, and dental cement. Standard operating procedures were performed to minimize animal discomfort. The mice were allowed to recover for 5 days before recording. Data acquisition and analyses were performed according to the methodology described in previous studies (Botanas et al., 2021; Custodio et al., 2021) but with some modifications. On the last recovery day, mice were allowed to acclimatize to the EEG apparatus for 2 h (unrecorded). On the next day, a baseline recording for drug-naïve mice was performed for 2 h with an initial 10 min of habituation using a computer-based software (Pinnacle Technology, Inc., Lawrence, KS, United States). One group of mice was treated with JWH-018 (0.3 mg/kg body weight) or vehicle (VEH) for 7 days. After the initial baseline recording (Day 0), EEG was recorded on the first and last day of treatment. EEG wave changes relative to the baseline recording were calculated. ## 2.3.7 Intravenous self-administration (SA) test The SA test was performed as reported previously (Custodio et al., 2022). Lever pressing training, which was based on food pellet reward under continuous reinforcement, was performed for three consecutive days [30 min per session (AM and PM sessions)]. In this study, lever pressing behavior was determined when mice earned 15 reinforcers at the end of the session on the third day and $75\%$ of responses were on the active lever (Thomsen and Caine, 2011). After training, surgery was performed to insert a 0.28 mm inner diameter catheter into the jugular vein and mid-scapular region of mice anesthetized with Zoletil® (50 mg/mL) and Rompun® (xylazine 23.32 mg/mL) prepared in SAL. The catheterized mice were allowed to recover for 5 days. During recovery, the catheters were infused daily with 0.02 mL of $0.9\%$ SAL containing heparin (20 IU/mL) and gentamicin (0.08 mg/mL). Catheter patency was determined by flushing catheters with a 0.02 mL of the Zoletil®/Rompun® mixture, and the loss of muscle tone was observed in mice within 10 s of infusion. During the SA experiment, catheters were flushed with SAL containing gentamicin or heparin immediately before and after each SA session. Mice were assessed daily in 2-h SA sessions under a fixed-ratio (FR) 1 schedule for 7 days, followed by assessment with an FR2 schedule for 3 days (Graphic State Notation 4; Coulbourn Instruments, Whitehall, PA, United States). Mice were fed a pellet diet (2 g) daily. In each session, both left and right levers were available. Left (active) lever pressing was followed by infusion of 0.05 mL JWH-018 (0.03 mg/kg body weight/infusion) or SAL over 10 s. During the infusion, the stimulus light over the lever was illuminated for 20 s. Lever presses during “time-out” periods were recorded but had no effect. Right (inactive) lever presses were also recorded, but not reinforced. Active and inactive lever presses and the number of infusions for each daily session were recorded for evaluating SA behavior. ## 2.3.8 Conditioned place preference (CPP) test The apparatus comprised two compartments (dimensions = 17.4 cm × 12.7 cm × 12.7 cm) with a removable guillotine door that separated the compartments. One compartment had smooth black walls and white flooring, whereas the other had white-dotted black walls and textured white flooring. An illumination of 12 lux was maintained throughout the experiment. A computer system (EthoVision) was used for recording animal movements and stay durations in the compartments. The protocol was performed according to previous studies with some modifications (Kim et al., 2019; Custodio et al., 2020; Sayson et al., 2020). The test comprised the following three phases: (A) habituation (days 1–3) and pre-conditioning (day 4; 15 min), (B) conditioning (days 5–12; 30 min), and (C) post-conditioning (day 13; 15 min). During habituation, mice were allowed to freely explore the entire apparatus. An initial trial (pre-conditioning) was performed to determine the stay duration of each mouse in each of the compartments. Mice were assigned to groups based on the pre-conditioning phase such that their non-preferred side was designated as the drug-paired compartment. In the conditioning phase, mice were administered JWH-018 (0.3 mg/kg body weight) or VEH and placed in the drug-paired compartment. On alternate days, the mice received VEH and were confined to the VEH-paired compartment. During the post-conditioning phase, the mice were not treated and were allowed to explore both compartments (similar to the pre-conditioning phase). The CPP score was calculated as the difference in the time spent by the mice in their respective drug-paired compartments between the post-conditioning and pre-conditioning phases. ## 2.4.1 RNA extraction and quantitative real-time polymerase chain reaction (qRT-PCR) Drug-naïve or JWH-018-treated mice (7 days) were euthanized and decapitated to excise the brain 30 min after the last treatment. The ventral tegmental area (VTA) and nucleus accumbens (NAC) were isolated using a mouse brain matrix on ice. The samples were rapidly frozen at −80°C before further processing. The subsequent steps followed previous methods (Custodio et al., 2022) and were according to MIQE guidelines. Total RNA was isolated with TRIzol® (Invitrogen, Carlsbad, CA, United States), following the manufacturer’s protocol, and purified using the Hybrid-RTM kit (Geneall Biotechnology, Seoul, Korea). RNA concentrations were determined using Colibri Microvolume Spectrometer (Titertek-Berthold, Pforzheim, Germany). Total RNA (1 μg) was reverse-transcribed into complementary DNA (cDNA) using AccuPower® CycleScript RT PreMix (Bioneer, Seoul, Korea), following the manufacturer’s instructions. Aliquots of cDNA were stored at −20°C. *Target* genes were amplified using custom sequence-specific primers (Cosmogenetech, Seoul, Korea) and detected using SYBR® Green (Solgent, Daejeon, Korea). The primer sequences are shown in Supplementary Table S1. The input cDNA concentration was 2.5 μg/μL. The PCR conditions were as follows: 94°C for 1 min (denaturing step), followed by annealing at primer-specific temperature for 1 min, and 72°C for 45 s. qRT-PCR was performed using samples in triplicate. The expression of target genes was normalized to that of Gapdh. The results are expressed as relative expression calculated using the 2−ΔΔCT method (VanGuilder et al., 2008). ## 2.4.2 Protein extraction and enzyme-linked immunosorbent assay (ELISA) Brains of a separate cohort of VEH or JWH-018-exposed (7-day treatment) mice were collected and isolated similarly as described in the previous section. Protein extraction was done according to previous methods with slight modifications (Sayson et al., 2020). Brain tissues were lysed in 400 μL homogenization buffer [radioimmunoprecipitation assay buffer (Biosesang Inc., Seongnam, Korea) supplemented with cOmplete™ ULTRA protease inhibitor cocktail tablets (05892791001, Sigma-Aldrich) and PhosSTOP™ phosphatase inhibitor cocktail tablets (04906845001, Sigma-Aldrich)]. The tissue extracts were centrifuged at 16,000 g at 4°C for 20 min. Dopamine concentration was determined using the dopamine ELISA kit (KA1887, Abnova, Taiwan), following the manufacturer’s instructions. Briefly, standards, controls, and samples were acylated and incubated with dopamine antiserum for 2 h at room temperature (20°C–25°C) on a shaker (approximately 600 rpm). Next, the samples in the wells were washed and incubated with the enzyme conjugate for 30 min. After washing the samples, the substrate was pipetted into individual wells, and the samples were incubated for 25 min. The stop solution was then added to each well. The absorbance of the reaction mixture was measured at 450 nm using an EMax Plus Microplate Reader (Molecular Devices; San Jose, CA, United States). All measurements were performed in duplicates. ## 2.4.3 Western blotting The striatum (STR) of VEH or JWH-018-exposed (7-day treatment) mice was isolated. Procedure was done according to previous studies (Sayson et al., 2020; Custodio et al., 2021). The samples were then heated at 95°C for 5 min. Protein lysates (20 μg) were subjected to sodium dodecyl sulfate-polyacrylamide gel electrophoresis on a $12\%$ gel. The resolved proteins were transferred onto nitrocellulose membranes. The membrane was blocked with $5\%$ bovine serum albumin (BSA) prepared in Tris-buffered saline containing $0.1\%$ Tween-20 (TBST) for 1 h and then incubated overnight with specific primary antibodies at 4°C. Then, the membrane was washed with TBST and incubated with horseradish peroxidase-conjugated anti-rabbit (1:3,000) or anti-mouse secondary antibodies (1:5,000) for 1 h. Protein bands were visualized based on enhanced chemiluminescence (Clarity Western ECL; Bio-Rad Laboratories, Hercules, CA, USA) using the ChemiDoc Imaging System (Image Lab software, version 6.0; Bio-Rad). The levels of phosphorylation-independent proteins were normalized to those of β-Actin. The levels of the phosphorylated form of proteins were normalized to those of the total form of proteins. Fold change was determined by normalizing the values of the test groups to those of the WT VEH group. The antibodies used for the Western blot analysis are listed in Supplementary Table S2. ## 2.4.4 Immunofluorescence Standard protocols were used, following previously described methods (Custodio et al., 2022). After the last SA session, the mice were intracardially perfused with perfusion solution [0.05 M phosphate-buffered saline (PBS)] and perfusate [$4\%$ paraformaldehyde (PFA) in 0.1 M phosphate buffer]. The brain tissues were stored in PFA solution at 4°C overnight, followed by incubation with $30\%$ sucrose solution at 4°C. The brain samples were sectioned to a thickness of 35 μm using a Leica CM1850 cryostat (Wetzlar, Germany), following mouse brain stereotaxic coordinates (Paxinos and Franklin, 2001). For this experiment, the STR was selected for analysis as this region is commonly implicated in inflammation-associated behavioral alterations, such as addiction (Krasnova et al., 2016; Vonder Haar et al., 2019). The brain sections were carefully washed with 1× PBS and subsequently incubated in a protein-blocking solution ($5\%$ BSA and $0.3\%$ Triton X-100 in 1× PBS) for 1 h at room temperature. Next, the sections were incubated with primary antibodies diluted in a protein-blocking solution for 3 days at 4°C. The primary antibody used in this analysis is listed in Supplementary Table S2. After washing, the samples were incubated overnight with Alexa fluor-555-conjugated goat anti-mouse (Thermo Fisher Scientific A32727, RRID: AB_2633276) antibody at 4°C. The samples were washed, incubated with Hoechst for 10 min, and mounted on 76 mm × 26 mm × 1 mm clean positively charged microscope slides (Walter Products Inc., Canada). Mounted sections were cured with Permount® Mounting Medium UN1294 (Fisher Chemical, NJ, United States), covered with 24 mm × 50 mm microscope cover glasses (Marienfeld Laboratory Glassware, Germany), and allowed to dry for 24 h at room temperature. The corrected total cell fluorescence (CTCF) level was measured in each sample using ImageJ 1.53k (NIH, Maryland, United States) as reported previously (Hammond, 2014). ## 2.5 Statistical analyses All mice were randomized for treatment. The researchers were blinded to the treatment of the animals while conducting the tests and analyzing the data. Statistical analyses were performed using GraphPad Prism v7 (GraphPad Software Inc., San Diego, CA, United States). Data are presented as the mean ± standard error of the mean (S.E.M.), unless specified otherwise. The means were analyzed using one-way or two-way analysis of variance (ANOVA) with or without repeated measures (RM), followed by Tukey’s or Bonferroni’s multiple comparison test, when appropriate. Differences were considered statistically significant at $p \leq 0.05.$ The p-value and detailed statistical analysis (e.g., genotype, treatment, time, days, etc.) are indicated. ## 3.1 Cryab KO mice exhibited hypoactivity Although controversial, inherent aberrant behaviors were previously correlated with substance abuse co-morbidity (Herrero et al., 2008; Volkow, 2009; Kabir et al., 2016), therefore we screened the general behavior of Cryab KO mice in various behavioral assays. OFT revealed that Cryab KO mice innately possessed lower spontaneous locomotor activity than WT mice, as evidenced by their lower total distance moved (Figure 2A; genotype: F1,18 = 29.6; $p \leq 0.001$; test day: F2,36 = 27.0; $p \leq 0.001$) and movement duration (Figure 2B; genotype: F1,18 = 37.3; $p \leq 0.001$; test day: F2,36 = 35.1; $p \leq 0.001$), even across multiple testing sessions. Spontaneous alternation (Figure 2C) in the Y-maze were similar between mice genotypes, while total entry (Figure 2D; $t = 5.14$, df = 17) also suggested lower locomotor activity in Cryab KO mice. Other behaviors in rota-rod (Figures 2E, F), cliff avoidance (Figures 2G, H), and EPM (Figures 2I, J) exhibited no differences between WT and Cryab KO mice. Drugs of abuse also modulate neural electrical activity (Newton et al., 2003; Malyshevskaya et al., 2017; Zanettini et al., 2019; Nukitram et al., 2021), suggesting that EEG alterations may co-manifest alongside the addiction-inducing effects of some psychoactive drugs. Aberrant EEG patterns may also indicate potentially modified neuropsychological responses to addictive drugs. Accordingly, 2-h baseline EEG recording data of mice (Figure 2K) showed that the delta-, theta-, alpha-, beta-, and gamma-wave levels of Cryab KO mice were lower than WT mice [absolute power (μV2) of WT mice was set to $100\%$]. Sample EEG traces of mice (Figure 2L) showed comparable patterns between genotype. **FIGURE 2:** *General behavior of wild-type (WT) and Cryab knockout (KO) mice. At 5–7 weeks old, pups were tested for their baseline behaviors. The open-field test shows the locomotor activity indicated by (A) distance moved (cm) and (B) movement duration (s) recorded for 10 min over three consecutive days. Y-maze indicates short-term memory in terms of (C) spontaneous alternating behavior (%) and (D) total entries. Latency of first fall (s) and falling frequency (n) in the (E,F) rota-rod and (G,H) cliff avoidance tests determine motor balance/coordination and impulsive tendencies, respectively. Anxiety is measured by the (I) percentage entry (%) and (J) percentage time spent (%) in the open arms in the elevated maze plus test. (K) Baseline electroencephalogram (EEG) results reveals the power of delta, theta, alpha, beta, and gamma in mice, recorded for 2 h (drug-free). (L) Sample EEG traces shows a 2-s activity during a 2-h recording. Data expressed as mean ± S.E.M., except (K) and (L). n = 9–10. ***p < 0.001 (vs. WT; Bonferroni post hoc analysis or t-test).* ## 3.2 Cryab KO mice exhibited increased responses in addiction-related behavioral assays and divergent gamma-wave power To evaluate the responses of mice to the reinforcing effect of JWH-018, we exposed WT and Cryab KO mice to JWH-018 SA. Cryab KO obtained higher active lever responses (Figure 3A) in FR1 (F3,38 = 3.94; $p \leq 0.05$) and FR2 [treatment (F3,38 = 2.99; $p \leq 0.05$); SA days (F2,76 = 4.38; $p \leq 0.05$)], along with greater number of infusions (Figure 3C) and FR1 (F3,38 = 4.9; $p \leq 0.01$) and FR2 (F3,38 = 3.49; $p \leq 0.05$), compared to WT mice. Average obtained infusions (Figure 3D) all throughout the SA sessions were also significantly higher (F3,24 = 51.3; $p \leq 0.001$) in Cryab KO mice compared to their WT and VEH counterparts. The varying behavior of mice during SA indicate the manifestation of JWH-018-induced reinforcing effects in Cryab KO mice. Aside from that, the rewarding property of JWH-018 was also evaluated in WT and Cryab KO mice through the CPP paradigm. JWH-018 appeared to induce rewarding effects in Cryab KO mice (Figure 3E), as evidenced by their higher CPP score (F3,24 = 5.59; $p \leq 0.01$) compared to their VEH counterpart, but not in WT mice. **FIGURE 3:** *Response of wild-type (WT) and Cryab knockout (KO) mice to JWH-018 addiction-related behavioral paradigms. Self-administration (SA) responses of mice are indicated by (A) active and (B) inactive lever responses and the (C) number of infusions, along with (D) mean number of infusions, which were recorded each day for a 2-h session. n = 9–11. Place preference is indicated by (E) Conditioned place preference (CPP) score of mice, which is obtained by the difference between the stay duration of mice in the drug-paired compartment between post- and pre-conditioning. n = 7. Changes in gamma-wave power of mice after (F) acute or (G) repeated treatments over 30-min bins were determined by 2-h electroencephalography (EEG) recordings, and the 2-h averages (H) were compared. n = 6–9. Data expressed as mean ± S.E.M. *p < 0.05, **p < 0.01, and ***p < 0.001 (vs. VEH; Tukey post hoc analysis). # p < 0.05, ## p < 0.01, and ### p < 0.001 (vs. WT; Tukey post hoc analysis).* Gamma-wave generation is also altered in chronic cannabis users owing to their continuous intake of cannabinoids (Skosnik et al., 2012; Liu et al., 2022), as evidenced by in vivo and in vitro studies (Sales-Carbonell et al., 2013). Thus, gamma wave alterations in mice following repeated JWH-018 administration were also determined through EEG. Gamma-wave power after acute JWH-018 treatment (Figure 3F) was significantly different among mice with a treatment-time interaction (F3,42 = 5.71; $p \leq 0.01$), specifically the change in gamma-wave power in Cryab KO mice was lesser than WT mice only after 60 min post-treatment. Repeated JWH-018 administration also induced significant differences in change in gamma-wave power (Figure 3G) among mice (F3,42 = 5.71; $p \leq 0.01$), showing that gamma-wave power in Cryab KO mice increased significantly from baseline, unlike WT mice, all throughout the recording period. The change in gamma-wave power in mice during the 2-h recording (Figure 3H) was significantly different among groups (F3,27 = 3.21; $p \leq 0.05$). The change in gamma-wave power of Cryab KO mice was significantly higher than WT mice after repeated JWH-018 treatment, but not after acute exposure. ## 3.3 Expressions of endocannabinoid system-related and dopamine-related genes, including accumbal dopamine concentration, were not markedly different between genotypes The abuse liabilities of THC and synthetic cannabinoids are usually attributed to their influence on CB1 receptors expressed in the VTA and NAC, which are major brain regions associated with the development of addiction-related behaviors (Nestler, 2001). Cannabinoids may also affect the expression of CB2 receptors (Sun et al., 2017), along with enzymes that degrade endocannabinoids (Li et al., 2019). Thus, we measured the mRNA levels of endocannabinoid-related genes in the VTA and NAC of mice repeatedly treated with JWH-018. Cnr1 (Figure 4A; F1,20 = 38.8; $p \leq 0.001$), Cnr2 (Figure 4B; F1,16 = 26.2; $p \leq 0.001$), and Mgll (Figure 4D; F1,16 = 41; $p \leq 0.001$) mRNA levels were downregulated in the VTA of WT and Cryab KO mice following repeated JWH-018 treatment. However, Cnr1, Cnr2, Faah, and Mgll mRNA levels in the VTA were not significantly different between WT and Cryab KO mice. In the NAC, Cnr2 (Figure 4F; F1,17 = 10.9; $p \leq 0.01$) and Mgll (Figure 4H; F1,18 = 16.4; $p \leq 0.001$) mRNA levels were also downregulated only in WT mice following repeated JWH-018 exposure. Similar to the VTA, Cnr1, Cnr2, Faah, and Mgll mRNA levels in the NAC were also not significantly different between genotypes. Faah expressions in both VTA and NAC (Figures 4C, G) were unaltered, although Cnr1 expression in NAC (Figure 4E) exhibited treatment differences between groups (F1,20 = 6.65; $p \leq 0.05$). **FIGURE 4:** *Expression levels of endocannabinoid system-related and dopamine-related genes and dopamine concentration in wild-type (WT) and Cryab knockout (KO) mice after repeated JWH-018 treatment. Expressions of Cnr1, Cnr2, Faah, and Mgll in the (A–D) ventral tegmental area (VTA) and (E–H) nucleus accumbens (NAC) were determined through quantitative real-time polymerase chain reaction (qRT-PCR) following repeated JWH-018 treatment. The expression levels of (I) Dat, (J) Drd1, (K) Drd2, and (L) Vmat2 in the NAC of mice following repeated JWH-018 treatment were also identified through qRT-PCR. Enzyme-linked immunosorbent assay determined the dopamine concentrations in mouse NAC after (M) acute or (N) repeated JWH-018 treatment. Data expressed as mean ± S.E.M. n = 4–6. *p < 0.05, **p < 0.01, ***p < 0.001 (vs. drug-naïve; Tukey post hoc analysis).* Since endocannabinoid signaling within the VTA may influence dopaminergic neurotransmission, divergent addiction responses in mice genotypes might be reflected by differential cannabinoid-mediated regulation of the VTA and distinct changes in dopamine-related markers in the NAC. Accordingly, we determined the mRNA levels of dopamine-related genes in the NAC of mice following repeated JWH-018 treatment. No significant alterations were detected in Drd1 (Figure 4J) and Drd2 (Figure 4K) mRNA levels, whereas the expression levels of Dat (Figure 4I; F1,16 = 8.23; $p \leq 0.05$) and Vmat2 (Figure 4L; F1,20 = 8.63; $p \leq 0.01$) in WT mice were downregulated. We also determined the accumbal dopamine concentration in mice following acute and repeated JWH-018 administration to verify the potential effect of cannabinoid treatment on dopamine-mediated responses. Accumbal dopamine concentrations in mice showed no significant differences between the genotypes following repeated JWH-018 treatment (Figure 4N), although significant differences (treatment: F1,16 = 10.4; $p \leq 0.01$; genotype: F1,16 = 6.04; $p \leq 0.05$) were observed among groups following acute JWH-018 treatment (Figure 4M). ## 3.4 Cryab KO mice demonstrated potentially higher neuroinflammatory state after JWH-018 exposure Striatal neuroinflammatory processes may possibly potentiate addiction-related behavior (Rodrigues et al., 2014; Kohno et al., 2019). Studies have mentioned that CRYAB may produce anti-inflammatory effects by inhibiting cytokine expression through various pathways (Shao et al., 2013; Qiu et al., 2016; Somade et al., 2019; Xu et al., 2019). Thus, CRYAB may influence addiction development through its impact on neuroinflammation. In here, we first identified the effect of CRYAB expression on the inflammation-related PI3K-AKT-GSK3 pathway (Xu et al., 2013), which may also modulate NF-κB expression (Xie and Wang, 2022), following repeated JWH-018 administration. Representative blots are shown in Figure 5A. JWH-018 exposure increased CRYAB expression (Figure 5B; genotype: F1,15 = 156; $p \leq 0.001$; treatment: F1,15 = 7.51; $p \leq 0.05$) in WT mice. PI3K (Figure 5C) and p-AKT levels (Figure 5D) were not significantly different between groups. p-GSK-3β levels (Figure 5E; treatment: F1,16 = 4.85; $p \leq 0.05$) were decreased in Cryab KO mice following repeated JWH-018 administration, while GSK-3β expression (Figure 5F; genotype: F1,16 = 15; $p \leq 0.01$, treatment: F1,16 = 11.5; $p \leq 0.01$) was significantly higher in Cryab KO mice than their WT and VEH counterpart. Interestingly, repeated JWH-018 administration in the absence of functional CRYAB resulted in a higher NF-κB expression (Figure 5G) in Cryab KO mice than WT mice (F1,15 = 17.2; $p \leq 0.001$). **FIGURE 5:** *Expression of Protein expressions along the PI3K-AKT-GSK3 pathway and inflammation-related proteins in wild-type (WT) and Cryab knockout (KO) mice following JWH-018 exposure. (A) Representative blots showing the expression of target proteins in mice STR. Western blotting revealed the expression levels of (B) CRYAB, (C) PI3K, (D) p-AKT, (E) GSK-3β, (F) p-GSK-3β, (G) NF-κB, (H) GFAP, (I) TNF-α, (J) IL-1β, and (K) IL-6 in the STR of JWH-018-treated mice. n = 4–5. Data expressed as mean ± S.E.M. *p < 0.05, **p < 0.01, and ***p < 0.001 (vs. VEH; Tukey’s post hoc analysis). # p < 0.05 and ### p < 0.001 (vs. WT; Tukey’s post hoc analysis).* NF-κB may also influence neuroinflammation by mediating inflammatory cytokine gene transcription (Liu et al., 2017). Accordingly, we verified the expression of glial cell activity and neuroinflammatory markers in mice STR. While TNF-α (Figure 5I) and IL-1β (Figure 5J) were unaltered in mice after JWH-018 exposure, GFAP (Figure 5H; F1,16 = 13.1; $p \leq 0.001$) and IL-6 (Figure 5K; F1,15 = 12.7; $p \leq 0.01$) expressions were higher in Cryab KO mice than WT mice. ## 3.5 Cryab KO mice exhibited potentially higher glutamate-dependent synaptic plasticity after JWH-018 exposure Glutamate reuptake in synapses is said to decrease, via GLT-1 downregulation, following an increase in cytokine expression and astrocyte activation (Boycott et al., 2008; Ramos et al., 2010), possibly altering glutamate concentration. Thus, we determined the mRNA expressions of glutamate transporter subtypes in JWH-018 exposed mice. Expression level of Eaat2 mRNA (Figure 6A) was significantly decreased in Cryab KO mice and was lower than WT mice (genotype: F1,16 = 10.4; $p \leq 0.01$; treatment: F1,16 = 6.51; $p \leq 0.05$) after repeated treatment of JWH-018. Eaat3 (Figure 6B) and Eaat4 (Figure 6C) mRNA levels were both increased in WT mice (Eaat3: F1,20 = 12.5; $p \leq 0.01$, Eaat4: F1,20 = 32.5; $p \leq 0.001$), while only Eaat4 mRNA was upregulated in Cryab KO mice after repeated JWH-018 administration. **FIGURE 6:** *Glutamate transporter mRNA levels and plasticity-related protein expressions in wild-type (WT) and Cryab knockout (KO) mice following JWH-018 exposure. mRNA expression levels of (A) Eaat2 (B) Eaat3, and (C) Eaat4 in JWH-018-exposed mice was determined by quantitative real-time polymerase chain reaction. n = 5–6. (D) Representative blots showing the expression of target proteins in mice STR. Western blotting revealed the expression levels of (E) GluA1, (F) GluA2, (G) p-CREB/CREB, (H) ΔFosB, (I) p-mTOR/mTOR, and (J) BDNF in the STR of JWH-018-exposed mice. n = 4–5. Data expressed as mean ± S.E.M. *p < 0.05 and **p < 0.01 (vs. VEH; Tukey’s post hoc analysis). # p < 0.05 (vs. WT; Tukey’s post hoc analysis).* Alterations in glutamate neurotransmission have been previously associated with the development of drug addiction, including those induced by psychostimulants (Kalivas, 2007) and cannabinoids (Cohen et al., 2019). These are generally characterized by changes in glutamate receptors and markers of synaptic plasticity in brain areas mediating addiction-related behaviors (Kauer and Malenka, 2007; Park et al., 2015). In here, we determined the expressions of glutamate receptors and synaptic plasticity markers in the STR (Figure 6D). GluA1 (Figure 6E; F1,16 = 5.92; $p \leq 0.05$), but not GluA2 (Figure 6F), expression showed significant differences among treatment groups, but only an increased trend in JWH-018-exposed Cryab KO mice can be observed. While p-CREB expressions of mice (Figure 6G) exhibited no significant variations among groups, ΔFosB expression (Figure 6H; genotype: F1,16 = 7.69; $p \leq 0.01$; treatment: F1,16 = 11.4; $p \leq 0.05$) was significantly higher in JWH-018-exposed Cryab KO mice than WT. p-mTOR (Figure 6I; treatment: F1,16 = 10.6; $p \leq 0.01$) and BDNF (Figure 6J genotype/treatment interaction: F1,15 = 5.21; $p \leq 0.05$) levels were increased in Cryab KO mice after repeated JWH-018 administration. ## 3.6 Cryab KO mice displayed higher NF-κB immunoreactivity in the STR following JWH-018 SA To verify the potential involvement of increased NF-κB expression in the manifestation of cannabinoid-induced addiction-related behaviors, we determined the striatal NF-κB expression of mice that underwent JWH-018 SA. NF-κB CTCF (Figure 7B) was significantly greater in Cryab KO mice (genotype: F1,8 = 12.2; $p \leq 0.01$; treatment: F1,8 = 18.8; $p \leq 0.01$) compared to their WT and VEH counterparts after JWH-018 SA. **FIGURE 7:** *NF-κB immunoreactivity in mice from JWH-018 self-administration. (A) Immunofluorescence analysis of NF-κB and Hoechst staining in VEH- or JWH-018-exposed WT and Cryab KO mice at ×150 magnification (merged images are shown). (B) Analysis through ImageJ determined the corrected total cell fluorescence of NF-κB in mice following JWH-018 self-administration. n = 3. Data expressed as mean ± S.E.M. *p < 0.05 (vs. VEH; Tukey’s post hoc analysis). # p < 0.05 (vs. WT; Tukey’s post hoc analysis).* ## 4 Discussion Cryab KO mice displayed generally similar baseline behavior with WT mice (Figures 2C, E–J), other than the marked hypoactivity in OFT (Figures 2A, B). This kind of phenotype was previously observed to occur alongside other aberrant behaviors, such as anxiety (Wood et al., 1987), depression (Ota et al., 2018), or autism spectrum disorder (Wang et al., 2018; Angelakos et al., 2019). Animal models for depression, which were also observed to possess inherently low spontaneous locomotor activity (Skalisz et al., 2002; Rygula et al., 2005; Ota et al., 2018), demonstrated increased sensitivity to cocaine (Lepsch et al., 2005; Ribeiro Do Couto et al., 2009; Xu et al., 2020) and nicotine (Cruz et al., 2008). The hypoactive behavior of Cryab KO mice might then indicate a possibility for them to likewise exhibit aberrant responses to psychoactive drugs. Furthermore, Cryab KO and WT mice might differentially respond to addictive drugs also due to their divergent baseline EEG recordings (Figure 2K). Human EEG data demonstrated persistent changes in the delta, alpha, and gamma waves of subjects repeatedly exposed to addictive drugs, such as cocaine (Reid et al., 2006) and marijuana (Herning et al., 2008), indicating the involvement of EEG waves in the neuropsychological effects of addictive drugs. Together, the hypoactivity and baseline EEG of Cryab KO mice might render them potentially sensitive to psychoactive substances, probably including synthetic cannabinoids. We then subsequently investigated the behavioral responses of Cryab KO mice to the effects of the synthetic cannabinoid JWH-018 using paradigms that generally indicate the rewarding and reinforcing effects of drugs. Cryab KO mice exhibited potentially higher sensitivity to the addiction-related effects of JWH-018, as evidenced by their increased responses in intravenous SA (Figures 3A–C) and CPP (Figure 3E) tests compared to WT mice. While several studies have reported the addiction-like effects of synthetic cannabinoids, including JWH-018 (Hur et al., 2021), in healthy rodent subjects (Cha et al., 2014; de Luca et al., 2015), some cannabinoids still do not exhibit significant abuse potential in animal behavioral paradigms (Tampus et al., 2015; Bilel et al., 2019). This suggests the involvement of complex neurological mechanisms (that mediate the addictive effects of cannabinoids) other than the typical mesolimbic dopaminergic pathway. The results of the SA and CPP tests were consistent with this hypothesis, as WT mice did not exhibit significant preference or tendency for self-administering JWH-018 at 0.03 mg/kg/infusion or 0.3 mg/kg, unlike with previous studies. This discrepancy among studies investigating intravenous JWH-018 SA in WT mice might be attributed primarily to methodological differences, given that some studies required long-term exposure of mice in JWH-018 SA (de Luca et al., 2015), with some using adolescent mice with potentially different drug sensitivity (Margiani et al., 2022). However, the fact that Cryab KO mice exhibited modest SA responses and place preference for JWH-018 indicated cannabinoid-induced addiction-like behaviors, suggesting cannabinoid abuse susceptibility. EEG recordings revealed significantly different gamma-wave power changes in WT and Cryab KO mice following repeated exposures to JWH-018 (Figures 3F–H). Gamma oscillations are involved in both cognition and perceptive functions and may be mediated by CB1 receptors via GABA interneurons (Skosnik et al., 2012), indicating the potential involvement of gamma-wave alterations in cannabinoid-mediated effects, which may include addiction. Previous studies have also reported that repeated exposure to various addictive drugs increases gamma-wave production (Abiero et al., 2020; Custodio et al., 2020), which was also observed in Cryab KO mice after repeated JWH-018 exposure. However, gamma-wave production is generally associated with attention and perception (Müller et al., 2000; Tallon-Baudry et al., 2005), which may implicate the JWH-018-induced increase in Cryab KO mice gamma-wave power as an improvement in receptivity and cognition. While this may not entirely indicate an addiction response or vulnerability, the divergent changes in gamma-wave power between WT and Cryab KO mice might still indicate different neurophysiological modifications in brain substrates that may contribute to the observed disparity in the cannabinoid-induced, addiction-like responses between genotypes. Thus far, behavioral and EEG data suggest the Cryab KO mice to be a potential animal model of cannabinoid abuse susceptibility. As previously mentioned, psychoactive cannabinoids generally activate CB1 receptors on GABA/glutamatergic neurons located in the VTA and NAC, thus modulating the release of GABA/glutamate (Cristino et al., 2020; Spanagel, 2020) and influencing dopamine-mediated activities, such as addiction responses. Thus, a dysregulation in these pathways may influence cannabinoid-induced responses. Endocannabinoid-related mRNA expression levels (Figures 4A–H) suggest a possible downregulation of endocannabinoid signaling in WT and Cryab KO mice VTA, and perhaps also in WT mice NAC, after repeated JWH-018 treatment. While frequent exposure to cannabinoids potentially downregulates cannabinoid receptors in some brain regions (Scherma et al., 2016; van de Giessen et al., 2017; Kesner and Lovinger, 2021), similar to our qRT-PCR data, comparable JWH-018-induced mRNA alterations were detected between Cryab KO and WT mice, indicating possibly similar effects on dopaminergic signaling. Since alterations in cannabinoid receptor-encoding gene expressions may indicate GABA/glutamate signaling adaptations and dopaminergic activity dysregulation (Maldonado et al., 2011; Colizzi et al., 2016; Zehra et al., 2018; Pintori et al., 2021), comparable modifications in both Cryab KO and WT mice might suggest similarities in the expression of cannabinoid-induced behaviors. As this was not the case, given the SA and CPP results, the disparity between the JWH-018-induced behavioral responses of WT and Cryab KO mice might involve divergent modifications in dopaminergic signaling. Drug addiction is frequently attributed to alterations in dopamine neurotransmission, along with upregulated accumbal dopamine levels (Fleckenstein et al., 2007; MacNicol, 2017; Jiménez-González et al., 2022). Expressions of dopamine-related mRNA in the NAC of JWH-018-exposed mice (Figures 4I–L) suggest a possible dampening of dopaminergic activity. The same phenomenon was also observed in previous studies showing the blunting of dopaminergic activity following chronic cannabinoid exposure (van de Giessen et al., 2017; Kesner and Lovinger, 2021). It was previously suggested that genes influencing dopamine availability tend to also downregulate following chronic cannabinoid treatments (Perdikaris et al., 2018). The Dat and Vmat2 downregulation in WT mice may suggest a compensatory response to promote dopamine availability, which is a phenomenon also found during drug tolerance and in chronic methamphetamine users (Wilson et al., 1996; Graves et al., 2021). Although there are only limited studies describing the effect of cannabinoids on Vmat2 mRNA, its downregulation may denote possible dopamine release dysregulation (German et al., 2015) in WT mice in response to cannabinoids. One study suggested that reduced Vmat2 mRNA expression may lead to inhibition of vesicular dopamine and potentially increase cytosolic dopamine levels (Sun et al., 2014). The lack of such changes in Cryab KO mice may imply their resilience to cannabinoid-induced dopaminergic alterations, further suggesting dopamine-independent mechanisms mediating their higher cannabinoid sensitivity. Nevertheless, downregulation of endocannabinoid signaling may have contributed to possible dopaminergic desensitization following repeated JWH-018 administration, which is a potential theory behind cannabinoid addiction pathophysiology (Zehra et al., 2018). On the other hand, the involvement of dopamine mediation might still not be ruled out completely. Studies demonstrating dopamine level upregulation after acute cannabinoid administration (de Luca et al., 2015; Ossato et al., 2017; Pintori et al., 2021), along with our ELISA results following acute JWH-018 treatment (Figure 4M), may still implicate transient dopamine increments in addiction-related brain regions to the addictive properties of cannabinoids. However, probably due to the absence of sustained dopaminergic activation via endocannabinoid signaling downregulation, some cannabinoids may have appeared to lack robust abuse potential following prolonged exposure (Cha et al., 2014; Tampus et al., 2015), as opposed to recognized abused drugs that show persistent increases in dopamine concentration and activity even after repeated exposures (Nestby et al., 1997; Cadoni et al., 2000), resulting in evident abuse liability. Therefore, the differential responses between Cryab KO and WT mice to the addiction-inducing effects of JWH-018 may implicate the involvement of other neurological mechanisms beyond the endocannabinoid or dopaminergic systems. Studies have suggested the involvement of CRYAB and cannabinoids in the anti-inflammatory action of the PI3K-AKT-GSK3 pathway (Ozaita et al., 2007; Xu et al., 2013; Zhu et al., 2015), which may also modulate the expression of NF-κB (Bathina and Das, 2018), a transcription factor broadly associated with immune system-related and inflammation-related gene regulation (Beurel et al., 2015; Liu et al., 2017). Several factors (Beurel et al., 2015; Liu et al., 2017; Feng and Lu, 2021), including some heat shock proteins (Schell et al., 2005), were reported to potentially influence neuroinflammation by regulating NF-κB expression/activity, and when modulated, altered complex behaviors such as addiction (Rodrigues et al., 2014; Nennig and Schank, 2017; Morcuende et al., 2021). More recently, CRYAB was described to potentially regulate NF-κB nuclear translocation (Shao et al., 2013; Qiu et al., 2016), which is essential for inflammatory cytokine and chemokine transcription (Somade et al., 2019). Based on the possible correlation between cannabinoid addiction and neuroinflammation, CRYAB, via NF-κB, may potentially influence addiction-related behaviors. Increased CRYAB (Figure 5B) and negligible changes along the PI3K-AKT-GSK3 pathway (Figures 5C–F) in JWH-018-exposed WT mice may imply the occurrence of only minor neuroinflammation. In contrast, JWH-018-exposed Cryab KO mice seemed to suggest potential downstream neuroinflammatory alterations, given by increased GSK-3β (Figure 5F) and NF-κB (Figure 5G) expressions. From this observation, functional CRYAB expression might have a role in the anti-inflammatory effect of cannabinoids, as the presence or absence of CRYAB resulted in significant genotype differences in the expression of NF-κB following JWH-018 exposure. Furthermore, NF-κB expression and translocation may also influence neuroinflammation by mediating inflammatory cytokine gene transcription and astroglia activation (Young et al., 2011; Liu et al., 2017). While JWH-018 exposure induced no changes in WT mice, the higher expression of glial fibrillary acidic protein (GFAP; Figure 5H) and IL-6 (Figure 5K) in Cryab KO mice may indicate a higher state of neuroinflammation compared to WT mice, which may have been a consequence of increased NF-κB expression following repeated JWH-018 administration. Intriguingly, this had no effect on TNF-α and IL-1β expressions, unlike in previous reports (Nennig and Schank, 2017), which could suggest that the alteration of CRYAB expression may have specificity to IL-6 transcription. Our results contradict some previous studies, given that cannabinoids are understood to be anti-inflammatory and JWH-018 seemed to potentially induce inflammation in Cryab KO mice. However, according to previous studies (Pintori et al., 2021; Margiani et al., 2022), JWH-018 may potentially promote neuroinflammation in mice following previous repeated exposures. Thus, the absence of CRYAB might have exacerbated the neuroinflammatory effect of JWH-018 in Cryab KO mice, resulting in the observed alterations in NF-κB, GFAP, and IL-6 expressions, given also that CRYAB, possessing an anti-inflammatory function, might have rendered the Cryab KO mice susceptible to inflammatory insults. Increased neuroinflammation, specifically due to increased cytokine and astrocyte activation (Boycott et al., 2008), may result to the downregulation of glutamate transporter (GLT-1) (Ramos et al., 2010), potentially increasing synaptic glutamate concentration. Glutamate reuptake was also reported to be reduced in the STR of cocaine-exposed rodents (Parikh et al., 2014; Smaga et al., 2020), potentially increasing the availability of glutamate in the synapse. Taking together the alterations in glutamate transporter mRNA (Figures 6A–C), there might be an overall decrease in glutamate reuptake in Cryab KO mice, suggesting potentially higher glutamate levels in the STR of Cryab KO mice, compared to that in WT after JWH-018 exposure, which may have implications in glutamate-mediated long-term potentiation. While glutamate-mediated cannabinoid addiction may entail their regulation of glutamate activation of medium spiny neurons in the NAC (Cohen et al., 2019), other drugs of abuse elicit glutamate-mediated addiction through long-term potentiation of the glutamatergic synapse (LaLumiere and Kalivas, 2008; Gipson et al., 2014; Griffin et al., 2015) via potential neuroinflammatory mechanisms (Hutchinson et al., 2012; Zhu et al., 2018). Similarly, the expressions of synaptic plasticity markers in Cryab KO mice (Figures 6G–J) may suggest the occurrence of neuronal adaptations following JWH-018 exposure, which may have contributed to the development and maintenance of drug-induced addiction (Tsai, 2007; Post and Kalivas, 2013; Zhang et al., 2014; Sutton and Caron, 2015; Ucha et al., 2020; García-García et al., 2021) in Cryab KO mice. While it is still uncertain whether these changes were indeed a result of increased glutamate receptor activation, given the lack of genotype variations in GluA1 and GluA2 expressions, although with marked differences among groups (Figures 6E, F), the lack of CRYAB during JWH-018 administration might still ultimately induce positive effects on drug cue reinforcement. Since glutamate receptor activation may reinforce addiction-related behaviors through postsynaptic alterations (Gass and Olive, 2008; Joffe et al., 2014; Nennig and Schank, 2017), the greater striatal synaptic adaptations in drug-exposed Cryab KO mice may have induced stronger improved habit-associated learning from neuroinflammation-mediated long-term potentiation. This can be supported by NF-κB fluorescent signaling in Cryab KO mice from JWH-018 SA (Figures 7A, B), implying that upregulated NF-κB expression might indeed be involved in the manifestation of cannabinoid-induced addiction-like behaviors in Cryab KO mice, even through various means of JWH-018 exposure (daily repeated treatment or self-administered). Therefore, functional CRYAB deficiency may have enhanced the occurrence of increased striatal synaptic plasticity via NF-κB-modulated neuroinflammation following JWH-018 exposure, potentially corroborating Cryab in neuroinflammation-mediated cannabinoid addiction. In conclusion, Cryab KO mice exhibit higher cannabinoid-induced addiction than WT mice. Their divergent responses to cannabinoids compared to WT may involve neuroinflammation-mediated synaptic plasticity, due to functional CRYAB deletion (Figure 8). Such behavior might not also involve genotype differences in cannabinoid-induced alterations in the endocannabinoid or dopaminergic systems. Given these findings, future studies are imperative to investigate the behavioral responses of Cryab KO mice to other types of cannabinoids to further verify the involvement of Cryab in general cannabinoid addiction. Taken together, the Cryab KO mice might represent one of the potential mechanisms for enhanced cannabinoid abuse susceptibility and be an efficient tool for screening the abuse potential of novel synthetic cannabinoids, contributing to more reliable and accurate evaluation regarding the potential dangers of these substances in humans. **FIGURE 8:** *Schematic diagram for a proposed mechanism underlying the enhanced JWH-018 abuse susceptibility of Cryab knockout (KO) mice. Repeated administration of cannabinoids, such JWH-018, may result to a reduction in cannabinoid receptor (CBR) expression, leading to a desensitized dopamine (DA) system in both Cryab KO and wild-type (WT) mice. Due to CRYAB deficiency, repeated JWH-018 administration resulted in higher striatal neuroinflammation, leading to higher neuroadaptations. These changes may potentially contribute to the abuse susceptibility of Cryab KO mice to cannabinoids.* ## Data availability statement The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors. ## Ethics statement The animal study was reviewed and approved by Animal Care and Use Guidelines of Sahmyook University. ## Author contributions LS, JC, and HK were responsible for the study design. LS, DO, HL, MK, RC, JY, CL, YL, and HC contributed to data acquisition, analysis, and interpretation of data. LS drafted the manuscript. All authors contributed to and approved the final version of the manuscript. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2023.1135929/full#supplementary-material ## References 1. Abiero A., Botanas C. J., Custodio R. J., Sayson L. V., Kim M., Lee H. J.. **4-MeO-PCP and 3-MeO-PCMo, new dissociative drugs, produce rewarding and reinforcing effects through activation of mesolimbic dopamine pathway and alteration of accumbal CREB, deltaFosB, and BDNF levels**. *Psychopharmacol. Berl.* (2020) **237** 757-772. DOI: 10.1007/s00213-019-05412-y 2. Adinoff B.. **Neurobiologic processes in drug reward and addiction**. *Harv Rev. Psychiatry* (2004) **12** 305-320. DOI: 10.1080/10673220490910844 3. Angelakos C. C., Tudor J. C., Ferri S. L., Jongens T. A., Abel T.. **Home-cage hypoactivity in mouse genetic models of autism spectrum disorder**. *Neurobiol. Learn Mem.* (2019) **165** 107000. DOI: 10.1016/J.NLM.2019.02.010 4. Bathina S., Das U. N.. **Dysregulation of PI3K-Akt-mTOR pathway in brain of streptozotocin-induced type 2 diabetes mellitus in Wistar rats**. *Lipids Health Dis.* (2018) **17** 168. DOI: 10.1186/s12944-018-0809-2 5. Bayazit H., Selek S., Karababa I. F., Cicek E., Aksoy N.. **Evaluation of oxidant/antioxidant status and cytokine levels in patients with cannabis use disorder**. *Clin. Psychopharmacol. Neurosci.* (2017) **15** 237-242. DOI: 10.9758/cpn.2017.15.3.237 6. Beurel E., Grieco S. F., Jope R. S.. **Glycogen synthase kinase-3 (GSK3): Regulation, actions, and diseases**. *Pharmacol. Ther.* (2015) **148** 114-131. DOI: 10.1016/j.pharmthera.2014.11.016 7. Bilel S., Tirri M., Arfè R., Stopponi S., Soverchia L., Ciccocioppo R.. **Pharmacological and behavioral effects of the synthetic cannabinoid AKB48 in rats**. *Front. Neurosci.* (2019) **13** 1163. DOI: 10.3389/fnins.2019.01163 8. Botanas C. J., Perez Custodio R. J., Kim H. J., de la Pena J. B., Sayson L. V., Ortiz D. M.. **R (−)-methoxetamine exerts rapid and sustained antidepressant effects and fewer behavioral side effects relative to S (+)-methoxetamine**. *Neuropharmacology* (2021) **193** 108619. DOI: 10.1016/j.neuropharm.2021.108619 9. Boycott H. E., Wilkinson J. A., Boyle J. P., Pearson H. A., Peers C.. **Differential involvement of TNFα in hypoxic suppression of astrocyte glutamate transporters**. *Glia* (2008) **56** 998-1004. DOI: 10.1002/glia.20673 10. Cadoni C., Solinas M., di Chiara G.. **Psychostimulant sensitization: Differential changes in accumbal shell and core dopamine**. *Eur. J. Pharmacol.* (2000) **388** 69-76. DOI: 10.1016/S0014-2999(99)00824-9 11. Calderwood S. K., Repasky E. A., Neckers L., Hightower L. E.. **The IXth CSSI international symposium on heat shock proteins in biology and medicine: Stress responses in health and disease: Alexandria old town, alexandria, Virginia, november 10–13, 2018**. *Cell. Stress Chaperones* (2019) **24** 1-6. DOI: 10.1007/S12192-018-00966-W 12. Cha H. J., Lee K. W., Song M. J., Hyeon Y. J., Hwang J. Y., Jang C. G.. **Dependence potential of the synthetic cannabinoids JWH-073, JWH-081, and JWH-210:**. *Biomol. Ther. Seoul.* (2014) **22** 363-369. DOI: 10.4062/biomolther.2014.039 13. Cohen K., Weizman A., Weinstein A.. **Modulatory effects of cannabinoids on brain neurotransmission**. *Eur. J. Neurosci.* (2019) **50** 2322-2345. DOI: 10.1111/EJN.14407 14. Colizzi M., McGuire P., Pertwee R. G., Bhattacharyya S.. **Effect of cannabis on glutamate signalling in the brain: A systematic review of human and animal evidence**. *Neurosci. Biobehav Rev.* (2016) **64** 359-381. DOI: 10.1016/j.neubiorev.2016.03.010 15. Cristino L., Bisogno T., di Marzo V.. **Cannabinoids and the expanded endocannabinoid system in neurological disorders**. *Nat. Rev. Neurol.* (2020) **16** 9-29. DOI: 10.1038/s41582-019-0284-z 16. Cruz F. C., DeLucia R., Planeta C. S.. **Effects of chronic stress on nicotine-induced locomotor activity and corticosterone release in adult and adolescent rats**. *Addict. Biol.* (2008) **13** 63-69. DOI: 10.1111/j.1369-1600.2007.00080.x 17. Custodio R. J. P., Botanas C. J., de la Peña J. B., dela Peña I. J., Kim M., Sayson L. V.. **Overexpression of the thyroid hormone-responsive (THRSP) gene in the striatum leads to the development of inattentive-like phenotype in mice**. *Neuroscience* (2018) **390** 141-150. DOI: 10.1016/j.neuroscience.2018.08.008 18. Custodio R. J. P., Kim M., Sayson L. V., Lee H. J., Ortiz D. M., Kim B. N.. **Low striatal T3 is implicated in inattention and memory impairment in an ADHD mouse model overexpressing thyroid hormone-responsive protein**. *Commun. Biol.* (2021) **4** 1101-1114. DOI: 10.1038/s42003-021-02633-w 19. Custodio R. J. P., Kim M., Sayson L. V., Ortiz D. M., Buctot D., Lee H. J.. **Regulation of clock and clock-controlled genes during morphine reward and reinforcement: Involvement of the period 2 circadian clock**. *J. Psychopharmacol.* (2022) **36** 875-891. DOI: 10.1177/02698811221089040 20. Custodio R. J. P., Sayson L. V., Botanas C. J., Abiero A., You K. Y., Kim M.. **25B-NBOMe, a novel N-2-methoxybenzyl-phenethylamine (NBOMe) derivative, may induce rewarding and reinforcing effects via a dopaminergic mechanism: Evidence of abuse potential**. *Addict. Biol.* (2020) **25** e12850. DOI: 10.1111/adb.12850 21. Dai A., Guo X., Yang X., Li M., Fu Y., Sun Q.. **Effects of the CRYAB gene on stem cell-like properties of colorectal cancer and its mechanism**. *J. Cancer Res. Ther.* (2022) **18** 1328-1337. DOI: 10.4103/JCRT.JCRT_212_22 22. de Luca M. A., Bimpisidis Z., Melis M., Marti M., Caboni P., Valentini V.. **Stimulation of**. *Neuropharmacology* (2015) **99** 705-714. DOI: 10.1016/j.neuropharm.2015.08.041 23. de Luca M. A., Fattore L.. **Therapeutic use of synthetic cannabinoids: Still an open issue?**. *Clin. Ther.* (2018) **40** 1457-1466. DOI: 10.1016/j.clinthera.2018.08.002 24. dela Peña I. J. I., Botanas C. J., de la Peña J. B., Custodio R. J., dela Peña I., Ryoo Z. Y.. **The atxn7-overexpressing mice showed hyperactivity and impulsivity which were ameliorated by atomoxetine treatment: A possible animal model of the hyperactive-impulsive phenotype of adhd**. *Prog. Neuropsychopharmacol. Biol. Psychiatry* (2019) **88** 311-319. DOI: 10.1016/j.pnpbp.2018.08.012 25. Delis F., Polissidis A., Poulia N., Justinova Z., Nomikos G. G., Goldberg S. R.. **Attenuation of cocaine-induced conditioned place preference and motor activity via cannabinoid CB2 receptor agonism and cb1 receptor antagonism in rats**. *Int. J. Neuropsychopharmacol.* (2017) **20** 269-278. DOI: 10.1093/ijnp/pyw102 26. Diao X., Huestis M. A.. **New synthetic cannabinoids metabolism and strategies to best identify optimal marker metabolites**. *Front. Chem.* (2019) **7** 109. DOI: 10.3389/fchem.2019.00109 27. Drug Enforcement Administration (2015). Drugs of abuse 2015 edition: A dea resource guide. Springfield: Drug Enforcement Administration.. *Drugs of abuse 2015 edition: A dea resource guide* (2015) 28. Fagundo A. B., de la Torre R., Jiménez-Murcia S., Agüera Z., Pastor A., Casanueva F. F.. **Modulation of the endocannabinoids N-arachidonoylethanolamine (AEA) and 2-arachidonoylglycerol (2-AG) on executive functions in humans**. *PLoS One* (2013) **8** e66387. DOI: 10.1371/journal.pone.0066387 29. Feng Y., Lu Y.. **Immunomodulatory effects of dopamine in inflammatory diseases**. *Front. Immunol.* (2021) **12** 663102. DOI: 10.3389/fimmu.2021.663102 30. Fleckenstein A. E., Volz T. J., Riddle E. L., Gibb J. W., Hanson G. R.. **New insights into the mechanism of action of amphetamines**. *Annu. Rev. Pharmacol. Toxicol.* (2007) **47** 681-698. DOI: 10.1146/annurev.pharmtox.47.120505.105140 31. García-García F., Priego-Fernández S., López-Muciño L. A., Acosta-Hernández M. E., Peña-Escudero C.. **Increased alcohol consumption in sleep-restricted rats is mediated by delta FosB induction**. *Alcohol* (2021) **93** 63-70. DOI: 10.1016/J.ALCOHOL.2021.02.004 32. Gass J. T., Olive M. F.. **Glutamatergic substrates of drug addiction and alcoholism**. *Biochem. Pharmacol.* (2008) **75** 218-265. DOI: 10.1016/J.BCP.2007.06.039 33. German C. L., Baladi M. G., McFadden L. M., Hanson G. R., Fleckenstein A. E.. **Regulation of the dopamine and vesicular monoamine transporters: Pharmacological targets and implications for disease**. *Pharmacol. Rev.* (2015) **67** 1005-1024. DOI: 10.1124/pr.114.010397 34. Gipson C. D., Kupchik Y. M., Kalivas P. W.. **Rapid, transient synaptic plasticity in addiction**. *Neuropharmacology* (2014) **76** 276-286. DOI: 10.1016/j.neuropharm.2013.04.032 35. Graves S. M., Schwarzschild S. E., Tai R. A., Chen Y., Surmeier D. J.. **Mitochondrial oxidant stress mediates methamphetamine neurotoxicity in substantia nigra dopaminergic neurons**. *Neurobiol. Dis.* (2021) **156** 105409. DOI: 10.1016/j.nbd.2021.105409 36. Griffin W. C., Ramachandra V. S., Knackstedt L. A., Becker H. C.. **Repeated cycles of chronic intermittent ethanol exposure increases basal glutamate in the nucleus accumbens of mice without affecting glutamate transport**. *Front. Pharmacol.* (2015) **6** 27. DOI: 10.3389/fphar.2015.00027 37. Guo Y. S., Liang P., Lu S. Z., Chen R., Yin Y., Zhou J.. **Extracellular αB-crystallin modulates the inflammatory responses**. *Biochem. Biophys. Res. Commun.* (2019) **508** 282-288. DOI: 10.1016/j.bbrc.2018.11.024 38. Hammond L.. **Measuring cell fluorescence using ImageJ — the open Lab book v1.0. The open Lab book**. (2014) 39. Herning R. I., Better W., Cadet J. L.. **EEG of chronic marijuana users during abstinence: Relationship to years of marijuana use, cerebral blood flow and thyroid function**. *Clin. Neurophysiol.* (2008) **119** 321-331. DOI: 10.1016/j.clinph.2007.09.140 40. Herrero M. J., Domingo-Salvany A., Torrens M., Brugal M. T., de Hoz L. D. L. F., Gómez R. B.. **Psychiatric comorbidity in young cocaine users: Induced versus independent disorders**. *Addiction* (2008) **103** 284-293. DOI: 10.1111/j.1360-0443.2007.02076.x 41. Huffman J. W., Zengin G., Wu M. J., Lu J., Hynd G., Bushell K.. **Structure-activity relationships for 1-alkyl-3-(1-naphthoyl)indoles at the cannabinoid CB 1 and CB 2 receptors: Steric and electronic effects of naphthoyl substituents. New highly selective CB 2 receptor agonists**. *Bioorg Med. Chem.* (2005) **13** 89-112. DOI: 10.1016/j.bmc.2004.09.050 42. Hur K. H., Ma S. X., Lee B. R., Ko Y. H., Seo J. Y., Ryu H. W.. **Abuse potential of synthetic cannabinoids: Am-1248, cb-13, and pb-22**. *Biomol. Ther. Seoul.* (2021) **29** 384-391. DOI: 10.4062/biomolther.2020.212 43. Hutchinson M. R., Northcutt A. L., Hiranita T., Wang X., Lewis S. S., Thomas J.. **Opioid activation of toll-like receptor 4 contributes to drug reinforcement**. *Soc. Neurosci.* (2012) **32** 11187-11200. DOI: 10.1523/JNEUROSCI.0684-12.2012 44. Hyatt W. S., Fantegrossi W. E.. **Δ9-THC exposure attenuates aversive effects and reveals appetitive effects of K2/’Spice’ constituent JWH-018 in mice**. *Behav. Pharmacol.* (2014) **25** 253-257. DOI: 10.1097/FBP.0000000000000034 45. Javed H., Azimullah S., Haque M. E., Ojha S. K.. **Cannabinoid type 2 (CB2) receptors activation protects against oxidative stress and neuroinflammation associated dopaminergic neurodegeneration in rotenone model of Parkinson’s disease**. *Front. Neurosci.* (2016) **10** 321. DOI: 10.3389/fnins.2016.00321 46. Jiménez-González A., Gómez-Acevedo C., Ochoa-Aguilar A., Chavarría A.. **The role of glia in addiction: Dopamine as a modulator of glial responses in addiction**. *Cell. Mol. Neurobiol.* (2022) **42** 2109-2120. DOI: 10.1007/s10571-021-01105-3 47. Joffe M., Grueter C. A., Grueter B. A.. **Biological substrates of addiction**. *Wiley Interdiscip. Rev. Cogn. Sci.* (2014) **5** 151-171. DOI: 10.1002/wcs.1273 48. Kabir Z. D., Lee A. S., Rajadhyaksha A. M.. **L-Type Ca2+ channels in mood, cognition and addiction: Integrating human and rodent studies with a focus on behavioural endophenotypes**. *J. Physiology* (2016) **594** 5823-5837. DOI: 10.1113/JP270673 49. Kalivas P. W.. **Cocaine and amphetamine-like psychostimulants: Neuro circuitry and glutamate neuroplasticity**. *Dialogues Clin. Neurosci.* (2007) **9** 389-397. DOI: 10.31887/DCNS.2007.9.4/PKALIVAS 50. Kauer J. A., Malenka R. C.. **Synaptic plasticity and addiction**. *Nat. Rev. Neurosci.* (2007) **8** 844-858. DOI: 10.1038/nrn2234 51. Kesner A. J., Lovinger D. M.. **Cannabis use, abuse, and withdrawal: Cannabinergic mechanisms, clinical, and preclinical findings**. *J. Neurochem.* (2021) **157** 1674-1696. DOI: 10.1111/jnc.15369 52. Kim M., Custodio R. J., Botanas C. J., de la Peña J. B., Sayson L. V., Abiero A.. **The circadian gene, Per2, influences methamphetamine sensitization and reward through the dopaminergic system in the striatum of mice**. *Addict. Biol.* (2019) **24** 946-957. DOI: 10.1111/adb.12663 53. Kinsey S. G., Mahadevan A., Zhao B., Sun H., Naidu P. S., Razdan R. K.. **The CB2 cannabinoid receptor-selective agonist O-3223 reduces pain and inflammation without apparent cannabinoid behavioral effects**. *Neuropharmacology* (2011) **60** 244-251. DOI: 10.1016/j.neuropharm.2010.09.004 54. Kohno M., Link J., Dennis L. E., McCready H., Huckans M., Hoffman W. F.. **Neuroinflammation in addiction: A review of neuroimaging studies and potential immunotherapies**. *Pharmacol. Biochem. Behav.* (2019) **179** 34-42. DOI: 10.1016/j.pbb.2019.01.007 55. Kozela E., Pietr M., Juknat A., Rimmerman N., Levy R., Vogel Z.. **Cannabinoids Delta(9)-tetrahydrocannabinol and cannabidiol differentially inhibit the lipopolysaccharide-activated NF-kappaB and interferon-beta/STAT proinflammatory pathways in BV-2 microglial cells**. *J. Biol. Chem.* (2010) **285** 1616-1626. DOI: 10.1074/jbc.M109.069294 56. Krasnova I. N., Justinova Z., Cadet J. L.. **Methamphetamine addiction: Involvement of CREB and neuroinflammatory signaling pathways**. *Psychopharmacol. Berl.* (2016) **233** 1945-1962. DOI: 10.1007/s00213-016-4235-8 57. Kuipers H. F., Yoon J., van Horssen J., Han M. H., Bollyky P. L., Palmer T. D.. **Phosphorylation of αB-crystallin supports reactive astrogliosis in demyelination**. *Proc. Natl. Acad. Sci. U. S. A.* (2017) **114** E1745-E1754. DOI: 10.1073/PNAS.1621314114/SUPPL_FILE/PNAS.1621314114.SD01.XLSX 58. LaLumiere R. T., Kalivas P. W.. **Glutamate release in the nucleus accumbens core is necessary for heroin seeking**. *J. Neurosci.* (2008) **28** 3170-3177. DOI: 10.1523/JNEUROSCI.5129-07.2008 59. Le Boisselier R., Alexandre J., Lelong-Boulouard V., Debruyne D.. **Focus on cannabinoids and synthetic cannabinoids**. *Clin. Pharmacol. Ther.* (2017) **101** 220-229. DOI: 10.1002/cpt.563 60. Lepsch L. B., Gonzalo L. A., Magro F. J. B., Delucia R., Scavone C., Planeta C. S.. **Exposure to chronic stress increases the locomotor response to cocaine and the basal levels of corticosterone in adolescent rats**. *Addict. Biol.* (2005) **10** 251-256. DOI: 10.1080/13556210500269366 61. Leung J., Chan G. C. K., Hides L., Hall W. D.. **What is the prevalence and risk of cannabis use disorders among people who use cannabis? A systematic review and meta-analysis**. *Addict. Behav.* (2020) **109** 106479. DOI: 10.1016/j.addbeh.2020.106479 62. Li R., Fukumori R., Takeda T., Song Y., Morimoto S., Kikura-Hanajiri R.. **Elevation of endocannabinoids in the brain by synthetic cannabinoid JWH-018: Mechanism and effect on learning and memory**. *Sci. Rep.* (2019) **9** 9621. DOI: 10.1038/S41598-019-45969-4 63. Lin X., Dhopeshwarkar A. S., Huibregtse M., MacKie K., Hohmann A. G.. **Slowly signaling G protein-biased CB2 cannabinoid receptor agonist LY2828360 suppresses neuropathic pain with sustained efficacy and attenuates morphine tolerance and dependence**. *Mol. Pharmacol.* (2018) **93** 49-62. DOI: 10.1124/mol.117.109355 64. Liu T., Zhang L., Joo D., Sun S. C.. **NF-κB signaling in inflammation**. *Signal Transduct. Target Ther.* (2017) **2** 17023. DOI: 10.1038/sigtrans.2017.23 65. Liu Y., Chen Y., Fraga-González G., Szpak V., Laverman J., Wiers R. W.. **Resting-state EEG, substance use and abstinence after chronic use: A systematic review**. *Clin. EEG Neurosci.* (2022) **53** 344-366. DOI: 10.1177/15500594221076347 66. MacDonald K., Pappas K.. **WHY not pot?: A review of the brain-based risks of cannabis**. *Innov. Clin. Neurosci.* (2016) **13** 13-22 67. MacNicol B.. **The biology of addiction**. *Can. J. Anesth.* (2017) **64** 141-148. DOI: 10.1007/s12630-016-0771-2 68. Maldonado R., Berrendero F., Ozaita A., Robledo P.. **Neurochemical basis of cannabis addiction**. *Neuroscience* (2011) **181** 1-17. DOI: 10.1016/j.neuroscience.2011.02.035 69. Malyshevskaya O., Aritake K., Kaushik M. K., Uchiyama N., Cherasse Y., Kikura-Hanajiri R.. **Natural (Δ9-THC) and synthetic (JWH-018) cannabinoids induce seizures by acting through the cannabinoid CB1 receptor**. *Sci. Rep.* (2017) **7** 10516. DOI: 10.1038/s41598-017-10447-2 70. Margiani G., Castelli M. P., Pintori N., Frau R., Ennas M. G., Orrù V.. **Adolescent self-administration of the synthetic cannabinoid receptor agonist JWH-018 induces neurobiological and behavioral alterations in adult male mice**. *Psychopharmacology* (2022) **239** 3083-3102. DOI: 10.1007/S00213-022-06191-9 71. **Development of sensitized screening method for addiction/dependence of abuse drugs. MFDS**. (2021) 72. Morcuende A., Navarrete F., Nieto E., Manzanares J., Femenía T.. **Inflammatory biomarkers in addictive disorders**. *Biomolecules* (2021) **11** 1824. DOI: 10.3390/biom11121824 73. Müller M. M., Gruber T., Keil A.. **Modulation of induced gamma band activity in the human EEG by attention and visual information processing**. *Int. J. Psychophysiol.* (2000) **38** 283-299. DOI: 10.1016/S0167-8760(00)00171-9 74. **Is marijuana safe and effective as medicine? National Institute on drug abuse (NIDA). National institutes of health**. (2021) 75. Nennig S. E., Schank J. R.. **The role of NFkB in drug addiction: Beyond inflammation**. *Alcohol Alcohol.* (2017) **52** 172-179. DOI: 10.1093/alcalc/agw098 76. Nestby P., Vanderschuren L. J. M. J., de Vries T. J., Hogenboom F., Wardeh G., Mulder A. H.. **Ethanol, like psychostimulants and morphine, causes long-lasting hyperreactivity of dopamine and acetylcholine neurons of rat nucleus accumbens: Possible role in behavioural sensitization**. *Psychopharmacol. Berl.* (1997) **133** 69-76. DOI: 10.1007/s002130050373 77. Nestler E. J.. **Molecular basis of long-term plasticity underlying addiction**. *Nat. Rev. Neurosci.* (2001) **2** 119-128. DOI: 10.1038/35053570 78. Newton T. F., Cook I. A., Kalechstein A. D., Duran S., Monroy F., Ling W.. **Quantitative EEG abnormalities in recently abstinent methamphetamine dependent individuals**. *Clin. Neurophysiol.* (2003) **114** 410-415. DOI: 10.1016/S1388-2457(02)00409-1 79. Nukitram J., Cheaha D., Kumarnsit E.. **Spectral power and theta-gamma coupling in the basolateral amygdala related with methamphetamine conditioned place preference in mice**. *Neurosci. Lett.* (2021) **756** 135939. DOI: 10.1016/j.neulet.2021.135939 80. Ossato A., Uccelli L., Bilel S., Canazza I., di Domenico G., Pasquali M.. **Psychostimulant effect of the synthetic cannabinoid JWH-018 and AKB48: Behavioral, neurochemical, and dopamine transporter scan imaging studies in mice**. *Front. Psychiatry* (2017) **8** 130. DOI: 10.3389/fpsyt.2017.00130 81. Ota S. M., Suchecki D., Meerlo P.. **Chronic social defeat stress suppresses locomotor activity but does not affect the free-running circadian period of the activity rhythm in mice**. *Neurobiol. Sleep. Circadian Rhythms* (2018) **5** 1-7. DOI: 10.1016/J.NBSCR.2018.03.002 82. Ozaita A., Puighermanal E., Maldonado R.. **Regulation of PI3K/Akt/GSK-3 pathway by cannabinoids in the brain**. *J. Neurochem.* (2007) **102** 1105-1114. DOI: 10.1111/j.1471-4159.2007.04642.x 83. Parikh V., Naughton S. X., Shi X., Kelley L. K., Yegla B., Tallarida C. S.. **Cocaine-induced neuroadaptations in the dorsal striatum: Glutamate dynamics and behavioral sensitization**. *Neurochem. Int.* (2014) **75** 54-65. DOI: 10.1016/J.NEUINT.2014.05.016 84. Park H., Han K.-S., Seo J., Lee J., Dravid S. M., Woo J.. **Channel-mediated astrocytic glutamate modulates hippocampal synaptic plasticity by activating postsynaptic NMDA receptors**. *Mol. Brain* (2015) **8** 7. DOI: 10.1186/s13041-015-0097-y 85. Parsons L. H., Hurd Y. L.. **Endocannabinoid signalling in reward and addiction**. *Nat. Rev. Neurosci.* (2015) **16** 579-594. DOI: 10.1038/nrn4004 86. Paxinos G., Franklin K. B. J.. **Paxinos and franklin’s the mouse brain in stereotaxic coordinates**. (2001) 87. Perdikaris P., Tsarouchi M., Fanarioti E., Natsaridis E., Mitsacos A., Giompres P.. **Long lasting effects of chronic WIN55,212-2 treatment on mesostriatal dopaminergic and cannabinoid systems in the rat brain**. *Neuropharmacology* (2018) **129** 1-15. DOI: 10.1016/j.neuropharm.2017.11.005 88. Pintori N., Castelli M. P., Miliano C., Simola N., Fadda P., Fattore L.. **Repeated exposure to JWH-018 induces adaptive changes in the mesolimbic and mesocortical dopaminergic pathways, glial cells alterations, and behavioural correlates**. *Br. J. Pharmacol.* (2021) **178** 3476-3497. DOI: 10.1111/bph.15494 89. Polissidis A., Chouliara O., Galanopoulos A., Marselos M., Papadopoulou-Daifoti Z., Antoniou K.. **Behavioural and dopaminergic alterations induced by a low dose of WIN 55,212-2 in a conditioned place preference procedure**. *Life Sci.* (2009) **85** 248-254. DOI: 10.1016/j.lfs.2009.05.015 90. Post R. M., Kalivas P.. **Bipolar disorder and substance misuse: Pathological and therapeutic implications of their comorbidity and cross-sensitisation**. *Br. J. Psychiatry* (2013) **202** 172-176. DOI: 10.1192/BJP.BP.112.116855 91. Qiu J., Yan Z., Tao K., Li Y., Li Y., Li J.. **Sinomenine activates astrocytic dopamine D2 receptors and alleviates neuroinflammatory injury via the CRYAB/STAT3 pathway after ischemic stroke in mice**. *J. Neuroinflammation* (2016) **13** 263. DOI: 10.1186/s12974-016-0739-8 92. Ramos K. M., Lewis M. T., Morgan K. N., Crysdale N. Y., Kroll J. L., Taylor F. R.. **Spinal upregulation of glutamate transporter GLT-1 by ceftriaxone: Therapeutic efficacy in a range of experimental nervous system disorders**. *Neuroscience* (2010) **169** 1888-1900. DOI: 10.1016/J.NEUROSCIENCE.2010.06.014 93. Reid M. S., Flammino F., Howard B., Nilsen D., Prichep L. S.. **Topographic imaging of quantitative EEG in response to smoked cocaine self-administration in humans**. *Neuropsychopharmacology* (2006) **31** 872-884. DOI: 10.1038/sj.npp.1300888 94. Ren Z., Yang M., Guan Z., Yu W.. **Astrocytic α7 nicotinic receptor activation inhibits amyloid-β aggregation by upregulating endogenous αB-crystallin through the PI3K/akt signaling pathway**. *Curr. Alzheimer Res.* (2018) **16** 39-48. DOI: 10.2174/1567205015666181022093359 95. Ribeiro Do Couto B., Aguilar M. A., Lluch J., Rodríguez-Arias M., Miñarro J.. **Social experiences affect reinstatement of cocaine-induced place preference in mice**. *Psychopharmacol. Berl.* (2009) **207** 485-498. DOI: 10.1007/s00213-009-1678-1 96. Rodrigues L. C. M., Gobira P. H., de Oliveira A. C., Pelição R., Teixeira A. L., Moreira F. A.. **Neuroinflammation as a possible link between cannabinoids and addiction**. *Acta Neuropsychiatr.* (2014) **26** 334-346. DOI: 10.1017/neu.2014.24 97. Rodríguez-Arias M., Roger-Sánchez C., Vilanova I., Revert N., Manzanedo C., Miñarro J.. **Effects of cannabinoid exposure during adolescence on the conditioned rewarding effects of WIN 55212-2 and cocaine in mice: Influence of the novelty-seeking trait**. *Neural Plast.* (2016) **2016** 6481862. DOI: 10.1155/2016/6481862 98. Rygula R., Abumaria N., Flügge G., Fuchs E., Rüther E., Havemann-Reinecke U.. **Anhedonia and motivational deficits in rats: Impact of chronic social stress**. *Behav. Brain Res.* (2005) **162** 127-134. DOI: 10.1016/j.bbr.2005.03.009 99. Sales-Carbonell C., Rueda-Orozco P. E., Soria-Gómez E., Buzsáki G., Marsicano G., Robbe D.. **Striatal GABAergic and cortical glutamatergic neurons mediate contrasting effects of cannabinoids on cortical network synchrony**. *Proc. Natl. Acad. Sci. U. S. A.* (2013) **110** 719-724. DOI: 10.1073/pnas.1217144110 100. Sayson L. V., Custodio R. J. P., Ortiz D. M., Lee H. J., Kim M., Jeong Y.. **The potential rewarding and reinforcing effects of the substituted benzofurans 2-EAPB and 5-EAPB in rodents**. *Eur. J. Pharmacol.* (2020) **885** 173527. DOI: 10.1016/j.ejphar.2020.173527 101. Schell M. T., Spitzer A. L., Johnson J. A., Lee D., Harris H. W.. **Heat shock inhibits NF-kB activation in a dose- and time-dependent manner**. *J. Surg. Res.* (2005) **129** 90-93. DOI: 10.1016/j.jss.2005.05.025 102. Scherma M., Dessì C., Muntoni A. L., Lecca S., Satta V., Luchicchi A.. **Adolescent Δ 9-tetrahydrocannabinol exposure alters WIN55,212-2 self-administration in adult rats**. *Neuropsychopharmacology* (2016) **41** 1416-1426. DOI: 10.1038/npp.2015.295 103. Schmitz N., Richert L.. **Pharmacists and the future of cannabis medicine**. *J. Am. Pharm. Assoc.* (2020) **60** 207-211. DOI: 10.1016/J.JAPH.2019.11.007 104. Shao W., Zhang S. Z., Tang M., Zhang X. H., Zhou Z., Yin Y. Q.. **Suppression of neuroinflammation by astrocytic dopamine D2 receptors via αb-crystallin**. *Nature* (2013) **494** 90-94. DOI: 10.1038/nature11748 105. Skalisz L. L., Beijamini V., Joca S. L., Vital M. A. B. F., da Cunha C., Andreatini R.. **Evaluation of the face validity of reserpine administration as an animal model of depression-Parkinson’s disease association**. *Prog. Neuropsychopharmacol. Biol. Psychiatry* (2002) **26** 879-883. DOI: 10.1016/S0278-5846(01)00333-5 106. Skosnik P. D., D’Souza D. C., Steinmetz A. B., Edwards C. R., Vollmer J. M., Hetrick W. P.. **The effect of chronic cannabinoids on broadband eeg neural oscillations in humans**. *Neuropsychopharmacology* (2012) **37** 2184-2193. DOI: 10.1038/npp.2012.65 107. Smaga I., Gawlińska K., Frankowska M., Wydra K., Sadakierska-Chudy A., Suder A.. **Extinction training after cocaine self-administration influences the epigenetic and genetic machinery responsible for glutamatergic transporter gene expression in male rat brain**. *Neuroscience* (2020) **451** 99-110. DOI: 10.1016/J.NEUROSCIENCE.2020.10.001 108. Somade O. T., Ajayi B. O., Tajudeen N. O., Atunlute E. M., James A. S., Kehinde S. A.. **Camphor elicits up-regulation of hepatic and pulmonary pro-inflammatory cytokines and chemokines via activation of NF-kB in rats**. *Pathophysiology* (2019) **26** 305-313. DOI: 10.1016/j.pathophys.2019.07.005 109. Sora I., Li B., Igari M., Hall F. S., Ikeda K.. **Transgenic mice in the study of drug addiction and the effects of psychostimulant drugs**. *Ann. N. Y. Acad. Sci.* (2010) **1187** 218-246. DOI: 10.1111/J.1749-6632.2009.05276.X 110. Spanagel R.. **Cannabinoids and the endocannabinoid system in reward processing and addiction: From mechanisms to interventions**. *Dialogues Clin. Neurosci.* (2020) **22** 241-250. DOI: 10.31887/DCNS.2020.22.3/RSPANAGEL 111. Spanagel R., Sanchis-Segura C.. **The use of transgenic mice to study addictive behavior**. *Clin. Neurosci. Res.* (2003) **3** 325-331. DOI: 10.1016/S1566-2772(03)00094-X 112. Sun L., Dong R., Xu X., Yang X., Peng M.. **Activation of cannabinoid receptor type 2 attenuates surgery-induced cognitive impairment in mice through anti-inflammatory activity**. *J. Neuroinflammation* (2017) **14** 138. DOI: 10.1186/s12974-017-0913-7 113. Sun Y., Li Y.-S., Yang J.-W., Yu J., Wu Y.-P., Li B.-X.. **Exposure to atrazine during gestation and lactation periods: Toxicity effects on dopaminergic neurons in offspring by downregulation of Nurr1 and VMAT2**. *Int. J. Mol. Sci.* (2014) **15** 2811-2825. DOI: 10.3390/ijms15022811 114. Sutton L. P., Caron M. G.. **Essential role of D1R in the regulation of mTOR complex1 signaling induced by cocaine**. *Neuropharmacology* (2015) **99** 610-619. DOI: 10.1016/J.NEUROPHARM.2015.08.024 115. Szumlinski K. K., Lominac K. D., Campbell R. R., Cohen M., Fultz E. K., Brown C. N.. **Methamphetamine addiction vulnerability: The glutamate, the bad, and the ugly**. *Biol. Psychiatry* (2017) **81** 959-970. DOI: 10.1016/j.biopsych.2016.10.005 116. Tai S., Fantegrossi W. E.. **Synthetic cannabinoids: Pharmacology, behavioral effects, and abuse potential**. *Curr. Addict. Rep.* (2014) **1** 129-136. DOI: 10.1007/s40429-014-0014-y 117. Tallon-Baudry C., Bertrand O., Hénaff M.-A., Isnard J., Fischer C.. **Attention modulates gamma-band oscillations differently in the human lateral occipital cortex and fusiform gyrus**. *Cereb. Cortex* (2005) **15** 654-662. DOI: 10.1093/cercor/bhh167 118. Tampus R., Yoon S. S., de la Peña J. B., Botanas C. J., Kim H. J., Seo J. W.. **Assessment of the abuse liability of synthetic cannabinoid agonists JWH-030, JWH-175, and JWH-176**. *Biomol. Ther. Seoul.* (2015) **23** 590-596. DOI: 10.4062/biomolther.2015.120 119. Thomsen M., Caine S. B.. **False positive in the intravenous drug self-administration test in C57BL/6J mice**. *Behav. Pharmacol.* (2011) **22** 239-247. DOI: 10.1097/FBP.0b013e328345f8f2 120. Tsai S. J.. **Increased central brain-derived neurotrophic factor activity could be a risk factor for substance abuse: Implications for treatment**. *Med. Hypotheses* (2007) **68** 410-414. DOI: 10.1016/J.MEHY.2006.05.035 121. Ucha M., Roura-Martínez D., Ambrosio E., Higuera-Matas A.. **The role of the mTOR pathway in models of drug-induced reward and the behavioural constituents of addiction**. *J. Psychopharmacol.* (2020) **34** 1176-1199. DOI: 10.1177/0269881120944159 122. van de Giessen E., Weinstein J. J., Cassidy C. M., Haney M., Dong Z., Ghazzaoui R.. **Deficits in striatal dopamine release in cannabis dependence**. *Mol. Psychiatry* (2017) **22** 68-75. DOI: 10.1038/mp.2016.21 123. VanGuilder H. D., Vrana K. E., Freeman W. M.. **Twenty-five years of quantitative PCR for gene expression analysis**. *Biotechniques* (2008) **44** 619-626. DOI: 10.2144/000112776 124. Vlachou S., Panagis G.. **Regulation of brain reward by the endocannabinoid system: A critical review of behavioral studies in animals**. *Curr. Pharm. Des.* (2014) **20** 2072-2088. DOI: 10.2174/13816128113199990433 125. Volkow N. D.. **Substance use disorders in Schizophrenia - clinical implications of comorbidity**. *Schizophr. Bull.* (2009) **35** 469-472. DOI: 10.1093/schbul/sbp016 126. Vonder Haar C., Ferland J. M. N., Kaur S., Riparip L. K., Rosi S., Winstanley C. A.. **Cocaine self-administration is increased after frontal traumatic brain injury and associated with neuroinflammation**. *Eur. J. Neurosci.* (2019) **50** 2134-2145. DOI: 10.1111/ejn.14123 127. Wang S.-H., Hsiao P.-C., Yeh L.-L., Liu C.-M., Liu C.-C., Hwang T.-J.. **Polygenic risk for schizophrenia and neurocognitive performance in patients with schizophrenia**. *Genes. Brain Behav.* (2018) **17** 49-55. DOI: 10.1111/gbb.12401 128. Weinstein A. M., Rosca P., Fattore L., London E. D.. **Synthetic cathinone and cannabinoid designer drugs pose a major risk for public health**. *Front. Psychiatry* (2017) **8** 156. DOI: 10.3389/fpsyt.2017.00156 129. Wilson J. M., Kalasinsky K. S., Levey A. I., Bergeron C., Reiber G., Anthony R. M.. **Striatal dopamine nerve terminal markers in human, chronic methamphetamine users**. *Nat. Med.* (1996) **2** 699-703. DOI: 10.1038/nm0696-699 130. Wood N. C., Hamilton I., Axon A. T. R., Khan S. A., Quirke P., Mindham R. H. S.. **Abnormal intestinal permeability**. *Br. J. Psychiatry* (1987) **150** 853-856. DOI: 10.1192/bjp.150.6.853 131. Xi Z. X., Peng X. Q., Li X., Song R., Zhang H. Y., Liu Q. R.. **Brain cannabinoid CB- receptors modulate cocaine's actions in mice**. *Nat. Neurosci.* (2011) **14** 1160-1166. DOI: 10.1038/nn.2874 132. Xie S., Wang X.. **CRYAB reduces cigarette smoke-induced inflammation, apoptosis, and oxidative stress by retarding PI3K/Akt and NF-κB signaling pathways in human bronchial epithelial cells**. *Allergol. Immunopathol. Madr.* (2022) **50** 23-29. DOI: 10.15586/AEI.V50I5.645 133. Xu F., Yu H., Liu J., Cheng L.. **αb-crystallin regulates oxidative stress-induced apoptosis in cardiac H9c2 cells via the PI3K/AKT pathway**. *Mol. Biol. Rep.* (2013) **40** 2517-2526. DOI: 10.1007/s11033-012-2332-2 134. Xu H., Cheng C. L., Chen M., Manivannan A., Cabay L., Pertwee R. G.. **Anti-inflammatory property of the cannabinoid receptor-2-selective agonist JWH-133 in a rodent model of autoimmune uveoretinitis**. *J. Leukoc. Biol.* (2007) **82** 532-541. DOI: 10.1189/JLB.0307159 135. Xu L., Nan J., Lan Y.. **The nucleus accumbens: A common target in the comorbidity of depression and addiction**. *Front. Neural Circuits* (2020) **14** 37. DOI: 10.3389/fncir.2020.00037 136. Xu W., Guo Y., Huang Z., Zhao H., Zhou M., Huang Y.. **Small heat shock protein CRYAB inhibits intestinal mucosal inflammatory responses and protects barrier integrity through suppressing IKKβ activity**. *Mucosal Immunol.* (2019) **12** 1291-1303. DOI: 10.1038/s41385-019-0198-5 137. Yazdani N., Parker C. C., Shen Y., Reed E. R., Guido M. A., Kole L. A.. **Hnrnph1 is A quantitative trait gene for methamphetamine sensitivity**. *PLoS Genet.* (2015) **11** e1005713. DOI: 10.1371/journal.pgen.1005713 138. Young A. M. H., Campbell E., Lynch S., Suckling J., Powis S. J.. **Aberrant NF-kappaB expression in autism spectrum condition: A mechanism for neuroinflammation**. *Front. Psychiatry* (2011) **2** 27. DOI: 10.3389/fpsyt.2011.00027 139. Zanettini C., Scaglione A., Keighron J. D., Giancola J. B., Lin S.-C., Newman A. H.. **Pharmacological classification of centrally acting drugs using EEG in freely moving rats: An old tool to identify new atypical dopamine uptake inhibitors**. *Neuropharmacology* (2019) **161** 107446. DOI: 10.1016/j.neuropharm.2018.11.034 140. Zehra A., Burns J., Liu C. K., Manza P., Wiers C. E., Volkow N. D.. **Cannabis addiction and the brain: A review**. *J. Neuroimmune Pharmacol.* (2018) **13** 438-452. DOI: 10.1007/s11481-018-9782-9 141. Zhang J. F., Liu J., Wu J. L., Li W. F., Chen Z. W., Yang L. S.. **Progression of the role of CRYAB in signaling pathways and cancers**. *Onco Targets Ther.* (2019) **12** 4129-4139. DOI: 10.2147/OTT.S201799 142. Zhang Y., Crofton E. J., Li D., Lobo M. K., Fan X., Nestler E. J.. **Overexpression of DeltaFosB in nucleus accumbens mimics the protective addiction phenotype, but not the protective depression phenotype of environmental enrichment**. *Front. Behav. Neurosci.* (2014) **8** 297. DOI: 10.3389/fnbeh.2014.00297 143. Zhu R., Bu Q., Fu D., Shao X., Jiang L., Guo W.. **Toll-like receptor 3 modulates the behavioral effects of cocaine in mice**. *J. Neuroinflammation* (2018) **15** 93-11. DOI: 10.1186/s12974-018-1130-8 144. Zhu Z., Li R., Stricker R., Reiser G.. **Extracellular α-crystallin protects astrocytes from cell death through activation of MAPK, PI3K/Akt signaling pathway and blockade of ROS release from mitochondria**. *Brain Res.* (2015) **1620** 17-28. DOI: 10.1016/j.brainres.2015.05.011
--- title: Neutrophilic inflammation promotes SARS-CoV-2 infectivity and augments the inflammatory responses in airway epithelial cells authors: - Ben A. Calvert - Erik J. Quiroz - Zareeb Lorenzana - Ngan Doan - Seongjae Kim - Christiana N. Senger - Jeffrey J. Anders - Wiliam D. Wallace - Matthew P. Salomon - Jill Henley - Amy L. Ryan journal: Frontiers in Immunology year: 2023 pmcid: PMC10061003 doi: 10.3389/fimmu.2023.1112870 license: CC BY 4.0 --- # Neutrophilic inflammation promotes SARS-CoV-2 infectivity and augments the inflammatory responses in airway epithelial cells ## Abstract ### Introduction In response to viral infection, neutrophils release inflammatory mediators as part of the innate immune response, contributing to pathogen clearance through virus internalization and killing. Pre- existing co-morbidities correlating to incidence to severe COVID-19 are associated with chronic airway neutrophilia. Furthermore, examination of COVID-19 explanted lung tissue revealed a series of epithelial pathologies associated with the infiltration and activation of neutrophils, indicating neutrophil activity in response to SARS-CoV-2 infection. ### Methods To determine the impact of neutrophil-epithelial interactions on the infectivity and inflammatory responses to SARS-CoV-2 infection, we developed a co-culture model of airway neutrophilia. This model was infected with live SARS-CoV-2 virus the epithelial response to infection was evaluated. ### Results SARS-CoV-2 infection of airway epithelium alone does not result in a notable pro-inflammatory response from the epithelium. The addition of neutrophils induces the release of proinflammatory cytokines and stimulates a significantly augmented proinflammatory response subsequent SARS-CoV-2 infection. The resulting inflammatory responses are polarized with differential release from the apical and basolateral side of the epithelium. Additionally, the integrity of the \epithelial barrier is impaired with notable epithelial damage and infection of basal stem cells. ### Conclusions This study reveals a key role for neutrophil-epithelial interactions in determining inflammation and infectivity. ## Graphical Abstract ## Introduction Novel coronavirus infectious disease 2019, COVID-19, is caused by the severe acute respiratory distress syndrome related coronavirus 2, SARS-CoV-2 [1, 2]. While COVID-19 is associated with high hospitalization and mortality rates, a substantial proportion of the population is asymptomatic or only experiences mild symptoms. In response to viral infection neutrophils are the first and predominant immune cells recruited to the respiratory tract [3]. Neutrophils release inflammatory mediators as part of the innate immune response and contribute to pathogen clearance through virus internalization and killing [4]. While the protective versus pathological role of neutrophils in the airways during viral response is poorly understood, it has been shown that the number of neutrophils in the lower respiratory tract correlates to COVID-19 disease severity (5–7). Infiltration of neutrophils is also characteristic of other lung diseases associated with chronic infection and inflammation, such as asthma, chronic obstructive pulmonary disease (COPD) and cystic fibrosis (CF). All these respiratory diseases have been associated with an increased risk of developing severe COVID-19 [8]. Evaluating the relationship between SARS-CoV-2 infection and pre-existing airway neutrophilia may provide critical insight into how host and viral factors contribute to disease severity. Neutrophils have an inherent capacity to recognize infectious agents, in addition to acting as sites of infection and, in both cases, result in an acute inflammatory response [9]. Understanding the precise nature of the inflammatory response and the pathophysiological consequences, could identify pathways for therapeutic intervention based on early detection of a prognostic signature for COVID-19 outcomes. An uncontrolled, hyper-inflammatory response, known as a “cytokine storm” can result from a massive influx of innate leukocytes, inclusive of neutrophils and monocytes [10], and has been heavily implicated in patients with severe COVID-19 [11, 12]. Cytokine storm and presence of pro-inflammatory mediators can be a predictor of disease severity and often leads to acute respiratory distress syndrome (ARDS), and eventually respiratory failure [13]. Retrospective studies have also demonstrated that elevated levels of interleukin-6 (IL-6) are a strong predictor of mortality over resolution [14], and tumor necrosis factor alpha (TNFα) is increased in severe compared to moderate cases [15]. Despite their importance in anti-viral immunity and response to viral pathogens, neutrophils have been somewhat overlooked for their role in the pathogenesis of SARS-CoV-2 infection (16–18). It has been shown that the number of neutrophils in the lower respiratory tract correlates to disease severity in other viral infections, including influenza A infection [19] and, more recently, to also be a feature of COVID-19 pathology [18]. Several studies have highlighted the importance of neutrophils in the response to SARS-CoV-2 infection [17, 18, 20, 21] and clinically neutrophil-lymphocyte ratios (NLR) are becoming an important hallmark of severe COVID-19 [22]. Furthermore, the expression of angiotensin converting enzyme 2 (ACE2) on neutrophils has also been demonstrated (23–25). These studies, however, have primarily focused on the recruitment of neutrophils post-infection and the production of neutrophil extracellular traps and lack insights into the infection of airways with pre-existing neutrophilia and other neutrophil functional responses such as inflammatory cytokine production and viral internalization. In this study, the relationship between SARS-CoV-2 infection and pre-existing airway neutrophilia in differentiated airway epithelium was evaluated through the adaption of a co-culture infection model previously used to study viral infections in vitro [26]. Primary neutrophils were isolated from peripheral blood and co-cultured with differentiated primary tracheo-bronchial airway epithelium prior to infection with live SARS-CoV-2 virus for 4 hours to characterize the earliest stages of infection. Changes in the inflammatory profile and epithelial response were comprehensively evaluated to determine the impact of pre-existing neutrophilia on SARS-CoV-2 infection of the airway epithelium. ## Isolation of neutrophils from peripheral blood Neutrophils were isolated from fresh human peripheral blood with patient consent and approval of the Institutional Review Board (IRB) of the University of Southern California (USC), protocol #HS-20-00546. CD15-expressing neutrophils were isolated using the EasySep™ direct neutrophil isolation kit (Stem Cell Technologies, Seattle, WA) within 1 hour of the blood draw as per the manufacturer’s instructions. Briefly, 5 ml of peripheral blood was collected into 10 ml EDTA vacutainers (Becton Dickinson, Franklin Lakes, NJ). From this, 3 ml was diluted 1:1 with PBS (Thermo Fisher Scientific, Waltham, MA) and kept on ice for purity analysis by flow cytometry. The remaining 2 ml was transferred to a 5 ml polystyrene round bottomed tube (Genesee Scientific, San Diego, CA) and gently combined with 100 μl of isolation cocktail and 100 μl of RapidSpheres™ (Stem Cell Technologies). After incubation at room temperature for 5 mins, 1.8 ml of 1 mM EDTA was added, gently mixed, and placed into the EasySep™ Magnet (Stem Cell Technologies) for 5 mins. The enriched cell suspension was placed into the EasySep™ Magnet for an additional 5 mins and decanted into a fresh tube. Approximately 4.25 x 106 cells were isolated from 5 ml of peripheral blood. ## Flow activated cell sorting To validate the purity of neutrophils isolated from peripheral blood; 1x107 CD15+ freshly isolated human neutrophils were resuspended in 100 ul FACS buffer (PBS, 0.5mM EDTA, $1\%$ FBS, $0.1\%$ BSA) and fresh whole human blood diluted 1:5 in FACS buffer and supplemented with 5 ul of human TruStain Fc receptor blocker (Biolegend, San Diego, CA) for 5 mins on ice. Cells were then incubated with anti-human CD15 PE (Biolegend) for 1 hour prior to FACS analysis. Cells were analyzed on the SORP FACS Symphony cell sorter (BD Biosciences) in the Flow Cytometry Facility at USC using FACS Diva software and all analyses was carried out in Flow Jo V10.8.0 (BD Biosciences). ## Air-liquid interface differentiation of airway epithelium Primary human airway basal epithelial cells (HBECs) were isolated from explant human lung tissue as previously described [27] and with approval of IRB at USC (protocol #HS-18-00273). For this study, HBEC donors were randomly paired with blood neutrophil donors (detailed in supplemental table S1&2). HBECs were expanded for 1 to 4 passages in airway epithelial cell growth media (AEGM, Promocell, Heidelberg, DE) and transitioned to Pneumacult Ex+ (Stem Cell Technologies) for 1 passage, prior to growth on Transwells. Cells were routinely passaged at $80\%$ confluence using Accutase™ (Stem Cell Technologies) and seeded at 5 x 104 cells per 6.5 mm polyethylene (PET) insert with 0.4 µm pores (Corning, Corning, NY). Media was changed every 24-48 hours and transepithelial electrical resistance (TEER) was monitored every 24-48 hours using an EVOM3 epithelial volt-ohm meter (World Precision Instruments, Sarasota, FL). At resistances ≥ 450Ω ^cm2, cells were air lifted by removing the apical media and washing the apical surface with phosphate buffered saine (PBS, Sigma-Aldrich, St Louis, MO). The basolateral media was replaced with Pneumacult ALI media (Stem Cell Technologies) and changed every 2 to 3 days for up to 40 days. ## SARS-CoV-2 culture Vero E6 cells overexpressing ACE2 (VeroE6-hACE2) were obtained from Dr. Jae Jung and maintained in DMEM high glucose (Thermo Fisher Scientific), supplemented with $10\%$ FBS (Thermo Fisher Scientific, Waltham, MA), 2.5 µg/ml puromycin (Thermo Fisher Scientific) at 37°C, $5\%$ CO2 in a humidified atmosphere in the Hastings Foundation and The Wright Foundation Laboratories BSL3 facility at USC. SARS-CoV-2 virus (BEI resources, Manassas, VA) was cultured and passaged 4 times in VeroE6-hACE2 cells and harvested every 48 hours post-inoculation. Plaque forming units (PFU) were determined using a plaque assay by infecting a monolayer of VeroE6-hACE2 cells with serial dilutions of virus stocks and layering semi-solid agar. Plaques were counted at day 3 post infection to determine PFU. Virus stocks were stored at -80°C. ## SARS-CoV-2 infection Differentiated airway epithelium at ALI was cultured with addition of 50 µl of PBS to the apical surface and incubated at 37°C, $5\%$ CO2 in a humidified atmosphere. After 10 minutes PBS was removed to eliminate the mucus build-up on the apical surface. The basolateral culture media was removed and replaced with 400 µl of assay media (Bronchial Epithelial Growth Media (BEGM), Lonza, Walkersville, MA), without the addition of bovine pituitary extract, hydrocortisone & GA-1000, for 1 hour prior to the addition of neutrophils. Freshly isolated neutrophils were diluted to 5 x106 cells/ml in Hank’s Balanced Salt Solution (with Mg2+ and Ca2+) (Thermo Fisher Scientific) and 20 µl of this suspension was seeded onto the apical surface of the ALI cultures. Monocultures of airway epithelium and neutrophils were used as controls. The neutrophil-epithelial co-cultures were incubated for 1 hour during which they were transferred to the BSL3 facility for infection. Co-cultures were infected with 1x104 PFU of SARS-CoV-2 in 100 µl of OptiMEM (Thermo Fisher Scientific) added to the apical surface to a final MOI of 0.1 relative to neutrophils. Infected cell cultures were incubated for 4 hours at 37°C, $5\%$ CO2 in a humidified atmosphere. After infection, 50µl of apical and 400µl basolateral supernatants were collected, and SARS-CoV-2 was inactivated with a 10x solution of Triton-X (Sigma-Aldrich) in PBS for 1 hour to a final concentration of $1\%$ Triton-X. Culture supernatants were stored at -20°C until required. For neutrophil monocultures, freshly isolated CD15+ neutrophils were seeded into black walled 96 well plates (Thermo Fisher Scientific) at 2x104 cells per well in 50µl Hank’s Balanced Salt Solution (with Mg2+ and Ca2+) with or without 50 ng/ml IFNγ (Peprotech, Cranbury, NJ) for 1 hour. These plates were transferred to the BSL3 facility for infection. Neutrophil monocultures were infected 2 x103 PFU in 50 µl of OptiMEM of SARS-CoV-2 to a final MOI of 0.1. After 4 hours of infection 90µl of cell culture supernatants were collected, and SARS-CoV-2 inactivated with 10x Triton-X solution in PBS for 1 hour to a final concentration of $1\%$ Triton-X. Culture supernatants were stored at -20°C until required. ## Validation of virus inactivation SARS-CoV-2 virus was inactivated by addition of $10\%$ Triton-X to supernatants to generate a final concentration of Triton-X of $1\%$ and incubating at room temperature for 1 hour. PFU was quantified using a plaque forming assay with ACE2 over-expressing Vero E6 cells (VeroE6-hACE2). Serial dilutions of SARS-CoV-2 virus were performed from a stock concentration of 1x105 PFU/ml and inactivated with $1\%$ Triton-X at room temperature for 1 hour and used to infect Vero E6 cells for a total of 4 days. Cells were monitored routinely for cytopathic effects using the Revolve microscope (Echo Laboratories, San Diego, CA). ## RNA isolation and qRT-PCR RNA was collected in 100 µl of Trizol (Thermo Fisher Scientific) per insert and incubated for 15 mins at room temperature. Cell isolates were gently mixed by pipetting up and down. An additional 900 µl of Trizol was added and cell isolates were collected and stored at -80°C until required. Cellular RNA was isolated by either phenol/chloroform extraction or using the Direct-zol RNA Microprep kit (Zymo Research, Irvine, CA). RT-qPCR was performed in 384 well plates on an Applied Biosystems 7900HT Fast Real-Time PCR system using the QuantiTect Virus Kit (Qiagen, Redwood City, CA) and SARS-CoV-CDC RUO primers and probes (Integrated DNA Technologies (IDT), Coralville, IA). Briefly, each 5 µl reaction contained 1 µl 5x QuantiTect Virus Master Mix, 500 nM forward primer, 500 nM Reverse Primer, 125 nM Probe, 10 ng DNA, 0.05 µl QuantiTect Virus RT Mix, and DNAse/RNAse-free water up to a final volume of 5 µl. Calibration curves for RNAseP primers/probe was performed with 10-fold dilutions of RNA from uninfected Calu3 cells (ATCC, Manassas, VA) from 100 ng to 0.01 ng per reaction. Calibration curves for N1 primers were performed on 5 ng of RNA from uninfected Calu3 cells per reaction spiked with 10-fold dilutions from 50 ng to 0.005 ng of RNA from Calu3 cells collected 48 hours post infection. *Relative* gene expression was calculated using the Pfaffl method [28]. ## Immunohisto-/cyto-chemistry Primary human lung tissue from post-mortem or surgical resection donors (detailed in supplemental table S3) was fixed in $10\%$ neutral buffered Formalin (Thermo Fisher Scientific). The tissue was then dehydrated in $70\%$ ethanol (Thermo Fisher Scientific) prior to embedding in paraffin blocks for sectioning. Tissue sections were mounted on positively charged slides (VWR, Visalia, CA) and tissue was rehydrated through sequentially decreasing concentrations of ethanol ($100\%$ - $70\%$) and finally water. Slides were stained sequentially with Hematoxylin and then Eosin and imaged on the Olympus microscope IX83 (Olympus, Waltham, MA). Alternatively, tissue slides were incubated overnight at 60°C in Tris-based antigen unmasking solution (Vector Laboratories, Burlingame, CA) before permeabilization in $3\%$ BSA, $0.3\%$ Triton-X 100 in PBS for 1 hour and blocking in $5\%$ normal donkey serum (Jackson ImmumoResearch, West Grove, PA) for 1 hour at room temperature. In vitro co-cultures were fixed in $4\%$ PFA (Thermo Fisher Scientific) for 1 hour at room temperature and stored in PBS at 4°C to be used for immunohisto/cytochemistry. Co-cultures were then permeabilized and blocked in $3\%$ BSA, $0.3\%$ Triton-X 100 in PBS for 1 hour and blocking in $5\%$ normal donkey serum (Jackson ImmumoResearch, #017-000-121) for 1 hour at room temperature. Tissue sections and in vitro cultures were subsequently stained with the antibodies or RNAScope probes listed in supplemental table S4. Slides were mounted in Fluoromount-G (Thermo Fisher Scientific) and imaged on a DMi8 fluorescent microscope (Leica, Buffalo Grove, IL) or a Zeiss LSM 800 confocal microscope (Zeiss, Dublin, CA). ## Image analysis 5 random 20x regions were imaged for each ALI section. Sections were prepared from each of the conditions for three random donor pairings. Images were blinded, randomized and analyzed by an independent investigator. Cell frequency was determined by nuclei staining for total cells and KRT5+ staining for basal cells. Infected cells were quantified through positive SARS-CoV-2 staining within an individual cell. Signal thresholds were determined using unstained, uninfected and secondary only control-stained slides. For cell layer thicknesses, 10 random regions from each image were used to measure the thickness in µm. All images were analyzed with Image J 1.53t (Fiji, Bethesda, MD) ## Trans epithelial electrical resistance Pre-warmed assay media (200 μl) was added to the apical surface of the cultures and trans epithelial electrical resistance (TEER) was measured using an EVOM-3 meter (World Precision Instruments). ## Meso scale discovery cytokine assay 50 μl of cell culture supernatants were analyzed for cytokines using the Meso Scale Discovery (MSD) Proinflammatory panel 1, Human kit, Lot:K0081459 & K0081080 (Meso Scale Diagnostics, Rockville, MD) as per the manufacturer’s instructions. Briefly, 1:5 dilutions of cell supernatant samples were diluted in PBS containing $1\%$ Triton-X. Samples were added to the MSD plate along with a 7-point 4-fold serial dilution (concentrations related to certificate of analysis for each individual standard) of protein standards diluted in PBS with $1\%$ Triton-X. The MSD plate was sealed, and samples incubated at room temperature for 2 hours on a plate shaker (ThermoFisher Scientific) at 700RPM. The plate was washed 3x in wash buffer and 25 μl of secondary antibody was added to each well. Plates were sealed and incubated at room temperature on a plate shaker at 700RPM for a further 2 hours in the dark. Plates were washed 3x with wash buffer and 50 μl of 2x read buffer (MSD R92TC) was added to each well. The plates were read on the MESO Sector S 600 (Meso Scale Diagnostics), and concentrations determined against the standard curves. ## Meso scale discovery SARS-CoV-2 spike protein assay 25 μl of cell culture supernatants were analyzed for cytokines using the MSD S-plex SARS-CoV-2 Spike Kit Lot: Z00S0021 as per the manufacturer’s instructions. Briefly, plates were washed 3x in wash buffer (PBS $0.05\%$ Tween-20) and coated with 50 μl of coating solution (1:40 dilution of Biotin SARS-CoV-2 spike antibody; 1:200 dilution of S-PLEX Coating reagent C1 in Diluent 100) and incubated at room temperature on a plate shaker at 700RPM for 1 hour. Plates were then washed 3x in wash buffer and blocked in 25 μl blocking solution (1:100 dilution of Blocker s1 in Diluent 61) per well. Samples were added to the MSD plate along with a 7-point 4-fold serial dilution (concentrations related to certificate of analysis for each individual standard) of protein standards diluted in PBS with $1\%$ Triton-X. Plates were incubated at room temperature on a plate shaker at 700RPM for 1.5 hours. Plates were washed 3x in wash buffer and 50 μl per well of TURBO-BOOST antibody (1:200 dilution of TURBO-BOOST SARS-CoV-2 Spike antibody in Diluent 59) was added to each well and plates were incubated at room temperature on a plate shaker at 700RPM for 1 hour. Plates were washed 3x in wash buffer and 50 μl per well of Enhance Solution (1:4 dilution of S-plex Enhance E1 1:4 dilution of S-plex Enhance E2 and 1:200 dilution of S-plex Enhance E3 in molecular biology grade water) was added. Plates were incubated at room temperature on a plate shaker at 700RPM for 30 mins. Plates were washed 3x in wash buffer and 50 μl of Detection solution (1:4 dilution of S-plex Detect D1 and 1:200 dilution of S-plex detect D2 in molecular biology grade water) was added to each well. Plates were incubated at 37°C on a plate shaker at 700RPM for 1 hour. Plates were washed 3x in wash buffer and 150 μl on MSD GOLD Read Buffer B was added to each well. Plates were read immediately on an MSD 1300 MESO QuickPlex SQ 120 plate reader (Meso Scale Diagnostics) and concentrations determined against the standard curve. ## Viral internalization assay CD15+ neutrophils were seeded at 20,000 cells per well in in HBSS with or without 15 μM Cytochalasin D (Sigma Aldrich) black walled 96 well plates (Thermo Fisher Scientific) for 1 hour to allow for attachment. Cells were then infected with SARS-CoV-2 at 2 MOI (80 μl at 5x10^5 PFU/ml) for 4 hours. Cells were then washed 2 x with PBS and fixed in $4\%$ PFA. Cells were stained for SARS-CoV-2 RNA via RNAScope and DAPI as per the manufacturer’s instructions. Whole wells were supplemented with 50 μl of PBS post staining and well were scanned on the DMi8 fluorescent microscope (Leica, Buffalo Grove, IL). Total cell number was determined by total frequency of DAPI particles and infected cells determined by SARS-CoV-2 particle signal in proximity to DAPI. Images were analyzed with ImageJ software 1.52n (National Institute of Health, Bethesda, MD). ## Data analysis and statistics All data are presented as mean ± S.E.M. Statistical analysis is dependent upon the data set and is specifically indicated in each figure. For comparisons of 2 groups. a two-tailed unpaired Student’s T-test was used. For more than 2 groups, an analysis of variance (ANOVA) was used with a post hoc Tukey test. Significance is determined to be $p \leq 0.05.$ *All data* represents a minimum of three independent biological replicates ($$n = 3$$), each with 3 experimental replicates ($$n = 3$$). Data was presented and analyzed using Graph Pad prism v8.4.3 (GraphPad, San Diego, CA). All Key reagents for this study are detailed in supplemental table S5. ## In vitro models of neutrophilic airways have significant, polarized inflammatory responses to SARS-CoV-2 infection Given the prevalence of neutrophilia in the airways of patients with chronic airway disease [29] and its association with other SARS-CoV-2 co-morbidities, such as hypertension [30, 31], the impact of chronic neutrophilic airway inflammation in the initial stages of SARS-CoV-2 infection was evaluated. We adapted a neutrophilic airway in vitro model, previously described by Deng and colleagues [26], co-culturing CD15+ peripheral blood polymorphonuclear leukocytes (PMNs) with primary HBECs differentiated at the ALI and infected these cultures with live SARS-CoV-2 virus for 4 hours, shown in the schematic in Figure 1A. This 4-hour time point allows for profiling of the initial stages of infection and acute phase cellular viral response, i.e., neutrophil degranulation. The short time frame for analysis was chosen to eliminate significant viral replication and thus anticipate any detectible intracellular viral load is because of the initial infection [32]. It also allows for optimal investigation into neutrophil function without loss of viability interfering with the assays due to the relatively short half-life of neutrophils. Prior to infection we confirmed the expression of ACE2 and Transmembrane Serine Protease 2 (TMPRSS2) in our in vitro airway epithelium models (Supplementary Figure S1). While ACE2 RNA was relatively low in expression across basal, secretory and multiciliated cells (Supplementary Figure S1A-C) at the protein level a predominant colocalization was detected with multiciliated cells in the airways (Supplementary Figure S1A, D-F). This data is supported by similar analysis of human lung tissues (Supplementary Information) and Supplementary Figure S2) where we observed a similarly low level of expression in RNA in basal, secretory and multiciliated cells (Supplementary Figure S2A-B) while protein, detected by IF, was associated with multiciliated cells and cells in submucosal glands (Supplementary Figure S2C-F). Confirmation of ACE2 expression at the RNA and protein level in human lung tissues and our in vitro model supports currently published data evaluating ACE2 in human lung tissue (33–35). **Figure 1:** *Polarized inflammatory response of neutrophils in co-culture with human airway epithelium, infected with SARS-CoV-2. (A) Schematic of the in vitro model of neutrophilic airways denoting neutrophils in co-culture with differentiated airway epithelial cells and infected with live SARS-CoV-2 virus. Inflammatory profiles of apical (B) and basolateral (C) supernatants collected 4 hours post infection in the neutrophilic airway model. (D) Inflammatory profile of naïve or IFN-γ (50ng/ml 1 hour) neutrophil monocultures infected with SARS-CoV-2 virus for 4 hours. Data is expressed as Tukey method box & whiskers plots. Significance is determined by analysis of variance (ANOVA) followed by Tukey’s post hoc analysis. *p<0.05,**p<0.01, ***p<0.001, ****,<0.0001 from n=3 experimental repeats from N=3 donors. ns, not significant, ND, not done as an experiment.* In our model system the apical side of the epithelium predominantly comprises of multiciliated, and secretory cells directly exposed to neutrophils and the virus, the basolateral side predominantly comprises of basal cells. To understand the immediate inflammatory response of the airway epithelium to SARS-CoV-2 infection we evaluated both the apical and basolateral cell culture supernatants using the MSD cytokine assay. All experiments were carried out using three independent HBEC donors and three independent neutrophil donors ensuring significant biological variability in our model system. As shown in Figures 1B, C a differential inflammatory profile exists between the apical and basolateral compartments. Focusing first on the apical cytokine and chemokine release, in the absence of neutrophils there were, surprisingly, no significant changes in cytokine release from the airway epithelial cells upon SARS-CoV-2 infection (Figure 1B). The addition of neutrophils to the model, creating a neutrophil-epithelial co-culture in the absence of any infection, resulted in a significant secretion of interferon gamma (IFNγ) ($p \leq 0.01$) and IL-10 ($p \leq 0.01$) at the apical surface with notable, but not statistically significant, increases in tumor necrosis factor alpha (TNFα) (Figure 1B). In the airway only cultures, only the basolateral release of interleukin-8 (IL-8, $p \leq 0.001$), and IL-10 ($p \leq 0.05$) were significantly changed in response to SARS-CoV-2 infection (Figure 1C). As IL-8 is a major chemoattractant for neutrophils this suggests that the basolateral surface responds to viral infection by releasing IL-8 to recruit neutrophils to infection site (36–38). The addition of neutrophils to the airway stimulated the release of IFNγ ($p \leq 0.05$) and IL-10 ($p \leq 0.01$) apically (Figure 1B) and additionally, significantly increased the release of IFNγ ($p \leq 0.0001$) IL-1β ($p \leq 0.0001$), IL-4 ($p \leq 0.0001$), IL-6 ($p \leq 0.05$), IL-10 ($p \leq 0.0001$) and TNFα ($p \leq 0.01$) from the basolateral surface (Figure 1C). Interestingly, the presence of neutrophils did not stimulate significant changes in IL-8 secretion from the basolateral surface supporting the role for IL-8 in the recruitment phase of airway neutrophilia, already established in our neutrophilic airway model (Figure 1C) [39, 40]. This data demonstrates that a pro-inflammatory niche is driven primarily by the neutrophils, likely though degranulation. Based on this information we added neutrophils to our airway epithelium to create a pro-inflammatory niche recreating aspects of chronic airway inflammation in the human lung in an in vitro model. Infection of the neutrophilic airway models with live SARS-CoV-2 virus was compared directly to both the infection in the absence of neutrophils and the neutrophilic airway in the absence of infection. Changes in inflammatory cytokine release from both the apical and basolateral surfaces was significantly augmented compared to both the infected epithelial monocultures and the non-infected co-cultures, demonstrating an exacerbation of pro-inflammatory cytokine release in the infected co-cultures (Figures 1B, C). Compared to the infected epithelial monocultures, infection of the co-culture model resulted in a significant increase in the apical secretion of IFNγ ($p \leq 0.0001$), IL1-β ($p \leq 0.05$), IL-6 ($p \leq 0.0001$) and IL-10 ($p \leq 0.001$) (Figure 1B) and in the basolateral secretion of IFNγ ($p \leq 0.05$), IL-4, IL-6 both ($p \leq 0.0001$), IL1-β, IL-10 and TNFα (all $p \leq 0.001$) (Figure 1C). Compared to the uninfected neutrophil-epithelial co-cultures, co-culture infection resulted in a significant increase in the apical secretion of IFNγ, IL-6 (both $p \leq 0.0001$) IL1-β and IL-10 (both $p \leq 0.05$) and in the basolateral secretion of IFNγ, IL-10 (both p<-.001), IL-1β, IL-4, IL-6 and TNFα (all $p \leq 0.0001$) (Figure 1B). The only instance where TNFα was significantly changed in the apical supernatants was in the infected co-cultures increased when compared to uninfected epithelial cell monocultures ($p \leq 0.01$). This data supports a significant augmentation of the inflammatory response to SARS-CoV-2 infection occurs in the presence of pre-existing airway neutrophilia. Importantly, this secretion profile closely reflects the cytokine biomarkers that have been clinically identified in patients hospitalized with severe COVID-19 disease (41–43), highlighting the importance of the co-culture models in recapitulating features associated with more severe responses to SARS-CoV-2 and demonstrating a role for neutrophils in the inflammatory profile observed in patients with severe COVID-19. The innate reactivity of neutrophils in isolation was evaluated independently of the co-culture model. We noted a significant apical increase in IFNγ in the co-cultures in the absence of any stimulation or infection (Figure 1B). As IFNγ is a known activator of neutrophils we evaluated inflammatory cytokine release from neutrophils pre-stimulated with IFNγ in response to SARS-CoV-2 infection using naïve neutrophils as controls to determine whether there was innate recognition of SARS-CoV-2 by neutrophils. Initially we assessed the response of naïve, non-activated neutrophils to SARS-CoV-2 infection. Very small, <1 pg/ml, responses from naïve neutrophils, in the absence of infection or stimulation, was observed for all cytokines assessed except for IL-8. An increase in IL-8 secretion was observed, however this increase equates to an increase of <$0.1\%$ of the response to infection. In infected naïve neutrophils, significant increases in cytokine release of IL1-β, IL-4, IL-6, IL-8 (all $p \leq 0.001$) and IL-10 ($p \leq 0.05$) was noted compared to uninfected controls (Figure 1D). Uninfected neutrophils activated with IFNγ produced no significant cytokine release compared to naïve uninfected neutrophils. The observed increase in IFNγ was likely due to the exogenous recombinant IFNγ used to activate the neutrophils and not a response of the neutrophils. Activation of neutrophils with IFNγ produced significant increases in IL-1β, IL-4, IL-6, IL-8, IL-10 and TNFα (all $p \leq 0.0001$). It is worth noting that there was also a significant ($p \leq 0.01$) decrease in IFNγ from the infected neutrophils activated with IFNγ; the presence of exogenous recombinant IFNγ complicates interpretation of this finding (Figure 1D). Finally, we compared IFNγ activated neutrophils with naïve neutrophils after SARS-CoV-2 infection. IFNγ ($p \leq 0.05$), IL-1β, IL-4, IL-6 and TNFα (all $p \leq 0.0001$) were all significantly increased and IL-8 significantly decreased ($p \leq 0.0001$) (Figure 1D). This data demonstrates that naive neutrophils have an innate recognition of SARS-CoV-2 in the absence of any activation and highlight an exacerbation of the response in IFNγ activated neutrophils. ## Increased SARS-CoV-2 infection of the airway epithelium is associated with neutrophilia and disruption of epithelial barrier integrity To determine whether a proinflammatory niche, such as that observed in the presence of pre-existing neutrophilia, impacts epithelial barrier integrity and viral load of the epithelial cells we evaluated barrier resistance and viral content of the airway epithelium. TEER was recorded at 4 and 24 hours after introduction of neutrophils to the airway epithelium. The presence of neutrophils significantly reduced the TEER and, therefore, epithelial barrier integrity, by 23 ± $9\%$, ($p \leq 0.05$) after 4 hours. This reduction in TEER was sustained through 24 hours (22 ± $4\%$, $p \leq 0.05$), all data are compared to epithelial monocultures (Figure 2A). Evaluation of intracellular viral load by qRT-PCR for SARS-CoV-2 nucleocapsid RNA in the epithelial cells under the same conditions indicated a concurrent and significant increase in infection after the addition of neutrophils by 3.1 ± 1.1-fold ($p \leq 0.05$) (Figure 2B). In the absence of infection, no SARS-CoV-2 RNA was detected (data not shown). To determine if the change in epithelial barrier function allowed for increased passage of viral particles from the apical to basolateral surface of the airway epithelium, we also evaluated SARS-CoV-2 spike protein expression in the supernatants (Figures 2C-D). The presence of neutrophils significantly decreased the apical viral load from 69204 ± 9200.1 fg/ml to 6655.6 ± 475.61 fg/ml ($p \leq 0.01$) (Figure 2C) with a concurrent increase in the basolateral viral load from 488.23 ± 129.12 fg/ml to 2307.7 ± 238.94 fg/ml ($p \leq 0.01$) (Figure 2D). This data shows that the presence of neutrophils is allows for increased migration of virus from the apical to the basolateral surface. To determine whether the physical presence of neutrophils is essential or whether the pro-inflammatory cytokines released from neutrophils in epithelial co-cultures (Figure 2) and stimulated by SARS-CoV-2 infection, could induce similar changes in epithelial barrier function, we supplemented the culture media with IFNγ (10 ng/ml), IL-1β (10 ng/ml), IL-6 (10 ng/ml) and TNFα (10 ng/ml) (referred to as cytomix). In the presence of cytomix TEER decreased after 4 hours (18 ± $7\%$, not significant) with a further and significant decline of 30 ± $5\%$, $p \leq 0.05$ after 24 hours (Figure 2E). This decrease in TEER corresponded to an increase in viral infection of the airway epithelium (2.6 ± 0.5-fold, $p \leq 0.05$) in the presence of cytomix (Figure 2F). Reflecting the observations in the presence of neutrophils the apical concentrations of SARS-CoV-2 were decreased from 76703 ± 8708.7 fg/ml to 35261 ± 3598.7 fg/ml ($p \leq 0.05$) and basolateral concentrations increased from 479.87 ± 129.21 fg/ml to 12344 ± 906.62 fg/ml ($p \leq 0.001$). This data supports the hypothesis that pro-inflammatory cytokines secreted by neutrophils allow for increased transition of virus from the apical to basolateral surfaces of the airway epithelium. **Figure 2:** *Neutrophils and pro-inflammatory cytokines break down the epithelial barrier and increase viral load in human airway epithelial cells. (A) TEER of human airway epithelial cells at the air-liquid interface in the presence, or absence (control), of neutrophils. (B) Intracellular viral load of SARS-CoV-2 RNA isolated from infected human airway epithelial cells with neutrophils present. (C) Apical supernatant SARS-CoV-2 spike protein concentration 4 hours post infection with neutrophils present. (D) Basolateral supernatant SARS-CoV-2 spike protein concentration 4 hours post infection with neutrophils present. (E) TEER of human airway epithelial cells cultured with a “cytomix” of TNFα, IL-1β, IL-6 and IFN-γ each at 10ng/ml. (F) Intracellular viral load of SARS-CoV-2 in airway epithelial cells cultured with cytomix. (G) Apical supernatant SARS-CoV-2 spike protein concentration 4 hours post infection from epithelial cells cultured with cytomix. (H) Basolateral supernatant SARS-CoV-2 spike protein concentration 4 hours post infection from epithelial cells cultured with cytomix. Data are expressed as mean ± SEM. Statistical significance of TEER data was determined by ANOVA and viral load data was analyzed using an unpaired two-tailed Student’s t-test. *p<0.05, **P<0.01, ***p<0.001. Experiments include n=3 experimental repeats of N=3 independent epithelial donors paired with 3 independent neutrophil donors.* ## Neutrophils increase SARS-CoV-2 infection of the epithelium including basal stem cells To investigate changes in airway pathology associated with SARS-CoV-2 infection we evaluated co-localization of SARS-CoV-2 virus in the presence or absence of neutrophils. Analysis of the airway structure by hematoxylin and eosin (H&E) highlights significant changes in pathology in the presence of neutrophils (Figures 3A-D). We use KRT5 as a marker to identify the sub-apical basal cell layer from the pseudostratified differentiated epithelium. In the absence of neutrophils and infection the airways comprise of a typical airway epithelium with KRT5+ basal cells residing on the basolateral surface and ciliated cells lining the airway lumen (Figure 3A). Despite the presence of pro-inflammatory cytokines produced by the neutrophils, epithelial cells appear to tolerate the presence of neutrophils, which can be observed near the apical ciliated cells in the culture model (Figure 3B). In an airway without neutrophils, the epithelial cells are capable of tolerating infection by SARS-CoV-2 after 4 hours of exposure with little evidence of cellular pathology by H&E and only sporadic infection observed in the columnar epithelial cells (Figure 3C and Supplemental Figure S3). Most notably, in the presence of neutrophils, significant cellular pathology is observed by H&E, with evidence for thickening of the basal cell layer, indicative of basal cell proliferation (Figure 3D). Furthermore, SARS-CoV-2 infection in epithelium is more widespread across the entire epithelial layer with KRT5+ basal cells also being infected (Figure 3D and Supplementary Figure S3). To corroborate these findings, we quantified infected KRT5+ basal cells and KRT5- differentiated epithelial cells using blinded image analysis by an independent investigator (Figure 3E). The infection rate in total epithelial cells increased ($p \leq 0.01$) in the presence of neutrophils when compared to monoculture controls. The infection rates increased in KRT5- differentiated epithelium (i.e., ciliated, goblet and club cells) (not significant) and in KRT5+ basal cells in the presence of neutrophils ($p \leq 0.001$). As demonstrated by the H&E staining in Figure 3D, infection in the presence of neutrophils caused significant cellular pathology compared to uninfected controls or infected airways in the absence of neutrophils. To quantify this, we also measured the thickness of the KRT5+ cellular layer and total cell layer and counted the total cells as part of our image analysis. In response to SARS-CoV-2 infection in the presence of neutrophils, the KRT5+ layer thickness increased ($p \leq 0.0001$) and the total cellular layer increased to ($p \leq 0.0001$) compared to uninfected epithelial layer monocultures. Overall, there were no significant changes in total cell numbers, suggesting that the change in thickness is a result of epithelial cellular pathology and remodeling rather than cell proliferation (Figure 3F). Interestingly, we observed a small, but significant decrease in total layer thicknesses in uninfected co-cultures ($p \leq 0.0001$) and infected monocultures ($p \leq 0.0001$) compared to the uninfected monoculture control. In our model system, neutrophils drive significant cellular pathology in response to infection by SARS-CoV-2. Infection of basal cells at such a short timepoint is likely to have significant implications on their function and subsequently airway regeneration. **Figure 3:** *Pre-existing neutrophils allow for SARS-CoV-2 infection of KRT5+ Basal cells. a-d representative hematoxylin and eosin (H&E) staining and immunofluorescent images of cross section culture models probed for KRT5 (green) SARS-CoV-2 (red) and alpha-tubulin (cyan). (A) uninfected monocultured epithelial cells. (B) uninfected epithelial:neutrophil co-cultures. (C) SARS-CoV-2 infected epithelial cell monoculture. (D) SARS-CoV-2 infected epithelial:neutrophil co-cultures. All IF images have nuclei counterstained with DAPI (blue) and scale bars represent 50 μm. (E) Image analysis quantification of infected KRT5-, KRT5+ and total cells. (F) Image analysis of cell layer thickness for KRT5+ Cells and Total cells All images are representative of 3 independent experimental repeats of 3 neutrophil and 3 epithelial random donor pairings. Data is expressed as Tukey method box & whiskers plots. Significance is determined by analysis of variance (ANOVA) followed by Tukey’s post hoc analysis. *p<0.05,**p<0.01, ***p<0.001, **** p<0.0001 from n=3 experimental repeats from N=3 donors. ns, not significant.* ## Airway epithelial pathologies are associated with neutrophil activity in severe COVID-19 The data presented from our in vitro models suggests that neutrophils play a role in the pathophysiology of early-stage epithelial infection in COVID-19. To further investigate continued neutrophil related pathologies in severe COVID-19 we evaluated epithelial cell related damage and neutrophil activity in post-mortem human tissues from COVID-19 subjects. Formalin-fixed paraffin embedded (FFPE) tissue sections from two post-mortem COVID-19 subjects, kindly provided by the autopsy service at the University of Vermont Medical Center (UVMMC) were assessed for infection-related pathologies through H&E staining. Pathologies were determined by an independent pathologist to be consistent with severe ARDS with mixed inflammatory cell infiltrates, inclusive of neutrophils, and organizing pneumonia (Figures 4A-D). Tissues from patient Au20-39 (detailed in supplementary table S1) contained a mild infiltrate of chronic inflammatory cells surrounding the bronchiole and arterial tissues with involvement in the adjacent surrounding alveolar tissue (Figure 4A and Supplementary Figure S4A). Scattered giant cells were identified in alveolar spaces and within the interstitium (Figure 4B, indicated by the red arrows and Supplementary Figure S4B). No well-formed granulomas or definite viral inclusions were evident in this patient. Images from the second patient; Au20-48 (Supplementary Table S3) also show severe organizing diffuse alveolar damage with evidence of barotrauma (Figure 4C and Supplementary Figure S4D). Alveolar spaces are lined by hyaline membranes or filled with polyps of organizing pneumonia and chronic inflammation (Supplementary Figure S4D). Alveolar walls are expanded with edema and a mixed inflammatory cell infiltrate including neutrophils (Supplementary Figure S4C-D). Bronchioles demonstrate chronic injury with peribronchiolar metaplasia and early squamous metaplasia (Figure 4D and Supplementary Figure S4C). Organizing pulmonary emboli are present in several arteries (Supplementary Figures S4C, D). There are frequent rounded airspaces lined by inflammatory cells and giant cells, consistent with barotrauma from ventilation injury (Supplementary Figure S4D). There are also scattered giant cells in the interstitium not associated with the barotrauma (Supplementary Figures S4C, D). Given the extensive infiltration of inflammatory cells, inclusive of neutrophils, we further evaluated the neutrophil-related epithelial tissue pathology in both patients. An array of airway tissue pathologies was evident in both tissues including 1) basal cell hyperplasia and small airway occlusion (Figure 4E), 2) epithelial damage and tissue remodeling of smaller ciliated airways (Figure 4F), 3) epithelial shedding of large cartilaginous airways (Figure 4G), 4) neutrophil invasion into the airway lumen (Figure 4H). and finally, 5) neutrophil invasion in the alveolar space with associated alveolar tissue damage and remodeling (Supplementary Figure S4e). In each of these examples, neutrophils were detected and frequently demonstrated strong neutrophil elastase (NE) activity (Figure 4E-I), and myeloperoxidase (MPO) expression (a common neutrophil marker) is frequently observed around centers of SARS-CoV-2 infection in postmortem COVID-19 tissues (Supplementary Figure S4F-G). We also observed sporadic formation of neutrophil extracellular traps (NETs) that stained for SARS-CoV-2 (Supplementary Figure 4G). From this data we conclude that neutrophils are a core part of the COVID-19 lung pathophysiology and significantly impact airway infection and injury in response to SARS-CoV-2 infection. **Figure 4:** *Neutrophil associated tissue pathology in post-mortem COVID19 human lung airways. a-d) Representative images of hematoxylin and eosin (H&E) staining of postmortem COVID-19 patient tissues showing patchy organizing pneumonia centered around a major artery and an airway (A); focally expanded interstitium by a mixed cellular infiltrate including scattered giant cells (red arrowheads) (B); diffuse alveolar damage from intense fibroinflammatory process and barotrauma induced rounded airspaces (C) and organizing diffuse alveolar damage with fibrin disposition replaced by organizing pneumonia, inflammatory cells and oedema (D). (E-H) Representative IF images of postmortem COVID-19 tissue probed for NE (cyan), KRT5 (green) and ACE2 (red). Images highlight; small airway occlusion resulting from basal cell hyperplasia with surrounding neutrophils present (E); epithelial damage with breaching neutrophils into the luminal space (F); epithelial shedding, inclusive of basal cell layer with neutrophil inclusion of mucosal surface (G); neutrophil breach into airway luminal space with high neutrophil elastase activity (H). All IF images have nuclei counterstained with DAPI (blue) and scale bars represent 100 μm. All images are representative of 3 independent regions per donor at least 2 independent donors.* ## Phagocytosis of SARS-CoV-2 is the predominant mechanism of viral internalization in neutrophils As previously mentioned, airway diseases, such as CF, that are co-morbidities for severe SARS-CoV-2 infection and progression to severe COVID-19, are also associated with significant infiltration of the airways with neutrophils (Supplementary Figure S5A-B). Interestingly, the neutrophils also colocalized with strong ACE2 expression (Supplementary Figure S5). Despite having significant ACE2 expression our data suggests that internalization of the virus in neutrophils is likely through phagocytosis. The apical concentration of SARS-CoV-2 in the presence of neutrophils was significantly smaller than the apical concentrations of SARS-CoV-2 in the presence of cytomix (Figure 4C, G) at 6655.65 ± 475.61 fg/ml compared to 35260.93 ± 3598.7 fg/ml, $p \leq 0.01.$ This suggests that viral clearance is taking place by the neutrophils in their functional role as professional phagocytes. In our experiments SARS-CoV-2 viral RNA was detected in the co-cultures by RNAscope confirming infection of the airway epithelium (Figure 5A). Interestingly, NE activity was heavily centered around sites of SARS-CoV-2 infection synonymous to that observed in post-mortem patient tissues (Supplementary Figure S4F), and internalization of SARS-CoV-2 by neutrophils was also confirmed by co-localization of staining for NE and SARS-CoV-2 viral RNA (Figure 5A) in vitro, indicated by the orange arrows. **Figure 5:** *Cytochalasin D inhibits internalization of SARS-CoV-2 in neutrophils. a) Representative IF images of ALI cultures probed for neutrophil elastase (NE) (Green) infected with SARS-CoV-2 (red) detected by RNAScope. b) Quantification or SARS-CoV-2 positive neutrophils relative to total number of neutrophils determined by DAPI (blue). Data expressed as mean ± SEM. **p<0.01 unpaired 2-tailed Student’s T-test. N=3 independent neutrophil donors, n=3 experimental replicates.* Finally, to determine whether the expression of ACE2 protein in neutrophils has a significant impact in the response of neutrophils to SARS-CoV-2, we evaluated whether neutrophils were being actively infected via a physical interaction of ACE2 and SARS-CoV-2 or functionally phagocytosing the SARS-CoV-2 virus. The decrease in apical spike protein concentrations when neutrophils are present, compared to epithelial cell monocultures, suggests that the neutrophils are clearing the virus at the apical surface through innate pattern recognition phagocytosis. To better understand this, the frequency of SARS-CoV-2 internalization in monocultures of neutrophils was quantified in the presence or absence of cytochalasin D (15 μM) to inhibit phagocytosis (Figure 5B). The number of neutrophils positive for SARS-CoV-2 RNA, reflecting viral internalization relative to the total number of neutrophils, was calculated after infection of the cells with SARS-CoV-2 (MOI = 2). Infection, detected by RNA scope, occurred at a rate of 7.9 ± $1\%$ of neutrophils in culture. This signal was significantly reduced by from 7.9 ± $1\%$ to 1.3 ± $0.3\%$ in the presence of cytochalasin D (Figure 5C). Disruption of the actin cytoskeleton, a core component of phagocytosis, therefore, significantly reduced viral uptake in neutrophils. This suggests the primary mechanism for SARS-CoV-2 internalization in neutrophils is phagocytosis. ## Discussion It is well established that neutrophils are critical in the development of pathological inflammation which can result in both acute and chronic tissue damage. Evaluation of post-mortem COVID-19 tissues indicated significant neutrophil presence and activation in regions of airway epithelial damage and pathology. In addition, we know that many SARS-CoV-2 co-morbidities, including chronic airway disease [29, 44], aging (45–47) and obesity (48–50), are also associated with chronic airway inflammation. In this study we developed a model of pre-existing airway neutrophilia akin to a model previously developed to investigate other respiratory viruses [26] and applied this to investigate the initial stages of SARS-CoV-2 airway infection. Using this model, we were able to conclude that the pre-existing presence of neutrophils in airway epithelium generates a pro-inflammatory niche, significantly augments initial proinflammatory responses to SARS-CoV-2 infection, increases viral load in basal stem cells and decreases airway epithelial barrier integrity. Our data, therefore, supports a key role for neutrophilic airway inflammation in determining the infectivity and outcome measures of COVID-19. Establishing a primary cell co-culture model of an inflammatory airway overcomes some of the limitations of using immortalized cell lines and more complex in vivo models. While in vivo models are perhaps considered gold standard in infection models, they have not been observed to closely mimic human lung pathophysiology, particularly with respect to SARS-COV-2. While infection can be detected, no animal model had closely reflected COVID-19 pathogenesis that leads to severe symptoms and fatal lung disease [5, 51]. Furthermore, studying neutrophilia in animal models is challenging, several depleted or knockout models exist [52], however evaluation of elevated lung neutrophilia typically requires pro-inflammatory stimulation with lipopolysaccharide (LPS) [53], this could complicate interpretation of findings in relation to viral infection. Our models use primary HBECs, some of the first cells exposed to inhaled viral particles, that express endogenous levels of ACE2 and TMPRSS2. This allowed for investigation of the initial stages of SARS-CoV-2 infection and characterization of acute phase inflammatory responses. Neutrophil phenotype and function, including those involved in resolving viral infections, is strongly regulated by signals received from their tissue micro-environment [54], in our study we considered neutrophil responses in the presence of an epithelial micro-environment. Our model mimics components of neutrophilic airway inflammation associated with other chronic lung diseases that have been linked with a predisposition to developing more severe COVID-19 disease. Perhaps our most striking finding is the presence of a differential polarized inflammatory response in response to neutrophils and/or SARS-CoV-2. IL-8, the core chemoattractant for neutrophils (36–38, 55), is secreted only on the basolateral surface of the epithelial monocultures, demonstrates that epithelial cells are capable of recognizing neutrophils within their niche and downregulate this chemokine secretion as a result and that the model recapitulates the directionality required to recruit circulating neutrophils into an infected epithelial environment. Furthermore, despite seeding neutrophils on the apical surface of our model, we observed a predominant pro-inflammatory basolateral niche, with increases in IL-1β, IL-4, IL-6 and TNFα. Through paired comparisons to primary airway epithelial cells in monoculture, we were able to demonstrate key differences in the secretion of pro- (IFNγ, IL1β, IL-6, IL-8 and TNFα) and anti-inflammatory (IL-4 and IL-10) mediators, epithelial barrier integrity and infectivity of epithelial cells (Figures 1, 2), which would have been over-looked in monoculture experiments involving airway infection only. Importantly, the secretion of pro-inflammatory cytokines in our model is consistent with clinical studies that have reported an elevated inflammatory profile associated with severe COVID-19 disease. In patient peripheral blood samples, IL-6 (56–59) IL-10 [57, 58] are consistently higher in COVID-19 patients and correlate with disease severity. Additionally, IL-6 and IL-8 are even higher in ICU than the IMU [60]. Our data also closely mimics responses observed in primate models of the disease [61]. The lack of robust inflammatory response of the epithelium alone may also provide rational for why some people are predisposed to more severe responses than others. In fact, our data evaluating the response of the more proximal, cartilaginous airways may highlight the importance of a robust proximal airway defense mechanism that controls the progression to severe COVID-19 associated with ARDS and distal airway dysfunction. IFNγ is a known activator of neutrophils [62] and widely studied in virology [63, 64]. In addition to IL-10, IFNγ was the only other cytokine increased apically after inclusion of neutrophils in the cultures creating a pro-inflammatory niche. We therefore assessed if IFN activation of neutrophils was required for innate recognition of SARS-CoV-2. We found that IFN activated neutrophils exacerbated their inflammatory response to SARS-CoV-2, however naïve neutrophils still recognized and responded to SARS-CoV-2 (Figure 1D). There are caveats to our neutrophil monoculture analysis. IL-4 and IL-10 concentrations are so low that whilst the assay is sensitive enough to detect such small concentrations it is questionable whether these concentrations would have any significant biological impact. Further, exogenous IFNγ used to activate the neutrophils clearing had a downstream impact in the IFNγ measure in our assay, however, Interestingly we did observe a significant decrease in IFNγ concentration in the IFNγ-treated neutrophil monocultures after SARS-CoV-2 infection (Figure 1D). IFNγ has direct anti-viral mechanisms [63] which may account for a reduction in its expression in the presence of SARS-CoV-2. Pro-inflammatory cytokines, including IFNγ, IL-1β, IL-6 and TNFα, have extensively been shown to disrupt barrier integrity and permeability of the epithelium [65, 66]. This breakdown in barrier integrity exists to allow for leukocyte migration to sites of stress and infection. Theoretically, any tight-junction breakdown that allows for more leukocyte migration, would also allow for increased permeability for viral particles to sub-apical and sub-epithelial structures, thus increasing infectivity and cellular viral loads. Our data supports this phenomenon with both neutrophils and cytomix synonymously decreasing barrier integrity (Figure 2) whilst increasing intracellular viral loads and virus concentrations in sub-apical compartments. This association of epithelial barrier integrity with an increase in intracellular epithelial viral loads, especially in the basal stem cells, suggests that epithelial barrier integrity plays an important functional role in SARS-CoV-2 infection. The changes in airway gross pathology are indicative of responses to neutrophil degranulation and are likely a result of increased reactive oxidative species (ROS) production, we are continuing work to define the mechanisms of action. Finally, we addressed the key question of whether neutrophils, as professional phagocytes [67, 68], are capable of recognition of SARS-CoV-2 as an invading pathogen through innate recognition pathways, and/or are capable of infection by SARS-CoV-2 through ACE2. Our data supports a high level of expression of ACE2 at the protein level, but not the RNA level in neutrophils; an observation recently reported by Veras and colleagues [23]. Furthermore, infection is facilitated by TMPRSS2, and we did not see any evidence for expression on neutrophils by RNA and protein (data not shown). Using cytochalasin D to breakdown actin filament organization we significantly reduced virus internalization, supporting a predominant role for phagocytosis in the internalization of SARS-CoV-2 in neutrophils. Reports are, however, emerging that suggest a significant role for cytoskeletal rearrangement in SARS-CoV-2 entry and, therefore, we cannot entirely rule out infection [69]. The use of blocking antibodies has potential to elucidate the mechanisms of internalization, however, neutrophils express copious amounts of Fc receptors [70] and likely to recognize antigens and opsonize through phagocytosis. Our assay attempted to investigate an innate recognition, i.e., a non-humoral opsonization of the SARS-CoV-2 virus. To determine whether the expression of ACE2 on neutrophils is functionally relevant in SARS-CoV-2 infection further investigation will be essential. In conclusion, we have developed a model to study neutrophil-epithelial interactions which more closely reflects an in vivo and more clinically relevant infection of airways than monocultures. Our findings demonstrate that the co-presence of neutrophils generates a polarized pro-inflammatory niche with the conducting airway epithelium that is significantly augmented with SARS-CoV-2 infection. This pro-inflammatory niche breaks down the epithelial barrier integrity allowing for increased epithelial infection including basal stem cells. Overall, this study reveals a key role for pre-existing chronic airway neutrophilia in determining infectivity and outcomes in response to SARS-CoV-2 infection that highlight neutrophilia as a potential target for prevention of severe COVID-19 disease. ## Data availability statement The original contributions presented in the study are included in the article/ Supplementary Material. Further inquiries can be directed to the corresponding author. ## Ethics statement The studies involving human participants were reviewed and approved by Institutional Review Board (IRB) of the University of Southern California (USC), protocol #HS-20-00546. The patients/participants provided their written informed consent to participate in this study. ## Author contributions Conceptualization: BC and AR. Methodology: BC and AR. Formal analysis: JA and MS. Investigation: BC, EQ, ZL, ND, CS, SK, WW, JH, and AR. Writing - original draft: BC and AR. Writing – review and editing: BC and AR. Funding acquisition: AR. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1112870/full#supplementary-material ## References 1. Wu Z, McGoogan JM. **Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: Summary of a report of 72314 cases from the Chinese center for disease control and prevention**. *JAMA* (2020) **323**. DOI: 10.1001/jama.2020.2648 2. Guan WJ, Ni ZY, Hu Y, Liang WH, Ou CQ, He JX. **Clinical characteristics of coronavirus disease 2019 in China**. *N Engl J Med* (2020) **382**. DOI: 10.1056/NEJMoa2002032 3. Pechous RD. **With friends like these: The complex role of neutrophils in the progression of severe pneumonia**. *Front Cell Infect Microbiol* (2017) **7**. DOI: 10.3389/fcimb.2017.00160 4. Grommes J, Soehnlein O. **Contribution of neutrophils to acute lung injury**. *Mol Med* (2011) **17** 293-307. DOI: 10.2119/molmed.2010.00138 5. Munoz-Fontela C, Dowling WE, Funnell SGP, Gsell PS, Riveros-Balta AX, Albrecht RA. **Animal models for COVID-19**. *Nature* (2020) **586**. DOI: 10.1038/s41586-020-2787-6 6. Zhang B, Zhou X, Zhu C, Song Y, Feng F, Qiu Y. **Immune phenotyping based on the neutrophil-to-Lymphocyte ratio and IgG level predicts disease severity and outcome for patients with COVID-19**. *Front Mol Biosci* (2020) **7**. DOI: 10.3389/fmolb.2020.00157 7. Song C-Y, Xu J, He JQ, Lu YQ. **COVID-19 early warning score: a multi-parameter screening tool to identify highly suspected patients**. *medRxiv* (2020) 2020.03.05.20031906. DOI: 10.1101/2020.03.05.20031906 8. Aveyard P, Gao M, Lindson N, Hartmann-Boyce J, Watkinson P, Young D. **Association between pre-existing respiratory disease and its treatment, and severe COVID-19: a population cohort study**. *Lancet Respir Med* (2021). DOI: 10.1016/S2213-2600(21)00095-3 9. Galani IE, Andreakos E. **Neutrophils in viral infections: Current concepts and caveats**. *J Leukoc Biol* (2015) **98**. DOI: 10.1189/jlb.4VMR1114-555R 10. Bordon J, Aliberti S, Fernandez-Botran R, Uriarte SM, Rane MJ, Duvvuri P. **Understanding the roles of cytokines and neutrophil activity and neutrophil apoptosis in the protective versus deleterious inflammatory response in pneumonia**. *Int J Infect Dis* (2013) **17**. DOI: 10.1016/j.ijid.2012.06.006 11. Borges L, Pithon-Curi TC, Curi R, Hatanaka E. **COVID-19 and neutrophils: The relationship between hyperinflammation and neutrophil extracellular traps**. *Mediators Inflamm 2020* (2020) **p** 8829674. DOI: 10.1155/2020/8829674 12. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y. **Clinical features of patients infected with 2019 novel coronavirus in wuhan, China**. *Lancet* (2020) **395** 497-506. DOI: 10.1016/S0140-6736(20)30183-5 13. Fajgenbaum DC, June CH. **Cytokine storm**. *N Engl J Med* (2020) **383**. DOI: 10.1056/NEJMra2026131 14. Ruan Q, Yang K, Wang W, Jiang L, Song J. **Correction to: Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from wuhan, China**. *Intensive Care Med* (2020) **46**. DOI: 10.1007/s00134-020-06028-z 15. Chen G, Wu D, Guo W, Cao Y, Huang D, Wang H. **Clinical and immunological features of severe and moderate coronavirus disease 2019**. *J Clin Invest* (2020) **130**. DOI: 10.1172/JCI137244 16. Hemmat N, Derakhshani A, Bannazadeh Baghi H, Silvestris N, Baradaran B, De Summa S. **Neutrophils, crucial, or harmful immune cells involved in coronavirus infection: A bioinformatics study**. *Front Genet* (2020) **11**. DOI: 10.3389/fgene.2020.00641 17. Shi H, Zuo Y, Yalavarthi S, Gockman K, Zuo M, Madison JA. **Neutrophil calprotectin identifies severe pulmonary disease in COVID-19**. *J Leukoc Biol* (2021) **109**. DOI: 10.1101/2020.05.06.20093070 18. Veras FP, Pontelli M, Silva C, Toller-Kawahisa J, de Lima M, Nascimento D. **SARS-CoV-2 triggered neutrophil extracellular traps (NETs) mediate COVID-19 pathology**. *medRxiv* (2020) 2020.06.08.20125823. DOI: 10.1101/2020.06.08.20125823 19. Radermecker C, Sabatel C, Vanwinge C, Ruscitti C, Marechal P, Perin F. **Locally instructed CXCR4(hi) neutrophils trigger environment-driven allergic asthma through the release of neutrophil extracellular traps**. *Nat Immunol* (2019) **20**. DOI: 10.1038/s41590-019-0496-9 20. Li G, He X, Zhang L, Ran Q, Wang J, Xiong A. **Assessing ACE2 expression patterns in lung tissues in the pathogenesis of COVID-19**. *J Autoimmun* (2020) **112** 102463. DOI: 10.1016/j.jaut.2020.102463 21. Tomar B, Anders HJ, Desai J, Mulay SR. **Neutrophils and neutrophil extracellular traps drive necroinflammation in COVID-19**. *Cells* (2020) **9**. DOI: 10.3390/cells9061383 22. Qin C, Zhou L, Hu Z, Zhang S, Yang S, Tao Y. **Dysregulation of immune response in patients with coronavirus 2019 (COVID-19) in wuhan, China**. *Clin Infect Dis* (2020) **71**. DOI: 10.1093/cid/ciaa248 23. Veras FP, Pontelli MC, Silva CM, Toller-Kawahisa JE, de Lima M, Nascimento DC. **SARS-CoV-2-triggered neutrophil extracellular traps mediate COVID-19 pathology**. *J Exp Med* (2020) **217**. DOI: 10.1084/jem.20201129 24. Arcanjo A, Logullo J, Menezes CCB, de Souza Carvalho Giangiarulo TC, Dos Reis MC, de Castro GMM. **The emerging role of neutrophil extracellular traps in severe acute respiratory syndrome coronavirus 2 (COVID-19)**. *Sci Rep* (2020) **10** 19630. DOI: 10.1038/s41598-020-76781-0 25. Janiuk K, Jablonska E, Garley M. **Significance of NETs formation in COVID-19**. *Cells* (2021) **10** 151. DOI: 10.3390/cells1001015 26. Deng Y, Herbert JA, Robinson E, Ren L, Smyth RL, Smith CM. **Neutrophil-airway epithelial interactions result in increased epithelial damage and viral clearance during respiratory syncytial virus infection**. *J Virol* (2020) **94**. DOI: 10.1128/jvi.02161-19 27. Randell SH, Walstad L, Schwab UE, Grubb BR, Yankaskas JR. **Isolation and culture of airway epithelial cells from chronically infected human lungs.**. *Cell Dev Biol Anim* (2001) **37**. DOI: 10.1290/1071-2690(2001)037<0480:iacoae>2.0.co;2 28. Pfaffl MW. **A new mathematical model for relative quantification in real-time RT-PCR**. *Nucleic Acids Res* (2001) **29** e45. DOI: 10.1093/nar/29.9.e45 29. Jasper AE, McIver WJ, Sapey E, Walton GM. **Understanding the role of neutrophils in chronic inflammatory airway disease**. *F1000Res* (2019) **8** 557. DOI: 10.12688/f1000research.18411.1 30. Taylor S, Dirir O, Zamanian RT, Rabinovitch M, Thompson AAR. **The role of neutrophils and neutrophil elastase in pulmonary arterial hypertension**. *Front Med (Lausanne)* (2018) **5**. DOI: 10.3389/fmed.2018.00217 31. Florentin J, Zhao J, Tai YY, Vasamsetti SB, O'Neil SP, Kumar R. **Interleukin-6 mediates neutrophil mobilization from bone marrow in pulmonary hypertension**. *Cell Mol Immunol* (2021) **18**. DOI: 10.1038/s41423-020-00608-1 32. Zhu N, Wang W, Liu Z, Liang C, Wang W, Ye F. **Morphogenesis and cytopathic effect of SARS-CoV-2 infection in human airway epithelial cells**. *Nat Commun* (2020) **11** 3910. DOI: 10.1038/s41467-020-17796-z 33. Jia HP, Look DC, Shi L, Hickey M, Pewe L, Netland J. **ACE2 receptor expression and severe acute respiratory syndrome coronavirus infection depend on differentiation of human airway epithelia**. *J Virol* (2005) **79**. DOI: 10.1128/JVI.79.23.14614-14621.2005 34. Jia HP, Look DC, Tan P, Shi L, Hickey M, Gakhar L. **Ectodomain shedding of angiotensin converting enzyme 2 in human airway epithelia**. *Am J Physiol Lung Cell Mol Physiol* (2009) **297**. DOI: 10.1152/ajplung.00071.2009 35. Zhang H, Rostami MR, Leopold PL, Mezey JG, O'Beirne SL, Strulovici-Barel Y. **Expression of the SARS-CoV-2 ACE2 receptor in the human airway epithelium**. *Am J Respir Crit Care Med* (2020) **202**. DOI: 10.1164/rccm.202003-0541OC 36. Baggiolini M, Walz A, Kunkel SL. **Neutrophil-activating peptide-1/interleukin 8, a novel cytokine that activates neutrophils**. *J Clin Invest* (1989) **84**. DOI: 10.1172/JCI114265 37. Yoshimura T, Matsushima K, Tanaka S, Robinson EA, Appella E, Oppenheim JJ. **Purification of a human monocyte-derived neutrophil chemotactic factor that has peptide sequence similarity to other host defense cytokines**. *Proc Natl Acad Sci U.S.A.* (1987) **84**. DOI: 10.1073/pnas.84.24.9233 38. Parsons PE, Fowler AA, Hyers TM, Henson PM. **Chemotactic activity in bronchoalveolar lavage fluid from patients with adult respiratory distress syndrome**. *Am Rev Respir Dis* (1985) **132**. DOI: 10.1164/arrd.1985.132.3.490 39. Azevedo MLV, Zanchettin AC, Vaz de Paula CB, Motta Junior JDS, Malaquias MAS, Raboni SM. **Lung neutrophilic recruitment and IL-8/IL-17A tissue expression in COVID-19**. *Front Immunol* (2021) **12**. DOI: 10.3389/fimmu.2021.656350 40. Pease JE, Sabroe I. **The role of interleukin-8 and its receptors in inflammatory lung disease: implications for therapy**. *Am J Respir Med* (2002) **1** 19-25. DOI: 10.1007/BF03257159 41. Del Valle DM, Kim-Schulze S, Huang HH, Beckmann ND, Nirenberg S, Wang B. **An inflammatory cytokine signature predicts COVID-19 severity and survival**. *Nat Med* (2020) **26**. DOI: 10.1038/s41591-020-1051-9 42. Han H, Ma Q, Li C, Liu R, Zhao L, Wang W. **Profiling serum cytokines in COVID-19 patients reveals IL-6 and IL-10 are disease severity predictors**. *Emerg Microbes Infect* (2020) **9**. DOI: 10.1080/22221751.2020.1770129 43. Liu QQ, Cheng A, Wang Y, Li H, Hu L, Zhao X. **Cytokines and their relationship with the severity and prognosis of coronavirus disease 2019 (COVID-19): a retrospective cohort study**. *BMJ Open* (2020) **10** e041471. DOI: 10.1136/bmjopen-2020-041471 44. Gernez Y, Tirouvanziam R, Chanez P. **Neutrophils in chronic inflammatory airway diseases: can we target them and how**. *Eur Respir J* (2010) **35**. DOI: 10.1183/09031936.00186109 45. Kulkarni U, Zemans RL, Smith CA, Wood SC, Deng JC, Goldstein DR. **Excessive neutrophil levels in the lung underlie the age-associated increase in influenza mortality**. *Mucosal Immunol* (2019) **12**. DOI: 10.1038/s41385-018-0115-3 46. Chen MM, Palmer JL, Plackett TP, Deburghgraeve CR, Kovacs EJ. **Age-related differences in the neutrophil response to pulmonary pseudomonas infection**. *Exp Gerontol* (2014) **54**. DOI: 10.1016/j.exger.2013.12.010 47. Sapey E, Patel JM, Greenwood HL, Walton GM, Hazeldine J, Sadhra C. **Pulmonary infections in the elderly lead to impaired neutrophil targeting, which is improved by simvastatin**. *Am J Respir Crit Care Med* (2017) **196**. DOI: 10.1164/rccm.201704-0814OC 48. Kordonowy LL, Burg E, Lenox CC, Gauthier LM, Petty JM, Antkowiak M. **Obesity is associated with neutrophil dysfunction and attenuation of murine acute lung injury**. *Am J Respir Cell Mol Biol* (2012) **47**. DOI: 10.1165/rcmb.2011-0334OC 49. Maia LA, Cruz FF, de Oliveira MV, Samary CS, Fernandes MVS, Trivelin SAA. **Effects of obesity on pulmonary inflammation and remodeling in experimental moderate acute lung injury**. *Front Immunol* (2019) **10**. DOI: 10.3389/fimmu.2019.01215 50. Manicone AM, Gong K, Johnston LK, Giannandrea M. **Diet-induced obesity alters myeloid cell populations in naive and injured lung**. *Respir Res* (2016) **17** 24. DOI: 10.1186/s12931-016-0341-8 51. Kumar S, Yadav PK, Srinivasan R, Perumal N. **Selection of animal models for COVID-19 research**. *Virusdisease* (2020) **p** 1-6. DOI: 10.1007/s13337-020-00637-4 52. Stackowicz J, Jonsson F, Reber LL. **Mouse models and tools for the in vivo study of neutrophils**. *Front Immunol* (2019) **10**. DOI: 10.3389/fimmu.2019.03130 53. Corteling R, Wyss D, Trifilieff A. **In vivo models of lung neutrophil activation. comparison of mice and hamsters**. *BMC Pharmacol* (2002) **2** 1. DOI: 10.1186/1471-2210-2-1 54. Parkos CA. **Neutrophil-epithelial interactions: A double-edged sword**. *Am J Pathol* (2016) **186**. DOI: 10.1016/j.ajpath.2016.02.001 55. Kunkel SL, Standiford T, Kasahara K, Strieter RM. **Interleukin-8 (IL-8): the major neutrophil chemotactic factor in the lung**. *Exp Lung Res* (1991) **17** 17-23. DOI: 10.3109/01902149109063278 56. Yang PH, Ding YB, Xu Z, Pu R, Li P, Yan J. **Increased circulating level of interleukin-6 and CD8(+) T cell exhaustion are associated with progression of COVID-19**. *Infect Dis Poverty* (2020) **9** 161. DOI: 10.1186/s40249-020-00780-6 57. Liu J, Li S, Liu J, Liang B, Wang X, Wang H. **Longitudinal characteristics of lymphocyte responses and cytokine profiles in the peripheral blood of SARS-CoV-2 infected patients**. *EBioMedicine* (2020) **55** 102763. DOI: 10.1016/j.ebiom.2020.102763 58. Godkin A, Humphreys IR. **Elevated interleukin-6, interleukin-10 and neutrophil : lymphocyte ratio as identifiers of severe coronavirus disease 2019**. *Immunology* (2020) **160**. DOI: 10.1111/imm.13225 59. Huang H, Zhang M, Chen C, Zhang H, Wei Y, Tian J. **Clinical characteristics of COVID-19 in patients with preexisting ILD: A retrospective study in a single center in wuhan, China**. *J Med Virol* (2020) **92**. DOI: 10.1002/jmv.26174 60. Pandolfi L, Fossali T, Frangipane V, Bozzini S, Morosini M, D'Amato M. **Broncho-alveolar inflammation in COVID-19 patients: a correlation with clinical outcome**. *BMC Pulm Med* (2020) **20** 301. DOI: 10.1186/s12890-020-01343-z 61. Fahlberg MD, Blair RV, Doyle-Meyers LA, Midkiff CC, Zenere G, Russell-Lodrigue KE. **Cellular events of acute, resolving or progressive COVID-19 in SARS-CoV-2 infected non-human primates**. *Nat Commun* (2020) **11** 6078. DOI: 10.1038/s41467-020-19967-4 62. Ellis TN, Beaman BL. **Interferon-gamma activation of polymorphonuclear neutrophil function**. *Immunology* (2004) **112** 2-12. DOI: 10.1111/j.1365-2567.2004.01849.x 63. Kang S, Brown HM, Hwang S. **Direct antiviral mechanisms of interferon-gamma**. *Immune Netw* (2018) **18**. DOI: 10.4110/in.2018.18.e33 64. Lee AJ, Ashkar AA. **The dual nature of type I and type II interferons**. *Front Immunol* (2018) **9**. DOI: 10.3389/fimmu.2018.02061 65. Capaldo CT, Nusrat A. **Cytokine regulation of tight junctions**. *Biochim Biophys Acta* (2009) **1788**. DOI: 10.1016/j.bbamem.2008.08.027 66. Al-Sadi R, Boivin M, Ma T. **Mechanism of cytokine modulation of epithelial tight junction barrier**. *Front Biosci (Landmark Ed)* (2009) **14**. DOI: 10.2741/3413 67. Silva MT, Correia-Neves M. **Neutrophils and macrophages: the main partners of phagocyte cell systems**. *Front Immunol* (2012) **3**. DOI: 10.3389/fimmu.2012.00174 68. Uribe-Querol E, Rosales C. **Phagocytosis: Our current understanding of a universal biological process**. *Front Immunol* (2020) **11**. DOI: 10.3389/fimmu.2020.01066 69. Wen Z, Zhang Y, Lin Z, Shi K, Jiu Y. **Cytoskeleton-a crucial key in host cell for coronavirus infection**. *J Mol Cell Biol* (2020) **12**. DOI: 10.1093/jmcb/mjaa042 70. Wang Y, Jonsson F. **Expression, role, and regulation of neutrophil fcgamma receptors**. *Front Immunol* (2019) **10**. DOI: 10.3389/fimmu.2019.01958
--- title: Improved computation of Lagrangian tissue displacement and strain for cine DENSE MRI using a regularized spatiotemporal least squares method authors: - Sona Ghadimi - Mohamad Abdi - Frederick H. Epstein journal: Frontiers in Cardiovascular Medicine year: 2023 pmcid: PMC10061004 doi: 10.3389/fcvm.2023.1095159 license: CC BY 4.0 --- # Improved computation of Lagrangian tissue displacement and strain for cine DENSE MRI using a regularized spatiotemporal least squares method ## Abstract ### Introduction In displacement encoding with stimulated echoes (DENSE), tissue displacement is encoded in the signal phase such that the phase of each pixel in space and time provides an independent measurement of absolute tissue displacement. Previously for DENSE, estimation of Lagrangian displacement used two steps: first a spatial interpolation and, second, least squares fitting through time to a Fourier or polynomial model. However, there is no strong rationale for such a through-time model, ### Methods To compute the Lagrangian displacement field from DENSE phase data, a minimization problem is introduced to enforce fidelity with the acquired Eulerian displacement data while simultaneously providing model-independent regularization in space and time, enforcing only spatiotemporal smoothness. A regularized spatiotemporal least squares (RSTLS) method is used to solve the minimization problem, and RSTLS was tested using two-dimensional DENSE data from 71 healthy volunteers. ### Results The mean absolute percent error (MAPE) between the Lagrangian displacements and the corresponding Eulerian displacements was significantly lower for the RSTLS method vs. the two-step method for both x- and y-directions (0.73±0.59 vs 0.83 ±0.1, $p \leq 0.05$) and (0.75±0.66 vs 0.82 ±0.1, $p \leq 0.05$), respectively. Also, peak early diastolic strain rate (PEDSR) was higher (1.81±0.58 (s-1) vs. 1.56±0. 63 (s-1), $p \leq 0.05$) and the strain rate during diastasis was lower (0.14±0.18 (s-1) vs 0.35±0.2 (s-1), $p \leq 0.05$) for the RSTLS vs. the two-step method, with the former suggesting that the two-step method was over-regularized. ### Discussion The proposed RSTLS method provides more realistic measurements of Lagrangian displacement and strain from DENSE images without imposing arbitrary motion models. ## Introduction Many myocardial strain imaging methods are referred to as “tracking” methods, such as speckle tracking, tag tracking, and feature tracking, as they employ image processing techniques to track specific patterns or image features through a time sequence of images by searching for the most probable correspondence from one frame to the next [1]. Harmonic phase (HARP) imaging is also a feature-tracking method as it tracks pixels of a common phase from frame to frame [2]. In contrast, displacement encoding with stimulated echoes (DENSE) [3] provides fundamentally different data than tracking-based methods, and, accordingly, displacement and strain analysis of DENSE images do not involve tracking features from frame to frame. In cine DENSE [4], the phase of each pixel of the myocardium (or other tissue of interest) in space and time provides an independent measurement of absolute tissue displacement relative to the time when the initial displacement-encoding pulses were applied, typically upon detection of the ECG R-wave. In other words, DENSE intrinsically measures the Eulerian displacement of each pixel. To compute the Lagrangian displacement, where one can observe the pathline of an element of myocardium starting from the beginning of the cardiac cycle as it moves through time, displacement field estimation can be formulated as a regularized least squares minimization problem seeking to find the Lagrangian displacement field with the least mean squared error relative to the measured Eulerian displacements subject to regularization to reduce the effects of noise in the measurements. Previously for DENSE, Lagrangian displacement estimation used two steps, including a first step of spatial interpolation or application of a spatial model (spatial interpolation with spatial regularization) combined with a second step of least squares fitting through time to fifth-order *Fourier basis* functions or a polynomial model (5–10). Alternatively, analysis of DENSE data has used spatial interpolation or modeling without exploiting the time dimension, i.e., treating each frame independently of other frames [11]. Limitations of these prior methods are that there are no strong rationales for particular temporal models such as fifth-order *Fourier basis* functions or polynomial functions and that treating the data independently from frame to frame does not exploit temporal information that is available to denoise the resulting Lagrangian displacement and strain. In the present study, we develop a method to compute the Lagrangian displacement (and, subsequently, strain) for cine DENSE that uses a least squares minimization method to enforce fidelity with the acquired Eulerian displacement data while simultaneously providing model-independent regularization in space and time enforcing only spatiotemporal smoothness (Figure 1). Furthermore, we demonstrate improved quantification of cardiac mechanics using the new method compared to the prior two-step method using data from healthy subjects. **Figure 1:** *Schematic diagrams of the proposed regularized spatiotemporal least squares method (green path) and the commonly used two-step method (red path) to estimate Lagrangian displacement trajectories from DENSE phase images. The input images are the segmented and phase-unwrapped x- and y-encoded cine DENSE phase images and the output is the left-ventricular myocardial Lagrangian displacement trajectories.* ## Lagrangian displacement estimation Prior to computing the myocardial Lagrangian displacement, we assume that the myocardial tissue has been segmented, and if phase wrapping occurred, phase unwrapping would be applied such that for each pixel the phase is directly proportional to Eulerian tissue displacement. These steps may require some manual input by a user or may be completed automatically [12]. To compute the Lagrangian displacement field from the Eulerian displacement field, we formulate a minimization problem as: where *Lf is* the desired Lagrangian displacement trajectory field of frame f, *Ef is* the Eulerian displacement computed directly from the unwrapped phase of myocardial pixels in frame f, and A is the spatial bilinear interpolation matrix. B is the operator taking second derivatives in each spatial direction. ‖(ALf−Ef)‖2 enforces agreement of the Lagrangian displacement with the measured Eulerian displacement data, ‖(BLf)‖2 represents regularization enforcing spatial smoothness of the Lagrangian displacement, ‖Lf−Lf−1‖2 represents regularization enforcing temporal smoothness of the Lagrangian displacement, and λ and μ are the weights for the regularization terms. To develop a least squares solution to Eq. 1, we rewrite it as: where F is the total number of image frames, and we assumed there is no Lagrangian displacement before the first frame (L0=0). Assuming that A^is a full-rank matrix, the Lagrangian displacement can be computed using a least squares solution of Eq. 2 given by Eq. 3 (refer to Appendix I for a derivation): The minimization problem of Eq. 1 and its solution given by Eq. 3 are applied separately for each displacement-encoding direction when more than one displacement-encoding direction is employed. In the present study, because our datasets are from two-dimensional (2D) DENSE imaging, we confine our Lagrangian displacement estimations to 2D, although, in theory, the method should be applicable to 3D data. To further denoise the resulting Lagrangian displacement, Lf, as a final step, we apply a moving mean filter in time with a kernel size of three frames. In addition to denoising, this filter will also add temporal smoothing. More implementation details regarding A^ and E^ are provided in Appendix II. Figure 2 shows examples of short-axis 2D Lagrangian displacement trajectories computed using the proposed RSTLS method and the prior two-step method that first applies agreement with the measured data and spatial regularization (Eq. 1 with μ=0) and subsequently performs through-time fitting of a 10th order polynomial. Figure 2B shows three magnified example Lagrangian trajectories computed with both the two-step and RSTLS methods, and Figure 2C shows the same magnified trajectories projected to the Eulerian domain. Also shown in Figure 2C are the raw Eulerian trajectories (at all discrete time points) calculated directly from the unwrapped DENSE phase data. Example Lagrangian displacement movies and circumferential strain movies computed using the two-step method and the RSTLS method are shown in the supplementary presentation. These examples illustrate that RSTLS better captures abrupt changes in Lagrangian and Eulerian trajectories and shows better agreement with the raw *Eulerian data* whereas the two-step method tends to over-smooth the Lagrangian and Eulerian trajectories and shows worse agreement with the raw Eulerian data. **Figure 2:** *(A) An example of 2D Lagrangian displacement trajectories computed using the two-step method with a through-time polynomial function and the proposed regularized spatiotemporal least squares (RSTLS) method from a healthy subject. Panel (B) shows three magnified example Lagrangian trajectories computed with both the two-step and RSTLS methods, and panel (C) shows the same magnified trajectories projected to the Eulerian domain. The raw Eulerian trajectories (at all discrete time points) calculated directly from the unwrapped DENSE phase data are also shown in (C). These examples demonstrate that abrupt changes in trajectories are over-smoothed using the two-step method but are better depicted using the RSTLS method. The Lagrangian trajectories projected into the Eulerian domain show that RSTLS maintains better agreement than the two-step method with the raw Eulerian measurements.* ## Data acquisition protocol For this study, we used two-dimensional (2D) short-axis cine DENSE MRI data acquired from 71 healthy volunteers. All CMR was performed in accordance with a protocol approved by the Institutional Review Board for Human Subjects Research at our institution, and all experiments were performed in accordance with relevant guidelines and regulations. Data were acquired using a 3 T system (Magnetom Prisma, Siemens, Erlangen, Germany). Cine DENSE image acquisition parameters included a pixel size of 3.4 mm2 × 3.4 mm2, FOV = 200 mm2 (using outer volume suppression), slice thickness = 8 mm, a temporal resolution of 15 ms (with view sharing), 2D in-plane displacement encoding using the simple three-point method [13], displacement-encoding frequency = 0.06 or 0.1 cycles/mm, ramped flip angle with a final flip angle of 15°, echo time = 1.08 or 1.26 ms, and a spiral k-space trajectory with 4 interleaves, providing a breath-hold scan time of 14 heartbeats. For most volunteers, cine DENSE images were acquired at basal, mid-ventricular, and apical levels. ## Comparison of displacement, strain, and strain rate computed with the two-step and RSTLS methods To quantitively evaluate and compare the RSLTS and two-step methods, we first computed the agreement between the computed Lagrangian displacements and the corresponding Eulerian displacements obtained from DENSE unwrapped phase images for each frame. Specifically, the mean absolute percent error (MAPE) was computed as: where n is the total number of myocardial pixels in frame f, Li,f is the estimated Lagrangian displacement, and Ei,f is the measured Eulerian displacement of pixel i in frame f. The Lagrangian displacement Li,f is multiplied by the bilinear interpolation matrix A to compute the Eulerian displacement. The purpose of choosing the percent error as opposed to the absolute error was to determine how close an estimated displacement is to a measured displacement regardless of the displacement magnitude, which differs substantially between various cardiac phases. To investigate the effects of RSTLS vs. the two-step method on strain and strain rate, global (whole-slice) and segmental circumferential strain (Ecc) and strain rate curves were computed from Lagrangian displacements calculated using both methods. End-systolic strain is widely used as an important metric of systolic function, and strain rate is widely considered an important metric of diastolic function (14–16). The 2D Lagrangian finite strain tensor E was computed using Eq. 5 where I is the identity matrix and F is the deformation gradient tensor. F represents the relationship between the myocardium in the undeformed configuration (first frame) and in a deformed configuration (e.g., a phase in cardiac systole). Let the positions of the myocardial points in the undeformed and deformed configurations be X and x, respectively. Then, In Eq. 6, dX is the position difference between myocardial points in the undeformed (first) frame, which is transformed to dx in the deformed frame. Each entry of this tensor (Fij) is defined as ∂xi/∂Xjand determines how the distance between any two elements along the jth direction in the reference configuration is projected to the ith direction in the deformed configuration. After computing F and E in the Cartesian coordinate system, strain tensors are mapped to the heart’s short-axis polar coordinate system using the rotation matrix shown in Eq. 7, in which Epolar[1,1] is the radial (Err) and Epolar[2,2] is the circumferential strain (Ecc). Once circumferential strain is calculated for all myocardial points and all time points, the strain rate is obtained by taking the difference of strain values in two consecutive frames divided by the repetition time (TR). Finally, to explore how the proposed RSTLS method and the two-step method propagate errors in the calculation of Lagrangian displacement, we arbitrarily added simulated measurement errors to a specific pixel in various time frames and visually compared the resulting computed Lagrangian trajectories of that pixel to uncorrupted trajectories. The simulated error displacement vector was perpendicular to the original vector with a magnitude of $50\%$ of the uncorrupted displacement vector. ## Results Figure 3A shows an example of the calculation of MAPE for the RSTLS and two-step methods. While the time of 330 ms represented end systole in this example, the highest MAPE values occurred in early- and late-diastolic phases, some of which have small absolute displacements. Using paired t-tests to compare the MAPE (averaged over time) for Lagrangian displacement computed using the RSTLS and two-step methods for displacement encoding applied in the x- and y-directions, Figure 3B shows that MAPE was significantly lower for the RSTLS method in both the x- and y-directions (0.73 ± 0.59 vs. 0.83 ± 0.1, $p \leq 0.05$), (0.75 ± 0.66 vs. 0.82 ± 0.1, $p \leq 0.05$), respectively. **Figure 3:** *(A) Examples of the mean absolute percentage error for the two-step and RSTLS methods as a function of time in the cardiac cycle for displacement measured in the x- and y-directions. The greatest percent of errors appear in early systole and early and late diastole. (B) Comparison of mean absolute percentage error for healthy subjects averaged over time. The graphs are plotted based on 182 slices from 71 volunteers. Error bars represent standard error. *p < 0.05.* Figure 4 demonstrates segmental and global Ecc strain and strain rate vs. time curves of a healthy volunteer. Qualitatively, it can be observed that RSTLS preserves the visualization of post-systolic shortening, which is over-smoothed by the two-step method. In addition, model-driven strain oscillations that are commonly observed in diastasis using the two-step method, related to the though-time polynomial fit, are avoided using the RSTLS method. To quantify the advantages of RSTLS vs. the two-step method for the analysis of diastole, we computed the peak early diastolic strain rate (PEDSR) and the strain rate during diastasis. For global strain and strain-rate data, Figure 5 shows the mean ± standard deviation of PEDSR and of the strain rate during diastasis. The PEDSR was higher (1.81±0.58 (s-1) vs. 1.56±0. 63 (s-1), $p \leq 0.05$) and the strain rate during diastasis was lower (0.14±0.18 (s-1) vs 0.35±0.2 (s-1), $p \leq 0.05$) for the RSTLS vs. the two-step method. The higher PEDSR likely reflects less temporal over-smoothing, and the lower diastasis strain rate likely reflects the absence of model-induced oscillation artifacts for the RSTLS method. **Figure 4:** *An example from a heathy subject comparing circumferential strain (Ecc) curves and strain rate (Ecc/s) curves computed using the two-step and RSTLS methods. In both segmental (A, B) and global (C, D) assessments, the RSTLS method better captures features such as post-systolic shortening, early diastole, and diastasis, that are over-smoothed or have oscillation artifacts when computed using the two-step method. PSS = post-systolic shortening, and PEDSR = peak early diastolic strain rate.* **Figure 5:** *Comparison of global peak early diastolic strain rate and strain rate during diastasis for healthy subjects. The graphs are plotted based on 182 slices from 71 volunteers. Error bars represent standard deviations. *p < 0.05.* Figure 6 illustrates how errors added to the specific pixel at different time frames propagate through the Lagrangian displacement calculations. Figure 6A depicts the uncorrupted Eulerian displacement derived from DENSE unwrapped phase data from one pixel at all time points. The dark blue arrow corresponds to the Eulerian displacement of the time frame selected for adding a simulated measurement error. The new corrupted Eulerian displacement with a simulated measurement error is shown in Figure 6B, where the error displacement vector (blue vector) is perpendicular to the original vector. Next, Figure 6C shows the computed Lagrangian trajectories using the RSTLS method without and with the simulated error (the dotted black line shows the uncorrupted Lagrangian trajectory and the solid red line shows the corrupted Lagrangian trajectory). The blue dots in the Lagrangian trajectories show the time point where the simulated error was inserted. Figure 6D is the same as Figure 6C, except it was generated using the two-step method instead of the RSLTS method for computing the Lagrangian trajectories. For both the two-step and RSTLS methods, these computations show that when errors occur, instead of leading to further and increasing errors through time along the trajectory, both methods demonstrate a self-correction property, as the trajectories quickly return to their uncorrupted form within just a few time frames after the errors were inserted. **Figure 6:** *Examples of errors added to measured Eulerian displacements and their effects on computed Lagrangian displacements. (A) depicts the uncorrupted Eulerian displacements derived from DENSE unwrapped phase data from one pixel at all time points. The dark blue arrow corresponds to the Eulerian displacement of the time frame selected for adding a simulated measurement error. The new corrupted Eulerian displacements with simulated measurement errors are shown in (D), where the error displacement vector (blue vector) is perpendicular to the original vector. Panel (C) shows the computed Lagrangian trajectories using the RSTLS method without and with the simulated error (the dotted black line shows the uncorrupted Lagrangian trajectory and the solid red line shows the corrupted Lagrangian trajectory). The blue dots in the Lagrangian trajectories show the time points where the simulated error was inserted. Panel (D) is the same as (C), except it was generated using the two-step method instead of the RSLTS method for computing the Lagrangian trajectories.* ## Discussion We have developed an improved approach to estimate Lagrangian displacement and strain from DENSE phase images that supports spatial and temporal regularization, enforces fidelity with the acquired Eulerian displacement data, and does not impose model-based assumptions on the displacement solution. While our experiments did not include a gold standard reference for measurements of cardiac mechanics, comparisons with the prior two-step displacement estimation method suggest that RSTLS provides a better depiction of the strain–time curve, particularly with regard to post-systolic shortening, early diastolic strain, and diastasis. Specifically, the proposed RSTLS method likely avoids temporal over-smoothing and model-driven oscillations. The model-driven oscillations are often seen in diastasis (as in Figure 4), but they can also manifest in other ways, such as false prestretch at the beginning of the cardiac cycle, as shown in Figure 7. **Figure 7:** *Example demonstrating artifactual pre-stretch in early systole in the apical-lateral wall using the two-step method that is not observed using the RSTLS method from a patient with ischemic heart disease.* An important characteristic of the RSTLS method is that it processes spatiotemporal DENSE data in an integrated fashion, as opposed to separately processing the spatial and temporal data. Prior methods for the analysis of DENSE phase images first perform spatial interpolation, and subsequently model the through-time dimension or simply apply the spatial calculations to each frame independent of other frames. These other methods include the radial point interpolation method described by Kar et al. [ 17], another finite element method described by Young et al. [ 8], and our previous study that first performed spatial interpolation and then handled the time dimension [6]. To the best of our knowledge, the present RSTLS method is the first to treat the 2D + time DENSE data in a unified way. Our investigations showed that measurement errors introduced at one cardiac phase do not propagate to subsequent cardiac phases, but instead the calculation of the Lagrangian trajectory has a self-correction property. This property occurs because fidelity with the acquired data is enforced for each cardiac phase. This is an important property of DENSE and is an advantage compared to traditional block-matching methods, where an error in one phase can propagate and accumulate over time [18, 19]. Applications, where RSTLS may have advantages, include the assessment of late mechanical activation in patients who may be candidates for cardiac resynchronization therapy [20], as model-driven oscillations in the strain–time curve using the two-step method can mimic early stretch in the circumferential direction (as shown in Figure 6), and RSTLS avoids these model-driven oscillations. In addition, due to the advantages of RSTLS for measuring diastolic strain rate, this method may be preferred when the assessment of diastolic function is important such as for heart failure with preserved ejection fraction [14] and pulmonary hypertension [15]. Our study had limitations. First, we confined our investigations to two dimensions, while, in reality, myocardial displacement and strain are three-dimensional. In the future, the RSTLS method can be extended to three dimensions. Second, we did not include patient data in our analysis and also there is a lack of multi-center validation in this study. Third, while in Appendix II, we give recommendations for suggested values of the regularization weights, μ and λ, based on our experience applying RSTLS in human subjects [21, 22], we have not provided a rigorous optimization of these values. To rigorously optimize μ and λ, ideally, we would make use of computer simulations with known displacements that closely match in vivo myocardial mechanics, both spatially and temporally. We could then apply RSTLS to simulated DENSE data and identify values of μ and λ that lead to RSTLS estimates of Lagrangian displacement that most closely agree with the known values. However, while very good models exist for mimicking the spatial mechanics of the heart [23], to the best of our knowledge, there are no such models that comprehensively mimic spatiotemporal myocardial mechanics. Since RSTLS incorporates spatiotemporal motion, the lack of an appropriate spatiotemporal model is an obstacle to rigorous optimization of the regularization weights. Finally, Eq. 1, which describes the RSTLS model, utilizes the one-sided first-order difference for temporal regularization, whereas the central difference would be more accurate for estimating the derivative. We chose the one-sided first-order difference because it facilitates the use of a simple least squares solution to Eq. 1 by Eq. 3. If we instead used the central difference in Eq. 1, the least squares method would not be suitable for solving it and a more complicated iterative method would need to be used to solve the minimization equation. A disadvantage of the RSTLS method compared to the two-step method may be less robustness to noise. To address this problem, noise reduction filtering with a mean filter was applied after using the RSTLS method to compute the Lagrangian displacement trajectories. Although fitting a through-time polynomial model imposes some artifacts and some over-smoothing, the two-step method may provide better results for very noisy data. ## Conclusion The present study developed an approach to estimate Lagrangian displacement from DENSE images using a regularized spatiotemporal least squares method. Evaluations using images from healthy subjects demonstrated that the RSTLS method combines spatial regularization, temporal regularization, and agreement with measured Eulerian displacement to provide a model-free computation of myocardial Lagrangian displacement and strain that provides reduced mean absolute percent error and higher peak early diastolic strain rate, suggesting better accuracy and less over-regularization compared to the competing two-step method. ## Data availability statement Requests to access these datasets should be directed to fhe6b@virginia.edu. ## Ethics statement The studies involving human participants were reviewed and approved by University of Virginia Institutional Review Board. The patients/participants provided their written informed consent to participate in this study. ## Author contributions SG developed the methods, analyzed data, and helped draft the manuscript. MA acquired some of the healthy volunteer data. FE provided the intellectual background necessary and helped draft the manuscript. All authors contributed to the article and approved the submitted version. ## Funding This work was supported by the National Institute of Health (NIH), and the National Heart, Lung, and Blood Institute (NHLBI) Research Project (R01) R01 HL147104. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcvm.2023.1095159/full#supplementary-material ## References 1. Amzulescu MS, De Craene M, Langet H, Pasquet A, Vancraeynest D, Pouleur AC. **Myocardial strain imaging: review of general principles, validation, and sources of discrepancies**. *Eur Heart J Cardiovasc Imaging* (2019) **20** 605-19. DOI: 10.1093/ehjci/jez041 2. Osman NF, Kerwin WS, McVeigh ER, Prince JL. **Cardiac motion tracking using CINE harmonic phase (HARP) magnetic resonance imaging**. *Magn Reson Med* (1999) **42** 1048-60. DOI: 10.1002/(SICI)1522-2594(199912)42:6<1048::AID-MRM9>3.0.CO;2-M 3. Aletras AH, Ding S, Balaban RS, Wen H. **DENSE: displacement encoding with stimulated echoes in cardiac functional MRI**. *J Magn Reson* (1999) **137** 247-52. DOI: 10.1006/jmre.1998.1676 4. Kim D, Gilson WD, Kramer CM, Epstein FH. **Myocardial tissue tracking with two-dimensional cine displacement-encoded MR imaging: development and initial evaluation**. *Radiology* (2004) **230** 862-71. DOI: 10.1148/radiol.2303021213 5. Young AA, Hunter PJ, Smaill BH. **Estimation of Epicardial strain using the motions of coronary bifurcations in biplane Cineangiography**. *IEEE Trans Biomed Eng* (1992) **39** 526-31. DOI: 10.1109/10.135547 6. Spottiswoode BS, Zhong X, Hess AT, Kramer CM, Meintjes EM, Mayosi BM. **Tracking myocardial motion from cine DENSE images using spatiotemporal phase unwrapping and temporal fitting**. *IEEE Trans Med Imaging* (2007) **26** 15-30. DOI: 10.1109/TMI.2006.884215 7. Zhong X, Spottiswoode BS, Meyer CH, Kramer CM, Epstein FH. **Imaging three-dimensional myocardial mechanics using navigator-gated volumetric spiral cine DENSE MRI**. *Magn Reson Med* (2010) **64** 1089-97. DOI: 10.1002/mrm.22503 8. Young AA, Li B, Kirton RS, Cowan BR. **Generalized spatiotemporal myocardial strain analysis for DENSE and SPAMM imaging**. *Magn Reson Med* (2012) **67** 1590-9. DOI: 10.1002/mrm.23142 9. Suever JD, Wehner GJ, Haggerty CM, Jing L, Hamlet SM, Binkley CM. **Simplified post processing of cine DENSE cardiovascular magnetic resonance for quantification of cardiac mechanics**. *J Cardiovasc Magn Reson* (2014) **16** 94. DOI: 10.1186/s12968-014-0094-9 10. Nasiraei Moghaddam A, Saber N, Wen H, Finn JP, Ennis D, Gharib M. **Analytical method to measure three-dimensional strain patterns in the left ventricle from single slice displacement data**. *J Cardiovasc Magn Reson* (2010) **12** 1-18. DOI: 10.1186/1532-429X-12-33 11. Ernande L, Thibault H, Bergerot C, Moulin P, Wen H, Derumeaux G. **Systolic myocardial dysfunction in patients with type 2 diabetes mellitus: identification at MR imaging with cine displacement encoding with stimulated echoes**. *Radiology* (2012) **265** 402-9. DOI: 10.1148/radiol.12112571 12. Ghadimi S, Auger DA, Feng X, Sun C, Meyer CH, Bilchick KC. **Fully-automated global and segmental strain analysis of DENSE cardiovascular magnetic resonance using deep learning for segmentation and phase unwrapping**. *J Cardiovasc Magn Reson* (2021) **23** 1-13. DOI: 10.1186/s12968-021-00712-9 13. Zhong X, Helm PA, Epstein FH. **Balanced multipoint displacement encoding for DENSE MRI**. *Magn Reson Med* (2009) **61** 981-8. DOI: 10.1002/mrm.21851 14. He J, Yang W, Wu W, Li S, Yin G, Zhuang B. **Early diastolic longitudinal strain rate at MRI and outcomes in heart failure with preserved ejection fraction**. *Radiology* (2022) **302** E5-E92. DOI: 10.1148/radiol.2021219026 15. Liu BY, Wu WC, Zeng QX, Liu ZH, Niu LL, Tian Y. **Left ventricular peak early diastolic strain rate detected by two-dimensional speckle tracking echocardiography and disease severity in pre-capillary pulmonary hypertension**. *Pulm Circ* (2019) **9**. DOI: 10.1177/2045894019865158 16. Zhu J, Shi F, You T, Tang C, Chen J. **Global diastolic strain rate for the assessment of left ventricular diastolic dysfunction in young peritoneal dialysis patients: a case control study**. *BMC Nephrol* (2020) **21** 1-11. DOI: 10.1186/s12882-020-01742-8 17. Kar J, Knutsen AK, Cupps BP, Zhong X, Pasque MK. **Three-dimensional regional strain computation method with displacement ENcoding with stimulated echoes (DENSE) in non-ischemic, non-valvular dilated cardiomyopathy patients and healthy subjects validated by tagged MRI**. *J Magn Reson Imaging* (2015) **41** 386-96. DOI: 10.1002/jmri.24576 18. Zhu Y, Hall TJ. **A modified block matching method for real-time freehand strain imaging**. *Ultrason Imaging* (2002) **24** 161-76. DOI: 10.1177/016173460202400303 19. Qian Z, Liu Q, Metaxas DN, Axel L. **Identifying regional cardiac abnormalities from myocardial strains using nontracking-based strain estimation and spatio-temporal tensor analysis**. *IEEE Trans Med Imaging* (2011) **30** 2017-29. DOI: 10.1109/TMI.2011.2156805 20. Auger DA, Bilchick KC, Gonzalez JA, Cui SX, Holmes JW, Kramer CM. **Imaging left-ventricular mechanical activation in heart failure patients using cine DENSE MRI: validation and implications for cardiac resynchronization therapy**. *J Magn Reson Imaging* (2017) **46** 887-96. DOI: 10.1002/jmri.25613 21. D’Errico J. (2023) 22. Jamal S.. (2021) 23. Perotti LE, Verzhbinsky IA, Moulin K, Cork TE, Loecher M, Balzani D. **Estimating cardiomyofiber strain in vivo by solving a computational model**. *Med Image Anal* (2021) **68** 101932. DOI: 10.1016/j.media.2020.101932
--- title: 'Risk factors for the recurrence of cervical cancer using MR-based T1 mapping: A pilot study' authors: - Jie Liu - Shujian Li - Qinchen Cao - Yong Zhang - Marcel Dominik Nickel - Yanglei Wu - Jinxia Zhu - Jingliang Cheng journal: Frontiers in Oncology year: 2023 pmcid: PMC10061013 doi: 10.3389/fonc.2023.1133709 license: CC BY 4.0 --- # Risk factors for the recurrence of cervical cancer using MR-based T1 mapping: A pilot study ## Abstract ### Objectives This study aimed to identify risk factors for recurrence in patients with cervical cancer (CC) through quantitative T1 mapping. ### Methods A cohort of 107 patients histopathologically diagnosed with CC at our institution between May 2018 and April 2021 was categorized into surgical and non-surgical groups. Patients in each group were further divided into recurrence and non-recurrence subgroups depending on whether they showed recurrence or metastasis within 3 years of treatment. The longitudinal relaxation time (native T1) and apparent diffusion coefficient (ADC) value of the tumor were calculated. The differences between native T1 and ADC values of the recurrence and non-recurrence subgroups were analyzed, and receiver operating characteristic (ROC) curves were drawn for parameters with statistical differences. Logistic regression was performed for analysis of significant factors affecting CC recurrence. Recurrence-free survival rates were estimated by Kaplan–*Meier analysis* and compared using the log-rank test. ### Results Thirteen and 10 patients in the surgical and non-surgical groups, respectively, showed recurrence after treatment. There were significant differences in native T1 values between the recurrence and non-recurrence subgroups in the surgical and non-surgical groups ($P \leq 0.05$); however, there was no difference in ADC values ($P \leq 0.05$). The areas under the ROC curve of native T1 values for discriminating recurrence of CC after surgical and non-surgical treatment were 0.742 and 0.780, respectively. Logistic regression analysis indicated that native T1 values were risk factors for tumor recurrence in the surgical and non-surgical groups ($$P \leq 0.004$$ and 0.040, respectively). Compared with cut-offs, recurrence-free survival curves of patients with higher native T1 values of the two groups were significantly different from those with lower ones ($$P \leq 0.000$$ and 0.016, respectively). ### Conclusion Quantitative T1 mapping could help identify CC patients with a high risk of recurrence, supplementing information on tumor prognosis other than clinicopathological features and providing the basis for individualized treatment and follow-up schemes. ## Introduction Cervical cancer (CC) is the second leading gynecological malignancy that seriously affects women’s health worldwide [1]. According to a 2018 epidemiological investigation, approximately 570,000 women have CC every year, with a mortality rate of $54.6\%$ [2]. The International Federation of Gynecology and Obstetrics (FIGO) staging provides the basis for optimal CC treatment at the time of diagnosis. Different treatment options are recommended for each CC stage. Surgery is the first-line therapy for early-stage CC, whereas concurrent chemoradiotherapy (CCRT) is the primary treatment for advanced CC [3]. A previous study showed that the 5-year recurrence rate of CC is approximately $28\%$ [4], ranging from 10–$20\%$ for patients who underwent surgery [5, 6] and approximately $32\%$ for patients who did not undergo surgery [7]. In addition to the differentiation and heterogeneity of the tumor, recurrence is also related to inaccurate staging and insufficient evaluation in peripheral invasion [8]. The current clinical options for further treatment of recurrent lesions are limited, with the one-year survival rate ranging from 15–$20\%$ [9, 10]. Timely identification of patients with a high risk of recurrence will aid the development of individualized treatment and follow-up plans [11, 12]. Previous studies suggested that most risk factors, such as tumor size, lymph node metastasis, and parametrial invasion, could only be accurately evaluated based on postoperative pathologic examination and are, therefore, of limited value in guiding therapeutic decisions. Thus, it is necessary to seek reliable biomarkers to improve the capability of identifying patients with a high risk of recurrence before treatment. Medical imaging is of critical clinical importance in the diagnosis and prediction of cancer prognosis [7]. Of the numerous imaging methods used for examining patients with cancer, magnetic resonance imaging (MRI) is the best for evaluating the pathologic features and prognostic factors of CC owing to its high soft tissue resolution, safety, and diverse imaging modes and parameters [13, 14]. Several relevant studies based on functional MRI have been conducted to predict the prognosis of CC after definitive therapy. For example, in one retrospective study of 31 patients with CC treated with radiation therapy, the pre-treatment ADC mean values for primary CC tumors with recurrence were lower than those without recurrence [15]. However, Heo et al. demonstrated that the pre-treatment ADCmean values of CC tumors were significantly higher in the recurrence group than in the non-recurrence group [16]. Apart from the number and heterogeneity of the patient population and retrospective study design, the conflicting results of these previous studies may also be attributed to different MRI imaging protocols and non-standardized parameter settings. T1 mapping is a quantitative MRI diagnosis technology that is independent from technical implications [17]. Two techniques are used to acquire T1 maps; inversion recovery and saturation recovery [18]. The former technique is more widely used in clinical practice because of its demonstrated high accuracy [19]. The Look-Locker inversion recovery sequence is among the most efficient methods for T1 measurement as it can capture multiple images after each inversion pulse [20]. The T1 value, also known as the longitudinal relaxation time, is the decay constant for the exponential recovery of the longitudinal magnetization toward its equilibrium state [21]. Quantitative T1 mapping can directly reflect the microscopic alterations and potential pathophysiologic processes in tissues by measuring their T1 value [22]. In the early stages of most diseases, tissues show biochemical changes and increases in water content [23, 24]. Thus, T1 relaxation time, mainly determined based on interstitial tissue water [25], has been recommended as a biomarker for early diagnosis of diseases [26]. Lescher et al. [ 27] and Qin et al. [ 11] found that T1 mapping helps monitor tumor progression and prognosis in patients with glioblastoma and hepatocellular carcinoma, respectively. Other studies have confirmed that T1 mapping is beneficial for assessing the histopathological features of tumors and diagnosing tumor recurrence [28, 29]. However, although T1 mapping is increasingly used in tumor studies [30, 31], the impact of utilizing T1 values in assessing the prognosis of patients with CC has not been investigated. In this study, we used conventional diffusion-weighted imaging (DWI) as a reference to explore whether post-treatment recurrence of patients with CC can be reflected in MRI-based T1 mapping. ## Patient population A total of 153 patients who were histopathologically diagnosed with CC between May 2018 and April 2021 were enrolled in this prospective study. The inclusion criteria for this study were as follows: i) patients diagnosed with CC based on the results of histopathological staining, ii) patients who underwent surgery or standard CCRT within 1 month after MRI examination, and iii) patients with tumors ≥1 cm in size. The exclusion criteria were as follows: i) patients who underwent MRI less than 7 days after the cervical biopsy, ii) patients who received previous interventional treatment, and iii) patients with a history of other malignant tumors. The final study cohort consisted of 107 patients with CC (Table 1, Figure 1). The tumor types and degrees of pathological differentiation were classified based on the World Health Organization classification. This study was approved by the Ethics Committee of the First Affiliated Hospital of Zhengzhou University (approval number: 2021-KY-0133-002). Written informed consent was obtained from all the patients before this study's enrolment. ## Treatment Patients in the surgical group underwent radical hysterectomy and pelvic lymphadenectomy with or without adjuvant therapy. In contrast, those in the non-surgical group received volume-modulated radiotherapy of 45 Gy (total dose) administered at 1.8–2 Gy per session, five times per week, according to the National Comprehensive Cancer Network Guidelines for Cervical Cancer (version 3.2019) [32]. The specific irradiation target areas included the CC, parauterine tissue, and some lymph node drainage areas (including the internal, external, common iliac, and obturator lymph node chains). A 30 mg/m2 dose of cisplatin-sensitized chemotherapy was administered simultaneously with radiation. Intravenous drips were administered on radiotherapy days 1, 8, 15, 22, and 29. Intracavitary afterloading radiotherapy was administered as well. The total radiotherapy dose was 35 Gy, administered 5–7 Gy per session, 1–2 times per week. The total duration of treatment was 5–6 weeks. ## Follow-up All patients were clinically and radiologically followed up for 6 months to 3 years, and recurrence (including distant metastasis) and recurrence time were recorded. Recurrence was diagnosed through medical imaging (PET/CT, CT, or MRI) or pathological confirmation. Follow-up was conducted every 3–4 months in the first 2 years and every 6 months in the third year after treatment. The follow-up phase lasted until November 1, 2021. ## Magnetic resonance imaging protocols All MRI examinations were performed using a 3T MR scanner (MAGNETOM Skyra; SiemensHealthcare, Erlangen, Germany) with an 18-channel body coil and an integrated 32-channel spine matrix coil. Patients were asked to eat nothing for 4–6 hours before the examination but to drink some water to ensure that the bladder was moderately filled. The examination position was the head-first supine position. The image acquisition range was from the upper edge of the bilateral iliac bone wings to the level of the femoral neck. The patients were instructed to keep their bodies motionless and breathing calm during the scanning process. The MRI protocol included T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), T1 mapping, and DWI. The detailed MRI parameters were as follows: Both apparent diffusion coefficient (ADC) and T1 maps were automatically generated inline after image acquisition. ## Image data analysis and processing The T1 pre-enhancement (native T1) and ADC values of the patients with CC were independently analyzed by two experienced radiologists (with 5 and 10 years of experience in the diagnosis of gynecological tumors) using a post-processing workstation (syngo.via; Siemens Healthcare, Erlangen, Germany). The region of interest (ROI) was manually depicted on the T1 and ADC maps, regarding the conventional T2WI and DWI images, avoiding the cystic or necrotic areas within the lesion (Figures 2, 3). Native T1 and ADC values on all slices of the whole tumor were measured. The average values of these measurements were used for statistical analyses. The maximum tumor diameter was quantitatively evaluated on T2WI. Both radiologists were blinded to the clinicopathological findings. In the surgical group, lymph node metastasis was assessed based on the postoperative pathological findings. In the non-surgical group, any lymph nodes with a short-axis diameter of >10 mm identified on MRI were considered positive indications of metastatic lymph nodes. The diameters were measured using the transverse plane on T2WI, with a slice thickness of 5 mm. **Figure 2:** *Native T1 mapping and apparent diffusion coefficient (ADC) images of recurrent and nonrecurrent cervical cancer (CC) in the surgical group. (A, B) A 45-year-old patient with recurrence during the follow-up period. (A) Axial T1 mapping pseudo-color map and (B) axial ADC image. The native T1 and ADC values were 1619.20 ms and 0.68 × 10−3 mm2/s, respectively. (C, D) A 61-year-old patient without recurrence during the follow-up period. (C) Axial T1 mapping pseudo-color map and (D) axial ADC image. The native T1 and ADC values were 1480.19 ms and 0.82 × 10−3 mm2/s, respectively. The white arrows in A-D indicate the locations of the lesions.* **Figure 3:** *Native T1 mapping and apparent diffusion coefficient (ADC) images of recurrent and nonrecurrent cervical cancer (CC) in the non-surgical group. (A, B) A 55-year-old patient with recurrence during the follow-up period. (A) Axial T1 mapping pseudo-color map and (B) axial ADC image. The native T1 and ADC values were 1556.65 ms and 0.77 × 10−3 mm2/s, respectively. (C, D) A 51-year-old patient without recurrence during the follow-up period. (C) Axial T1 mapping pseudo-color map and (D) axial ADC image. The native T1 and ADC values were 1489.09 ms and 0.84 × 10−3 mm2/s, respectively. The white arrows in A–D indicate the locations of the lesions.* ## Statistical analysis Statistical analyses were performed using SPSS statistical software (version 22; SPSS, Chicago, IL, USA). The differences in clinicopathological variables between recurrence and non-recurrence subgroups in the surgical and non-surgical groups were compared using the Chi-square test. The normality of the distributions of all continuous variables was evaluated using the Kolmogorov–Smirnov test. The normally distributed variables are expressed as mean ± standard deviation. An independent samples t-test was used to compare the differences between the native T1 and ADC values of patients who showed post-treatment recurrence and those who did not. The diagnostic performances of statistically different parameter values were determined using receiver operating characteristic (ROC) curves, which were drawn using the MedCalc V19.0 software (MedCalc Software, Mariakerke, Belgium). The area under the curve (AUC), sensitivity, and specificity were calculated, and the cut-off value for predicting CC recurrence after treatment was obtained using the Youden index. Logistic regression analysis was performed to test the factors that affect the post-treatment recurrence of CC. The Kaplan–Meier method was used to compute the recurrence-free survival rate (RFSR), and the log-rank test was used to compare patient groups. The interobserver variability of the acquired quantitative values was analyzed using the intraclass correlation coefficient (ICC). P values <0.05 were considered statistically significant. ## Results A total of 107 patients (77 in the surgical group and 30 in the non-surgical group) were enrolled in this study. The median age of the patients was 52.1 years (range: 25–73 years). Based on the FIGO system, clinical staging of the tumors revealed that 25 patients had stage Ib cancer, 52 had stage IIa cancer, 10 had stage IIb cancer, 12 had stage III cancer, and eight had stage III-IV cancer. The patients were further categorized into recurrence and non-recurrence subgroups according to their follow-up results. Thirteen patients in the surgical group showed recurrence over 3–27 months of follow-up (average, 14.7 months; recurrence rate, $16.9\%$). Ten patients in the non-surgical group showed recurrence over 2–21 months of follow-up (average, 10.1 months; recurrence rate, $33.3\%$) (Table 1). The ICC of the native T1 values (ICC, 0.923; $95\%$ CI, 0.874–0.966), ADC values (ICC, 0.956; $95\%$ CI, 0.916–0.974), and maximum tumor size (ICC, 0.992; $95\%$ CI, 0.988–0.995) showed significant interobserver agreement [33]. Lymph node status was significantly different between the recurrence and non-recurrence subgroup in the surgical group ($P \leq 0.05$), while the FIGO stage, histology type, tumor differentiation, and maximum tumor size were not ($P \leq 0.05$) (Table 2). Regardless of the surgical or non-surgical group, the native T1 value of the recurrence subgroup was significantly higher than that of the non-recurrence subgroup ($P \leq 0.05$). However, there was no significant difference between the ADC values of the recurrence and non-recurrence subgroups in both the surgical and non-surgical groups ($P \leq 0.05$) (Table 3). The AUC of the native T1 value for the prediction of postoperative CC recurrence was 0.742. When native T1=1480.19 ms, its sensitivity and specificity were $76.9\%$ and $70.3\%$, respectively. The AUC of the native T1 value for predicting the recurrence of CC after non-surgical treatment was 0.780. When native T1=1494.00 ms, its sensitivity and specificity were $80.0\%$ and $75.0\%$, respectively (Figure 4). Finally, logistic regression analysis showed that associated risk factors included lymph node invasion and the native T1 value in the surgical group ($$P \leq 0.003$$ and 0.004, respectively); meanwhile, only the native T1 value was a significant risk factor of recurrence in patients with CC after non-surgical treatment ($$P \leq 0.040$$) (Table 4). Regardless of the surgical group or the non-surgical group, recurrence-free survival rates of CC with native T1 values higher than the optimal cut-offs (1480.19 and 1494.00 ms, respectively) were significantly lower compared with those with values lower than the optimal cut-offs ($$P \leq 0.000$$ and 0.016, respectively) (Figure 5). ## Discussion Previous studies have demonstrated that FIGO stage, histology type, histology grade, tumor size, and lymph node invasion were important prognostic factors of CC, but these variables are insufficient to accurately predict clinical outcomes [7, 12, 14]. In this study, we assessed the feasibility of T1 mapping to reflect the recurrence of CC. The results showed that lymph node invasion was significantly associated with tumor recurrence only in the surgical group ($P \leq 0.05$). Moreover, as quantitative parameters of T1 mapping, native T1 values of tumors in the surgical and non-surgical groups could be used to effectively identify patients with a high risk of relapse after therapy ($P \leq 0.05$). However, the ADC values of the two subgroups were not significantly different ($P \leq 0.05$). The results also indicated that patients with higher native T1 values (≥cut-offs) tend to have higher incidences of CC recurrence. As is well elucidated in the literature, lymph node metastasis plays an important role in determining the oncological prognosis and treatment method in patients with CC [34]. Even in early-stage CC, Tsunoda et al. found that the incidence rate of lymph node metastasis ranges from 17–$33\%$ [35]. Üreyen et al. reported on 27 early-stage CC patients with recurrence and found that lymph node invasion was significantly relevant to recurrence after surgical treatment [36]. Another study by Mabuchi et al. revealed that it was the presence instead of the number and location of lymph node metastasis that independently affected the survival in patients with CC treated by salvage hysterectomy plus lymphadenectomy [37]. Our result showed that the presence of metastatic lymph nodes was a significant risk factor for the recurrence of CC in the surgical group, which was in line with the previously mentioned reports. It is reported that more than $80\%$ of metastatic lymph nodes are smaller than 10 mm and more than $50\%$ are smaller than 5 mm [38]. Hence, the size criterion for evaluating lymph node status by radiography has some limitations, potentially explaining why lymph node metastasis was not a significant risk factor in the non-surgical group. Native T1 values represent critical physical parameters of MRI and are related to many factors, such as tissue water content, cell density, and macromolecular concentration [39]. These values are particularly sensitive to alterations in water content and can distinguish microscopic changes in tissues that are not easily displayed on conventional T1WI [22]. Olsen et al. [ 40] proposed that native T1 values significantly correlate with the Ki-67 index, a biomarker of tumor cell proliferation activity [41]. Due to variations in tumor cell proliferation activities, water content varies between tissues, leading to differences in the corresponding native T1 values [27]. Moreover, previous research on liver cancer has shown that recurrence is associated with tumor heterogeneity and type [42, 43]. It has been reported that a decrease in tumor heterogeneity generally corresponds to improved outcomes [44]. Ditmer et al. [ 45] used texture analysis to discriminate high- and low-grade gliomas quantitatively and proposed that tumor grade is strongly correlated with heterogeneity. Adams et al. [ 30] analyzed the native T1 values of patients with renal clear cell carcinoma and showed that native T1 values gradually increase with increasing grades. They also reported that there was a statistical difference between the low-level and high-level groups in their study ($P \leq 0.05$). Thus, we speculated that the increased native T1 values of the patients in the recurrent subgroup in the present study might be related to increased tissue water content, active cell proliferation, and evident heterogeneity. In addition, we found that there was no significant difference in ADC values between the recurrence and non-recurrence subgroups in the surgical and non-surgical groups. Although DWI has been widely used to predict tumor treatment outcomes, Somoye et al. [ 46] did not find any evidence of a relationship between survival in patients with CC and pre-treatment baseline ADCmean and suggested that it was insufficient for ADCmean to predict the prognosis of tumors. We speculated that the absence of significant difference might be due to the integrated effects of diffusion and microperfusion on ADC values calculated based on the Gaussian distribution model [15]. The present study's logistic regression analysis showed that the native T1 values helped identify patients with CC at high risk for recurrence. Furthermore, we calculated the cut-off value of native T1 using the Youden index in the ROC curve and analyzed the patients who received standardized treatment for CC. The results indicated that the optimal cutoff native T1 value for predicting the recurrence of CC after surgical and non-surgical treatment is 1480.19 ms and 1494.00 ms, respectively. According to Kaplan–Meier analysis, if the native T1 value of a patient who underwent surgery is ≥1480.19 ms, and that of a patient who received non-surgical treatment is ≥1494.00 ms, clinicians should be highly vigilant and strongly consider the possibility of recurrence; this will facilitate the timely adjustment of the subsequent treatment plan and time interval for the follow-up to reduce the risk of treatment failure and improve the quality of life and survival rates of patients with CC. This study had some limitations. First, the study population was relatively small, especially the number of patients in the recurrence subgroup. Second, the ROI of lesions may contain small necrotic areas that are invisible to the naked eye, which may have affected the accuracy of measurements. Further studies on whole lesion texture analysis based on T1 mapping are needed to rectify any effect of selection bias on the results of the present study. Third, the follow-up duration was relatively short. Furthermore, only one scanner and a single T1 mapping sequence were used for image acquisition. In addition, reproducibility across different MRI devices and imaging protocols was not tested; thus, the results may not be generalizable. In the future, we will validate our findings and promote the clinical application of this technique using multicenter studies with larger patient cohorts and long-term follow-up periods. Compared with ADC, the pre-treatment native T1 value is a significant risk factor for CC recurrence. Furthermore, risk assessment of recurrence using a noninvasive method will provide a rational basis for further improvement of therapeutics. ## Data availability statement The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author. ## Ethics statement The studies involving human participants were reviewed and approved by Ethics Committee of the First Affiliated Hospital of Zhengzhou University. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article. ## Author contributions Guarantor of integrity of the entire study: JL and SL. Study concepts and design: JL and JC. Literature research: JL. Clinical studies: JL, SL, and QC. Experimental studies/data analysis: JL, SL, QC, YW, and JZ. Statistical analysis: JL. Manuscript preparation: JL. Manuscript editing: YZ, MN, YW, and JZ. All authors read and approved the final manuscript. All authors contributed to the article and approved the submitted version. ## Conflict of interest Authors MN, YW, and JZ are employed by Siemens Healthcare. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## References 1. Tsikouras P, Zervoudis S, Manav B, Tomara E, Iatrakis G, Romanidis C. **Cervical cancer: screening, diagnosis and staging**. *J Buon* (2016) **21** 2. Arbyn M, Weiderpass E, Bruni L, de Sanjosé S, Saraiya M, Ferlay J. **Estimates of incidence and mortality of cervical cancer in 2018: a worldwide analysis**. *Lancet Glob Health* (2020) **8**. DOI: 10.1016/S2214-109X(19)30482-6 3. Wang PY, Thapa D, Wu GY, Sun Q, Cai H, Tuo F. **A study on diffusion and kurtosis features of cervical cancer based on non-Gaussian diffusion weighted model**. *Magn Reson Imaging* (2018) **47**. DOI: 10.1016/j.mri.2017.10.016 4. Benedet JL, Odicino F, Maisonneuve P, Beller U, Creasman WT, Heintz AP. **Carcinoma of the cervix uteri**. *J Epidemiol Biostat* (2001) **6** 7-43. DOI: 10.1016/S0020-7292(03)90115-9 5. Landoni F, Maneo A, Colombo A, Placa F, Milani R, Perego P. **Randomised study of radical surgery versus radiotherapy for stage ib–IIa cervical cancer**. *Lancet* (1997) **350**. DOI: 10.1016/S0140-6736(97)02250-2 6. Keys HM, Bundy BN, Stchman FB, Muderspach LI, Chafe WE, Suggs CL. **Cisplatin, radiation and adjuvant hysterectomy compared with radiation and adjuvant hysterectomy for bulky stage ib cervical carcinoma**. *N Engl J Med* (1999) **340**. DOI: 10.1056/NEJM199904153401503 7. Park SH, Hahm MH, Bae BK, Chong GO, Jeong SY, Na S. **Magnetic resonance imaging features of tumor and lymph node to predict clinical outcome in node-positive cervical cancer: a retrospective analysis**. *Radiat Oncol* (2020) **15**. DOI: 10.1186/s13014-020-01502-w 8. Park H, Kim KA, Jung JH, Rhie J, Choi SY. **MRI Features and texture analysis for the early prediction of therapeutic response to neoadjuvant chemoradiotherapy and tumor recurrence of locally advanced rectal cancer**. *Eur Radiol* (2020) **30**. DOI: 10.1007/s00330-020-06835-4 9. Friedlander M. **Guidelines for the treatment of recurrent and metastatic cervical cancer**. *Oncologist* (2002) **7**. DOI: 10.1634/theoncologist.2002-0342 10. Li N, Sun Q, Yu Z, Gao X, Pan W, Wan X. **Nuclear-targeted photothermal therapy prevents cancer recurrence with near-infrared triggered copper sulfide nanoparticles**. *ACS Nano* (2018) **12**. DOI: 10.1021/acsnano.7b06870 11. Qin XL, Yang TF, Huang ZK, Long L, Zhou Z, Li W. **Hepatocellular carcinoma grading and recurrence prediction using T1 mapping on gadolinium-ethoxybenzyl diethylenetriamine pentaacetic acid-enhanced magnetic resonance imaging**. *Oncol Lett* (2019) **18**. DOI: 10.3892/ol.2019.10557 12. Kim H, Cho WK, Kim YJ, Kim YS, Park W. **Significance of the number of high-risk factors in patients with cervical cancer treated with radical hysterectomy and concurrent chemoradiotherapy**. *Gynecol Oncol* (2020) **157**. DOI: 10.1016/j.ygyno.2020.02.031 13. Li XS, Wang P, Li DC, Zhu H, Meng L, Song Y. **Intravoxel incoherent motion MR imaging of early cervical carcinoma: correlation between imaging parameters and tumor-stroma ratio**. *Eur Radiol* (2018) **28**. DOI: 10.1007/s00330-017-5183-3 14. Li D, Xu XX, Yan DD, Yuan S, Ni J, Lou H. **Prognostic factors affecting survival and recurrence in patients with early cervical squamous cell cancer following radical hysterectomy**. *J Int Med Res* (2020) **48**. DOI: 10.1177/0300060519889741 15. Okubo M, Itonaga T, Saito T, Shiraishi S, Yunaiyama D, Mikami R. **Predicting factors for primary cervical cancer recurrence after definitive radiation therapy**. *BJR Open* (2021) **3**. DOI: 10.1259/bjro.20210050 16. Heo SH, Shin SS, Kim JW, Lim HS, Jeong YY, Kang WD. **Pre-treatment diffusion-weighted MR imaging for predicting tumor recurrence in uterine cervical cancer treated with concurrent chemoradiation: value of histogram analysis of apparent diffusion coefficients**. *Korean J Radiol* (2013) **14**. DOI: 10.3348/kjr.2013.14.4.616 17. Staniszewski M, Klose U. **Improvement of fast model-based acceleration of parameter look-locker T1 mapping**. *Sensors (Basel)* (2019) **19**. DOI: 10.3390/s19245371 18. Fernandes JL, Rochitte CE. **T1 mapping technique and applications**. *Magn Reson Imaging Clin N Am* (2015) **23** 25-34. DOI: 10.1016/j.mric.2014.08.007 19. Gaspar AS, Maltês S, Marques H, Nunes RG, Ferreira A. **Myocardial T1 mapping with magnetic resonance imaging - a useful tool to understand the diseased heart**. *Rev Port Cardiol* (2022) **41**. DOI: 10.1016/j.repc.2021.04.005 20. Kim PK, Hong YJ, Im DJ, Suh YJ, Park CH, Kim JY. **Myocardial T1 and T2 mapping: Techniquesand clinical applications**. *Korean J Radiol* (2017) **18**. DOI: 10.3348/kjr.2017.18.1.113 21. Taylor AJ, Salerno M, Dharmakumar R, Jerosch-Herold M. **T1 mapping: Basic techniques and clinical applications**. *JACC Cardiovasc Imaging* (2016) **9** 67-81. DOI: 10.1016/j.jcmg.2015.11.005 22. Ma JC, Xu XT, Wang SY, Wang R, Yu N. **Quantitative assessment of early type 2 diabetic cataracts using T1, T2-mapping techniques**. *Br J Radiol* (2019) **92** 20181030. DOI: 10.1259/bjr.20181030 23. Chou MC, Tsai PH, Huang GS, Lee HS, Lee CH, Lin MH. **Correlation between the Mr T2 value at 4.7 T and relative water content in articular cartilage in experimental osteoarthritis induced by ACL transection**. *Osteoarthritis Cartilage* (2009) **17**. DOI: 10.1016/j.joca.2008.09.009 24. Friedrich MG. **Myocardial edema–a new clinical entity**. *Nat Rev Cardiol* (2010) **7**. DOI: 10.1038/nrcardio.2010.28 25. Jurcoane A, Wagner M, Schmidt C, Mayer C, Gracien RM, Hirschmann M. **Within-lesion differences in quantitative MRI parameters predict contrast enhancement in multiple sclerosis**. *J Magn Reson Imaging* (2013) **38**. DOI: 10.1002/jmri.24107 26. Kellman P, Hansen MS. **T1-mapping in the heart: accuracy and precision**. *J Cardiovasc Magn Reson* (2014) **16**. DOI: 10.1186/1532-429X-16-2 27. Lescher S, Jurcoane A, Veit A, Bähr O, Deichmann R, Hattingen E. **Quantitative T1 and T2 mapping in recurrent glioblastomas under bevacizumab: earlier detection of tumor progression compared to conventional MRI**. *Neuroradiology* (2015) **57** 11-20. DOI: 10.1007/s00234-014-1445-9 28. Zhou ZP, Long LL, Qiu WJ, Cheng G, Huang LJ, Yang TF. **Evaluating segmental liver function using T1 mapping on gd-EOB-DTPA-enhanced MRI with a 3.0 Tesla**. *BMC Med Imaging* (2017) **17** 20. DOI: 10.1186/s12880-017-0192-x 29. Wang B, Zhang Y, Zhao B, Zhao P, Ge M, Gao M. **Postcontrast T1 mapping for differential diagnosis of recurrence and radionecrosis after gamma knife radiosurgery for brain metastasis**. *AJNR Am J Neuroradiol* (2018) **39**. DOI: 10.3174/ajnr.A5643 30. Adams LC, Ralla B, Jurmeister P, Bressem KK, Fahlenkamp UL, Hamm B. **Native T1 mapping as an**. *Invest Radiol* (2019) **54**. DOI: 10.1097/RLI.0000000000000515 31. Wang S, Li J, Zhu D, Hua T, Zhao B. **Contrast-enhanced magnetic resonance (MR) T1 mapping with low-dose gadolinium-diethylenetriamine pentaacetic acid (Gd-DTPA) is promising in identifying clear cell renal cell carcinoma histopathological grade and differentiating fat-poor angiomyolipoma**. *Quant Imaging Med Surg* (2020) **10**. DOI: 10.21037/qims-19-723 32. Koh WJ, Abu-Rustum NR, Bean S, Bradley K, Campos SM, Cho KR. **Cervical cancer, version 3.2019, NCCN clinical practice guidelines in oncology**. *J Natl Compr Canc Netw* (2019) **17** 64-84. DOI: 10.6004/jnccn.2019.0001 33. Li SJ, Zhang ZX, Liu J, Zhang F, Yang M, Lu H. **The feasibility of a radial turbo-spin-echo T2 mapping for preoperative prediction of the histological grade and lymphovascular space invasion of cervical squamous cell carcinoma**. *Eur J Radiol* (2021) **139**. DOI: 10.1016/j.ejrad.2021.109684 34. Kido A, Nakamoto Y. **Implications of the new FIGO staging and the role of imaging in cervical cancer**. *Br J Radiol* (2021) **94**. DOI: 10.1259/bjr.20201342 35. Tsunoda AT, Marnitz S, Soares Nunes J, Mattos de Cunha Andrade CE, Scapulatempo Neto C, Blohmer JU. **Incidence of histologically proven pelvic and para-aortic lymph node metastases and rate of upstaging in patients with locally advanced cervical cancer: results of a prospective randomized trial**. *Oncology* (2017) **92**. DOI: 10.1159/000453666 36. Üreyen I, Aksoy Ü, Dündar B, Tapisiz ÖL, Karalök MA, Turan AT. **Does lymph node involvement affect the patterns of recurrence in stage IB cervical cancer**. *Turk J Med Sci* (2014) **44**. DOI: 10.3906/sag-1212-3 37. Mabuchi S, Komura N, Kodama M, Maeda M, Matsumoto Y, Kamiura S. **Significance of the number and the location of metastatic lymph nodes in locally recurrent or persistent cervical cancer patients treated with salvage hysterectomy plus lymphadenectomy**. *Curr Oncol* (2022) **29**. DOI: 10.3390/curroncol29070385 38. Benedetti-Panici P, Maneschi F, Scambia G, Greggi S, Cutillo G, D’Andrea G. **Lymphatic spread of cervical cancer: an anatomical and pathological study based on 225 radical hysterectomies with systematic pelvic and aortic lymphadenectomy**. *Gynecol Oncol* (1996) **62** 19-24. DOI: 10.1006/gyno.1996.0184 39. Hueper K, Peperhove M, Rong S, Gerstenberg J, Mengel M, Meier M. **T1-mapping for assessment of ischemia-induced acute kidney injury and prediction of chronic kidney disease in mice**. *Eur Radiol* (2014) **24**. DOI: 10.1007/s00330-014-3250-6 40. Olsen G, Lyng H, Tufto I, Solberg K, Bjørnaes I, Rofstad EK. **Measurement of proliferation activity in human melanoma xenografts by magnetic resonance imaging**. *Magn Reson Imaging* (1999) **17** 393-402. DOI: 10.1016/s0730-725x(98)00175-1 41. Su C, Liu C, Zhao L, Jiang J, Zhang J, Li S. **Amide proton transfer imaging allows detection of glioma grades and tumor proliferation: comparison with ki-67 expression and proton MR spectroscopy imaging**. *AJNR Am J Neuroradiol* (2017) **38**. DOI: 10.3174/ajnr.A5301 42. Liu K, Hao M, Ouyang Y, Zheng J, Chen D. **CD133**. *Sci Rep* (2017) **7**. DOI: 10.1038/srep41499 43. Barreto SG, Brooke-Smith M, Dolan P, Wilson TG, Padbury RTA, Chen JWC. **Cirrhosis and microvascular invasion predict outcomes in hepatocellular carcinoma**. *ANZ J Surg* (2013) **83**. DOI: 10.1111/j.1445-2197.2012.06196.x 44. Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ. **CT texture analysis: definitions, applications, biologic correlates, and challenges**. *RadioGraphics* (2017) **37**. DOI: 10.1148/rg.2017170056 45. Ditmer A, Zhang B, Shujaat T, Pavlina A, Luibrand N, Gaskill-Shipley M. **Diagnostic accuracy of MRI texture analysis for grading gliomas**. *J Neurooncol* (2018) **140**. DOI: 10.1007/s11060-018-2984-4 46. Somoye G, Harry V, Semple S, Plataniotis G, Scott N, Gilbert FJ. **Early diffusion weighted magnetic resonance imaging can predict survival in women with locally advanced cancer of the cervix treated with combined chemo-radiation**. *Eur Radiol* (2012) **22**. DOI: 10.1007/s00330-012-2496-0
--- title: β-mannanase supplementation in diets reduced in 85 kcal metabolizable energy/kg containing xylanase-phytase improves gain to feed ratio, nutrient usage, and backfat thickness in finisher pigs authors: - Jansller Luiz Genova - Paulo Evaristo Rupolo - Liliana Bury de Azevedo - Daniela Henz - Silvana Teixeira Carvalho - Marcos Kipper - Giovana de Arruda Castelo Gonçalves - Hellen Lazarino Oliveira Vilela - Tiago Junior Pasquetti - Newton Tavares Escocard de Oliveira - Andrei Roberto Manelli Dietrich - Paulo Levi de Oliveira Carvalho journal: Frontiers in Veterinary Science year: 2023 pmcid: PMC10061018 doi: 10.3389/fvets.2023.1144692 license: CC BY 4.0 --- # β-mannanase supplementation in diets reduced in 85 kcal metabolizable energy/kg containing xylanase-phytase improves gain to feed ratio, nutrient usage, and backfat thickness in finisher pigs ## Abstract This study aimed to assess the effects of β-mannanase supplementation in metabolizable energy (ME)-reduced diets containing xylanase-phytase on performance, fecal score, blood biochemical and immunological profile, apparent total tract digestibility (ATTD), digesta passage rate, fecal microbiome, carcass traits and meat quality in finisher pigs ($$n = 40$$ entire male hybrid, 26.0 ± 0.9 kg) randomly assigned to 1 of 4 dietary treatments: a control diet containing isolated phytase and xylanase valued at 40 kcal of ME/kg (CD0), CD0 + β-mannanase (0.3 g/kg valued at 30 kcal of ME/kg) (CD70), CD0 + β-mannanase (0.3 g/kg valued at 45 kcal of ME/kg) (CD85), and CD0 + β-mannanase (0.3 g/kg valued at 60 kcal of ME/kg) (CD100), with 10 pen replicates. Pigs fed CD0 diet showed ($$P \leq 0.002$$) greater ADFI. However, pigs fed CD0 diet showed ($$P \leq 0.009$$) lower G:F than those provided CD70 or CD85 diets. A greater ($P \leq 0.001$) superoxide dismutase concentration was observed in pigs fed CD70 diet. Pigs fed CD85 diet showed ($$P \leq 0.002$$) greater digestible protein than pigs fed CD0 or CD100 diets. Pigs fed CD70 diet showed an increase of $11.3\%$ in digestible protein than those fed CD0 diet. In addition, greater ($P \leq 0.001$) digestible energy was observed in pigs fed CD85 diet. Pigs fed CD0 or CD100 diets showed greater ($P \leq 0.05$) Firmicutes:Bacteroidota ratio than those fed CD85 diet. The Muribaculaceae was more abundant ($$P \leq 0.030$$) in pigs fed CD70 diet than in those fed CD0 diet. The Prevotella was more abundant ($$P \leq 0.045$$) in pigs fed CD85 diet than in those fed CD100 diet. In conclusion, β-mannanase supplementation in diets containing xylanase-phytase allows reducing 85 kcal of ME/kg because it improves gain to feed ratio, energy and protein usage, and backfat thickness without metabolic and intestinal ecosystem disorders in finisher pigs. ## 1. Introduction Plant-based ingredients widely used in the diets of pigs possess significant amounts of antinutritional factors [1, 2]. These antinutritional substances, such as β-mannans (1, 3–5), phytate molecules [3, 6], and xylans [4, 6], are not digested by endogenous enzymes, and compromise the use of nutrients and energy metabolism in non-ruminant animals [5]. Based on this, dietary supplementation of β-mannanase has been attributed to the hydrolysis of β-mannans reducing the immune response capacity induced by feeding [2], and energy expenditure for immune system activation [5]. This nutritional strategy also allows the use of phytase enzyme, known to improve the availability of phosphorus and calcium in diets containing phytate molecules [3], and providing additional energy and improving energy efficiency [7]. In addition, the antinutritional effects of non-starch polysaccharides (NSP) provided by xylans highlight the importance of using the xylanase enzyme [1]. Xylanase breakdowns the plant cell wall releasing nutrients within the cell and reduce digesta viscosity [6]. Diets supplemented with a blend of these enzymes may be of economic-environmental-nutritional interest. Indeed, β-mannanase has been previously reported to reduce feed to gain ratio and increase nutrient ATTD [5]. Greater phosphorus and lower neutral detergent fiber digestibility were reported when combined xylanase-phytase were supplemented in the diet of grower pigs [6]. Greater blood glucose concentration and lower backfat thickness were observed in finisher pigs fed β-mannanase-xylanase [8]; however, no effect on ATTD in grower pigs provided diets containing phytase and β-mannanase were observed [3]. To date, no studies have been conducted to assess the effects of the dietary association of these enzymes on the fecal microbiome, total digesta passage rate, and fecal consistency score in finisher pigs. Here, a study was conducted based on the hypothesis that β-mannanase supplementation in ME-reduced diets improves ATTD, intestinal digesta viscosity, and intestinal microbiome, supporting growth performance and health compared to the diet without β-mannanase supplementation. Therefore, this study assessed the effects of β-mannanase associated with xylanase-phytase on growth performance, fecal score, biochemical and immunological blood profile, ATTD, total digesta passage rate, fecal microbiome, carcass traits, and meat quality in finisher pigs fed ME-reduced diets. ## 2.1. Animals, experimental design, housing, and dietary treatments A total of 40 entire male hybrid pigs (26.0 ± 0.9 kg BW) from a commercial line (Landrace × Large White) were used. Pigs were allotted to 1 of 4 dietary treatments in a randomized complete block design with 10 pen replicates and 1 animal per pen as the experimental unit. Blocks were based on the initial BW of pigs. At the beginning of the experiment, animals were weighed and identified with numbered ear tags. Pigs were housed in a masonry facility with 2 rows (with a central aisle) of concrete floor pens (6.3 m2). All pens were equipped with a semiautomatic front feeder and a nipple waterer. Room temperature and relative humidity were recorded by a data logger (Hygro-Thermometer, model RT811) located in the middle of the experimental facility. Temperature and ventilation were controlled via side curtains and trees on both sides of the facility. Room temperature and relative humidity averaged 20.4 ± 6.6°C and 63.6 ± $19.3\%$, respectively. The experimental period lasted 52 days and was divided into 2 phases: finisher I (d 0 to 22) and finisher II (d 22 to 52). Diets (Table 1) were formulated to meet the nutritional requirements of pigs in each phase [9] and offered as mash, and ad libitum. All diets were corn- and soybean meal-based with industrial amino acids, and were isonutritional with variations only in soybean oil and inert content. **Table 1** | Item | Finisher I | Finisher I.1 | Finisher I.2 | Finisher I.3 | Finisher II | Finisher II.1 | Finisher II.2 | Finisher II.3 | | --- | --- | --- | --- | --- | --- | --- | --- | --- | | | CD0 | CD70 | CD85 | CD100 | CD0 | CD70 | CD85 | CD100 | | Ingredients (%) | Ingredients (%) | Ingredients (%) | Ingredients (%) | Ingredients (%) | Ingredients (%) | Ingredients (%) | Ingredients (%) | Ingredients (%) | | Ground corn, 7.86% | 79.35 | 79.35 | 79.35 | 79.35 | 90.66 | 90.66 | 90.66 | 90.66 | | Soybean meal, 45.4% | 16.23 | 16.23 | 16.23 | 16.23 | 4.76 | 4.76 | 4.76 | 4.76 | | Dicalcium phosphate | 1.23 | 1.23 | 1.23 | 1.23 | 0.97 | 0.97 | 0.97 | 0.97 | | Limestone | 0.52 | 0.52 | 0.52 | 0.52 | 0.45 | 0.45 | 0.45 | 0.45 | | Inert (kaolin) | - | 0.33 | 0.51 | 0.69 | 0.59 | 0.92 | 1.10 | 1.28 | | Soybean oil | 1.03 | 0.67 | 0.49 | 1.03 | 0.72 | 0.36 | 0.19 | - | | Sodium chloride | 0.38 | 0.38 | 0.38 | 0.38 | 0.37 | 0.37 | 0.37 | 0.37 | | Premix1 | 0.30 | 0.30 | 0.30 | 0.30 | 0.30 | 0.30 | 0.30 | 0.30 | | Lysine sulfate, 54.6% | 0.57 | 0.57 | 0.57 | 0.57 | 0.71 | 0.71 | 0.71 | 0.71 | | DL-methionine, 99.5% | 0.12 | 0.12 | 0.12 | 0.12 | 0.10 | 0.10 | 0.10 | 0.10 | | L-threonine, 96.8% | 0.15 | 0.15 | 0.15 | 0.15 | 0.18 | 0.18 | 0.18 | 0.18 | | L-tryptophan, 99% | 0.03 | 0.03 | 0.03 | 0.03 | 0.05 | 0.05 | 0.05 | 0.05 | | L-valine, 95.5% | 0.02 | 0.02 | 0.02 | 0.02 | 0.08 | 0.08 | 0.08 | 0.08 | | β-mannanase | - | 0.03 | 0.03 | 0.03 | - | 0.03 | 0.03 | 0.03 | | Enramycin2 | 0.006 | 0.006 | 0.006 | 0.006 | 0.006 | 0.006 | 0.006 | 0.006 | | Calculated composition | Calculated composition | Calculated composition | Calculated composition | Calculated composition | Calculated composition | Calculated composition | Calculated composition | Calculated composition | | Metabolizable energy, kcal/kg | 3310 | 3280 | 3265 | 3250 | 3310 | 3280 | 3265 | 3250 | | Crude protein, % | 14.20 | 14.20 | 14.20 | 14.20 | 9.95 | 9.95 | 9.95 | 9.95 | | Lysine SID3, % | 0.89 | 0.89 | 0.89 | 0.89 | 0.69 | 0.69 | 0.69 | 0.69 | | Methionine + cysteine SID, % | 0.53 | 0.53 | 0.53 | 0.53 | 0.41 | 0.41 | 0.41 | 0.41 | | Threonine SID, % | 0.58 | 0.58 | 0.58 | 0.58 | 0.45 | 0.45 | 0.45 | 0.45 | | Tryptophan SID, % | 0.18 | 0.18 | 0.18 | 0.18 | 0.13 | 0.13 | 0.13 | 0.13 | | Valine SID, % | 0.62 | 0.62 | 0.62 | 0.62 | 0.48 | 0.48 | 0.48 | 0.48 | | Total calcium, % | 0.57 | 0.57 | 0.57 | 0.57 | 0.44 | 0.44 | 0.44 | 0.44 | | STTD phosphorus4, % | 0.28 | 0.28 | 0.28 | 0.28 | 0.21 | 0.21 | 0.21 | 0.21 | | Total sodium, % | 0.16 | 0.16 | 0.16 | 0.16 | 0.15 | 0.15 | 0.15 | 0.15 | Dietary treatments were: [1] a control diet containing isolated phytase and xylanase valued at 40 kcal of ME/kg (CD0), [2] CD0 + β-mannanase (0.3 g/kg valued at 30 kcal of ME/kg) (CD70), [3] CD0 + β-mannanase (0.3 g/kg valued at 45 kcal of ME/kg) (CD85), and [4] CD0 + β-mannanase (0.3 g/kg valued at 60 kcal of ME/kg) (CD100). ## 2.2. Traits of the tested enzymes Xylanase (Sunhy Biology Co., Ltd, Wuhan, HB, China; registration no. PR-08978 03462) was a product obtained from Trichoderma longibrachiatum. A U of xylanase is the amount of enzyme that releases 1 micromol of reducing sugar from a xylan solution (5 mg/mL) at 37°C and pH 5.5. Phytase (Sunhy Biology Co., Ltd, Wuhan, HB, China; registration no. PR 000267-4.000005) was a product from Aspergillus niger with the activity of 1,000 U/g of dry solid at 37°C and pH 5.5. β-mannanase (Elanco Animal Health, Inc., São Paulo, SP, Brazil; registration no. SP-59122 30011, HemicellTM HT) was obtained from Paenibacillus lentus. A U of β-mannanase is the amount of enzyme that releases 0.72 mcg of reducing sugars (equivalent to D-mannose) per min from goma locust (mannans concentration of $88\%$) at 40°C and pH 7.5. ## 2.3. Growth performance and fecal consistency score Animals had free access to diets and water throughout the experiment. Offered diets and leftovers were recorded daily using a digital scale (model UL-50, DIGI-TRON, Curitiba, PR, Brazil) to determine the average daily feed intake (ADFI, g/day). Pigs were weighed at the beginning and end of each experimental phase using a 2 bars digital scale (model ULB-3000, IWM bivolt, Curitiba, PR, Brazil). Initial BW (IBW, kg), final BW (FBW, kg), average daily gain (ADG, g/day), and gain to feed ratio (G:F, g:g) were determined. Fecal consistency score was assessed via partial feces collection at the end of finisher phases. Before feces collection, all pens (08:00) were cleaned and animals were monitored for a 12-h period. During this period, fecal samples were collected right after defecation, except for the lower part that was in contact with the floor. Feces were packed in plastic bags and kept in a thermal box (4°C) until the end of the collection period. Then, the samples were homogenized and 2 subsamples of 110 g each were weighed in a scale (model M4102, Bel engineering, Monza, Italy) and dried in a forced-air oven (Tecnalbrand, SF-325 NM model; Piracicaba, SP, Brazil) at 55°C for 72 h for dry matter determination [10]. Values were tabulated and classified according to fecal consistency, following the adapted methodology [11]. ## 2.4. Blood sampling and blood profile analysis Animals fasted for 8 h at the end of the finisher II phase. Blood samples (≅10 mL) were withdrawn from the anterior cranial vena cava using 1.2 × 40 mm needles and 20 mL syringes. Samples were transferred to 1 of 3 tubes containing potassium fluoride, EDTA, or no anticoagulant. All tubes were previously identified, placed into a thermal box (4°C), and sent to the blood laboratory for further analysis. Plasma or serum was isolated from blood by centrifugation (Centrilab analog centrifuge, model 80-2B) at 3,000 g for 10 min. Then, ≅3 mL of plasma or serum were transferred to previously identified polyethylene tubes (Eppendorf-type) and stored at −20°C until analysis of urea (enzymatic-colorimetric method), glucose (enzymatic-colorimetric method), total cholesterol (enzymatic-colorimetric method), total protein (enzymatic-biuret method), and albumin (bromocresol green colorimetric) of 10 animals per treatment. All analyses were performed in the blood laboratory of *Unioeste via* spectrophotometry with the aid of an analyzer (Bel SPECTRO S05) using commercial kits (Gold Analisa Diagnóstica—Belo Horizonte, MG, Brazil). Globulin was calculated as the difference between total protein and plasma albumin. Blood samples from 8 animals per treatment were stored at −80°C and sent to a private laboratory (Curitiba, PR, Brazil) where serum concentrations of superoxide dismutase (SOD), glutathione S-transferase, and immunoglobulins M were determined via the immunoturbidimetry method. ## 2.5. Apparent total tract digestibility and total digesta passage rate The insoluble acid ash marker (IAA, celite®) was added to the diets (10 g/kg diet) at the end of the finisher II phase to assess ATTD using partial feces collection (indirect method) [12]. The diets containing the marker were homogenized in a vertical mixer for 10 min. These diets were fed to pigs for 3 days before feces collection. On the fourth day, partial feces collection was performed following the adapted methodology [13]. The beginning and end of the diet supply and the feed intake per pen were recorded. Feces were collected for 12 h on the last day of the supply of the diets containing the marker. During collection, feces were packed in polyethylene plastic bags (previously identified) and kept in thermal boxes containing ice (4°C). After this period, the feces were stored at −18°C for further analysis. Afterward, the samples were thawed and homogenized. Two subsamples (110 g each) were weighed in a scale (bel engineering, model M4102, Monza, Italy) and dried in a forced-air oven (Tecnalbrand, SF-325 NM model; Piracicaba, SP, Brazil) at 55°C for 72 h, according to the methodologies [10]. Then, the samples were ground in a micro-powder grinding mill (R-TE-350; Tecnal Scientific Equipment, Piracicaba, SP, Brazil) and stored in plastic containers previously identified for laboratory analyses. Insoluble acid ash marker was analyzed via hydrochloric acid (4N) digestion, following the adapted procedures [13]. The chemical composition of diets and feces samples was determined according to the methodologies described [10]. The gross energy in diets and feces samples was determined in a bomb calorimeter (IKA®, model C200, USA). Based on the results of laboratory analyses, the recovery percentage of IAA and the ATTD coefficients of dry matter (ADCDM), organic matter (ADCOM), crude protein (ADCCP), and gross energy (ADCGE) were calculated. Digestible nutrients and energy were determined as a percentage of digestible dry matter (DDM), digestible organic matter (DOM), digestible protein (DP), and kcal/kg of digestible energy (DE), according to the established equations [12]. The total digesta passage rate was assessed via fecal marker excretion at the end of the finisher phases, according to the adapted methodology [14]. Before supplying the diets containing the marker, a quantified portion of the diet was weighed with $1.5\%$ of the marker (iron oxide) and homogenized to ensure the intake in a single meal. One h before the evaluation, all diet was removed from the feeder of pens and placed in identified containers to be returned to the respective feeder afterward. Diets containing the marker were supplied following the same sequence used to withdraw diets without marker. The supplying time and the time when animals consumed all the marked diet (h 0) were recorded per pen. Pens were monitored to identify the defection of marked feces. The defecation time was recorded accordingly. The total digesta passage rate was calculated based on the time (in min) between the marked diet consumption and the excretion of marked feces. ## 2.6. Fecal microbiome At the end of finisher II phase, rectum feces samples from 6 pigs per treatment were collected and immediately placed in sterile Eppendorf-type tubes using swabs. Right after collection, the samples were stored at −80°C until analysis. A commercial kit (ZR Fecal DNA MiniPrep® from Zymo Research) was used to extract DNA from samples following the manufacturer's instructions. The integrity of the extracted DNA was assessed via $1\%$ agarose gel electrophoresis. The extracted DNA was quantified via spectrophotometry at 260 nm. A segment of approximately 460 bases of the hypervariable region V3-V4 of the ribosomal gene 16S rRNA was amplified using the universal primers described by the methodology. The PCR conditions were as follows: 95°C for 3 min, 25 cycles at 95°C for 30 s, 55°C for 30 s, and 72°C for 30 s, followed by a step at 72°C for 5 min. A metagenomics library was built from the amplified using a commercial kit (Nextera DNA Library Preparation Kit, Illumina®). The amplified were pooled and sequenced in Illumina's MiSeqTM sequencer® [15]. Readings were analyzed in the quantitative insights into microbial ecology (QIIME2) platform [16]. The following procedures were performed: removal of low-quality sequences, filtration, chimera's removal, and taxonomic classification. Sequences were classified into bacterial genera via amplicon sequence variants (ASVs) identification, in this case, the homology between sequences when compared against a database. The 2019 edition (SILVA 138) of the SILVA ribosomal sequence database [17] was used to compare the sequences. *To* generate the classification of bacterial communities via ASVs identification, 25,610 readings per sample were used. Thus, data were normalized and samples with different number of readings were not compared. The samples of identifiers 29,160 and 29,167 were removed due to the low number of readings (< 15,000). They were retrieved after the quality filtering steps. ## 2.7. Slaughter procedures, carcass traits, and meat quality On day 52 of the experimental period, all animals ($$n = 10$$/treatment) were fasted for 12 h and then transported for 6 h (a total of 18 h of fasting) to a commercial abattoir (Medianeira, PR, Brazil) with federal certification. Pigs were slaughtered using carbon dioxide stunning, followed by exsanguination. All analyses were performed and calculated according to the methodologies described [18]. The quantitative carcass traits such as backfat thickness, muscle percentage in the carcass, lean meat percentage, and lean meat amount were measured in the slaughterhouse using a swine carcass typing pistol (model UltraFom 300, Carometec). The carcass weight was determined using a scale placed in the slaughter line. Then, hot carcass yield, meat yield, and amount of chilled meat were calculated. Carcass length was measured after a cold shock in the cold chamber. Measurements were taken from the cranial edge of the atlas to the cranial edge of the aitch bone. A sample (≅30 cm) of the l. thoracis muscle was collected between the last thoracic vertebra and the first lumbar vertebra (caudal to cranial direction). Samples were immediately packed in the identified polyethylene plastic bags, placed in thermal boxes (4°C), and transported to the Animal Products Technology Laboratory (APTL) belonging to Unioeste. Then, pH value in the l. thoracis muscle was measured using a portable pHmeter (model AK103, Asko produtos eletrônicos Ltda, São Leopoldo, RS, Brazil) in the area of the last rib 4 and 24 h post mortem. For the measurements taken 4 h post mortem, the carcasses were submitted to 180 min of cooler shock, as follows: first stage: from −18°C to −15°C; second stage: from −15°C to −12°C; and third stage: from −10°C to −8°C. At the APTL, samples were refrigerated (≅2°C) for 24 h and then the backfat thickness and loin depth were measured using a digital pachymeter (MTX, stainless hardened). To determine the loin eye area (LEA) of the l. thoracis muscle, samples were scanned using a scanner printer (Officejet 4500 Desktop - G510a, HP, São Paulo, SP, Brazil). A black box was used to block the lighting and improve the image quality. Then, readings were performed using a Software (imageJ 1.53e - Java). Meat color was assessed after muscle oxygenation via air exposure for 15 min. Color analyses were performed using a Minolta CR400 colorimeter device (Konica Minolta Holdings, inc. Tokyo, Japan) and the results were expressed using the CIELAB color system. Color parameters were measured as L* (luminosity), a* (red-green component), and b* (yellow-blue component), which represent the saturation (chroma or purity) and the tint (color or hue). With these results, the saturation of the l. thoracis muscle was calculated. Marbling was determined using photographic standards and a 7-point scale (1 = traces of marbling and, 7 = excessive marbling). The subjective color analysis was performed using a 6-point scale (1 = light color and, 6 = trend to red). Afterward, samples were boned and the l. thoracis muscle was cross-sectioned into four 2.5-cm subsamples. The subsamples were used to determine drip loss (DL), thaw loss (TL), cooking loss (CL), shear force (SF), and chemical analyses. Subsample 1 was used to assess DL. The remaining subsamples were packed in the identified polyethylene bags and stored at−18°C until analyses. The losses were expressed as the percentage of lost water in relation to the original sample weight. Cooking loss was performed sequentially in a grill (Britannia brand, multi grill 2). Shear force analysis was performed using 6 cores (1.5 cm) removed from subsample 2 (TL and CL sequentially) using a stainless-steel cylinder sampler. Subsequently, the cores were submitted to a TA.HD.plus texture meter (model Texture Analyser, Stable Micro Systems) equipped with a standard shear blade calibrated for force (15 g), deformation (20 mm), and speed (2.0 mm/s). Subsample 3 was thawed in a refrigerator at a controlled temperature (4°C). Fat and connective tissue were withdrawn using a knife. Then, the subsamples were ground in a microprocessor and packed in the originally identified bags to determine moisture, ash, and crude protein. The ether extract was performed according to the AOCS methodology (Am 5-04, 2017) using an Ankom extractor (model XT15, NY, USA). Subsample 4 was kept frozen as a backup. The in vivo loin depth and backfat thickness were assessed in the lumbar area P2 in finisher II pigs using an Aloka ultrasound (Echo Camera model - SSD-500 vet, Tokyo, Japan). ## 2.8. Statistical procedures A Student standardized residuals analysis was performed before one-way analysis of covariance (ANCOVA) and variance (ANOVA), in which values >3 standard deviations were considered outliers. The normality of experimental errors and the homogeneity of variance of errors among treatments were evaluated using Shapiro-Wilk and Levene tests, respectively. For antioxidant enzyme data, outliers were identified via ROUT test ($Q = 1$%) and the normality was assessed via D'Agostino-Pearson test. Data on growth performance were analyzed using the following model: The effects of the factors in the model were described as: Yijk = average observation of the dependent variable in each plot, measured in the i-th class of treatment, in the j-th block, and the k-th replication; μ = overall mean effect; Ti = fixed effect of treatment classes, i = (1, 2, 3, and 4); bj = random effect of block, j = (1 and 2); β = regression coefficient of Y over X; Xijk = average observation of the covariate (initial BW) in each plot, measured in the i-th class of treatment, in the j-th block, and the k-th replication; X¯… = overall mean for covariate X; εijk = random error of the plot associated with level i, block j, and replication k. For other variables, the statistical model used was the one mentioned above, no covariate effect. Treatment effect on dependent variables was verified via ANCOVA or ANOVA. Treatment significance was set at $P \leq 0.10$ when the power of the test was < $80\%$. Multiple comparisons among treatment means were performed according to the post hoc test of Tukey and t-Student at $5\%$ and $10\%$ of probability, respectively. All statistical analyses were performed using the procedures of the SAS University Edition (SAS Inst. Inc., Cary, NC, USA). All normally distributed data were reported as means and their pooled SEM. For the fecal microbiome, the statistical comparison among the groups in the analyses of alpha diversity and the relative abundances of taxa among all experimental groups was performed via Wilcoxon non-parametric test at $P \leq 0.05.$ Statistical analyses for beta diversity were performed through permutational multivariate analysis of variance (PERMANOVA) in the QIIME2 pipeline. A total of 10,000 permutations was used. Alpha diversity analyses were calculated using phyloseq [19] and microbiome [20] libraries. ## 3.1. Growth performance and fecal consistency score Pigs fed CD0 diet showed ($$P \leq 0.002$$) greater ADFI than pigs fed other dietary treatments (Table 2). However, pigs fed CD0 diet showed ($$P \leq 0.009$$) lower G:F than those provided CD70 or CD85 diets. Although no difference among dietary treatments was observed in the finisher II phase, pigs fed ME-reduced diets containing the enzymes combination-maintained growth performance. No dietary treatment effect on the fecal consistency score was observed in finisher pigs. **Table 2** | Item2 | Treatments 3 | Treatments 3.1 | Treatments 3.2 | Treatments 3.3 | SEM4 | P-value | | --- | --- | --- | --- | --- | --- | --- | | | CD0 | CD70 | CD85 | CD100 | | | | Finisher I (d 0 to 22) | Finisher I (d 0 to 22) | Finisher I (d 0 to 22) | Finisher I (d 0 to 22) | Finisher I (d 0 to 22) | Finisher I (d 0 to 22) | Finisher I (d 0 to 22) | | FBW, kg | 100.90 | 96.45 | 98.10 | 97.95 | 0.90 | 0.505 | | ADFI, g | 3,076a | 2,590b | 2,598b | 2,753b | 0.04 | 0.002 | | ADG, g | 1291 | 1232 | 1225 | 1249 | 0.01 | 0.525 | | G:F, g:g | 0.42b | 0.47a | 0.46a | 0.45ab | 0.66 | 0.009 | | FCS | 1.10 | 1.00 | 0.90 | 0.90 | 0.05 | 0.557 | | Finisher II (d 22 to 52) | Finisher II (d 22 to 52) | Finisher II (d 22 to 52) | Finisher II (d 22 to 52) | Finisher II (d 22 to 52) | Finisher II (d 22 to 52) | Finisher II (d 22 to 52) | | FBW, kg | 138.30 | 133.65 | 133.45 | 134.50 | 1.36 | 0.518 | | ADFI, g | 3214 | 3165 | 2972 | 3251 | 0.08 | 0.637 | | ADG, g | 1244 | 1241 | 1178 | 1219 | 0.05 | 0.962 | | G:F, g:g | 0.38 | 0.39 | 0.39 | 0.37 | 1.38 | 0.922 | | FCS | 1.10 | 0.79 | 0.80 | 0.78 | 0.10 | 0.614 | | Overall period (d 0 to 52) | Overall period (d 0 to 52) | Overall period (d 0 to 52) | Overall period (d 0 to 52) | Overall period (d 0 to 52) | Overall period (d 0 to 52) | Overall period (d 0 to 52) | | ADFI, g | 3155 | 2923 | 2815 | 3040 | 0.05 | 0.141 | | ADG, g | 1264 | 1237 | 1195 | 1231 | 0.03 | 0.467 | | G:F, g:g | 0.40 | 0.42 | 0.42 | 0.40 | 0.00 | 0.418 | ## 3.2. Blood biochemical and immune profile A greater ($P \leq 0.001$) SOD concentration was observed in pigs fed CD70 diet compared to other dietary treatments (Table 3). No dietary treatment effect on the biochemical blood profile was observed in finisher pigs. **Table 3** | Item2 | Treatments 3 | Treatments 3.1 | Treatments 3.2 | Treatments 3.3 | SEM4 | P-value | | --- | --- | --- | --- | --- | --- | --- | | | CD0 | CD70 | CD85 | CD100 | | | | Albumin, g/dL | 3.57 | 3.57 | 3.55 | 3.84 | 0.06 | 0.337 | | Total cholesterol, mg/dL | 86.86 | 98.60 | 92.31 | 92.14 | 2.52 | 0.485 | | Glucose, mg/dL | 77.45 | 73.09 | 69.45 | 68.86 | 1.8 | 0.303 | | Urea, mg/dL | 18.42 | 21.05 | 17.01 | 19.54 | 0.91 | 0.482 | | Total protein, g/dL | 6.12 | 5.79 | 6.34 | 6.04 | 0.13 | 0.569 | | Globulin, g/dL | 2.54 | 2.22 | 2.79 | 2.20 | 0.13 | 0.365 | | GST, μmol/min/mg protein | 5.57 | 5.98 | 6.52 | 6.98 | 0.32 | 0.467 | | SOD, U/mg protein | 219.70b | 279.10a | 216.90b | 221.40b | 5.94 | < 0.001 | | IgM, mg/dL | 83.01 | 99.41 | 95.24 | 69.53 | 5.09 | 0.153 | ## 3.3. Apparent total tract digestibility and total digesta passage rate Pigs fed CD85 diet showed ($$P \leq 0.002$$) greater DP than pigs fed CD0 or CD100 diets. Pigs fed CD70 diet showed an increase of $11.3\%$ in DP than those fed CD0 diet (Table 4). In addition, greater ($P \leq 0.001$) DE was observed in pigs fed CD85 diet compared to other dietary treatments. No effect of dietary treatments on the passage rate of total digesta was observed in finisher pigs. **Table 4** | Item2 | Treatments 3 | Treatments 3.1 | Treatments 3.2 | Treatments 3.3 | SEM4 | P-value | | --- | --- | --- | --- | --- | --- | --- | | | CD0 | CD70 | CD85 | CD100 | | | | ADCDM (%) | 83.50 | 84.09 | 84.01 | 83.02 | 0.3 | 0.598 | | ADCCP (%) | 74.36 | 76.89 | 78.14 | 75.94 | 0.76 | 0.341 | | ADCOM (%) | 86.09 | 86.91 | 87.03 | 86.08 | 0.31 | 0.576 | | ADCGE (%) | 82.14 | 83.89 | 84.54 | 83.47 | 0.41 | 0.187 | | DDM (%) | 82.75 | 83.27 | 83.06 | 81.95 | 0.32 | 0.509 | | DP (%) | 8.61c | 9.59ab | 9.71a | 8.99bc | 0.11 | 0.002 | | DOM (%) | 82.43 | 82.89 | 82.68 | 81.30 | 0.3 | 0.279 | | DE (kcal/kg) | 3,697b | 3,805b | 4,035a | 3,813b | 27.07 | < 0.001 | | TDPI on day 22 (min) | 1656 | 1604 | 1571 | 1475 | 36.3 | 0.357 | | TDPII on day 52 (min) | 2179 | 2409 | 2414 | 2353 | 117.12 | 0.895 | ## 3.4. Fecal microbiome No difference among treatments was observed via the alpha diversity test (Shannon, Evenness Pielou, Simpson Index, Fisher, total number of observed OTUs, and Chao 1) in finisher pigs (Figure 1). Beta diversity was estimated via Bray-Curtis, Jaccard, UniFrac, and Weighted Unifrac parameters (Figure 2); however, no differences among dietary treatments were observed in finisher pigs. **Figure 1:** *Alpha diversity estimated by parameters Chao1 (A), observed OTUs (B), Fisher (C), Simpson (D), Shannon (E), and Pielou (F) in finisher pigs fed 1 of 4 dietary treatments: a control diet containing isolated phytase and xylanase valued at 40 kcal of ME/kg (CD0), CD0 + β-mannanase (0.3 g/kg valued at 30 kcal of ME/kg) (CD70), CD0 + β-mannanase (0.3 g/kg valued at 45 kcal of ME/kg) (CD85), and CD0 + β-mannanase (0.3 g/kg valued at 60 kcal of ME/kg) (CD100). Data are averages of 6 pigs per dietary treatment.* **Figure 2:** *Beta diversity estimated by parameters Bray-Curtis (A), Jaccard (B), Unifrac (C), and Weighted Unifrac (D) in finisher pigs fed 1 of 4 dietary treatments: a control diet containing isolated phytase and xylanase valued at 40 kcal of ME/kg (CD0), CD0 + β-mannanase (0.3 g/kg valued at 30 kcal of ME/kg) (CD70), CD0 + β-mannanase (0.3 g/kg valued at 45 kcal of ME/kg) (CD85), and CD0 + β-mannanase (0.3 g/kg valued at 60 kcal of ME/kg) (CD100). Data are averages of 6 pigs per dietary treatment.* The most abundant phyla we observed were Firmicutes, Bacteroidota (previously described as Bacteroidetes), Proteobacteria, and Spirochaetota (previously described as Spirochaetes) (Figure 3A). The classes Clostridia, Bacteroidia, Negativicutes, Bacilli, Gammaproteobacteria, and Spirochaetia showed the largest populations (Figure 3B). The most abundant orders were Bacteroidales, Oscillospirales, Veillonellales, Lachnospirales, Clostridiales, Acidaminococcales, Christensenellales, Enterobacterales, Lactobacillales, Treponematales, Selenomonadales, and Peptostreptococcales (Figure 3C). **Figure 3:** *Relative abundance of phyla (A), classes (B), orders (C), families (D), genera (E), and species (F) presents in finisher pigs fed 1 of 4 dietary treatments: a control diet containing isolated phytase and xylanase valued at 40 kcal of ME/kg (CD0), CD0 + β-mannanase (0.3 g/kg valued at 30 kcal of ME/kg) (CD70), CD0 + β-mannanase (0.3 g/kg valued at 45 kcal of ME/kg) (CD85), and CD0 + β-mannanase (0.3 g/kg valued at 60 kcal of ME/kg) (CD100). Data are averages of 6 pigs per dietary treatment.* The most abundant families were Muribaculaceae, Bacteroidaceae, Oscillospiraceae, Lachnospiraceae, Megasphaeraceae, Acutalibacteraceae, Clostridiaceae, Acidaminococcaceae, Christensenellaceae, Ruminococcaceae, Treponemataceae, Succinivibriononaceae, Selenomonadaceae, Dialisteraceae, Streptococcaceaeaceae, and Lactobacillaceae (Figure 3D). The most abundant genera were Sodaliphilus, Prevotella, Megasphaera, Phascolarctobacterium, Christensenellaceae NSJ-63, Clostridium, Treponema, Succinivibrio, Oscillospiraceae UBA1777, Paramuribaculum, Dialister, Streptococcus, Oscillospiraceae ER4, and Ruminococcus (Figure 3E). The species Sodaliphilus sp004557565, Megasphaera elsdenii, Phascolarctobacterium succinatutens, NSJ-63 sp014384805, Prevotella sp000436595, Succinivibrio dextrinosolvens_B, Clostridium saudiense, UBA1777 sp002320035, Paramuribaculum intestinale, Megasphaera sp000417505, ER4 sp000765235, and Dialister sp900543165 showed the largest abundances (Figure 3F). In addition, pigs fed CD0 diet showed ($$P \leq 0.049$$) greater Firmicutes:Bacteroidota ratio (FBR) than those provided with CD85 diet (Figure 4). However, pigs fed CD100 diet showed ($$P \leq 0.011$$) greater FBR than those fed CD85 diet. We analyzed only the taxon that showed different ($P \leq 0.05$) average relative abundance among dietary treatments. Therefore, the Muribaculaceae family was more abundant ($$P \leq 0.030$$) in pigs fed CD70 diet than in those fed CD0 diet (Figure 5). In addition, the *Prevotella genus* was more abundant ($$P \leq 0.045$$) in pigs fed CD85 diet than in those fed CD100 diet (Figure 6). **Figure 4:** *Firmicutes:Bacteroidetes ratio in finisher pigs fed 1 of 4 dietary treatments: a control diet containing isolated phytase and xylanase valued at 40 kcal of ME/kg (CD0), CD0 + β-mannanase (0.3 g/kg valued at 30 kcal of ME/kg) (CD70), CD0 + β-mannanase (0.3 g/kg valued at 45 kcal of ME/kg) (CD85), and CD0 + β-mannanase (0.3 g/kg valued at 60 kcal of ME/kg) (CD100). Data are averages of 6 pigs per dietary treatment. Means differed by Wilcoxon test (P < 0.05).* **Figure 5:** *Differential abundance analysis of taxon of the Muribaculaceae family in finisher pigs fed 1 of 4 dietary treatments: a control diet containing isolated phytase and xylanase valued at 40 kcal of ME/kg (CD0), CD0 + β-mannanase (0.3 g/kg valued at 30 kcal of ME/kg) (CD70), CD0 + β-mannanase (0.3 g/kg valued at 45 kcal of ME/kg) (CD85), and CD0 + β-mannanase (0.3 g/kg valued at 60 kcal of ME/kg) (CD100). Data are averages of 6 pigs per dietary treatment. Means differed by Wilcoxon test (P < 0.05).* **Figure 6:** *Differential abundance analysis of taxon of the Prevotella genus in finisher pigs fed 1 of 4 dietary treatments: a control diet containing isolated phytase and xylanase valued at 40 kcal of ME/kg (CD0), CD0 + β-mannanase (0.3 g/kg valued at 30 kcal of ME/kg) (CD70), CD0 + β-mannanase (0.3 g/kg valued at 45 kcal of ME/kg) (CD85), and CD0 + β-mannanase (0.3 g/kg valued at 60 kcal of ME/kg) (CD100). Data are averages of 6 pigs per dietary treatment. Means differed by Wilcoxon test (P < 0.05).* ## 3.5. Carcass traits and meat quality Pigs fed CD0 diet showed ($$P \leq 0.094$$) greater backfat thickness measured with ultrasound than pigs fed CD70 or CD85 diets (Table 5). In addition, animals fed CD0 or CD85 diets showed ($$P \leq 0.060$$) greater pH24h in the l. thoracis muscle than those fed CD100 diet. **Table 5** | Item2 | Treatments 3 | Treatments 3.1 | Treatments 3.2 | Treatments 3.3 | SEM4 | P-value | | --- | --- | --- | --- | --- | --- | --- | | | CD0 | CD70 | CD85 | CD100 | | | | Quantitative traits | Quantitative traits | Quantitative traits | Quantitative traits | Quantitative traits | Quantitative traits | Quantitative traits | | CL (cm) | 96.05 | 98.77 | 100.66 | 101.40 | 1.27 | 0.466 | | HCW (kg) | 96.70 | 94.72 | 91.68 | 93.92 | 1.06 | 0.447 | | Musc (%) | 56.77 | 54.62 | 56.77 | 55.90 | 1.08 | 0.903 | | HCY (kg) | 69.23 | 70.74 | 69.28 | 69.87 | 0.56 | 0.785 | | LM (%) | 56.63 | 57.03 | 57.14 | 57.01 | 0.48 | 0.986 | | LM (kg) | 54.93 | 54.32 | 52.35 | 53.44 | 0.76 | 0.677 | | CM (%) | 51.59 | 53.34 | 50.72 | 51.20 | 0.68 | 0.588 | | MY (%) | 53.90 | 56.14 | 57.01 | 55.19 | 0.58 | 0.271 | | LEA (cm2) | 60.23 | 54.12 | 59.77 | 57.90 | 1.18 | 0.263 | | LDpaq (mm) | 63.40 | 63.04 | 61.88 | 63.31 | 0.88 | 0.932 | | LDult (mm) | 57.00 | 55.00 | 55.30 | 55.90 | 0.59 | 0.676 | | BFTpaq (mm) | 21.44 | 20.04 | 17.58 | 19.20 | 0.71 | 0.284 | | BFTpis (mm) | 18.62 | 17.67 | 17.38 | 18.00 | 0.79 | 0.958 | | BFTult (mm) | 17.90a | 15.33b | 15.20b | 16.90ab | 0.04 | 0.094 | | Qualitative traits | Qualitative traits | Qualitative traits | Qualitative traits | Qualitative traits | Qualitative traits | Qualitative traits | | pH4h | 6.23 | 6.15 | 6.11 | 6.08 | 0.04 | 0.631 | | pH24h | 6.14a | 5.75ab | 5.95a | 5.32b | 0.11 | 0.060 | | DL (%) | 7.03 | 6.34 | 7.98 | 7.06 | 0.27 | 0.214 | | TL (%) | 9.49 | 8.92 | 9.43 | 8.62 | 0.35 | 0.800 | | CL (%) | 28.27 | 25.37 | 26.10 | 29.05 | 0.75 | 0.262 | | SF (kgf/seg) | 4138 | 3493 | 3909 | 4367 | 142.97 | 0.159 | | L* | 45.33 | 46.09 | 46.39 | 45.33 | 0.29 | 0.481 | | a* | 5.59 | 5.67 | 5.25 | 5.26 | 0.13 | 0.571 | | b* | 3.07 | 3.57 | 3.13 | 2.84 | 0.10 | 0.107 | | Chroma | 6.38 | 6.72 | 6.12 | 5.99 | 0.15 | 0.387 | | Color score | 3.61 | 3.05 | 2.83 | 3.45 | 0.13 | 0.137 | | Marbling degree | 3.33 | 3.00 | 3.22 | 3.20 | 0.15 | 0.907 | | Ash (%) | 1.19 | 1.18 | 1.21 | 1.20 | 0.01 | 0.884 | | Crude protein (%) | 24.54 | 25.01 | 24.25 | 24.47 | 0.23 | 0.716 | | Ether extract (%) | 3.31 | 3.45 | 2.92 | 3.60 | 0.21 | 0.736 | | Moisture (%) | 73.55 | 73.13 | 73.47 | 73.55 | 0.16 | 0.777 | ## 4. Discussion In the present study, animals were healthy throughout the experiment. However, pigs fed diets supplemented with β-mannanase supported growth performance due to the combined effect of these enzymes in the hydrolysis of antinutritional factors, and as energy sparing and extra energy supply [2, 4, 7]. The energy-saving effect of diets supplemented with β-mannanase is attributed to an unnecessary immune deactivation caused by the β-mannans in plant products [2]. A lower SOD concentration is due to the different enzymatic antioxidant system in response to oxidative stress. When pigs are fed diets with reduced ME, the metabolism is changed to use body reserves such as energy and lipids [21]. As a result, the process of nutrient oxidation produces energy for animal metabolism; however, energy production is also a source of free radicals [22]. In the present study, the greater SOD activity in pigs was performed to eliminate reactive free radicals, as previously reported [23]. However, this improved antioxidant capacity did not favor greater energy and nutrient usage in pigs fed CD70 diet. A previous study [8] reported a higher glucose concentration in pigs fed diets supplemented with β-mannanase-xylanase. The authors mentioned above explained this result based on successful enzyme hydrolysis of NSP, unlike our study, where no differences among the dietary treatments in the biochemical blood profile were observed. The mechanisms of action of these enzymes are supported by the greater usage of hydrolyzed nutrients that favors absorption by the enterocytes in the small intestine [1]. However, in the present study, a reduction of 100 kcal of ME/kg diet did not promote greater ATTD in pigs, even with β-mannanase supplementation. This result did not impair the nutrient ATTD coefficients and did not affect the occurrence of intestinal disorders such as diarrhea or increasing digesta viscosity. On other hand, the results suggested that feeding the CD85 diet to finisher pigs promoted greater DP and DE compared to other dietary treatments, explained by a successful degradation of NSP [8] that improves nutrient usage and energy efficiency due to the increased effectiveness of host enzymes. This mechanism is performed by β-mannanase-xylanase enzymes via breaking down cell walls containing NSP [24] and reducing digesta viscosity [6]. However, we did not observe changes on fecal consistency score or the total digesta passage rate in pigs. Furthermore, β-mannanase supplementation in diets has been previously reported to stimulate the activity of endogenous enzymes [5] and hence favor a greater ATTD in pigs. The effects of phytase on ATTD of nutrients other than calcium and phosphorus have not been well established yet [6]. However, the association of dietary xylanase-phytase has been reported to break down cell walls and release more phytic acid to be broken down by phytase [25]. Usually, the consumption of diets with greater energy content promotes increased backfat thickness, as well as the additional energy effect that can be provided by phytase in pig diets [7], which agrees with the results we observed. Pigs fed CD85 diet showed lower backfat thickness measured in vivo with ultrasound due to greater energy digestibility even with reduced ME dietary content [8]. Although the animals fed CD70 diet had lower DE, the backfat thickness was positively influenced in the animals of this dietary treatment. Contrary to our observation, enzyme supplementation increased energy digestibility and no effect on backfat thickness in pigs due to dietary energy content was observed in a previous study [24]. However, our finding was similar to the one reported by [8], who also observed lower backfat thickness in pigs fed diets supplemented with β-mannanase-xylanase. Phosphorus and phytate grouped with arabinoxylans have been previously reported [24] to increase redness and reduce water retention in the meat of pigs when exposed to xylanase-phytase action. A similar effect was not observed in the present study; however, we observed dietary treatment effects on pH24, which is related to meat quality regarding water retention capacity, color, softness, juiciness, and flavor. Overall, our results agree with those reported by [26], who summarized the higher quality traits of meat from finisher pigs and estimated values of 5.54 for pH24h, L* of 46.6, CL of $25.8\%$, and chroma of 6.2. In the present study, differences in pH24h among dietary treatments are attributed to muscle glycogen concentration (although not determined in the present study), which largely depends on the diet provided to animals. The lower pH24h value in the meat of pigs fed CD100 diet is related to a greater rate of lactic acid-producing pyruvate, as evidenced by [27]. Based on these pH24h values in meat, pigs fed CD0 and CD100 diets showed meats classified as DFD and PSE, respectively [18]. Bacterial diversity in the gastrointestinal tract was assessed in the present study because this is crucial in modulating intestinal functionality and is essential for metabolism, ATTD, and nutrient usage. *In* general, the balance of the commensal microbiota plays a role in the health of the host. This role is attributed to the diversity of genera and species that possesses protective function, reduces pathogens, inhabit intestinal surfaces, and produces antimicrobial substances [28]. The above-mentioned roles attributed to intestinal microbiota promoted animal health during the experimental period. No effects of dietary treatments on alpha and beta diversity were observed in finisher pigs. This lack of effect could be attributed to the dynamism of microbial communities and different profiles in the gastrointestinal tract segments. In our study, the most prevalent bacterial phyla in pigs were Firmicutes, Proteobacteria, and Bacteroidota, which agrees with previous studies [29, 30]. Firmicutes and Bacteroidota are the phyla of greatest representation and paramount importance for gastrointestinal homeostasis [31]. An increased incidence of Firmicutes may also be negatively correlated with the presence of potentiality pathogenic bacteria in the intestine of pigs [32]. According to [33], a greater presence of organisms of the phylum Firmicutes may create a hostile intestinal environment for pathogenic bacteria colonization. In a previous study [34], FBR was reported as widely accepted as an evaluative parameter beneficial for intestinal health; therefore, changes in this proportion can trigger several pathologies (35–37). In a study conducted by [38], a greater FBR in pigs was related to improved energy efficiency and growth performance. In addition, similar results were reported in a study conducted on poultry [39]; however, these findings differ from our results. In the present study, the Ruminococcaceae, Lactobacillaceae, and Lachnospiraceae families showed relative abundance in pigs. These families compose the central microbiota of the distal intestine portion and are found in similar proportions in the colon and feces [40]. The CD70 diet has positively modulated the growth of Ruminococcaceae family in finisher pigs. This family produces xylanases, cellulases, α-glucosidases, α and β-galactosidases providing greater energy usage [41]. In addition, bacteria belonging to the Ruminococcaceae family degrade complex carbohydrates. A reduction in this family has been associated with the use of calorie-rich diets and/or enhanced with carbohydrates [42]. This effect could support the reduced Ruminococcaceae occurrence in pigs fed CD0 diet. Furthermore, no treatment effect on the abundance of Prevotellaceae and Rikenellaceae families was observed in pigs. This finding is associated, in several studies, with a low G:F. Similarly, the Christensenellaceae family was not affected by treatments, which was related to improvement G:F in pigs [43], although a lower G:F was observed in finisher I pigs fed CD0 diet. However, the Lachnospiraceae family was abundant in the pig gastrointestinal microbiota in both dietary treatments in the present study. This family is known to produce butyric acid [29], which plays a role in maintaining intestinal epithelium structure [41]. In addition, the fecal microbiome in finisher pigs showed a relative abundance of families associated with short-chain fatty acids synthesis as final products of sugar fermentation, for example, the Oscillospiraceae [44] and Christensenellaceae families [45]. Prevotella is part of the phylum Bacteroidetes, which participates in immune system modulation, metabolic syndromes, and brain-intestine axis regulation [46]. This genus was more abundant in pigs fed CD85 diet than in those fed CD100 diet. This result suggests that these changes are related to the different energy content among diets. In fact, Prevotella has been reported to play a role in carbohydrate metabolism, such as the degradation of polysaccharides and oligosaccharides usage [47]. When analyzed together, part of the modulations observed in pigs occurred in families and genera that play a crucial role in gastrointestinal tract homeostasis. ## 5. Conclusion Based on the assessing criteria in this study, β-mannanase supplementation in diets containing xylanase-phytase allows reducing 85 kcal of ME/kg because it improves the gain to feed ratio, energy and protein usage, and backfat thickness without metabolic and intestinal ecosystem disorders in finisher pigs. Furthermore, reducing dietary ME alters the fecal microbiome in finisher pigs regardless of the combined enzymes. ## Data availability statement The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author. ## Ethics statement All experimental procedures performed were approved by the Ethics Committee on the use of production animals at the Universidade Estadual do Oeste do Paraná (Authorization number $\frac{17}{2022}$). ## Author contributions PC, MK, and TP: conceptualization, data curation, and project management. JG, PR, LA, DH, and SC: methodology. JG and NO: software. JG, PR, and NO: statistical analysis, formal analysis, and writing—original draft preparation. PC, SC, and MK: validation. PR, PC, LA, DH, and SC: investigation. JG, PR, MK, GG, HV, and TP: writing—review and editing. PC, SC, AD, and MK: supervision. All authors contributed to the article and approved the submitted version. ## Conflict of interest MK was employed by Elanco Animal Health Incorporated Company. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## References 1. Liu S, Ma C, Liu L, Ning D, Liu Y, Dong B. **β-Xylosidase and β-mannosidase in combination improved growth performance and altered microbial profiles in weanling pigs fed a corn-soybean meal-based diet.**. *Asian-Australas J Anim Sci.* (2019) **32** 1734. DOI: 10.5713/ajas.18.0873 2. Vangroenweghe F, Poulsen K, Thas O. **Supplementation of a β-mannanase enzyme reduces post-weaning diarrhea and antibiotic use in piglets on an alternative diet with additional soybean meal**. *Porc Health Manag.* (2021) **7** 1-12. DOI: 10.1186/s40813-021-00191-5 3. Mok CH, Lee JH, Kim BG. **Effects of exogenous phytase and β-mannanase on ileal and total tract digestibility of energy and nutrient in palm kernel expeller-containing diets fed to growing pigs**. *Anim Feed Sci Technol.* (2013) **186** 209-13. DOI: 10.1016/j.anifeedsci.2013.10.008 4. Tiwari UP, Chen H, Kim SW, Jha R. **Supplemental effect of xylanase and mannanase on nutrient digestibility and gut health of nursery pigs studied using both**. *Anim Feed Sci Technol.* (2018) **245** 77-90. DOI: 10.1016/j.anifeedsci.2018.07.002 5. Kipper M, Andretta I, Quadros VRD, Schroeder B, Pires PGDS, Franceschina CS, França I. **Performance responses of broilers and pigs fed diets with β-mannanase**. *Rev Bras Zootec* (2020) **49** 1-11. DOI: 10.37496/rbz4920180177 6. Yang YY, Fan YF, Cao YH, Guo PP, Dong B, Ma YX. **Effects of exogenous phytase and xylanase, individually or in combination, and pelleting on nutrient digestibility, available energy content of wheat and performance of growing pigs fed wheat-based diets**. *Asian-Australas J Anim Sci.* (2017) **30** 57-63. DOI: 10.5713/ajas.15.0876 7. Silva CA, Callegari MA, Dias CP, Bridi AM, Pierozan CR, Foppa L. **Increasing doses of phytase from Citrobacter braakii in diets with reduced inorganic phosphorus and calcium improve growth performance and lean meat of growing and finishing pigs**. *PLoS ONE.* (2019) **14** e0217490. DOI: 10.1371/journal.pone.0217490 8. Cho JH, Kim IH. **Effects of beta mannanase and xylanase supplementation in low energy density diets on performances, nutrient digestibility, blood profiles and meat quality in finishing pigs**. *Asian J Anim Vet Adv.* (2013) **8** 622-30. DOI: 10.3923/ajava.2013.622.630 9. Rostagno HS, Albino LFT, Hannas MI, Donzele JL, Sakomura NK, Perazzo FG. *Tabelas Brasileiras para aves e su* (2017) 10. Silva DJ, Queiroz AC. *Análises de alimentos (métodos qu* (2002) 11. Hart GK, Dobb GJ. **Effect of a fecal bulking agent on diarrhea during enteral feeding in the critically ill**. *JPEN J Parenter Enteral Nutr.* (1988) **12** 465-8. DOI: 10.1177/0148607188012005465 12. Sakomura NK, Rostagno HS. *Métodos de pesquisa em nutrição de monogástricos.* (2016) 13. Kavanagh S, Lynch PB, O'Mara F, Caffrey PJ. **comparison of total collection and marker technique for the measurement of apparent digestibility of diets for growing pigs**. *Anim Feed Sci Technol.* (2001) **89** 49-58. DOI: 10.1016/S0377-8401(00)00237-6 14. Owusu-Asiedu AJFJ, Patience JF, Laarveld B, Van Kessel AG, Simmins PH, Zijlstra RT. **Effects of guar gum and cellulose on digesta passage rate, ileal microbial populations, energy and protein digestibility, and performance of grower pigs**. *J Anim Sci.* (2006) **84** 843-52. DOI: 10.2527/2006.844843x 15. Degnan PH, Ochman H. **Illumina-based analysis of microbial community diversity**. *ISME J.* (2012) **6** 183-94. DOI: 10.1038/ismej.2011.74 16. Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ. **Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample**. *Proc Natl Acad Sci.* (2011) **108** 4516-22. DOI: 10.1073/pnas.1000080107 17. Yilmaz P, Parfrey LW, Yarza P, Gerken J, Pruesse E, Quast C. **The SILVA and “all-species living tree project (LTP)” taxonomic frameworks**. *Nucleic Acids Res.* (2013) **42** D643-48. DOI: 10.1093/nar/gkt1209 18. Bridi AM, Silva CA. *Métodos de avaliação da carcaça e da carne suína* (2009) 19. McMurdie PJ, Holmes S. **phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data**. *PLoS One.* (2013) **8** e61217. DOI: 10.1371/journal.pone.0061217 20. 20.Lahti L, Shetty, S,. Introduction to the Microbiome R Package. (2018). Available online at: https://microbiome.github.io/tutorials/ (Accessed March 15, 2022).. *Introduction to the Microbiome R Package.* (2018) 21. Belhadj Slimen I, Najar T, Ghram A, Abdrrabba M. **Heat stress effects on livestock: molecular, cellular and metabolic aspects, a review**. *J Anim Physiol Anim Nutr.* (2016) **100** 401-12. DOI: 10.1111/jpn.12379 22. Celi P, Gabai G. **Oxidant/antioxidant balance in animal nutrition and health: the role of protein oxidation**. *Front Vet Sci.* (2015) **2** 48. DOI: 10.3389/fvets.2015.00048 23. Li Z, Tang L, Liu N, Zhang F, Liu X, Jiang Q. **Comparative effects of compound enzyme and antibiotics on growth performance, nutrient digestibility, blood biochemical index, and intestinal health in weaned pigs**. *Front Microbiol* (2021) **12** 768767. DOI: 10.3389/fmicb.2021.768767 24. Cho JH, Park JH, Lee DH, Lee JM, Song TH, Kim IH. **Effects of xylanase supplementation on growth performance, digestibility, fecal gas emission, and meat quality in growing–finishing pigs**. *Can J Anim Sci.* (2016) **97** 95-100. DOI: 10.1139/CJAS-2015-0198 25. Kim JC, Sands JS, Mullan BP, Pluske JR. **Performance and total-tract digestibility responses to exogenous xylanase and phytase in diets for growing pigs**. *Anim Feed Sci Technol.* (2008) **142** 163-72. DOI: 10.1016/j.anifeedsci.2007.07.004 26. Sardi L, Gastaldo A, Borciani M, Bertolini A, Musi V, Garavaldi A. **Pre-slaughter sources of fresh meat quality variation: The case of heavy pigs intended for protected designation of origin products**. *Animals.* (2020) **10** 2386. DOI: 10.3390/ani10122386 27. Yin Y, Liu Y, Duan G, Han M, Gong S, Yang Z. **The effect of dietary leucine supplementation on antioxidant capacity and meat quality of finishing pigs under heat stress**. *Antioxidants.* (2022) **11** 1373. DOI: 10.3390/antiox11071373 28. Duda-Chodak A, Tarko T, Satora P, Sroka P. **Interaction of dietary compounds, especially polyphenols, with the intestinal microbiota: a review**. *Eur J Nutr.* (2015) **54** 325-41. DOI: 10.1007/s00394-015-0852-y 29. Gresse R, Durand FC, Dunière L, Blanquet-Diot S, Forano E. **Microbiota composition and functional profiling throughout the gastrointestinal tract of commercial weaning piglets**. *Microorganisms* (2019) **7** 343. DOI: 10.3390/microorganisms7090343 30. Kim BR, Shin J, Guevarra RB, Lee JH, Kim DW, Seol KH. **Deciphering diversity indices for a better understanding of microbial communities**. *J Microbiol Biotechnol.* (2017) **27** 2089-93. DOI: 10.4014/jmb.1709.09027 31. Rychlik I. **Composition and function of chicken gut microbiota**. *Animals* (2020) **10** 103. DOI: 10.3390/ani10010103 32. Mulder IE, Schmidt B, Stokes CR, Lewis M, Bailey M, Aminov RI. **Environmentally-acquired bacteria influence microbial diversity and natural innate immune responses at gut surfaces**. *BMC Biol.* (2009) **7** 1-20. DOI: 10.1186/1741-7007-7-79 33. Molist F, Manzanilla EG, Pérez JF, Nyachoti CM. **Coarse, but not finelyground, dietary fibre increases intestinal firmicutes:bacteroidetes ratio and reduces diarrhoea induced by experimental infection in piglets**. *Br J Nutr.* (2012) **108** 9-15. DOI: 10.1017/S0007114511005216 34. Wang Z, Tang Y, Long L, Zhang H. **Effects of dietary L-theanine on growth performance, antioxidation, meat quality and intestinal microflora in white feather broilers with acute oxidative stress**. *Front Vet Sci* (2022). DOI: 10.3389/fvets.2022.889485 35. Magne F, Gotteland M, Gauthier L, Zazueta A, Pesoa S, Navarrete P, Balamurugan R. **The firmicutes/bacteroidetes ratio: a relevant marker of gut dysbiosis in obese patients?**. *Nutrients* (2020) **12** 1474. DOI: 10.3390/nu12051474 36. Stojanov S, Berlec A, Štrukelj B. **The influence of probiotics on the firmicutes/bacteroidetes ratio in the treatment of obesity and inflammatory bowel disease**. *Microorganisms.* (2020) **8** 1-16. DOI: 10.3390/microorganisms8111715 37. Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI. **An obesity-associated gut microbiome with increased capacity for energy harvest**. *Nature.* (2006) **444** 1027-31. DOI: 10.1038/nature05414 38. Zhao W, Wang Y, Liu S, Huang J, Zhai Z, He C. **The dynamic distribution of porcine microbiota across different ages and gastrointestinal tract segments**. *PLoS ONE.* (2015) **10** 1-13. DOI: 10.1371/journal.pone.0117441 39. Xu Y, Yang H, Zhang L, Su Y, Shi D, Xiao H. **High-through put sequencing technology to reveal the composition and function of cecal microbiota in Dagu chicken**. *BMC Microbiol.* (2016) **16** 1-9. DOI: 10.1186/s12866-016-0877-2 40. Gierse LC, Meene A, Schultz D, Schwaiger T, Karte C, Schröder C. **A multi-omics protocol for swine feces to elucidate longitudinal dynamics in microbiome structure and function**. *Microorganisms.* (2020) **8** 1-20. DOI: 10.3390/microorganisms8121887 41. Biddle A, Stewart L, Blanchard J, Leschine S. **Untangling the genetic basis of fibrolytic specialization by Lachnospiraceae and Ruminococcaceae in diverse gut communities**. *Diversity.* (2013) **5** 627-40. DOI: 10.3390/d5030627 42. Lagkouvardos I, Lesker TR, Hitch TCA, Gálvez EJC, Smit N, Neuhaus K. **Sequence and cultivation study of Muribaculaceae reveals novel species, host preference, and functional potential of this yet undescribed family**. *Microbiome.* (2019) **7** 28. DOI: 10.1186/s40168-019-0637-2 43. Quan J, Cai G, Ye J, Yang M, Ding R, Wang X. **A global comparison of the microbiome compositions of three gut locations in commercial pigs with extreme feed conversion ratios**. *Sci Rep.* (2018) **8** 4536. DOI: 10.1038/s41598-018-22692-0 44. Beaumont M, Cauquil L, Bertide A, Ahn I, Barilly C, Gil L. **Gut microbiota-derived metabolite signature in suckling and weaned piglets**. *J Proteome Res.* (2021) **20** 982-94. DOI: 10.1021/acs.jproteome.0c00745 45. Morotomi M, Nagai F, Watanabe Y. **Description of Christensenella minutagen. Nov., sp nov, isolated from human faeces, which forms a distinct branch in the order Clostridiales, and proposal of Christensenellaceae fam nov**. *Int J Syst Evol Microbiol.* (2012) **62** 144-9. DOI: 10.1099/ijs.0.026989-0 46. Gibiino G, Lopetuso LR, Scaldaferri F, Rizzatti G, Binda C, Gasbarrini A. **Exploring bacteroidetes: metabolic key points and immunological tricks of our gut commensals**. *Dig Liver Dis.* (2018) **50** 635-9. DOI: 10.1016/j.dld.2018.03.016 47. Zhang D, Liu H, Wang S, Zhang W, Wang J, Tian H. **Fecal microbiota and its correlation with fatty acids and free amino acids metabolism in piglets after a Lactobacillus strain oral administration**. *Front Microbiol.* (2019) **10** 785. DOI: 10.3389/fmicb.2019.00785
--- title: The postbiotic of hawthorn-probiotic ameliorating constipation caused by loperamide in elderly mice by regulating intestinal microecology authors: - Yu Wei - Na Huang - Xinyu Ye - Meng Liu - Meilian Wei - Yali Huang journal: Frontiers in Nutrition year: 2023 pmcid: PMC10061020 doi: 10.3389/fnut.2023.1103463 license: CC BY 4.0 --- # The postbiotic of hawthorn-probiotic ameliorating constipation caused by loperamide in elderly mice by regulating intestinal microecology ## Abstract ### Background Constipation is common gastrointestinal disorder with high prevalence and recurrence, making people suffering. However, the treatment for constipation remains ineffectual. We aimed to the study the effects and mechanisms of postbiotic of hawthorn-probiotic on loperamide modeled old KM mice. ### Methods Constipated mice were grouped and treated with $10\%$ lactulose (Y), hawthorn group (S), probiotic group (F) and postbiotic of hawthorn-probiotic (FS). Fecal changes were observed. AQP3 and Enac-γ were measured by RT-qPCR and Western blotting, intestinal barrier by H&E and immunofluorescence staining, cell proliferation and apoptosis by CCK8 and flow cytometry. Gut microbiota was further determined by 16 s rRNA sequence of feces. ### Results Postbiotic of hawthorn-probiotic improved intestinal movement and pathomorphology, elevated AQP3, Enac-γ and mucin-2 expression, accompanied by decreased serum TNF-α and cell apoptosis, but increased proliferation. Furthermore, it modified the gut microbiota of constipated mice, featured by upregulation of Lactobacillaceae. ### Conclusion Postbiotic of hawthorn-probiotic relieved constipation by combined effects of regulating intestinal water and sodium metabolism, maintain intestinal barrier and gut microflora. ## Introduction Constipation is one of the most frequent gastrointestinal disorders. It’s reported that the global incidence of constipation has reached $15\%$ just in 2020 [1], and the prevalence of constipation in the elderly is as high as $27\%$ [2], and the prevalence increased with the increase of age [3]. And this might be underestimated since partial patients do not seek medical help in hospitals, especially in developing countries. Meanwhile, constipation has a $50\%$ recurrence rate [4]. Constipation increases discomfort and may lead to abdominal cramping and straining on defecation that ultimately affect quality of life [5]. Prolonged constipation can also lead to neurasthenia, metabolic disorders, and even sepsis [6]. However, the treatment for constipation remains ineffectual. In recent years, studies have found that the intestinal microbial community of constipation patients has significantly changed compared with that of normal people [7, 8]. It has also been found that supplementing with prebiotic (nutrients designed to stimulate the growth of beneficial microbes), probiotic (microbes that confer a health benefit when consumed at sufficient levels) or synbiotic (a combination of a prebiotic and a probiotic) can relieve constipation symptoms such as frequency of defecation, fecal characteristics, colonic mucosa, colonic transit, gut microbiota, metabolite (9–14). However, these rely on external supplements or the patient’s own live bacteria. Inanimate microorganisms and/or their components that are beneficial to host health are called postbiotic [15]. It has been observed that the culture supernatants of certain probiotics maintain the same effectiveness of alive bacteria, furthermore, postbiotics are in some cases considered a valid and safer alternative to taking viable microorganisms [16]. Hawthorn is a kind of fruit, which has been widely used in the formulation of dietary supplements, functional foods and medical products. Hawthorn is rich in amino acids, minerals, pectin, vitamin C, polyphenols (chlorogenic acid, proanthocyanidin B2, epicatechin), flavonoids (proanthocyanidin, colloxanidine, quercetin, rutin) [17], because of which, hawthorn exerts extensive biological functions, like antioxidant, anti-inflammatory, anti-cancer, anti-cardiovascular disease and digestive promoting properties. Lactobacillus, one of the most beneficial probiotics in fermented food production, has been widely applied with a long-term history and safety [18]. The most important function of lactobacillus is to improve digestive and immune functions [19, 20]. Lactobacillus paracasei, one of seven species in the genus Lactobacillus, has two subspecies (L. paracasei subsp. paracasei and L. paracasei subsp. tolerans) [21]. It has been shown to be safe [22] and is used in the fermentation of dairy products and cheese [23, 24]. And studies have found that using these live bacteria can relieve constipation [2, 25, 26]. However, there is no report exploring the effects of its metabolites (postbiotic). In this study, we aimed to explore the effects of postbiotic of hawthorn-probiotic on constipation. The Lactobacillus paracasei isolated from the feces of infants was cultured with aqueous extract of hawthorn. After fermentation, the postbiotic was obtained by removing the living bacteria. The effects and possible mechanisms of the postbiotic on constipated elderly mice induced by loperamide was then explored. We found that postbiotic of hawthorn-probiotic exerts remarkable effects on constipation by regulating water and sodium metabolism, repairing intestinal barrier, relieving inflammation, and restoring microflora structure. This might offer a promising therapy for constipation. ## Postbiotic of hawthorn-probiotic increased intestinal motivity The overall experiment design was shown in Figure 1. After intervention, fecal quantities were measured 10 min after intragastric administration at a fixed time. Fecal quantities in the culture supernatant of Lactobacillus paracasei group (F) and hawthorn-probiotic group (FS) were significantly higher than those in the M group (Figure 1B). As demonstrated in Figure 1C, lactulose group (Y) presented the shortest time in term of the first black feces’ time after intragastrical administration with ink on the sacrifice day, followed by FS. Mice in F and hawthorn group (S) took longer time to defecate than mice in Y and FS group. The intestinal transport ratio in Y and FS group was significantly lower than that in the model group (M) (Figure 1D). The representative image of ink moving distance in each group was shown in Figure 1E. **Figure 1:** *Postbiotic of hawthorn-probiotic can increase intestinal motivity. (A) Animal models and treatment; (B) fecal quantities 10 min after intragastric administration; (C) time of first black feces after being intragastrically fed with ink on the day of sacrifice; (D) intestinal transport ratio (GI = ink movement length/total intestinal length*100%); (E) moving distances of each group of inks. NS; *p < 0.05; **p < 0.01; ***p < 0.001.* ## Postbiotic of hawthorn-probiotic increased fecal water content by regulating water and sodium metabolism The weight of fresh wet feces in F and FS group was heavier than that in M group (Figure 2A). But there were no differences in dry feces weight among these groups (Figure 2B). However, after calculating the fecal water content, we found that all the administration groups are better than the model group (Figure 2C). Combined with the above morphological changes, the effect of *Postbiotic is* the most obvious and stable. We used N, M, FS to explore the Postbiotic role. We can clearly see that the FS mice’s feces are more wet than those in M (Figure 2D). **Figure 2:** *Postbiotic of hawthorn-probiotic maintain the stability of water and sodium metabolism and increase fecal water content. (A) Wet feces weight; (B) Dry feces weight; (C) Fecal water content (wet feces weight/dry feces weight); (D) Fresh feces in three groups of mice; (E) Relative mRNA expression of AQP3 and Enac-γ; (F) AQP3 protein level. NS; *p < 0.05; **p < 0.01; ***p < 0.001.* We speculated that the above changes were induced by water and sodium metabolism changes. Then we tested the water and small-molecule channel aquaporin-3 (AQP3) and sodium channel epithelial 1 subunit gamma (Enac-γ) in different groups. It turned out that AQP3 and Enac-γ in M were significantly increased than those in normal group (N). However, the expression of these two genes was decreased in FS (Figure 2E). Moreover, AQP3 protein had a similar trend to gene in the three groups (Figure 2F). ## Postbiotic of hawthorn-probiotic preserved the intestinal barrier by promoting proliferation and reducing apoptosis As shown in Figure 3A, the colonic and ileum’s muscular layer in M was thinner (red arrows), and the glands were fewer and atrophic (black arrows). Colonic hyperemia and edema in M were obvious, and the muscular layer was separated from the submucosa (blue arrows), tipping inflammation. Moreover, ileum villi in M showed obvious necrosis and exfoliation. The FS group represented remission on the above pathological changes. In Figure 3B, Postbiotic of Hawthorn-Probiotic (FS) is statistically significant on the increase in colonic muscle thickness and glandular thickness. M edema is more serious than N, and FS can effectively reduce edema. **Figure 3:** *Postbiotic of Hawthorn-Probiotic help preserve the intestinal barrier. (A) Hematoxylin and eosin (H&E) staining in mice’s colon and ileum tissue; (B) muscle thickness, glandular thickness, edema width in colon; Muscle thickness, glandular thickness in ileum; (C,D) Mucin2 and FITC staining in colon tissue, Mucin2 is green, FITC is blue. NS; *p < 0.05; **p < 0.01; ***p < 0.001.* Mucin2 is a mucin mainly distributed in the digestive tract, secreted by goblet cells and glands. Its level is an important indicator of intestinal barrier function. As shown in Figure 3C, the level of mucin2 with green fluorescence in M was significantly decreased, tipping damage to the intestinal barrier. Then the mucin2 in FS was elevated than that in M (Figure 3D). We noticed that the intestinal cells in the FS group were fuller than those in the M group, with less necrosis and shedding. We speculated that these changes might be related to intestines inflammation since inflammation plays an important role in the pathogenesis of constipation, as demonstrated in Figure 3A. Therefore, we further tested serum TNFα of the constipated mice. It turned out that serum TNF-α was significantly elevated in M group and reversed in FS group (Figure 4A). **Figure 4:** *Postbiotic of hawthorn-probiotic help preserve the intestinal barrier. (A) The concentration of TNF-α in mice’s serum (pg/mL) by using Elisa; (B) Caco2 cells viability in normal and 5% postbiotic treated (FS) groups by using CCK8; (C,D) Caco2 cells relative apoptosis level (Q2 + Q3). NS; *p < 0.05; **p < 0.01; ***p < 0.001.* Since TNF-α is a key regulator and inducer of cell apoptosis, we speculated that apoptosis might be involved in intestinal barrier disruption. Then, Caco2 cell line was further applied to explore the mechanism by which postbiotic maintained the intestinal barrier. CCK-8 results showed that Caco2’s cell proliferation rates were significantly higher in $5\%$ postbiotic than in $5\%$ PBS at 12 and 24 h (Figure 4B). After 1 h of LPS stimulation, the $5\%$ postbiotic culture for 24 h reduced the rate of early and late apoptosis of Caco2 cells (Figure 4C). Figure 4D showed that the apoptosis rate of FS was $3\%$ (Q2 + Q3), while that of the LPS group was $7.5\%$. ## Postbiotic of hawthorn-probiotic was safe and promoted blood circulation and spleen immune cell growth Before and after modeling and administration, the mice in each group were weighed (Figure 5A). We found that the body weight of the mice was basically stagnated by loperamide modeling and recovered after administration. **Figure 5:** *Postbiotic of hawthorn-Probiotic are safe and promote blood circulation and spleen immune cell growth. (A) Mice’s body weight after modeling and administration; (B) liver organ index of mice after administration; (C) spleen organ index of mice after administration; (D) lung organ index of mice after administration; (E) hematoxylin and eosin (H&E) staining in mice’s livers, spleens and lungs tissue. NS; *p < 0.05; **p < 0.01; ***p < 0.001.* We then used organ index, a relatively sensitive index of drug toxicity, to explore the safety performance of postbiotic of hawthorn-probiotic. We weighed the lungs, livers and spleens of mice and found there was no significant changes in organ index (Figures 5B–D). H&E staining was performed on the tissue sections. And we found that the liver of mice in M presented generally blood stasis, with the boundaries between the red pulp and the white pulp of the spleen blurred, and the distribution of immune cells were reduced compared with that of mice in N group. The above pathological changes were improved in FS group. No abnormalities were observed in the lungs. ## Postbiotic of hawthorn-probiotic improved intestinal flora We conducted 16S rRNA to determine the changes of fecal microflora composition in the N, M and FS groups of mice. α- and β-diversity were used to explore the richness and evenness of gut microbiota. The results showed there was no statistical significance in terms of α-diversity among the three groups (Figure 6A). Principal coordinate analysis (pCoA) and non-metric multiscale analysis (NMDS) showed that the three groups were significantly clustered, with Stress = 0.1304 (less than 0.2 indicates the effectiveness of NMDS) (Figures 6B,C). Meanwhile, *Anosim analysis* showed that $R = 0.963$ (the closer it is to 1, the more effective the grouping is) (Figure 6D). Taken together, β-diversity analysis demonstrated that the gut flora patterns significantly differed among the three groups. By comparing the sequences of bacteria, we found that the microflora composition of the three groups was different (Figures 6E–G), but the structural composition within the groups was very similar. The intersection of common bacteria in the three groups was shown in Figure 6E. Three groups of bacteria with LDA > 4 are shown in Figure 6F. The result is similar to Figure 6G. The FS group was Bacteroidaceae–Bacteroides-phocaeicola sartorii, Lactobacillales–Lactobacillaceae–Ligilactobacillus, Campylobacterales–Campylobacterota–*Helicobacter were* mainly increased. According to the weighted phylogenetic tree, the bar diagram was formed by combining the bacteria groups. There was little difference within the three groups of bacteria, and they could be clearly distinguished from each other. **Figure 6:** *The structure of the three groups of bacteria is different. (A) PD whole tree in α-diversity; (B) principal coordinate analysis (pCoA) in β-diversity; (C) non-metric multiscale analysis (NMDS) in β-diversity; (D) anosim analysis; (E) Venn diagrams to compare the sequences of the three groups of bacteria; (F) histogram of LDA value distribution. The ordinate represents the taxonomic units with significant differences between groups, and the abscess represents the logarithmic score values of LDA analysis of each taxonomic unit in a bar chart. The taxon is sorted according to the size of the score value. The longer the length, the more significant the difference between the taxon is. The color of the bar graph indicates the sample group corresponding to the taxon with higher abundance; (G) phylogenetic tree and bar diagram to distinguish the three groups.* We identified the marker flora of the three groups from phylum, class, order, family, genus and species using LEfSe analysis (Figure 7A). Namely, Desulfovibroides-Desulfovibrionaceae and Lachnospirales-Lachnospiraceae were marker flora for N group, Erysipelotrichales-Erysipelotrichaceae-Allobaculum, Burkholderiales-sutterellaceae-Parastutterella-*Burkholderiales bacterium* YL45, and Muribaculaceae for M group, Bacteroidaceae-Bacteroides-Phocaeicola sartorii, Campylobacterales-Helicobacteraceae-Helicobacter, and Lactobacillales-Lactobacillaceae-Ligilactobaillus for FS group (the same to Figure 6E). Proteobacteria and Firmicutes were abundant in N group as in physical conditions and the most of flora in M group was pathogenic or potentially pathogenic. The decrease of the Lactobacillales-Lactobacillaceae-Ligilactobaillus could be used as the biomarker of M (LDA>4) (Figure 7E). **Figure 7:** *Postbiotic of hawthorn-probiotic decreases the abundance of intestinal or potential pathogens and increased the abundance of probiotics. (A) LEfSe analysis, the circles radiating from the inside out represent the classification levels from phylum to species; each small circle at a different classification level represents a classification at that level, and the diameter of the small circle is proportional to the relative abundance. The coloring principle is to color the species with no significant difference uniformly yellow, and color the other different species according to the group with the highest abundance. Different colors represent different groups, and different colored nodes represent the microbiome that plays an important role in the group represented by the color; (B) bar diagram of aerobic flora abundance at Family level in 3 groups; (C) bar diagram of the abundance of anaerobes at the Family level in 3 groups; (D) bar diagram of the abundance of facultative anaerobes at the Family level in 3 groups; (E) LDA analysis, the ordinate represents the classification units with significant differences between groups, and the abscess represents the logarithm score of LDA analysis of each classification unit in a bar graph. The classification unit is sorted according to the score value. The longer the length, the more significant the difference of the classification unit. The color of the bar graph indicates the sample group corresponding to the higher abundance of the classification unit; (F) bar diagram of the abundance of facultative contains mobile elements at the Family level in 3 groups; (G) bar diagram of the abundance of facultative potential pathogenic bacteria at the Family level in 3 groups; (H) bar diagram of the abundance of facultative bacteria who form biofilm at the Family level in 3 groups.* At the Family level, bacteria in the three groups were analyzed by abundance according to oxygen availability and three Features (Figures 7B–D,F). We found that microflora structure of FS and N was similar, and Verrucomicrobiaceae in M significantly increased, and postbiotic reduced the overall abundance of facultative anaerobes and reduced some bacteria who could form biofilms. It is well known that increased facultative anaerobic bacteria and biofilm formation of intestinal bacteria are associated with intestinal diseases. We also analyzed data from three groups at the Genus level (Table 1). Acetatifactor significantly increased in M, but postbiotic did not increase it. Encouragingly, postbiotic sharply reduced the abundance of Acinetobacter, a kind of recognized pathogen. Puzzlingly, the abundance of Akkermansia, a beneficial bacterium in the gut, increased in M, which accounted for a small proportion in FS, and almost none in N. This may be related to the age and individual differences of the mice. Alistipes, producing short-chain fatty acids and reducing intestinal inflammation, increased in M, but did not change in FS. It may be a compensatory response to changes in the microbiome. **Table 1** | Genus | M (mean) | M (se) | N (mean) | N (se) | T (mean) | T (se) | Multigroup (p) | | --- | --- | --- | --- | --- | --- | --- | --- | | A2 | 0.000264 | 0.000159 | 0.000859 | 0.000301 | 0.000447 | 0.000225 | 0.221101 | | ASF356 | 0.001374 | 0.000253 | 0.001325 | 0.000227 | 0.002247 | 0.000722 | 0.309511 | | Acetatifactor | 9.9e-05 | 9.9e-05 | 0.001188 | 0.000436 | 2.6e-05 | 2.6e-05 | 0.010101 | | Acinetobacter | 0.004912 | 0.001975 | 1.7e-05 | 8e-06 | 6e-06 | 6e-06 | 0.011175 | | Actinomadura | 0.0 | 0.0 | 0.0 | 0.0 | 6e-06 | 6e-06 | 0.391127 | | Aerococcus | 9e-06 | 9e-06 | 0.0 | 0.0 | 0.0 | 0.0 | 0.391127 | | Aeromonas | 0.0 | 0.0 | 0.0 | 0.0 | 1.6e-05 | 1.6e-05 | 0.391127 | | Agathobacter | 0.0 | 0.0 | 1.1e-05 | 1.1e-05 | 0.0 | 0.0 | 0.391127 | | Akkermansia | 0.005916 | 0.001222 | 0.0 | 0.0 | 0.000121 | 0.000111 | 2.9e-05 | | Alistipes | 0.012515 | 0.001609 | 0.006836 | 0.001133 | 0.011167 | 0.00122 | 0.022686 | ## Postbiotic of hawthorn-probiotic may promote intestinal microbiotic restoration and relieve constipation by reducing pathogenic bacteria and increasing cell activity According to the predicted enrichment results, we find that pathogenic bacteria and parasite-related pathways were elevated in M (Figure 8A), while improvements were observed in FS (Figure 8B). Moreover, the cell activity of M was decreased (Figure 8C), and the cell activity was increased after administration (Figure 8D). **Figure 8:** *Postbiotic of hawthorn-probiotic reduces the pathogenic microbial pathway and increases the cell activity pathway by enrichment analysis. (A) The FAPROTAX Ecological function forecast between M and N; (B) the FAPROTAX Ecological function forecast between M and FS; (C) COG functional classification statistical chart between M and N; (D) COG functional classification statistical chart between M and FS. The figure on the left shows the abundance ratio of different functions in two samples or groups of samples. In the middle is the proportion of differences in functional abundance within the 95% confidence interval. The rightmost value is a value of p.* ## Discussion Constipation, as a common gastrointestinal disease, troubles people, especially the elderly. Dietary modification instead of medication is reckoned as optimal therapy for constipated patients. In our study, with loperamide modeled elderly KM mice, we found that postbiotic of hawthorn-probiotic exerted remarkable curative effects, without toxicity and side effects. It might attenuate the water and sodium metabolism, chronic inflammation of the intestine, accompanied by promoted cell proliferation and reduced cell apoptosis. Besides, it could maintain the intestinal microecology by repressing the harmful bacteria colonization and improving the abundance of probiotics. Water and sodium metabolism, intestinal inflammation are interacting factors during the disease course of constipation, which participate in intestinal motility and the balance of intestinal barrier (27–29). AQP3 is a water channel protein required to promote glycerol permeability and water transport across cell membranes [30]. There is increasing evidence demonstrating that AQP3 in involved in inflammatory diseases including atopic dermatitis, psoriasis, allergy, and cancer progression, using AQP3−/− mice and AQP3-knockdown cells (31–33). Enac-γ is a sodium permeable non-voltage-sensitive ion channel inhibited by the diuretic amiloride, mediating the electro-diffusion of the luminal sodium (and water, which follows osmotically) through the apical membrane of epithelial cells [34]. It plays an essential role in electrolyte and blood pressure homeostasis, but also in airway surface liquid homeostasis, which is important for proper clearance of mucus [35]. And it also controls the reabsorption of sodium in kidney, colon, lung and sweat glands, also plays a role in taste perception [34, 35]. These findings suggest that when Enac-γ is out of balance, it can cause electrolyte disturbances, blood pressure fluctuations, and changes in colon mucus. Mucin2 (MUC2) coats the epithelia of the intestines, airways, and other mucus membrane-containing organs. It is thought to be protective, lubricating barrier against particles and infectious agents at mucosal surfaces [36]. Major constituent of both the inner and outer mucus layers of the colon may play a role in excluding bacteria from the inner mucus layer [36]. Combined with various indexes of elderly KM mice in group M, we analyzed that intestinal motility decreased and fecal retention occurred in the colon during constipation. The expression of AQP3 and Enac-γ increased, and the metabolism of water and sodium increased. With increased intestinal permeability, water, mucus and sodium were absorbed in large quantities, and fecal water content decreased. The whole-body may have electrolyte disorders, blood pressure abnormalities, so the liver blood flow was not smooth. Intestinal pathogenic bacteria stimulate intestinal epithelial cells and destroy intestinal epithelial structure. Mucin2 is decreased in goblet cells and glands of the large intestine. Glandular atrophy, mucosal layer and epithelial cell shedding and necrosis reflect decreased intestinal barrier function. Pathogenic bacteria increase, immune cell infiltration, secretion of TNF-α, intestinal mucosal epithelium accelerated necrosis and shedding, also promote intestinal congestion and edema. The gut is in a state of inflammation. We found postbiotic of Hawthorn-Probiotic can effectively reduce water and sodium metabolism, maintain intestinal tissue integrity, restore intestinal barrier, and reduce inflammatory factors. The gut is home to trillions of microbes, and their changes indicate an imbalance of homeostasis of the body. The most immediate changes are local functional and organizational changes in the gut, and this is also an important factor in the occurrence of constipation [37]. There are many reports about the changes of intestinal microflora in constipation and the recovery of microflora after treatment (8, 38–40). The abundance of pathogenic bacteria and potential pathogenic bacteria increased in the disease state, the number of probiotics decreased, and improved after treatment. Our results not only showed the increase of pathogenic bacteria (Acinetobacter, Erusipelotrichaceae, etc.) in the model group, but also the increase of pathogenic bacteria forming biofilm. Biofilms have been found in a variety of intestinal diseases (tumor, inflammatory bowel disease, colitis, etc.) and are important factors in the development of drug resistance (41–45). When bacteria form biofilms, resistance can increase by up to 1,000 times [43, 46]. The increased biofilm of intestinal bacteria in constipated mice is a bad phenomenon. Among these bacteria increasing biofilm, *Xanomonadaceae is* a kind of pathogenic bacteria that secretes effector proteins to enhance the adaptability of bacteria to the external environment and mediate the virulence of bacteria to the host [47]. Verrucomicrobiaceae, a newly delineated group of bacteria that includes a handful of recognized species, has been found mostly in aquatic and soil environments, or in human feces [48]. It was discovered relatively recently. At present, the research on it mainly focuses on the changes of the flora of various diseases. And the change is irregular. So it’s impossible to infer its exact function, good or evil. It is certain that the formation of biofilms by these bacteria increases, boding ill for the future. Excitingly, this phenomenon in constipation is first reported by us. It also indicates that deserves more attention. What is more shocking to us is that postbiotic of hawthorn-probiotic can treat the biofilm and adjust the structure of the flora to a normal state. And then we are going to do more in vitro studies to investigate this phenomenon. Bacteroides is the main bacteria that can decompose polysaccharides in human body (49–51). It produces short chains and organic acids that can be absorbed by the host [52]. The increase of Bacteroides in FS may be caused by polysaccharides in hawthorn. Lactobacillus is a well-known probiotic, and it has a wide variety and is widely used. The increase of Lactobacillus should be a direct effect of Postbiotic. Akkermansiamuciniphila (Akk) is a kind of normal bacteria in human intestinal tract [53], which is a mucin-degrading bacteria [54, 55]. The strain MucT was isolated by Derrien in 2004 [56]. It is an ovoid, gram-negative anaerobe and a representative of the phylum vermicella. However, there was no Akk in normal old KM mice, which we thought might be related to the differences of age and individual mice. As for why there is Akk in M and FS, there are more M, which we analyze that reduction of intestinal MUC2 was associated. Similar to the increase of AQP3, Akk may increase mucin absorption and decomposition, reduce MUC2 content, and reduce intestinal barrier function. After Postbiotic treatment, Akk showed a downward trend. ## Study limitations Due to limited conditions, we did not perform metabonomics analysis. Caco2 cell line is commonly used to study the potential of drug absorption, the mechanism of drug transport (absorption and elimination mechanism), and the intestinal metabolism of drugs, nutrients and plant components. It’s okay for us to experiment with it. But it’s also a cancer cell, and we are missing that. Due to limited condition, we did not carry out the extraction and experiment of primary cells. We found an increase in TNF-α in the serum of the constipated mice, but local inflammation of the colon was not validated due to the absence of intestinal tissue. ## Key resources table Reagent or resourceSourceIdentifierAntibodiesAQP3AffinityDF6127β-actin (13E5)CST4970SMucin2ServicebioAF5222goat anti-rabbit IgG secondary antibody (1:2000)ServicebioG2210-2-AAlexa Fluor 488-conjugated Goat Anti-Rabbit IgGServicebioGB25303ChemicalsBlood agar plates (TSA with $5\%$ sheep blood)Thermo FisherR02050Radio immunoprecipitation assay (RIPA) lysis bufferThermo Scientific78,510horseradish peroxidase- conjugated goat anti-mouseServicebioG2211-1-AEDTA antigen retrieval solution (pH 8.0)ServicebioG1206anti-fade mounting mediumServicebioG140Liquid blocker penServicebioG6100Spontaneouss fluorescence quenching reagentServicebioG1221Penicillin–streptomycin solutionCORNING30,002,304Phosphate-buffered saline (PBS)CORNING19,117,004Fetal bovine serumGibco1,932,$5950.25\%$ Trypsin–EDTA (1×)Gibco2,042,337DMEM basic (1×)Gibco8,119,090Loperamide (imodium)Xian Janssen Pharmaceutical Ltd.LGJ0549Lactulose (Duphalac)AbbottH20171057Lipopolysaccharide (LPS)Sigma-AldrichL2630Critical commercial assaysAnnexin V-FITC/PIMelunbioMA0229Mouse TNF-alpha ELISA KitProteintechKE10002CCK-8SolarbioCA1210RNAiso PlusVazymeR401HiScriptII Q RT SuperMix for qPCR (+gDNA wiper)VazymeR223ChamQ SYBR qPCR Master MixVazymeQ311Software and algorithmsR version3.6.3The R FoundationN/AImage JNIHN/AFlowJo V10BDN/ASIMCAUmetrics ABN/ASequence of primersGenesSequence of primersAQP35′-CGCTGGTGTCTTCGTGTACC-3’5′-TGTGGGCCAGCTTCACATTC-3’Enac-γ5’-TGAGTGACCTCCTGACTGACTTGG-3’5’-GAAATCTGGGTGGTGTGCCTTCC-3’Universal bacterial primers5’-AGAGTTTGATCCTGGCTCAG-3’5’-GGTTACCTTGTTACGACTT-3’ ## Preparation of hawthorn aqueous extract and postbiotic Ten gram caseinase digestion, 10 g beef paste powder, 4 g yeast paste powder, 2 g triammonium citrate, 5 g sodium acetate, 0.2 g magnesium sulfate, 0.05 g manganese sulfate, 2 g dimethyl hydrogen phosphate, 20 g glucose and 1.08 g Tween 80 were added into each liter of distilled water. Adjust the final pH value of this liquid to 5.7 ± 0.2. Then autoclave it, 121°C for 20 min. The monoclonal Lactobacillus paracasei (isolated from baby feces, identified again by colony PCR, the sequence was shown in Supplementary Table S1) cultured on the blood agar plates was added into it. Then place it in a shaker at 35°C and 220 rpm for 8 h. The OD600 value measured was 0.8–1.2. Some of the liquids was retained, and the remaining was continued in a shaker at 35°C and 220 rpm for 72 h. The supernatants were centrifuged at 12,000 g for 10 min. Filter it with a 0.22-μm filter. The hawthorns were soaked in distilled water for 0.5 h, and then boiled at 100°C for 30 min. The liquids were concentrated to 1 g/ml and then filtered with a 0.22-μm filter. The retained liquids were centrifuged with 8,000 g for 10 min to obtain the bacteria. Then the sterile hawthorn liquids were poured into the bacteria container. After fermentation for 72 h in a shaker at 35°C and 220 rpm, the supernatants were centrifuged at 12,000 g for 10 min and filtered with a 0.22-μm filter to obtain the postbiotic of hawthorn-probiotic. ## Animals and groups Two hundred and forty days old, male KM mice obtained from the Guangzhou Regal Biotechnology Co., Ltd., (SCXK [Yue] 2018–0182, SYXK [Yue] 2021–0059) were pair-housed in plastic cages in a temperature-controlled (25 ± 2°C) colony room under a $\frac{12}{12}$-h light/dark cycle, with free access to food and water. All experimental protocols were approved by the Animal Center, Guangzhou University of Chinese Medicine. The aged KM male mice were administrated with distilled water throughout the whole course and 6 mice were grouped as the normal controls without any other intervention (N). The rest mice were treated with 5 mg/kg loperamide for 1 week and randomly divided into model group (M), positive drug group (Y), hawthorn group (S), probiotic group (F) and postbiotic of hawthorn-probiotic group (FS). Mice in Y group were intragastrically treated with $10\%$ lactulose (0.2 ml/day/per mouse), S group with 1 g/ml pure hawthorn solution (0.2 ml/day/per mouse), F group with Lactobacillus paracasei supernatant (0.2 ml/day/per mouse) and FS group with postbiotic of hawthorn-probiotic (0.2 ml/day/per mouse) for another week. The body weight and other health indexes, fecal water content, intestinal propulsion rate, the levels of inflammatory factors in vivo, the structure of ileum and colon, and fecal flora of mice were determined. ## Histopathology and immunofluorescence The lung, spleen, liver, ileum, and colon tissues were removed and fixed in $4\%$ paraformaldehyde at pH 7.4 for further pathological experiments. These tissue samples were then embedded in paraffin and cut into 4 μm sections. Sections were stained with hematoxylin–eosin (H&E). Slides were observed under a light microscope. Deparaffinize and rehydrate: incubate sections in 3 changes of Biodewax and Clear Solution, 10 min each. Dehydrate in 3 changes of pure ethanol for 5 min. Wash in distilled water. Antigen retrieval: immerse the slides in EDTA antigen retrieval buffer (pH 8.0) and maintain at a sub-boiling temperature for 8 min, standing for 8 min and then followed by another sub-boiling temperature for 7 min. Be sure to prevent buffer solution evaporate. Let air cooling. Wash three times with PBS (pH 7.4) in a Rocker device, 5 min each. Use the right antigen retrieval buffer and heat extent according to tissue characteristics. Circle and Serum blocking: eliminate obvious liquid, mark the objective tissue with liquid blocker pen. Add $3\%$ BSA to cover the marked tissue to block non-specific binding for 30 min. Cover objective area with $10\%$ donkey serum (for the case of primary antibody originated from goat) or $3\%$ BSA (for the case of primary antibody originated from others). Primary antibody: throw away the blocking solution slightly. Incubate slides with primary antibody (diluted with PBS appropriately) overnight at 4°C, placed in a wet box containing a little water. Secondary antibody: wash slides three times with PBS (pH 7.4) in a Rocker device, 5 min each. Then throw away liquid slightly. Cover objective tissue with secondary antibody (appropriately respond to primary antibody in species), incubate at room temperature for 50 min in dark condition. FITC counterstain in nucleus: wash three times with PBS (pH 7.4) in a Rocker device, 5 min each. Then incubate with FITC solution at room temperature for 10 min, kept in dark place. Spontaneous fluorescence quenching: wash three times with PBS (pH 7.4) in a Rocker device. 5 min each. Add spontaneous fluorescence quenching reagent to incubate for 5 min. Wash in running tap water for 10 min. Mount: Throw away liquid slightly, then cover slip with anti-fade mounting medium. Microscopy detection and collect images by Fluorescent Microscopy. ## RNA isolation and quantitative analysis (RT-qPCR) RNA was extracted from colon tissue using RNAiso Plus according to the instructions. Then, cDNA was obtained using the ImProm-II™ Reverse Transcription System (Promega) and RT-qPCR was carried out with custom designed oligonucleotides using the HiScriptII Q RT SuperMix for qPCR (+gDNA wiper) and the ChamQ SYBR qPCR Master Mix in a total volume of 20 μl:95°C for 1 min and 40 cycles of denaturation (95°C for 15 s) and extension (60°C for 1 min). Experiments were performed in triplicates. Following amplification, dissociation curve analyses were performed to confirm the amplicon specificity for each PCR run. The relative level of gene expression in mouse colon tissue was normalized against mouse β-actin, respectively. Analysis of relative expression was performed using the 2(−ΔΔCT) method. ## Western blotting Briefly, global colon tissue was dissected from treated mice and proteins extracted with radioimmunoprecipitation assay (RIPA) lysis buffer. The proteins were separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis and transferred onto polyvinylidene fluoride membranes. After blocking with $5\%$ nonfat dry milk in Tris-buffered saline (20 mM Tris–HCl, 500 mM NaCl, pH 7.4) with $0.2\%$ Tween-20, the membranes were probed with antibodies overnight at 4°C, followed by incubation with a horseradish peroxidase- conjugated goat anti-mouse or goat anti-rabbit IgG secondary antibody (1: 2000). Band intensity was quantified using ImageJ software (NIH). ## Cell culture Caco2 cells were obtained from the National Collection of Authenticated Cell Cultures. Cells were cultured in DMEM Medium with $10\%$ fetal bovine serum and $1\%$ Penicillin–streptomycin solution as routine. All cells were grown in a humidified incubator at 37°C with $5\%$ CO2. ## CCK-8 detection Eight thousand cells per well of Caco2 cells suspension was prepared in a 96-well plate. The control group’s (N) wells were added with ordinary cell medium containing $5\%$ PBS to 100 μl per well. In the experimental group (FS), cell mediums containing $5\%$ postbiotic were added to 100 μl per well. Replace the fresh cell culture medium and add 10 μl of CCK-8 solution to each well in 12 h and 24 h. ## Flow cytometry with the Tdt-mediated UTP nick-end labeling 1,000,000 cells per well of Caco2 cells suspension was prepared in a 6-well plate. After 24 h, the cells adhere. Replace the fresh cell culture medium in control group’s (N) wells. And replace cell medium containing 10 ng/ml LPS in Model (LPS) and experimental group’s (FS) wells. After 1 h, discard the two group’s medium and use PBS washing wells for three times. Replace the fresh cell culture medium in LPS’s wells. Replace the cell medium containing $5\%$ postbiotic in FS’s wells. After 24 h, wash all wells with PBS. Add 500 μl trypsin to each well. After 2 min, add 1 ml medium to each well. Centrifuge at 2000 rpm for 5 min. Discard the medium. Add 100 μl binding buffer (1X), 5 μl FITC-Annexin V, 5 μl PI for each sample. After 15 min, cell apoptosis was detected by flow cytometry. ## Bioinformatics analysis of 16 S rRNA from fecal bacteria Sequences of the V3-V4 region of 16S rRNA genes were detected using an Illumina HiSeq 2,500 platform (Biomarker Technology Co. Ltd., Beijing, China). OTUs present in $50\%$ or more of the fecal samples were identified as core OTUs. The observed species, Shannon, Simpson and PD whole tree were calculated with QIIME2 2020.6 [57, 58]. The abundance and diversity of the OTUs (beta diversity) were examined using principal-coordinate analysis (PCoA) with weighted UniFrac analysis in R software. The linear discriminant analysis (LDA) effect size analysis (LEfSe) was used with the Kruskal–Wallis rank sum test to detect features with significantly different abundances between assigned taxa, and linear discriminant analysis was performed to estimate the effect size of each feature. The bacterial groups with LDA score of 4.00 were presented as the significantly abundant group in the indicated group. The phylogenetic tree was constructed between the feature sequences (16S rRNA) and the Integrated Microbial Genome Database (IMG) Reference sequence alignment (aliign) using PICRUSt2, and the “nearest species” of the feature sequences were found. *The* gene information of unknown species was predicted based on the information of gene type and abundance of known species, and the pathway of the whole community was predicted based on the KEGG pathway information of genes. COG (Clusters of Orthologous Groups of proteins) was a commonly used protein functional classification database of prokaryotes. COG function prediction analysis method was basically the same as KEGG. Faprotax [59] forecast analysis was conducted according to the literature. ## Data availability statement The 16S sequencing datasets presented in this study can be found in NCBI. The accession number(s) is PRJNA939336. Other data has been aggregated into the Supplementary material. ## Ethics statement The animal study was reviewed and approved by Experimental Animal Ethics Committee, Guangzhou University of Chinese Medicine. ## Author contributions YH conceived and designed the study, coordinated the study, and obtained the fund. YW, NH, and ML conducted the experiments, performed the data collection and analysis. XY and MW were responsible for raising the mice. YW drafted the manuscript. YH critically revised the manuscript. All authors have read and approved the final manuscript. ## Funding This research was funded by grants from Chinese National Natural Science Foundation (Grant No. 31700288). ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2023.1103463/full#supplementary-material ## References 1. Bharucha AE, Lacy BE. **Mechanisms, evaluation, and management of chronic constipation**. *Gastroenterology* (2020) **158** 1232-1249.e3. DOI: 10.1053/j.gastro.2019.12.034 2. Chen CL, Chao SH, Pan TM. *Heliyon* (2020) **6** e3804. DOI: 10.1016/j.heliyon.2020.e03804 3. Gallegos-Orozco JF, Foxx-Orenstein AE, Sterler SM, Stoa JM. **Chronic constipation in the elderly**. *Am J Gastroenterol* (2012) **107** 18-25. DOI: 10.1038/ajg.2011.349 4. Nelson AD, Camilleri M, Chirapongsathorn S, Vijayvargiya P, Valentin N, Shin A. **Comparison of efficacy of pharmacological treatments for chronic idiopathic constipation: a systematic review and network meta-analysis**. *Gut* (2017) **66** 1611-22. DOI: 10.1136/gutjnl-2016-311835 5. Pasay D, Guirguis M, Shkrobot R, Slobodan J, Bresee L. **Association of Dissemination of an educational communication tool with docusate administration**. *JAMA Intern Med* (2017) **177** 1433-6. DOI: 10.1001/jamainternmed.2017.3605 6. Sumida K, Molnar MZ, Potukuchi PK, Thomas F, Lu JL, Yamagata K. **Constipation and risk of death and cardiovascular events**. *Atherosclerosis* (2019) **281** 114-20. DOI: 10.1016/j.atherosclerosis.2018.12.021 7. Vriesman MH, Koppen I, Camilleri M, Di Lorenzo C, Benninga MA. **Management of functional constipation in children and adults**. *Nat Rev Gastroenterol Hepatol* (2020) **17** 21-39. DOI: 10.1038/s41575-019-0222-y 8. Parthasarathy G, Chen J, Chen X, Chia N, O'Connor HM, Wolf PG. **Relationship between microbiota of the colonic mucosa vs feces and symptoms, colonic transit, and methane production in female patients with chronic constipation**. *Gastroenterology* (2016) **150** 367-379.e1. DOI: 10.1053/j.gastro.2015.10.005 9. Ford AC, Quigley EMM, Lacy BE, Lembo AJ, Saito YA, Schiller LR. **Efficacy of prebiotics, probiotics, and synbiotics in irritable bowel syndrome and chronic idiopathic constipation: systematic review and meta-analysis**. *Am J Gastroenterol* (2014) **109** 1547-61. DOI: 10.1038/ajg.2014.202 10. Linetzky WD, Alves PCC, Logullo L, Manzoni JT, Almeida D, Teixeira DSML. **Microbiota benefits after inulin and partially hydrolized guar gum supplementation: a randomized clinical trial in constipated women**. *Nutr Hosp* (2012) **27** 123-9. DOI: 10.1590/S0212-16112012000100014 11. Bazzocchi G, Giovannini T, Giussani C, Brigidi P, Turroni S. **Effect of a new synbiotic supplement on symptoms, stool consistency, intestinal transit time and gut microbiota in patients with severe functional constipation: a pilot randomized double-blind, controlled trial**. *Tech Coloproctol* (2014) **18** 945-53. DOI: 10.1007/s10151-014-1201-5 12. Dan LW, Luciana CL, Amanda FB, Raquel ST, Glaucia MS, Natalia PP. **Effect of synbiotic in constipated adult women – a randomized, double-blind, placebo-controlled study of clinical response**. *Clin Nutr* (2013) **32** 27-33. DOI: 10.1016/j.clnu.2012.08.010 13. Ying JL, Rosita J, Abu SH, Jin YC. **Effects of synbiotics among constipated adults in Serdang, Selangor, Malaysia—a randomised, double-blind, placebo-controlled trial**. *Nutrients* (2018) **10** 10. DOI: 10.3390/nu10070824 14. Ahmad K, Mozhgan S. **Role of Synbiotics in the treatment of childhood constipation: a double-blind randomized placebo controlled trial. Iran**. *J Pediatr* (2010) 20 15. Salminen S, Collado MC, Endo A, Hill C, Lebeer S, Quigley EMM. **The international scientific association of probiotics and prebiotics (ISAPP) consensus statement on the definition and scope of postbiotics**. *Nat Rev Gastroenterol Hepatol* (2021) **18** 671. DOI: 10.1038/s41575-021-00481-x 16. Panebianco C, Andriulli A, Pazienza V. **Pharmacomicrobiomics: exploiting the drug-microbiota interactions in anticancer therapies**. *Microbiome* (2018) **6** 92. DOI: 10.1186/s40168-018-0483-7 17. Zhang J, Chai X, Zhao F, Hou G, Meng Q. **Food applications and potential health benefits of hawthorn**. *Foods* (2022) **11** 11. DOI: 10.3390/foods11182861 18. Yoon KY, Woodams EE, Hang YD. **Probiotication of tomato juice by lactic acid bacteria**. *J Microbiol* (2004) **42** 315-8. PMID: 15650688 19. Park SY, Ji GE, Ko YT, Jung HK, Ustunol Z, Pestka JJ. **Potentiation of hydrogen peroxide, nitric oxide, and cytokine production in RAW 264.7 macrophage cells exposed to human and commercial isolates of**. *Int J Food Microbiol* (1999) **46** 231-41. DOI: 10.1016/s0168-1605(98)00197-4 20. Sanders ME. **Considerations for use of probiotic bacteria to modulate human health**. *J Nutr* (2000) **130** 384S-90S. DOI: 10.1093/jn/130.2.384S 21. Felis GE, Dellaglio F. **Taxonomy of Lactobacilli and Bifidobacteria**. *Curr Issues Intest Microbiol* (2007) **8** 44-61. PMID: 17542335 22. Jia X, Wang W, Song Y, Li N. **A 90-day oral toxicity study on a new strain of**. *Food Chem Toxicol* (2011) **49** 1148-51. DOI: 10.1016/j.fct.2011.02.006 23. Gardiner G, Ross RP, Collins JK, Fitzgerald G, Stanton C. **Development of a probiotic cheddar cheese containing human-derived**. *Appl Environ Microbiol* (1998) **64** 2192-9. DOI: 10.1128/AEM.64.6.2192-2199.1998 24. De Angelis M, Corsetti A, Tosti N, Rossi J, Corbo MR, Gobbetti M. **Characterization of non-starter lactic acid bacteria from Italian ewe cheeses based on phenotypic, genotypic, and cell wall protein analyses**. *Appl Environ Microbiol* (2001) **67** 2011-20. DOI: 10.1128/AEM.67.5.2011-2020.2001 25. Valerio F, Russo F, de Candia S, Riezzo G, Orlando A, Lonigro SL. **Effects of probiotic**. *J Clin Gastroenterol* (2010) **44** S49-53. DOI: 10.1097/MCG.0b013e3181d2dca4 26. Zhang X, Chen S, Zhang M, Ren F, Ren Y, Li Y. **Effects of fermented Milk containing**. *Nutrients* (2021) **13** 13. DOI: 10.3390/nu13072238 27. Negussie AB, Dell AC, Davis BA, Geibel JP. **Colonic fluid and electrolyte transport 2022: An Update**. *Cells* (2022) **11** 11. DOI: 10.3390/cells11101712 28. Jiang C, Xu Q, Wen X, Sun H. **Current developments in pharmacological therapeutics for chronic constipation**. *Acta Pharm Sin B* (2015) **5** 300-9. DOI: 10.1016/j.apsb.2015.05.006 29. Ikarashi N, Kon R, Sugiyama K. **Aquaporins in the colon as a new therapeutic target in diarrhea and constipation**. *Int J Mol Sci* (2016) **17** 17. DOI: 10.3390/ijms17071172 30. Roudier N, Ripoche P, Gane P, Le Pennec PY, Daniels G, Cartron JP. **AQP3 deficiency in humans and the molecular basis of a novel blood group system**. *GIL J Biol Chem* (2002) **277** 45854-9. DOI: 10.1074/jbc.M208999200 31. Hara-Chikuma M, Chikuma S, Sugiyama Y, Kabashima K, Verkman AS, Inoue S. **Chemokine-dependent T cell migration requires aquaporin-3-mediated hydrogen peroxide uptake**. *J Exp Med* (2012) **209** 1743-52. DOI: 10.1084/jem.20112398 32. Hara-Chikuma M, Satooka H, Watanabe S, Honda T, Miyachi Y, Watanabe T. **Aquaporin-3-mediated hydrogen peroxide transport is required for NF-kappaB signalling in keratinocytes and development of psoriasis**. *Nat Commun* (2015) **6** 7454. DOI: 10.1038/ncomms8454 33. Satooka H, Hara-Chikuma M. **Aquaporin-3 controls breast cancer cell migration by regulating hydrogen peroxide transport and its downstream cell signaling**. *Mol Cell Biol* (2016) **36** 1206-18. DOI: 10.1128/MCB.00971-15 34. Hobbs CA, Blanchard MG, Alijevic O, Tan CD, Kellenberger S, Bencharit S. **Identification of the SPLUNC1 ENaC-inhibitory domain yields novel strategies to treat sodium hyperabsorption in cystic fibrosis airway epithelial cultures**. *Am J Physiol Lung Cell Mol Physiol* (2013) **305** L990-L1001. DOI: 10.1152/ajplung.00103.2013 35. Hansson JH, Nelson-Williams C, Suzuki H, Schild L, Shimkets R, Lu Y. **Hypertension caused by a truncated epithelial sodium channel gamma subunit: genetic heterogeneity of Liddle syndrome**. *Nat Genet* (1995) **11** 76-82. DOI: 10.1038/ng0995-76 36. Johansson ME, Thomsson KA, Hansson GC. **Proteomic analyses of the two mucus layers of the colon barrier reveal that their main component, the Muc2 mucin, is strongly bound to the Fcgbp protein**. *J Proteome Res* (2009) **8** 3549-57. DOI: 10.1021/pr9002504 37. Mayer EA, Savidge T, Shulman RJ. **Brain-gut microbiome interactions and functional bowel disorders**. *Gastroenterology* (2014) **146** 1500-12. DOI: 10.1053/j.gastro.2014.02.037 38. Kashyap PC, Marcobal A, Ursell LK, Larauche M, Duboc H, Earle KA. **Complex interactions among diet, gastrointestinal transit, and gut microbiota in humanized mice**. *Gastroenterology* (2013) **144** 967-77. DOI: 10.1053/j.gastro.2013.01.047 39. Zhang X, Yang H, Zheng J, Jiang N, Sun G, Bao X. **Chitosan oligosaccharides attenuate loperamide-induced constipation through regulation of gut microbiota in mice**. *Carbohydr Polym* (2021) **253** 117218. DOI: 10.1016/j.carbpol.2020.117218 40. Zhang X, Zheng J, Jiang N, Sun G, Bao X, Kong M. **Modulation of gut microbiota and intestinal metabolites by lactulose improves loperamide-induced constipation in mice**. *Eur J Pharm Sci* (2021) **158** 105676. DOI: 10.1016/j.ejps.2020.105676 41. Drewes JL, White JR, Dejea CM, Fathi P, Iyadorai T, Vadivelu J. **High-resolution bacterial 16S rRNA gene profile meta-analysis and biofilm status reveal common colorectal cancer consortia**. *Npj Biofilms Microbiomes* (2017) **3** 34. DOI: 10.1038/s41522-017-0040-3 42. Mirzaei R, Ranjbar R. **Hijacking host components for bacterial biofilm formation: an advanced mechanism**. *Int Immunopharmacol* (2022) **103** 108471. DOI: 10.1016/j.intimp.2021.108471 43. Sears CL, Garrett WS. **Microbes, microbiota, and colon cancer**. *Cell Host Microbe* (2014) **15** 317-28. DOI: 10.1016/j.chom.2014.02.007 44. Dejea CM, Fathi P, Craig JM, Boleij A, Taddese R, Geis AL. **Patients with familial adenomatous polyposis harbor colonic biofilms containing tumorigenic bacteria**. *Science* (2018) **359** 592-7. DOI: 10.1126/science.aah3648 45. Kim JS, Park C, Kim YJ. **Role of flgA for flagellar biosynthesis and biofilm formation of**. *J Microbiol Biotechnol* (2015) **25** 1871-9. DOI: 10.4014/jmb.1504.04080 46. Roy R, Tiwari M, Donelli G, Tiwari V. **Strategies for combating bacterial biofilms: a focus on anti-biofilm agents and their mechanisms of action**. *Virulence* (2018) **9** 522-54. DOI: 10.1080/21505594.2017.1313372 47. Timilsina S, Potnis N, Newberry EA, Liyanapathiranage P, Iruegas-Bocardo F, White FF. *Nat Rev Microbiol* (2020) **18** 415-27. DOI: 10.1038/s41579-020-0361-8 48. Hou S, Makarova KS, Saw JH, Senin P, Ly BV, Zhou Z. **Complete genome sequence of the extremely acidophilic methanotroph isolate V4,**. *Biol Direct* (2008) **3** 26. DOI: 10.1186/1745-6150-3-26 49. Rakoff-Nahoum S, Coyne MJ, Comstock LE. **An ecological network of polysaccharide utilization among human intestinal symbionts**. *Curr Biol* (2014) **24** 40-9. DOI: 10.1016/j.cub.2013.10.077 50. Bolam DN, Sonnenburg JL. **Mechanistic insight into polysaccharide use within the intestinal microbiota**. *Gut Microbes* (2011) **2** 86-90. DOI: 10.4161/gmic.2.2.15232 51. Sonnenburg ED, Zheng H, Joglekar P, Higginbottom SK, Firbank SJ, Bolam DN. **Specificity of polysaccharide use in intestinal bacteroides species determines diet-induced microbiota alterations**. *Cells* (2010) **141** 1241-52. DOI: 10.1016/j.cell.2010.05.005 52. Porter NT, Luis AS, Martens EC. **Bacteroides thetaiotaomicron**. *Trends Microbiol* (2018) **26** 966-7. DOI: 10.1016/j.tim.2018.08.005 53. Wells JM, Brummer RJ, Derrien M, MacDonald TT, Troost F, Cani PD. **Homeostasis of the gut barrier and potential biomarkers**. *Am J Physiol Gastrointest Liver Physiol* (2017) **312** G171-93. DOI: 10.1152/ajpgi.00048.2015 54. Derrien M, Belzer C, de Vos WM. *Microb Pathog* (2017) **106** 171-81. DOI: 10.1016/j.micpath.2016.02.005 55. Everard A, Belzer C, Geurts L, Ouwerkerk JP, Druart C, Bindels LB. **Cross-talk between**. *Proc Natl Acad Sci U S A* (2013) **110** 9066-71. DOI: 10.1073/pnas.1219451110 56. Derrien M, Vaughan EE, Plugge CM, de Vos WM. *Int J Syst Evol Microbiol* (2004) **54** 1469-76. DOI: 10.1099/ijs.0.02873-0 57. Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA. **Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2**. *Nat Biotechnol* (2019) **37** 852-7. DOI: 10.1038/s41587-019-0209-9 58. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. **DADA2: high-resolution sample inference from Illumina amplicon data**. *Nat Methods* (2016) **13** 581-3. DOI: 10.1038/nmeth.3869 59. Louca S, Parfrey LW, Doebeli M. **Decoupling function and taxonomy in the global ocean microbiome**. *Science* (2016) **353** 1272-7. DOI: 10.1126/science.aaf4507
--- title: Fucosylated TLR4 mediates communication between mutualist fucotrophic microbiota and mammalian gut mucosa authors: - Nanda N. Nanthakumar - Di Meng - David S. Newburg journal: Frontiers in Medicine year: 2023 pmcid: PMC10061023 doi: 10.3389/fmed.2023.1070734 license: CC BY 4.0 --- # Fucosylated TLR4 mediates communication between mutualist fucotrophic microbiota and mammalian gut mucosa ## Abstract ### Objective The glycans on the mucosa of suckling mice are predominantly sialylated; upon weaning, fucosylated glycans preponderate. This manifestation of mutualism between fucotrophic bacteria and the mature host utilizes a sentinel receptor in the intestinal mucosa; this receptor was isolated to distinguish its structural and functional features. ### Design Provisional identification of the sentinel gut receptor as fuc-TLR4 was through colonization of germ-free mutant mice. Conventional mice whose microbiota was depleted with a cocktail of antibiotics were used to further define the nature and functions of fuc-TLR4 sentinel, and to define the role of the fucotrophic microbiota in gut homeostasis and recovery from insult. The nature of the sentinel was confirmed in cultured human HEL cells. ### Results Fuc-TLR4 activity is distinct from that of TLR4. Activated mucosal fuc-TLR4 induces a fuc-TLR4 dependent non-inflammatory (ERK and JNK dependent, NF-κB independent) signaling cascade, initiating induction of fucosyltransferase 2 (secretor) gene transcription. In vitro, either defucosylation or TLR4 knockdown abrogates FUT2 induction, indicating that fuc-TLR4 activity requires both the peptide and glycan moieties. In vivo, fucose-utilizing bacteria and fucose-binding ligands induce mucosal fucosylation. Activation of this pathway is essential for recovery from chemically induced mucosal injury in vivo. ### Conclusion In mature mice, fucosyl-TLR4 mediated gut fucosylation creates a niche that supports the healthy fucose-dependent mutualism between the mammalian gut and its fucotrophic microbes. Such microbiota-induced Fuc-TLR4 signaling supports initial colonization of the secretor gut, recovery from dysbiosis, and restoration or preservation of intestinal homeostasis. ## Introduction A popular supposition in the evolution of life is that the genesis of eukaryotes from a world filled with prokaryotes was accompanied by many successful eukaryotes participating in symbiotic mutualism with prokaryotes. In a mutualistic relationship, the specific pairing goes beyond providing essential nutrients to one another, and depends upon each mutualist binding to specific receptors in the other partner to mediate reciprocal interkingdom communication. Such mutualism between mammalian intestinal mucosa and the gut microbiota is of high relevance to human health. The large and complex microbial ecosystem within the mammalian gastrointestinal tract had long been considered to be comprised of commensals, but increasing evidence indicates that the relationship is mutualistic (1–6). Both benefit. The mucosa provides a protected warm, wet, nutrient-rich niche for the microbiota. In return, harmonious cohabitation with mutualistic microbes maintains homeostasis of the intestinal mucosa [3, 4, 7, 8] and protects against various forms of insult. Mutualism involves reciprocal communication. Upon contact, microbes and host undergo mutually accommodating gene expression and signaling [4, 7, 9, 10]. Most prior research on bacterial signaling in mammalian gut focused on activation of the inflammatory component of an immune response to pathogens, often mediated by toll-like receptors (TLRs). Toll-like receptor 4 is activated by lipopolysaccharide (LPS), a cell wall component of Gram-negative bacteria, initially resulting in assembly of a signaling complex that includes MyD88. This complex activates a signal transduction cascade that enables nuclear translocation of transcription factors including AP1 and NF-κB (10–12), which activate transcription of pro-inflammatory genes to elicit IL-8 and TNF-α. The canonical signaling cascades activated by TLRs all lead to inflammation. Such inflammatory bacterial signaling by the innate immunity receptors TLR2 and TLR4 is also requisite for recovery from intestinal injury [13, 14]. In stark contrast to this paradigm, specific signaling pathways activated by pioneering species of fucotrophic bacteria during early colonization of adult murine gut mediate adaptive gene expression without activating inflammation [1, 9]. Specifically, these microbes induce fut2 gene expression in the intestinal epithelium, leading to a highly fucosylated mucosa. The fut2 (secretor) gene encodes the galactoside 2-α-L-fucosyltransferase II (FucT II; EC 2.4.1.69), an inducible enzyme that adds α1,2 linked L-fucose to a terminal D-galactose of glycans. In the intestinal epithelial cell, these fucosylated glycans are generally transported to the extracellular glycocalyx of the intestinal mucosa [1, 9, 15]. This fucosylated niche is hospitable to a microbial ecosystem that contains fucotrophic mutualists supportive of host resilience to a variety of mucosal insults (15–17). This fut2 expression is induced by early colonization via the ERK and JNK signaling pathways without stimulating the inflammatory NF-κB pathway [9]. In conventional mice, fully colonized post-weaning gut is heavily fucosylated, whereas colonized suckling gut exhibits a preponderance of sialylated glycans, with distinctly low fucosylation (17–19). The crossover at weaning suggested four potential regulatory mechanisms: [1] change in diet; [2] innate timing by developmentally sensitive genes; [3] modified hormonal milieu; or [4] a shift in microbiota. These possibilities were differentiated in germ-free mice, where the shift in fucosylation did not occur at weaning, eliminating change in diet, innate developmental gene control, and hormonal shifts as candidates [9]. This conclusion is reinforced by the converse experiment, in which adult germ-free mice, whose gut mucosa are predominantly sialylated, upon being colonized by normal adult murine microbiota, initiate fucosylation of their mucosa. Moreover, when conventional colonized mice are treated with a cocktail of broad-spectrum antibiotics, their microbiota become depleted and dysbiotic, and the fucosylation of their gut reverts to the immature, sialylated state [9, 15]. When such germ-free or antibiotic-treated bacterially-depleted mice are exposed to conventional fecal microbiota, mucosal fucosylation is rapidly induced. Furthermore, if germ-free or antibiotic-treated mice are inoculated solely with an individual fucose-utilizing bacterium species of normal mammalian microbiota, either Bacteriodes thetaiotaomicron or B. fragilis, mucosal fucosylation is rapidly induced [15, 20]. This implies active regulation of gut fucosylation by normal intact microbiota and its major individual fucose-utilizing (fucotrophic) bacterial mutualists. Other systems also display a link between bacteria and fut2 activation. For example, mucosal fut2 expression is activated by IL-22 of lymphoid cells in a bacteria-dependent manner [21] and expression of epithelial IL-22 receptor enhances fucose-dependent host-microbe mutualism [22]. Conversely, mucosal glycans of the distal gut, along with indigestible residual dietary glycans, modulate composition and function of the microbiota [23]. Epithelial fucosylation regulates microbial metabolism and reduces bacterial virulent gene expression, thereby protecting the host [21, 24]. These data are all consistent with interdependence between microbiota colonization and fucosylation of the intestinal mucosa (1–4, 7). Mice whose microbiota has been disrupted by antibiotics and are unable to maintain intestinal fucosylation are also less able to recover from mucosal injury. Restoration of the microbiota re-establishes fucosylation and recovery from injury [9, 15]. Colonization with only B. fragilis [9343], a fucotrophic gut bacterium, also fully reinstates fucosylation and recovery of homeostasis [15], whereas a mutant of B. fragilis (Δgmd-fclΔfkp) that is unable to utilize fucose does not restore fut2 expression, mucosal fucosylation, or recovery of homeostasis. The concordance of fucosylation, colonization, and mucosal resilience implies a strong and active mutualism that underlies healthy homeostasis of the gut and recovery from insult. To prove this mutualism, the molecules mediating bidirectional interkingdom communication needed to be identified and defined. The studies described herein are focused on discovering and defining the molecule that mediates communication from mutualist fucotrophic bacteria in the murine gut to the nucleus of the intestinal mucosal cell. Accordingly, this study addresses the following hypothesis: mature mammalian gut, when not colonized with fucotrophic bacteria at or after weaning, expresses a “sentinel” receptor molecule on the mucosal surface that, upon exposure to pioneering mutualistic fucotrophic bacteria, signals the intestinal epithelial cell nucleus and activates fut2, thereby inducing or renewing the accommodating fucosylated niche. Three postulates are tested: sentinel binding to fucotrophic bacteria induces fut2 expression through activation of an ERK and JNK non-inflammatory signaling cascade. The sentinel molecule has unique characteristics. Activation of this sentinel molecule is necessary and sufficient to promote fucosylation of the intestinal mucosa, thereby reinforcing intestinal colonization by fucotrophic mutualists and homeostasis of adult gut. ## Chemicals Phenyl-β-D-galactoside, BSA, 2-mercaptoethanol, 2,4,6-t rinitrobenzene sulfonic acid (TNBS), and oxazalone (OXA) were from Sigma Chemical Co. (St. Louis, MO, United States). 10 mM GDP-[14C] Fucose (specific activity = 1.8 mCi/mmol) was from New England Nuclear Life Sciences (Boston, MA, United States). Taqman reverse transcription kits and enzymes were from Applied Biosystems (InVitrogen®, San Diego, CA, United States). Biotinylated and fluorescein-conjugated *Ulex europaeus* agglutinin-1 (UEA-I) were from E-Y Laboratories (San Mateo, CA, United States). Anti-Toll-like receptor-4 and anti-E-cadherin were from Santa Cruz Biotechnology (Santa Cruz, CA, United States). All other reagents were of analytical or molecular biology grade from Fisher Scientific (Fairlawn, NJ) or Sigma Chemical Co. Strains of *Bacteroides fragilis* were a gift from Dr. Laurie Comstock, Brigham and Women’s Hospital, Boston, MA, United States. ## Mice C57B/6, C3H/Hej, C3H/Ouj, BALB/c mice, TLR4−/−, TLR2−/−, and MyD88−/− mutant mice were from Jackson Labs, Bar Harbor, ME. Germ-free mice were maintained germ-free until immediately before being euthanized at 6 week of age. Ex-germ-free mice were produced by removing germ-free mice from their pristine environment at 4 or 6 weeks age and exposing them to conventional microbiota as a slurry of fresh fecal and cecal contents from age-matched conventional control mice (1 conventional mouse: 5 germ-free) through orogastric intubation, drinking water, and housing with conventional mice. All animals were euthanized between 12 and 3 PM [9]. At sacrifice, mouse colons were harvested. Mice were depleted of luminal bacteria by an antibiotic cocktail (100 μl antibiotic cocktail·mouse−1·day−1) in the drinking water for 2 weeks, whereupon commensal bacteria were introduced for 2 weeks, the mice were euthanized, and fucosyltransferase activity and fut2 mRNA levels measured in each colon. The antibiotic cocktail contained Kanamycin (8 mg/ml), Gentamicin (0.7 mg/ml), Colistin (34,000 U/ml), Metronidazole (4.3 mg/ml), and Vancomycin (0.9 mg/ml). After the antibiotic cocktail was introduced, fresh fecal samples were collected daily from mouse and the presence of bacteria assessed in five different aerobic and anaerobic culture media [9, 15]. ## DSS-treatment Mice received $3.5\%$ (wt/vol) DSS (40,000 kDa; ICN Biochemicals) in their drinking water ad libitum for 5 days, then ordinary drinking water [13, 15, 16]. The amount of DSS water consumed per animal was similar across strains. Control mice received water only. For survival studies, mice were followed 12 days post start of DSS-treatment. Mice were weighed on alternate days, with % weight change calculated as: (weight at day x–day 0/ weight at day 0). Animals were monitored for rectal bleeding, diarrhea, and morbidity, including hunched posture and failure to groom. Similarly, consequences of $2.5\%$ 2,4,6-trinitrobenzene sulfonic acid (TNBS) and oxazalone (OXA)-induced colitis [13, 15, 16] were compared to determine if phenomena observed in recovery from DSS could be generalized to recovery from mucosal injury per se, irrespective of cause. The three models produced essentially similar data, thus only DSS data are shown. Care of mice followed institutional guidelines under a protocol approved by the Institutional Animal Care Committee at the Massachusetts General Hospital and Virginia Tech. ## Fucosyltransferase activity The entire small intestine and colon was removed and thoroughly flushed with ice-cold $0.9\%$ NaCl. Fucosyltransferase activitiy was measured in samples of colon as described [9, 15]. A $10\%$ mucosal homogenate was prepared from colon in 0.1 M Tris–HCl buffer (pH 7.4), and the homogenate was centrifuged at 1,000 × g for 15 min to remove nuclei and cellular debris. The supernatant was then centrifuged at 105,000 × g for 1 h, resulting in a membrane fraction and soluble cell fluid. The resulting pellets were re-suspended in homogenization buffer (0.1 M Tris–HCl, pH 7.4), frozen as aliquots at −80°C, and used for the fucosyltransferase assay. α1,$\frac{2}{3}$-Fucosyltransferase enzyme activity was assessed using phenyl-β-D-galactoside as the acceptor [9, 15]. The reaction mixture for each assay contained, in a total volume of 0.1 ml, 25 mM phenyl-β-D- galactoside, 20 mM sodium phosphate buffer (pH 6.1), 10 mM fucose, 5 mM ATP, 20 mM MgCl2, 50 mM NaCl, $0.5\%$ Triton X-100, 10 nmol GDP-[14C]fucose (0.1 μCi, specific activity 11 mCi/mmol; New England Nuclear), and homogenate containing 50–100 μg protein. GDP-fucose concentration was at saturation, and product formation was linear for 2 h of incubation for up to 100 μg of enzyme protein at 37°C. After 2 h, the reaction was terminated by addition of 100 μl of ethanol and dilution with 1 ml of 4°C H2O followed by centrifugation at 15,000 × g for 5 min. The supernatant was applied to C-18 Bond Elute cartridges (500 mg) that had previously been washed with 6 ml of acetonitrile followed by 6 ml water. After application of the sample, the cartridges were washed with 5 ml water to remove the radiolabeled precursor. The product, [14C]-fucosylphenyl-β-D-galactoside, was eluted with 1.5 ml $50\%$ acetonitrile directly into scintillation vials. Five milliliters of scintillation cocktail (Ready Safe, Beckman, Fullerton, CA, United States) was added to each vial, and radioactivity determined by scintillation counting of the clear solution. Specific activity is expressed as nmol [14C] fucose incorporated/h/mg protein. ## Cell cultures Human erythroleukemia cells (HEL cells), HeLa, and T84 (ATCC, Manassas, VA, United States) were grown in Dulbecco’s modified eagle medium (DMEM) supplemented with $10\%$ FBS, $1\%$ nonessential amino acids, 50 IU/ml penicillin, 50 μg/ml streptomycin, and $1\%$ HEPES buffer. Cells were grown at 37°C in $95\%$ O2 and $5\%$ CO2. Cells were stimulated with LPS (1–100 ng/ml) and UEA1 (0.1–1 μg/ml); total RNA was isolated after 16 h and analyzed for mRNA by RT-PCR [9, 15]. Media were collected for IL-8 quantification. ## IL-8 ELISA Microtiter plates (96 well; Nunc-Maxisorp, Fisher Scientific, Pittsburgh, PA, United States) were coated overnight with anti-human IL-8 (R&D Systems, Minneapolis, MN, United States), and incubated for 70 min at 37°C with 100 μl of cell supernatant. After sequential incubations with rabbit anti-human IL-8 (Endogen, Woburn, MA, United States) followed by horseradish peroxidase (HRP)-conjugated goat anti-rabbit IgG (Biosource, Camarillo, CA, United States) and 2,2′ azino-bis (3-ethylbenz-thiazoline-6-sulfonic acid; ABTS), absorbance was measured at 405 nm. IL-8 concentrations determined from a standard curve of purified recombinant human IL-8 (R&D Systems, Minneapolis, MN, United States) and normalized to total cellular protein. ## Protein determination Protein was measured by bicinchoninic acid binding (Pierce, Rockford, IL, United States) according to the manufacturer’s protocol, but modified for 96-well microtiter plates. Each protein sample (10 μl), was added to 200 μl working reagent and incubated at 37°C for 30 min. Absorbance at 560 nm was measured on a microtiter reader (BT 2000 Microkinetics Reader Spectrophotometer, Fisher Biotech, Pittsburgh, PA, United States). Concentration was calculated from a standard curve of bovine serum albumin [25, 26]. ## Lectin histochemistry with UEA-1 Expression of fucosyl glycoconjugates on the mucosal surface was measured on frozen tissue sections using FITC-conjugated *Ulex europaeus* agglutinin-1 (UEA-1; Vector Laboratories, Burlingame, CA, United States). The middle 1 cm of the colon was fixed for 4 h at 4°C in $4\%$ paraformaldehyde, washed in ice-cold PBS containing $30\%$ sucrose overnight at 4°C, and embedded in optimal cutting temperature compound. Frozen sections (6–7 μm thick) were blocked with PBS containing $2\%$ BSA and then stained with labeled lectin for 1 h (10 μg/ml). Sections were then washed three times in ice-cold PBS, mounted using Anti-Fade (Vector Labs), and analyzed by confocal microscopy [9]. ## SDS PAGE analysis Protein samples (30 or 50 μg) mixed with SDS sample buffer were loaded on 10–$20\%$ SDS Tris·HCl ready gels and transferred to Immun-Blot polyvinylidene difluoride membranes (Bio-Rad, Hercules, PA, United States). The membranes were blocked in blot A, $5\%$ (wt/vol) Carnation nonfat dry milk (Nestlé, Solon, OH, United States) in Tris-buffered saline supplemented with $0.05\%$ Tween 20 at room temperature for 1 h, then incubated overnight at 4°C with antibody or lectin. Blots were washed three times for 10 min each in blot A and then incubated with horseradish peroxidase-conjugated secondary antibody for 1 h at room temperature. After two 10-min washes in blot A and three 10-min washes in Tris-buffered saline, blots were developed via enhanced chemiluminescence (Supersignal; Pierce) [9, 25, 26]. ## Total RNA isolation and quantitative RT-PCR The RNA RNeasy Mini kit (Quigen, Valencia, CA, United States) was used to extract total RNA from homogenized tissue. RNA was reverse transcribed with random hexamers using a GeneAmp RNA PCR kit (Applied Biosystems, Foster City, CA, United States), and the cDNA was amplified using iQ SYBR Green Supermix (Bio-Rad) and 5 μM of each primer [25, 26]. GAPDH primers were amplified in all samples. Duplicate cDNA samples were amplified 40 cycles for fut2, IL-8, and ICAM-1 and 42 cycles for fut1 for 1 min at 95°C and 1 min at 72°C. The threshold cycle (CT) was the cycle number at which fluorescence of the amplified product crossed a specified threshold value during exponential amplification. Mean ΔCT values of each mRNA were normalized by subtracting the mean CT value of its control GAPDH mRNA. The primer sequences and calculations for relative quantification were carried out using the comparative CT method (2−ΔCT), as described [25, 26]. ## Statistical analysis The statistical significance of differences between treatment and control groups in all categorical comparisons was determined by factorial ANOVA, with results reported as mean ± SEM. Time to recovery was compared using Kaplan Meier survival analysis, with significant differences assessed by comparing the areas under the receiver operator curve (AUROC) by χ2. Statistical significance was set at α ≤ 0.05. Statistical analyses were performed using the XLStat software (Addinsoft, Brooklyn, NY). ## Colonization induces fucosylation of adult mouse gut mucosa Depleting bacteria via an oral cocktail of antibiotics in 4–8-week-old mature mice decreased intestinal fucosylation and resulted in reversion to the predominantly sialylated mucosa typical of suckling gut (Figure 1 and supplement). Discontinuing the antibiotics and replenishing with adult mouse microbiota through exposure to the feces and bedding of conventional adult mice results in recovery of gut fucosylation, measured as increased α1,$\frac{2}{3}$-fucosyltransferase (FucT) activity (Figure 1A) and fut2 mRNA levels (Figure 1B). This occurred in all strains of mouse tested (Figures 1A–C), consistent with this aspect of mutualism being a general phenomenon. **Figure 1:** *α1,2/3-fucosyltransferase (FucT) activity and fut2 mRNA in conventionally raised (CONV), bacteria-depleted (BD), and bacteria-repleted (XBD) mice. (A) Black Swiss, (B) C3H, (C,D) C57/B6. C3H/HeJ is isogenic strain of C3H/Ouj, but with an inactive TLR4 point mutation. The wild-type C57/B6 mice and TLR2−/− exhibited normal colonization dependent induction of FucT activity and fut2 mRNA. TLR4−/− and MyD88−/− mutants displayed low expression FucT activity and fut2 mRNA, irrespective of colonization status. Thus, TLR4-mediated signaling is necessary for bacterial induction of fucosyltransferase expression. Data are mean ± SEM, n = 6–8 (*p < 0.01; **p < 0.001).* ## TLR4 is required for colonization-induced fucosylation Toll-like receptors are known universal Gram-negative microbial sensors. Accordingly, TLRs were tested as potential mediators of bacteria-induced FucT activation and fut2 mRNA expression in colonic mucosa (Figure 1). TLR4−/−, TLR2−/−, and MyD88−/− mice [12, 13] were used to identify signaling pathways essential for colonization-dependent fucosylation. Neither TLR4−/− nor MyD88−/− mice exhibited the colonization-dependent fucosylation observed in the wild type, while TLR2−/− mice did. Thus, both TLR4 and its downstream mediator, MyD88, are essential for bacteria-dependent up-regulation of fut2 mRNA, FucT activity, and fucosylation of the mucosal surface, but TLR2 is not involved. Likewise, another strain of mouse, C3H/HeJ, is unable to express functional TLR4 [27], and also was unable to express fut2 mRNA and FucT activity in response to colonization (Figure 1B), while its wild type C3H/OuJ could do so. Moreover, conventionally raised TLR4 mutant mice never achieved full fucosylation. These data are consistent with TLR4 activation being essential for initiation (and maintenance) of mucosal fucosylation, but whether TLR4 activation is sufficient was investigated next. ## In bacteria depleted mice, TLR4 stimulation induces fucosylation In bacteria-depleted (BD) mice, orally administered TLR4 ligand, lipopolysaccharide (LPS), was able to induce fut2 mRNA, FucT activity, and fucosylation ($p \leq 0.01$; Figure 2). Peptidoglycan (PG), the TLR2 ligand, did not. Germ-free mice manifest the same phenomena. Moreover, a commensal Gram-negative E. coli that expresses LPS on its surface also induced fucosylation ($p \leq 0.01$), while Gram-positive *Lactobacillus plantarum* (LP), which does not produce LPS, did not induce fucosylation in BD mice (Figure 2). Thus, luminal stimulation of TLR4 in BD mice is the critical signal necessary for colonization-mediated mucosal fucosylation. In all of these model systems, fucosylation is induced without activating inflammation, suggesting that TLR4 in BD mouse mucosa was different from prototypic TLR4. **Figure 2:** *Recovery of FucT and fut2 mRNA by recolonization. (A) α1,2/3-fucosyltransferase (FucT) activity and (B) fut2 mRNA in BD mice following recolonization with normal microflora (XBD), non-pathogenic Gram-negative E. coli (F18), or by ultra-pure LPS treatment. FucT activity and fut2 mRNA expression was not induced by Gram-positive bacteria Lactobacillus plantarum (L.p) or by peptidoglycan (PG) treatment. Thus, in bacteria depleted mice, ligands for TLR4, but not TLR2, induce FucT activity and fut2 mRNA. Data are mean ± SEM n = 8–10 (*p < 0.01; **p < 0.001).* ## Non-colonized adult gut epithelium expresses a unique fucosylated TLR4 The TLR4 from colonic epithelium of conventionally colonized (CONV), germ-free (GF) and newly recolonized germ-free (XGF) mice was isolated by immunoprecipitation (IP) and characterized by western blot (WB). The quantity of TLR4 protein (measured by binding to anti-TLR4 IgG2a) was similar for the three conditions, as was E-cadherin, a constitutive protein (Figure 3A). The TLR4s from all three conditions were sialylated, measured as *Sambucus nigra* agglutinin (SNA) lectin binding. In contrast, only the TLR4s from germ-free mouse colon bound *Ulex europaeus* agglutinin 1 (UEA1) lectin, indicating the presence of α1,2-linked fucosylation in addition to the sialylation. In the inverse experiment, protein from the three colonic epithelia was precipitated by UEA1 lectin, and only the precipitate from the germ-free (GF) mice was positive for TLR4 when probed by TLR4-specific IgG2a antibody (Supplementary Figure 1A), confirming that only the germ-free TLR4 was fucosylated (fuc-TLR4). In other control experiments, the precipitate of an isotype specific control antibody, an IgG2a that does not bind to TLR4, did not immuno-precipitate TLR4. Although fuc-TLR4 was quite evident in colonic epithelium, it was not found in other tissues, including peritoneal macrophages or liver of germ-free mice. **Figure 3:** *A novel fucosylated form of TLR-4 (fuc-TLR4) is present in intestinal epithelial cells of germfree mice (A). UEA-I, which binds α1,2-fucosylglycans, did not bind the TLR4 of conventional (CONV) mice, but binds strongly to TLR4 of germ-free (GF) mice; colonization of germ-free (XGF) mice returns TLR4 to its non-fucosylated state. TLR4 sialylation, measured by SNA binding, was constitutive irrespective of colonization. E-cadherin a constitutive epithelial membrane protein, is used as the gel loading control; n = 6–8. UEA1 lectin binds α1,2-fucosylglycans. Feeding the fucose ligand UEA1 restored (B) α1,2/3-fucosyltransferase activity (FUT2) and (C) fut2 mRNA expression in bacteria-depleted (BD) mice, to levels equivalent to gut mucosa of colonized mice. This UEA1 induction does not occur in TLR4 knock-out mice, but does in TLR2 knock-outs. (D) When BD mice are recolonized (XBD), fed LPS, or fed UEA1, the increase in phosphorylation of ERK1/2 kinases and ATF2 (nuclear transcription factor 2) indicates activation of the ERK signaling pathway. Phosphorylation of JNK kinases and transcription factor c-Jun indicate activation of the JNK signaling pathway. This indicates that fucose binding per se is sufficient to induce recovery of the FucT activity and fut2 mRNA levels, that this activation requires TLR4, but not TLR2, dependent activation of ERK and JNK pathways. Data are mean ± SEM n = 6–8 (*p < 0.01; **p < 0.001).* The expression of fucosylated TLR4 (fuc-TLR4) declined greatly after 2 weeks of colonization (XGF), and after 4 weeks, the TLR4 immuno-precipitate was indistinguishable from TLR4 from the gut of normal colonized mice. This led to the hypothesis that fuc-TLR4 could be a “sentinel” receptor on colonic epithelium, in which binding to its α1,2-linked fucosylated moiety of the fuc-TLR4 would induce fucosylation. ## UEA1 induces intestinal fucosylation In bacteria-depleted BD mice, activation of TLR4 was sufficient to induce fucosylation, and the TLR4 was expressed as the unique glycoform of fuc-TLR4. The next hypothesis tested was that the fucose α1,2 linked moiety of the fuc-TLR4 is the essential feature of a sentinel receptor, and hence binding to the fucose moiety would be sufficient to initiate intestinal fucosylation. Bacteria-depleted mice were fed UEA1 while continuing antibiotic treatment to prevent confounding stimulation by re-colonization (Figures 3B,C). Feeding UEA1 to BD mice induced FucT activity ($p \leq 0.01$) and fut2 mRNA expression ($p \leq 0.001$). Moreover, the level of fucosylation induced by only UEA1 was comparable to that induced by restitution of native mouse microbiota (Figures 2, 3B). As controls, other lectins that do not bind fucose, such as wheat germ agglutinin and *Maackia amurensis* agglutinin I, did not activate mucosal fucosylation, nor did fucose or fructosyloligosaccharides. Moreover, in antibiotic-treated TLR4−/− and MyD88−/− mutant BD mice, UEA1 did not induce colonic fut2 mRNA (Figure 3B, 3rd from right), consistent with the inability of these mutants to express the fuc-TLR4 sentinel receptor, but UEA1 did induce fut2-TLR4 in antibiotic-treated BD TLR2−/− mice (Figure 3B, right), whose TLR4 expression is intact. Taken together, these data strongly support the hypothesis that specific binding to the fucosylated glycan of fuc-TLR4 is sufficient to mimic the bacteria-induced fucosylation of the colonic mucosa. If fuc-TLR4 is the receptor that mediates fucosylation of BD mouse colon upon re-colonization, it should activate trans-cellular pathways that are essential to fut2 induction. ## Fuc-TLR4 signaling is mediated through ERK and JNK pathways Re-colonization of BD mice requires ERK and JNK trans-cellular signaling pathways for induction of fucosylation; if activation of fuc-TLR4 is the requisite proximal event to induce fucosylation, feeding BD mice UEA1 and LPS should activate these same pathways. LPS and UEA1 induced intestinal ERK and JNK signaling intermediates at levels comparable to induction by re-colonization of XBD (Figure 3D); specific inhibitors of these signaling pathways significantly attenuated fuc-TLR4 mediated colonic fucosylation. Thus, fuc-TLR4 binding was both necessary and sufficient to specifically induce the same trans-cellular signaling of fut2 induction through the ERK and JNK signaling pathways as are activated by re-colonization. However, feeding UEA1 in vivo does not provide direct evidence that UEA1 is binding specifically to the fuc-TLR4 molecule; this was tested by mechanistic studies in cell culture models. ## UEA-1 and LPS induce FUT2 in HEL and HeLa cells expressing fucosylated-TLR4 The HEL cell, a human erythroleukemia cell line, constitutively expresses fucosylated TLR4, and most of the remaining cell surface proteins are not usually fucosylated, providing a model to study fuc-TLR4 activation. HeLa cells transfected with the TLR4 gene also produce fucosylated TLR4. When HEL or TLR4 transfected HeLa cells were treated with UEA1 or LPS, within 16 h the other membrane proteins became highly fucosylated (Figures 4A,B), indicating that fuc-TLR4, when activated by its ligands, induces α1,2 fucosytransferase gene expression in these human cell models. The ability of UEA1 to induce FUT2 mRNA and to activate membrane fucosylation is consistent with UEA1 activating fuc-TLR4. Moreover, treating cells with an α1,2-specific fucosidase to remove the fucose from fuc-TLR4 abrogated the ability of UEA1 to induce fut2 mRNA expression (Figure 4A). Both UEA1 and LPS induced fut2 mRNA in a dose dependent manner ($p \leq 0.01$), and did not induce fut1 mRNA ($p \leq 0.4$; Supplementary Figure 2). The concentration of LPS needed for inducing FUT2 expression in HEL cells is two orders of magnitude greater than that required for inducing IL-8 expression via non-fucosylated TLR4. This indicates involvement of TLR4 in FUT2 induction, but no involvement in this signaling at typical levels of LPS. Conversely, T84 or CaCo-2 (colonic epithelial) cells [16, 17] do not produce endogenous fuc-TLR4, and neither LPS nor UEA1 is able to induce FUT2 expression or cell-surface fucosylation in these cells. TNF-α induced release of IL-8 in HEL cells and release of ICAM-1 in HeLa cells ($p \leq 0.01$), indicating that inflammatory signaling pathways are intact in these cell lines. In these cells, UEA1 activates the fuc-TLR4 dependent fucosylation without inducing inflammation, indicated by the lack of concomitant increases in IL-8 or ICAM-1 release ($p \leq 0.3$; Figure 4). These models are concordant with the response of bacteria depleted mice to UEA1. Thus, fucosylation of TLR4 is necessary for UEA1 to induce fut2 mRNA levels in human cells in vitro, as it is in mice in vivo. This conclusion could be ascertained by direct evidence that the fucosylated molecule activated by UEA1 was TLR4. **Figure 4:** *Cell models for testing UEA1 activation of fucosylated TLR4 (fuc-TLR4). Fuc-TLR4 is expressed constitutively in the HEL cell line derived from human erythroleukemia (A), and HeLa cells tranfected with the TLR4 gene fucosylate the protein to form fuc-TLR4 (B). Both of these models recapitulated the phenomena observed in intestinal mucosa of uncolonized mouse gut. Stimulation of HEL cells (A) or tranfected HeLa cells by fut-TLR4 ligands UEA1 or LPS induces cell surface fucosylation. UEA1 stimulated induction of fut2 mRNA was abolished in both models by prior removal of fucose from fuc-TLR4 by fucosidase. Stimulation of fuc-TLR4 by UEA1 does not stimulate proinflammatory pathways that lead to IL-8 production (A) or ICAM-1 production (B); TNF-α is the positive control. UEA1, ligands specific for fucose or TLR4 induce fut2 mRNA, and consequent elevated fucosylation of the cell surface; the fucose specific ligand does not elicit an inflammatory response. Data are mean ± SEM n = 5–7 (*p < 0.01; **p < 0.001).* ## UEA1- and LPS-induced fucosylation of HEL cells requires TLR4 expression To confirm that fuc-TLR4 per se mediates induction of fut2 mRNA transcription by UEA1, the expression of TLR4 in HEL cells was knocked down with siRNA (Figure 5). The efficiency of TLR4 knockdown was measured as reduction in TLR4 mRNA levels (Supplementary Figure 3) and in reduced cell surface expression of TLR4 as measured by FACS analysis (Figure 5B; Supplementary Figure 4). Control cells treated with scrambled siRNA remained sensitive to UEA1 induction of fut2 mRNA expression and cell surface fucosylation without the activation of IL-8 mRNA production, and toward LPS eliciting all three. However, after siRNA knockdown of TLR4 expression, UEA1 no longer induced fut2-mRNA and cell surface fucosylation. Likewise, LPS no longer induced fut2-mRNA, cell surface fucosylation, or IL-8-mRNA expression (Figures 5A,B). These data confirm that TLR4 is required for induction of fut2 mRNA and its downstream sequellae; that its fucosylation is essential is verified in Figure 4. Thus, these HEL cell studies provide independent confirmation that UEA1 induction of fut2 mRNA transcription and surface fucosylation is mediated through fuc-TLR4. The next question addressed whether fuc-TLR4 signaling has a functional role in maintaining mucosal homeostasis. **Figure 5:** *TLR4 expression is essential for induction of fut2 mRNA expression and cell surface fucosylation by UEA1 and LPS. To test whether TLR4 expression is sine qua non for Hel cell induction of fut2 mRNA, the consequence of TLR4 knock-down by siRNA on response to the TLR4 ligands was measured (A). The induction of FUT2 mRNA transcription by LPS (right) or by UEA1 (left) is inhibited by a TLR4 specific siRNA, but not by a scrambled siRNA. In contrast, only induction of IL-8 transcription by LPS was affected by the TLR4 siRNA, as the UEA1 did not induce IL-8. The induced cell surface fucosylation, measured by FACS analysis, followed the same pattern (B), consistent with TLR4 mediation of FUT2 mRNA induction regulating cell surface fucosylation; CD49 was the constitutive control. Thus, activation of fucosylated TLR-4 is necessary for inducing fut2 mRNA and cellular fucosylation. Data are mean ± SEM n = 4 (*p < 0.01; **p < 0.001).* ## Fuc-TLR4 signaling is necessary for recovery from mucosal injury during dysbiosis The functional role of fuc-TLR4 signaling in mucosal repair and recovery of homeostasis was investigated by inducing colitis in BD mice. Fully colonized conventional control mice were able to fully recover from chemically induced mucosal injury induced by $3.5\%$ DSS, whereas the same insult in BD mice resulted in $60\%$ mortality (Figure 6A). Restitution of typical mouse microbiota (XBD) allowed full recovery from DSS injury, as does treatment with a fuc-TLR4 ligand, either UEA1 or LPS (Figure 6B). Consistent with the in vitro model, the amount of LPS required to rescue the BD mice from colitis-induce mortality is two orders of magnitude higher than that needed for LPS-mediated sepsis in fully colonized mice. Lectins that do not bind fucose, such as wheat germ agglutinin and *Maackia amurensis* agglutinin I, did not activate mucosal fucosylation, nor restore the ability to recover from mucosal insult of BD mice. Thus, activation of fuc-TLR4 is the critical feature supporting a return to mucosal homeostasis in BD mice. The essential nature of fuc-TLR4 signaling was reinforced by the inability of UEA1, conventional microbiota, or their combination to reverse DSS-induced damage in TLR4−/− mutants (Figure 6C) or MyD88−/− mutants. Likewise, fuc-TLR4 signaling is also required for recovery from TNBS and OXA, which induce related forms of chemically-induced mucosal injury. Thus, fuc-TLR4 signaling is a critical component for recovery from injury in dysbiotic gut through restoration of mucosal homeostasis. **Figure 6:** *Fuc-TLR4 signaling is indispensable for intestinal homeostasis and recovery from injury provided by gut microbes. Conventionally colonized mice recover from the chemically induced mucosal injury caused by ingestion of DSS (A). In contrast, only a fraction of mice whose microbiota are depleted by a cocktail of antibiotics recover from DSS-induced injury (BD mice). BD mice recolonized by mouse microbiota recover from this mucosal injury. Almost all TLR4−/− mutants are unable to recover from DSS mucosal injury, consistent with the essential nature of TLR4 mediating bacterial signaling needed for recovery and maintenance of mucosal integrity. BD mice that are treated with a fuc-TLR4 ligand, either UEA1 or LPS, also recover fully (B). Thus, activation of fuc-TLR4 in the absence of recolonization also allows full recovery. Thus, fuc-TLR4 signaling is necessary and sufficient for recovery of intestinal homeostasis following DSS induced injury of the gut. Conversely, neither UEA1 nor LPS can rescue TLR4−/− mice (nor MyD88−/− mice, not shown) from the injury caused by DSS treatment (C). The data are consistent with fuc-TLR4 signaling being the essential signaling agent for microbiota mediated mucosal restoration of homeostasis. (n = 15–20; *p < 0.01; **p < 0.001).* In aggregate, these data are consistent with the newly discovered fuc-TLR4 being a unique “sentinel” sensor molecule on the surface of intestinal mucosa of uncolonized mature gut. An initial inoculum of fucose utilizing bacteria activates fuc-TLR4, which induces fucosylation of the colon, creating a niche that fosters colonization by fucotrophic mutualists of the microbiota. ## Discussion A reciprocated beneficial symbiotic relationship is defined as mutualism [2, 3, 28, 29]. Mutualism is a widespread feature of life that includes bidirectional communication to facilitate harmonious coexistence. Dialogue between animals and their bacterial mutualists can be through direct receptor-mediated trans-cellular signaling, but this had not been defined for mammalian gut microbiota [2, 9]. TLRs are the foremost microbial pattern recognition receptors of the vertebrate innate immune system found in the intestinal mucosa; they induce an inflammatory response that preserves life but causes collateral damage to the host (10–12). When mammalian gut microbes were considered commensal, this immune response was thought necessary for their confinement to the gut lumen. Mutualists, on the other hand, usually communicate with their hosts to initiate mutual adaptation without inducing detrimental inflammation [28, 29]. Removal of the symbiont typically results in reversion to the pre-mutualistic state. The interactions of mice with their microbiota observed in this study are consistent with this general pattern of mutualist interkingdom communication. Consumption of the cocktail of broad-spectrum antibiotics caused reduction and disruption of mutualist microbiota, and was accompanied by reversion of the fucosylated intestinal mucosa to the sparsely fucosylated form. This was observed in all segments of the intestine, but the phenomenon was most pronounced in the colon, which exhibits the highest degree of colonization, the highest induction of signaling, and was the primary site of injury by chemically-induced colitis. Therefore, this study focused on the colon, where the robust appearance of the novel fucosylated isoform of TLR4, fuc-TLR4, during weaning or recolonization was accompanied by fucosylation of the mucosa. Stimulation of this fuc-TLR4 did not induce inflammation, even when exposed to typical physiologic levels of LPS, but activated a signaling cascade that resulted in copious fucosylation of the colonic mucosa. Thus, fuc-TLR4 could be functioning as a sentinel that restores fucosylation when the renewed opportunity for recolonizing with fucotrophic symbionts is detected. This system would allow expenditure of resources needed to maintain mutualism (fucosylation) only when there is the possibility of reciprocation. Saccharolytic species of gut microbiota engage in glycan foraging [1, 20, 30, 31], and major human bacterial mutualists grow best when their media contains fucosylated glycans, especially those containing the fucose α1,2 moiety [30]. We propose that this heavily fucosylated mucosal niche induced by putative pioneering or principal species forms a base for a fucose-dependent heterotrophic microbial food web that underlies mutualist gut microbiota of mice and men. This is consistent with the observation that fucose-utilizing bacteroides are often the largest component of the adult mammalian gut microbiota [1, 2, 20, 23]. The same mechanism observed at weaning in mice could likewise control the developmental transition to a fucotroph-dominated microbiota in humans. This fucose dependent mammalian microbial community promotes homeostasis of the intestinal mucosa and resilience to several forms of injury. Thus, the studies described herein support a central role of fuc-TLR4 as a putative sentinel molecule mediating the initiation of this strongly mutualistic relationship between humans and their fucotrophic microbiota. If so, this raises the possibility that analogous microbial food webs could exist around other sugars, such as sialic acid or galactose; these could be the base for alternate heterotrophic gut microbiota, potentially a sialic based microbiota in suckling mammals, and a galactose dominant microbiota in non-secretor humans. This seems a promising topic of further research. This novel fucosylated form of TLR4 exhibits unique functional features that would allow detection of fucotrophic bacteria by the gut mucosa with activation of signaling pathways that lead to mucosal fucosylation without inducing inflammation. These changes persist until the gut becomes colonized, further supporting the role of fuc-TLR4 as a sentinel whose high levels attenuate toward conventional TLR4 levels upon restitution of the microbiota. Glycosylation of TLRs has precedent in other systems, where glycosylation can alter TLR expression, sub-cellular localization, and function. For example, glycosylation of TLR3 alters both subcellular trafficking and recognition of its ligand, single-stranded RNA [32]. Likewise, specific point mutations in human TLR4 eliminate asparagine N-glycosylation sites, and the resulting alterations in glycosylation modify trafficking to the cell surface, formation of receptor complexes, and ligand-induced JNK dependent IL-8 transcription [33, 34]. Different ligand affinity with differential glycosylation is consistent with the extreme differences observed herein between fuc-TLR4 and conventional non-fucosylated TLR4. Not only does fuc-TLR4 acquire the ability to be stimulated by fucose ligands, but activation of fuc-TLR4, even if by LPS, also results in non-inflammatory biological outcomes that are distinct from those of non-fucosylated TLR4. The established ligand for standard non-fucosylated TLR4 is LPS. At low ng/mL levels, LPS activates inflammatory signaling mediated through NF-κB, but not mucosal fucosylation. Conventional TLR4 is insensitive to UEA-1 and other fucose ligands. In contrast, LPS induces the NF-κB cascade of fuc-TLR4 only at greater than 100 fold its usual threshold (low μg/mL, Supplementary Figure 2). Measurements of activation by UEA-1 supports the conclusion that binding to the fucosylated glycan moiety of fuc-TLR4 is functionally equivalent to activation by low ng/mL levels of LPS. In both in vitro models in this study, fuc-TLR4 binding does not activate the NF-κB cascade, but only the ERK and JNK pathways. This results in phosphorylation of transcription factors ATF-2 and c-jun. In the nucleus, phosphorylated ATF-2 and c-jun bind to AP1 promoters to initiate fut2 transcription (Figure 7). FucT II enzyme (alpha1,2-fucoslytransferase II) is translated, processed, and activated in the ER-Golgi complex, resulting in extensive α1,2-fucosylation of mucosal epithelial cell surface glycans [9]. In contrast to other reported changes with alternate TLR glycosylation, fucosylation of TLR4 changed its fundamental function. The ability of a simple terminal α1,2-fucosylation to convert the TLR4 molecule from an innate immune signaling receptor that mounts pro-inflammatory defensive processes against bacteria into a mutualistic non-inflammatory signaling receptor that fosters close association with bacteria is biologically elegant. **Figure 7:** *Mature uncolonized mouse gut expresses a unique fucosylated TLR4 (fuc-TLR4). (i) Fucose utilizing or Gram-negative bacteria bind and activate fuc-TLR4. (ii) The complex activates Myd88, ERK, and JNK dependent signaling that activates the AP-1 domain of the fut2 gene. (iii) Fut2-mRNA is transcribed. (iv) FucT II is expressed and activated; glycans targeted to the apical glycocalyx are heavily fucosylated. (v) Luminal surface fucose serves as a base for a fucose-dependent heterotrophic microbial food web, the mutualistic human microbiota of mature secretors. A fully fucosylated and colonized gut resists insults to homeostasis.* Fuc-TLR4 was not apparent in tissues other than gut epithelium of uncolonized adult mice. However, its presence and activity in the two distinct human cell line models suggests the possibility of fuc-TLR4 inducing surface fucosylation that could alter additional functions in other tissues in response to diverse physiologic stimuli. That a minor change in receptor glycosylation can actuate distinct alternate signaling, resulting in alternate outcomes, goes beyond current emerging evidence of glycosylation altering receptor function. The data herein support a specific function of fuc-TLR4 as a sentinel molecule that induces gut fucosylation in response to the first presence of fucotrophic mutualists in newly mature gut, or the return of fucotrophic mutualists in a poorly colonized or dysbiotic adult gut. This system supports homeostasis of the intestinal mucosa. Recognition of microbiota by TLRs is required for intestinal homeostasis [13, 16]. The mucosal barrier requires glycosylation: distorted glycosylation alters tight junctions, signaling, microbiota composition and fermentation products, and exclusion of pathogens [35]. An active fut2 gene in the mucosa supports colonization by characteristic fucose dependent microbiota in a high-glucose polysaccharide-deficient diet [23]. Homeostasis of the intestinal mucosa requires full fucosylation [13, 15]. Fecal metabolic products produced by fut2-deficient mammals are distinct from patterns of metabolic products in wild-type animals [23], and these differences coincide with differing intestinal mucosal function. Therefore, the relationship between fuc-TLR4 function, colonization, and homeostasis was investigated. DSS in drinking water induces mucosal injury from which colonized mature mice fully recover. In the absence of colonization and mucosal fucosylation, the intestinal injury becomes more severe and can become lethal (Figure 6). With restitution of mouse microbiota, the mice survive. Recovery of mucosal resilience also followed treatment with UEA1, a more specific fuc-TLR4 ligand than total microbiota (Figure 6B); lectins with other specificities were ineffective in enhancing recovery from colitis. Consistent with these data, neither UEA1 nor recolonization was able to rescue TLR4−/− mice or MyD88−/− mice from the mucosal injury. Thus, fucosylation induced by mutualistic bacteria via the fuc-TLR4 sentinel is required to recover from gut injury and restore homeostasis in the intestinal mucosa following antibiotic-induced dysbiosis. If fuc-TLR4 is essential to colonization, and colonization is essential to health, the absence of fuc-TLR4 or its downstream fucosylation should result in pathology. FX mutant mice, unable to generate GDP-fucose and therefore unable to fucosylate glycans, are normal at birth, but die shortly after weaning from colitis and diarrhea [36]. In light of our findings, their inability to fucosylate the mucosa would preclude establishment of a fucose-utilizing mature microbial ecosystem. This would increase opportunity for pathobionts to colonize, and decrease homeostasis and resilience, consistent with the reported bacterial infiltration and inflammatory changes in FX mice. Mucosal fucosylation is also associated with recovery from enteric infection. Mucosal lymphocytes responding to infection induce epithelial fucosylation that can be foraged by protective microbiota [37], offsetting a decline in dietary energy due to inflammatory anorexia. This was purported to decrease the degree of inflammation, increasing host resilience and accelerating recovery [21, 24]. The role of mucosal fucosylation in these animal models is concordant with observations in humans. Strong clinical associations between FUT2 expression and disease risk (38–42) are consistent with the mucosal fucosylation system being a significant component of immune homeostasis. Approximately 20–$25\%$ of human populations of European, Asian, and African descent have homozygous recessive inactive point mutations in their FUT2 genes, and such “non-secretors” lack appreciable α1,2 fucosylated glycans in exocrine secretions and mucosa [43]. The central hypothesis herein predicts that this genetic polymorphism in non-secretors would preclude the health benefit of bidirectional communication between mucosa and fucotrophic microbes through the fuc-TLR4 communication system. In addition to the glycans expressed on the apical mucosal membrane, early colonization by microbiota can also be influenced by the oligosaccharides in milk. Although all milk contains oligosaccharides, the types, amounts, and timing across lactation differ among species and within individuals of a species, especially between secretor and non-secretor lactating humans. Early human milk of secretors is rich in fucosyloligosaccharides, suggesting a possible role in supporting early colonization of the young gut by fucotrophs, but fucosyloligosaccharides are quite sparse in the milk of non-secretor mothers [44]. Hence, in contrast to the mucosa of suckling mice, in humans, the newborn gut of secretors may be fucosylated; accordingly the fucotroph interaction with fuc-TLR4 would be of primary importance in the secretor newborn. A non-secretor infant receiving non-secretor milk from its mother would lack a source of fucose from the milk or from the intestinal mucosa, providing little support to colonization by fucotrophic bacteria. Genome-wide association studies identify non-secretors as being predisposed to chronic inflammatory conditions involving gut microbiota, including Crohn’s disease, primary sclerosing cholangitis, celiac disease, type-1 diabetes, and obesity (38–42). FUT2 status influences both alpha and beta diversity of fecal microbiota in these populations [23]. Moreover, non-secretor premature infants are at significantly higher risk of developing the inflammatory condition necrotizing enterocolitis (NEC) than comparable secretor premature infants [45]. The microbiome of secretor and non-secretor neonates differ significantly, and premature infants at highest risk of developing NEC have microbiota whose composition is distinct from early microbiota of those at low risk [45]. These associations, in concert with the biological models herein, support FUT2 expression being central to reciprocal communication between the human intestinal mucosa and human mutualists, thereby fostering mucosal immune homeostasis. To summarize, this report identifies a novel fucosylated glycoform of TLR4 that has a unique signaling function independent of the NF-κB cascade. Appending an α1,2-fucosylation to the terminus of a preexisting glycan converts TLR4 from an innate immune signaling receptor that mounts pro-inflammatory defensive processes against foreign bacteria into a mutualistic signaling receptor that fosters close association with mutualistic bacteria. In its presence, fucose-utilizing bacteria activate ERK and JNK dependent fut2 transcription and mucosal fucosylation. This fucosylation can support full colonization by a mutualist-dominated microbial community. This interkingdom reciprocal communication is critical to adult colonization in mice, recovery of microbiota devastated by antibiotic treatment, and restoration of homeostasis after intestinal injury. The data herein is, to our knowledge, the first description of how specific fucosylated moieties of TLR4 glycoforms can convert its function from one of exclusion, i.e., signaling inflammation in response to Gram-negative bacteria, to one of inclusion, i.e., inducing a fucosylated niche to promote colonization by Gram-negative mutualists. This mutualist-dominated gut microbiota is associated with recovery from the dysbiosis induced by antibiotic treatment and other pathologic insults, and with restoration of homeostasis of the intestinal mucosa. Defining the etiology and pathobiology of inflammatory diseases associated with mutations of the FUT2 gene and with dysbiosis may result in novel approaches toward treating some chronic clinical maladies. ## Data availability statement The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author. ## Ethics statement The animal study was reviewed and approved by IACUC Harvard. ## Author contributions NN, DM, and DN contributed to experimental design and interpretation of data, and data were obtained by DM and NN. The manuscript was written by DN and NN. All authors contributed to the article and approved the submitted version. ## Funding This work was supported by National Institutes of Health HD 013021, HD 059140, AI 075663, and HD 059126, and a pilot feasibility grant from the Center for the Study of Inflammatory Bowel Diseases at the Massachusetts General Hospital. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmed.2023.1070734/full#supplementary-material ## References 1. Coyne MJ, Reinap B, Lee MM, Comstock LE. **Human symbionts use a host-like pathway for surface fucosylation**. *Science* (2005) **307** 1778-81. DOI: 10.1126/science.1106469 2. Sonnenburg JL, Xu J, Leip DD, Chen CH, Westover BP, Weatherford J. **Glycan foraging in vivo by an intestine-adapted bacterial symbiont**. *Science* (2005) **307** 1955-9. DOI: 10.1126/science.1109051 3. Ley RE, Hamady M, Lozupone C, Turnbaugh PJ, Ramey RR, Bircher JS. **Evolution of mammals and their gut microbes**. *Science* (2008) **320** 1647-51. DOI: 10.1126/science.1155725 4. Rakoff-Nahoum S, Comstock LE. **Starve a fever, feed the microbiota**. *Nature* (2014) **514** 576-7. DOI: 10.1038/nature13756 5. Pandiyan P, Bhaskaran N, Zou M, Schneider E, Jayaraman S, Huehn J. **Microbiome dependent regulation of Tregs and Th17 cells in mucosa**. *Front Immunol* (2019) **10** 426. DOI: 10.3389/fimmu.2019.00426 6. Goto Y. **Epithelial cells as a transmitter of signals from commensal bacteria and host immune cells**. *Front Immunol* (2019) **10** 2057. DOI: 10.3389/fimmu.2019.02057 7. Garrett WS, Gordon JI, Glimcher LH. **Homeostasis and inflammation in the intestine**. *Cells* (2010) **140** 859-70. DOI: 10.1016/j.cell.2010.01.023 8. Kononova S, Litvinova E, Vakhitov T, Skalinskaya M, Sitkin S. **Acceptive immunity: the role of Fucosylated Glycans in human host-microbiome interactions**. *Int J Mol Sci* (2021) **22** 3854. DOI: 10.3390/ijms22083854 9. Meng D, Newburg DS, Young C, Baker A, Tonkonogy SL, Sartor RB. **Bacterial symbionts induce a FUT2-dependent fucosylated niche on colonic epithelium via ERK and JNK signaling**. *Am J Physiol Gastrointes Liver Physiol* (2007) **293** G780-7. DOI: 10.1152/ajpgi.00010.2007 10. Janeway CA, Medzhitov R. **Innate Immune Recognition**. *Annu Rev Immunol* (2002) **20** 197-216. DOI: 10.1146/annurev.immunol.20.083001.084359 11. Kawai T, Akira S. **TLR signaling**. *Semin Immunol* (2007) **19** 24-32. DOI: 10.1016/j.smim.2006.12.004 12. Fukata M, Abreu MT. **TLR4 signalling in the intestine in health and disease**. *Biochem Soc Trans* (2007) **35** 1473-8. DOI: 10.1042/BST0351473 13. Rakoff-Nahoum S, Paglino J, Eslami-Varzaneh F, Edberg S, Medzhitov R. **Recognition of commensal microflora by toll-like receptors is required for intestinal homeostasis**. *Cells* (2004) **118** 229-41. DOI: 10.1016/j.cell.2004.07.002 14. Ciesielska A, Matyjek M, Kwiatkowska K. **TLR4 and CD14 trafficking and its influence on LPS-induced pro-inflammatory signaling**. *Cell Mol Life Sci* (2021) **78** 1233-61. DOI: 10.1007/s00018-020-03656-y 15. Nanthakumar NN, Meng D, Newburg DS. **Glucocorticoids and microbiota regulate ontogeny of intestinal fucosyltransferase 2 requisite for gut homeostasis**. *Glycobiology* (2013) **23** 1131-41. DOI: 10.1093/glycob/cwt050 16. Elson CO, Cong Y, McCracken VJ, Dimmitt RA, Lorenz RG, Weaver CT. **Experimental models of inflammatory bowel disease reveal innate, adaptive, and regulatory mechanisms of host dialogue with the microbiota**. *Immunol Rev* (2005) **206** 260-76. DOI: 10.1111/j.0105-2896.2005.00291.x 17. Otte J-M, Cario E, Podolsky DK. **Mechanisms of cross hyporesponsiveness to toll-like receptor bacterial ligands in intestinal epithelial cells☆**. *Gastroenterology* (2004) **126** 1054-70. DOI: 10.1053/j.gastro.2004.01.007 18. Nanthakumar NN, Dai D, Newburg DS, Walker WA. **The role of indigenous microflora in the development of murine intestinal fucosyl- and sialyltransferases**. *FASEB J* (2002) **17** 44-6. DOI: 10.1096/fj.02-0031fje 19. Dai D, Nanthakumar NN, Savidge TC, Newburg DS, Walker WA. **Region-specific ontogeny of α-2,6-sialyltransferase during normal and cortisone-induced maturation in mouse intestine**. *Am J Physiol Gastrointes Liver Physiol* (2002) **282** G480-90. DOI: 10.1152/ajpgi.00531.2000 20. Bry L, Falk PG, Midtvedt T, Gordon JI. **A model of host-microbial interactions in an open mammalian ecosystem**. *Science* (1996) **273** 1380-3. DOI: 10.1126/science.273.5280.1380 21. Goto Y, Obata T, Kunisawa J, Sato S, Ivanov II, Lamichhane A. **Innate lymphoid cells regulate intestinal epithelial cell glycosylation**. *Science* (2014) **345** 1310-1321. DOI: 10.1126/science.1254009 22. Pham TAN, Clare S, Goulding D, Arasteh JM, Stares MD, Browne HP. **Epithelial IL-22RA1-mediated Fucosylation promotes intestinal colonization resistance to an opportunistic pathogen**. *Cell Host Microbe* (2014) **16** 504-16. DOI: 10.1016/j.chom.2014.08.017 23. Kashyap PC, Marcobal A, Ursell LK, Smits SA, Sonnenburg ED, Costello EK. **Genetically dictated change in host mucus carbohydrate landscape exerts a diet-dependent effect on the gut microbiota**. *Proc Natl Acad Sci* (2013) **110** 17059-64. DOI: 10.1073/pnas.1306070110 24. Pickard JM, Maurice CF, Kinnebrew MA, Abt MC, Schenten D, Golovkina TV. **Rapid fucosylation of intestinal epithelium sustains host–commensal symbiosis in sickness**. *Nature* (2014) **514** 638-41. DOI: 10.1038/nature13823 25. Nanthakumar NN, Fusunyan RD, Sanderson I, Walker WA. **Inflammation in the developing human intestine: a possible pathophysiologic contribution to necrotizing enterocolitis**. *Proc Natl Acad Sci U S A* (2000) **97** 6043-8. DOI: 10.1073/pnas.97.11.6043 26. Fusunyan RD, Nanthakumar NN, Baldeon ME, Walker WA. **Evidence for an innate immune response in the immature human intestine: toll-like receptors on fetal enterocytes**. *Pediatr Res* (2001) **49** 589-93. DOI: 10.1203/00006450-200104000-00023 27. Poltorak A, He X, Smirnova I, Liu MY, Huffel CV, du X. **Defective LPS signaling in C3H/HeJ and C57BL/10ScCr mice: mutations in Tlr4 gene**. *Science* (1998) **282** 2085-8. DOI: 10.1126/science.282.5396.2085 28. Oldroyd GED. **Speak, friend, and enter: signalling systems that promote beneficial symbiotic associations in plants**. *Nat Rev Microbiol* (2013) **11** 252-63. DOI: 10.1038/nrmicro2990 29. Kremer N, Philipp EER, Carpentier MC, Brennan CA, Kraemer L, Altura MA. **Initial symbiont contact orchestrates host-organ-wide transcriptional changes that prime tissue colonization**. *Cell Host Microbe* (2013) **14** 183-94. DOI: 10.1016/j.chom.2013.07.006 30. Yu Z-T, Chen C, Newburg DS. **Utilization of major fucosylated and sialylated human milk oligosaccharides by isolated human gut microbes**. *Glycobiology* (2013) **23** 1281-92. DOI: 10.1093/glycob/cwt065 31. Huang JY, Lee SM, Mazmanian SK. **The human commensal Bacteroides fragilis binds intestinal mucin**. *Anaerobe* (2011) **17** 137-41. DOI: 10.1016/j.anaerobe.2011.05.017 32. Sun J, Duffy KE, Ranjith-Kumar CT, Xiong J, Lamb RJ, Santos J. **Structural and functional analyses of the human toll-like receptor 3**. *J Biol Chem* (2006) **281** 11144-51. DOI: 10.1074/jbc.M510442200 33. da Silva Correia J, Ulevitch RJ. **MD-2 and TLR4 N-linked Glycosylations are important for a functional lipopolysaccharide receptor**. *J Biol Chem* (2002) **277** 1845-54. DOI: 10.1074/jbc.M109910200 34. Rudd PM, Elliott T, Cresswell P, Wilson IA, Dwek RA. **Glycosylation and the immune system**. *Science* (2001) **291** 2370-6. DOI: 10.1126/science.291.5512.2370 35. Sharon G, Garg N, Debelius J, Knight R, Dorrestein PC, Mazmanian SK. **Specialized metabolites from the microbiome in health and disease**. *Cell Metab* (2014) **20** 719-30. DOI: 10.1016/j.cmet.2014.10.016 36. Smith PL, Myers JT, Rogers CE, Zhou L, Petryniak B, Becker DJ. **Conditional control of selectin ligand expression and global fucosylation events in mice with a targeted mutation at the FX locus**. *J Cell Biol* (2002) **158** 801-15. DOI: 10.1083/jcb.200203125 37. Zen K, Liu Y, Cairo D, Parkos CA. **CD11b/CD18-dependent interactions of neutrophils with intestinal epithelium are mediated by fucosylated proteoglycans**. *J Immunol* (2002) **169** 5270-8. DOI: 10.4049/jimmunol.169.9.5270 38. McGovern DPB, Jones MR, Taylor KD, Marciante K, Yan X, Dubinsky M. **Fucosyltransferase 2 (FUT2) non-secretor status is associated with Crohn’s disease**. *Hum Mol Genet* (2010) **19** 3468-76. DOI: 10.1093/hmg/ddq248 39. Weiss FU, Schurmann C, Guenther A, Ernst F, Teumer A, Mayerle J. **Fucosyltransferase 2 (FUT2) non-secretor status and blood group B are associated with elevated serum lipase activity in asymptomatic subjects, and an increased risk for chronic pancreatitis: a genetic association study**. *Gut* (2014) **64** 646-56. DOI: 10.1136/gutjnl-2014-306930 40. Rueedi R, Ledda M, Nicholls AW, Salek RM, Marques-Vidal P, Morya E. **Genome-wide association study of metabolic traits reveals novel gene-metabolite-disease links**. *PLoS Genet* (2014) **10** e1004132. DOI: 10.1371/journal.pgen.1004132 41. Smyth DJ, Cooper JD, Howson JMM, Clarke P, Downes K, Mistry T. **FUT2 nonsecretor status links type 1 diabetes susceptibility and resistance to infection**. *Diabetes* (2011) **60** 3081-4. DOI: 10.2337/db11-0638 42. Folseraas T, Melum E, Rausch P, Juran BD, Ellinghaus E, Shiryaev A. **Extended analysis of a genome-wide association study in primary sclerosing cholangitis detects multiple novel risk loci**. *J Hepatol* (2012) **57** 366-75. DOI: 10.1016/j.jhep.2012.03.031 43. Kelly RJ, Rouquier S, Giorgi D, Lennon GG, Lowe JB. **Sequence and expression of a candidate for the human secretor blood group α(1,2)Fucosyltransferase gene (FUT2)**. *J Biol Chem* (1995) **270** 4640-9. DOI: 10.1074/jbc.270.9.4640 44. Chaturvedi P, Warren CD, Altaye M, Morrow AL, Ruiz-Palacios G, Pickering LK. **Fucosylated human milk oligosaccharides vary between individuals and over the course of lactation**. *Glycobiology* (2001) **11** 365-72. DOI: 10.1093/glycob/11.5.365 45. Morrow AL, Meinzen-Derr J, Huang P, Schibler KR, Cahill T, Keddache M. **Fucosyltransferase 2 non-secretor and low secretor status predicts severe outcomes in premature infants**. *J Pediatr* (2011) **158** 745-51. DOI: 10.1016/j.jpeds.2010.10.043
--- title: 'Economic and health impacts of the Change4Life Food Scanner app: Findings from a randomized pilot and feasibility study' authors: - Sundus Mahdi - Nicola J. Buckland - Jim Chilcott journal: Frontiers in Nutrition year: 2023 pmcid: PMC10061026 doi: 10.3389/fnut.2023.1125542 license: CC BY 4.0 --- # Economic and health impacts of the Change4Life Food Scanner app: Findings from a randomized pilot and feasibility study ## Abstract ### Introduction The UK Government developed the Change4Life Food Scanner app to provide families with engaging feedback on the nutritional content of packaged foods. There is a lack of research exploring the cost-effectiveness of dietary health promotion apps. ### Methods Through stakeholder engagement, a conceptual model was developed, outlining the pathway by which the Food Scanner app leads to proximal and distal outcomes. The conceptual model informed the development of a pilot randomized controlled trial which investigated the feasibility and acceptability of evaluating clinical outcomes in children and economic effectiveness of the Food Scanner app through a cost-consequence analysis. Parents of 4–11 years-olds ($$n = 126$$) were randomized into an app exposure condition ($$n = 62$$), or no intervention control ($$n = 64$$). Parent-reported Child Health Utility 9 Dimension (CHU9D) outcomes were collected alongside child healthcare resource use and associated costs, school absenteeism and parent productivity losses at baseline and 3 months follow up. Results for the CHU9D were converted into utility scores based on UK adult preference weights. Sensitivity analysis accounted for outliers and multiple imputation methods were adopted for the handling of missing data. ### Results 64 participants ($51\%$) completed the study (intervention: $$n = 29$$; control: $$n = 35$$). There was a mean reduction in quality adjusted life years between groups over the trial period of –0.004 (SD = 0.024, $95\%$ CI: –0.005; 0.012). There was a mean reduction in healthcare costs of –£30.77 (SD = 230.97; $95\%$ CI: –£113.80; £52.26) and a mean reduction in workplace productivity losses of –£64.24 (SD = 241.66, $95\%$ CI: –£147.54; £19.07) within the intervention arm, compared to the control arm, over the data collection period. Similar findings were apparent after multiple imputation. ### Discussion Modest mean differences between study arms may have been due to the exploration of distal outcomes over a short follow-up period. The study was also disrupted due to the coronavirus pandemic, which may have confounded healthcare resource data. Although measures adopted were deemed feasible, the study highlighted difficulties in obtaining data on app development and maintenance costs, as well as the importance of economic modeling to predict long-term outcomes that may not be reliably captured over the short-term. ### Clinical trial registration https://osf.io/, identifier 62hzt. ## 1. Introduction Childhood overweight and obesity is a growing public health problem. In the UK alone, approximately $23\%$ of 4–5 years old children and $38\%$ of 10–11 years old children are impacted [1]. Childhood obesity increases the risk of non-communicable diseases, such as asthma, sleep apnea, musculoskeletal problems, and psychological problems [2]. This creates a greater demand for healthcare resource use, therefore negatively impacting on limited healthcare budgets. Direct medical costs of obesity are estimated at £6.1 billion to the UK National Health Service (NHS) [3], and $14 billion in the United States [4, 5]. The rising trends in overweight and obesity has been associated with the growing availability of high density and nutritionally poor foods [6]. The use of smartphones has grown extensively. Recent figures suggest that $88\%$ of the UK online adult population engage with mobile applications [7], whilst over half of US smartphone users have used a health app [8]. Mobile apps have demonstrable beneficial impacts on weight reduction and dietary choices [9], whilst offering flexibility in their administration and use. They have the potential to reach diverse populations at low cost and may be provided by public health agencies as a public good. As such, there has been a growing number of dietary interventions delivered via smartphone apps [10, 11]. Despite being deemed a cost-effective method to deliver dietary interventions [12], few studies have considered economic and cost outcomes within their analyses, with little guidance available to aid this process. As such, it has been flagged that further research is needed on how best to integrate economic factors into intervention design [13]. Unlike conventional healthcare interventions (e.g., pharmaceutical), mobile apps have their own methodological issues within evaluations, therefore require specific guidance to aid cost-effectiveness analyses (13–15). Current recommendations for practice have included implications for resource use and benefit measurement pertaining to app evolvement [15], including development, implementation, and updates up to eventual obsolescence [14]; intervention costs based on study sample size or potential population reach [15]; extended health benefits such as spill-over effects of the intervention onto social networks [15]; and non-health care impacts such as productivity [15]. Given this, cost per quality adjusted life years (QALY) within economic analysis have been deemed unlikely to capture health and non-health impacts of mobile health (mHealth) interventions. Instead, cost-consequence analysis, where a clear breakdown of costs and various benefits, has been recommended [15]. This allows decision makers to use only the relevant aspects of this breakdown for their own local contexts. Economic evaluations of dietary app-based interventions are only just emerging. The SWAP-IT trial aimed to reduce energy-dense foods packed in lunchboxes. The intervention included an mHealth component which provided support on healthy lunchbox preparation to parents of primary school children in Australia [16]. The intervention adopted the use of an existing school app to communicate health promotion messages via push-notifications to support packing of healthy lunchboxes. Non-app components included the dissemination of resources to parents alongside lunchbox nutrition guidelines. Within a trial-based economic evaluation, costs relating to the mHealth component only included graphic design revisions and liaison time. Overall the intervention was deemed cost-effective at reducing energy from energy-dense, poor nutrient foods [17]. Similarly, LifeLab Plus targets improvements in dietary behaviors in adolescents in the UK. The multicomponent intervention included education modules, training for teachers, and an interactive mobile app component with gaming features. A Markov model was developed to estimate the costs, benefits and cost-effectiveness of the intervention in comparison to usual schooling [18]. The model assumed that intervention effects were sustained for 4 years, and then diminished to no effect over 10 years. The European Quality of Life 5 Dimensions 3 Level (EQ-5D-3L) was used to estimate quality of life outcomes. App costs were incorporated as capital costs and assumed to last 10 years. App maintenance costs were also assumed at $25\%$ of the development cost per year. Intervention effects were estimated based on best available evidence from the literature deeming the intervention cost-effective in accordance with the UK reference case [19]. In addition, a recent systematic review of dietary digital interventions concluded that mHealth interventions that are not cost-effective in the short-term may likely be cost-effective in the long-term due to cost-offsets and wider user reach [20]. In the absence of data, feasibility studies can provide insights into the suitability of study designs, methodological approaches, and economic outcomes [21]. The HelpMeDoIt randomized controlled trial tested the feasibility and acceptability of evaluating a mobile dietary app designed for weight loss amongst adults with overweight and obesity through mobilizing social networks [22]. Data collected for economic evaluation included NHS resource use, participant-borne costs (e.g., grocery shopping), interventions costs, health related quality of life (HRQoL) and capability wellbeing. App development and maintenance costs were valued, alongside quotes for future app maintenance [23]. This is an important consideration given that app design and software features need to be regularly updated to maintain user engagement and app function [14]. Although the study was not powered to detect significant changes, the intervention had potential to be effective, with modest decreases in BMI and sedentary time within the intervention group, thus generating moderate effect sizes. Evaluations of health promotion apps are lacking [24]. The Change4Life Food Scanner app was first released as part of a mass media campaign by Public Health England (PHE), a UK Government agency [25]. The app aims to raise awareness on the nutritional content of packaged foods through a barcode scanner feature. The Food Scanner app contains a series of evidence based components designed to effectively change behavior, i.e., Behavior Change Techniques (BCTs) [26], with some evidence to suggest it is effective in improving dietary behaviors in the short term when evaluated as part of the wider Change4Life campaign [25]. Little is known regarding whether the Change4Life Food Scanner app is cost-effective in improving dietary behaviors. This is important as the development of the app and its contents required substantial financial input and resources. There is limited available data and guidance surrounding the economic evaluation of public health mobile apps. The ways in which economic models are produced can highly affect final cost-effectiveness results. In order to inform the evaluation of the Change4Life Food Scanner app and to subsequently design a mathematical economic model, an understanding of the decision problem needs to be formed that captures the varying perspectives of the system and the causal relationships between factors within the system that lead to short-term and long-term behavior change and associated outcomes. The aims of this study were to [1] explore the feasibility of collecting cost and outcome data when evaluating the cost-effectiveness of the Food Scanner app; and [2] investigate whether randomized controlled trials offer a feasible approach to assessing whether the Food Scanner app is cost-effective in improving dietary choices. This was achieved through a multi-step process which firstly involved the engagement of stakeholders to design a conceptual model that would then inform the parameters of the feasibility study. ## 2.1. Stakeholder engagement Stakeholder engagement was carried out to inform the conceptual model of the Food Scanner app evaluation. This involved an interactive half-day workshop, and interviews for those unable to attend (one in-person interview with two stakeholders simultaneously and a single online video call) between November 2019 and January 2020. Participants were identified through available publications, existing networks and targeted decision makers working within policy. The total sample consisted of nine academics, two Government workers and one non-profit worker. Stakeholders had expertise within digital interventions, health economics and/or obesity research. Stakeholders were provided with a draft version of a conceptual model that was informed by the existing behavior change literature, and which informed the methods of the feasibility study. The stakeholder event aimed to, [1] discuss factors that need to be assessed within dietary digital interventions; [2] explore current perspectives of the causal pathway by which a dietary app may lead to obesity prevention and improved health and wellbeing outcomes within a complex system; and [3] discuss potential issues and recommendations of evaluating the effectiveness and cost-effectiveness of dietary apps. Discussions involved mapping out the decision problem (i.e., revising the conceptual model), identifying the short-term and long-term priority outcomes for evaluation, and identifying resource use and associated costs of the Change4Life Food Scanner app from an intervention, user, healthcare and societal perspective. The conceptual model was then updated to reflect the stakeholders’ feedback. Stakeholders identified the pathways by which the Change4Life Food Scanner app impacts on dietary intake and childhood obesity prevention (see Figure 1). The model is split into two sections; the upper section describes the pathways to behavioral outcomes leading from app uptake, whilst the lower section describes contextual factors that may facilitate, or hinder behavior change success. The model begins with the provision of the Food Scanner app, which comprises of eight BCTs through which behavior is shaped [26]. Alongside BCTs are app design features that are important to maintaining user engagement. Through using the app, users’ nutrition knowledge and psychological predictors of behavior change may improve, leading to a general increase in awareness of healthy diets. These are considered proximal outcomes. **FIGURE 1:** *Conceptual model of the Change4Life Food Scanner app.* Although intermediate outcomes are changes in behavior, they often precede the main desired effects. Within the model, changes in purchased items, habit formation, and healthiness of home environment are predicted to lead to parental outcomes, child mediators of change and environmental outcomes. Environmental outcomes are a result of the food system responding to consumer demands and changes in behavior. Parental and child outcomes describe how changes in sugar intake, lead to changes in dietary and energy intake, which may have an impact on body weight. These are considered medium-term outcomes, whilst environmental outcomes are considered distal. Increases in body weight may lead to changes in metabolic trajectories in the lead up to disease, and changes in weight and diet-related disease incidence. In the long-term this is predicted to lead to increased use of healthcare resources, increased sick days off school or work, and a negative impact on physical and mental HRQoL and wellbeing. Childhood outcomes will continue into adolescence and will get worse into adulthood. These are considered distal outcomes. Ideally, the Food Scanner app will lead to improvements in knowledge and awareness of nutrition in the short-term. This will lead to a decrease in sugar consumption and thus a reduction in total energy intake in the short to medium-term. This will then lead to a reduction in BMI in the medium-term, which will be protective of ill-health in the long-term. Contextual factors consider other aspects within the system that may facilitate or hinder behavior change. App engagement may interact with contextual factors and/or other policies within the system which may have additional positive impacts on behavioral outcomes. ## 2.2.1. Study design Outcomes from the stakeholder engagement and conceptual model were used to inform trial design. The study was conducted as part of a pilot randomized controlled trial, which tested the feasibility, acceptability, and sustainability of evaluating the Change4Life Food Scanner app in reducing overall energy intake and sugar consumption in 4–11 years-old children through parental behavior change. Using a non-blinded parallel trial design, participants were randomized into an intervention condition or usual practice control condition in a 1:1 allocation ratio. A randomization sequence of 50 was produced at first using Microsoft Excel, with 20 sequences following thereafter per block (a total of four blocks). Random allocation sequence, participant enrollment and participant assignment to conditions was conducted by the study team. The trial was registered in the Open Science Framework [27]. Ethical approval was obtained by the University of Sheffield Research Ethics Committee [026380] in August 2019. The study adhered to the Consolidated Standards of Reporting Trials (CONSORT) for pilot and feasibility studies [28]. ## 2.2.2. Participants and recruitment Recruitment took place between January and June 2020 in Yorkshire and the Humber region of the UK. The recruitment strategy included recruitment from primary schools. This occurred via school communication methods (e.g., signposting in school newsletters, SMS services, school app), and distribution of flyers provided by the study team to be sent home to parents. Online recruitment methods were also implemented, which included adverts distributed via the University’s mailing lists, online study recruitment, and social media platforms (Facebook and Twitter). A weblink directed interested volunteers to the online information sheet and consent form. Participants were informed that the study was exploring parents’ views on dietary online programs or mobile apps. The eligibility criteria for participation included being a parent of a primary school child aged 4–11 years, owning a smartphone with data access and sufficient storage space, an active grocery shopper in the household or involved in child’s food provisions, grocery shopping dominantly undertaken at a grocery store or supermarket, not currently using the Change4Life Food Scanner app, and the child has no health condition that affects diet (excluding allergies), e.g., cystic fibrosis. Upon study completion, participants received either a £35 (intervention) or £30 (control) shopping voucher for reimbursement of their time. In addition, participants who completed the study were entered into a prize draw for a £150 Virgin Experience Days gift card. As this was a feasibility study, participants who withdrew were contacted and asked to complete a short survey to detail reasons for withdrawing. To incentivize completing this survey participants were entered into a prize draw for a £20 Love2Shop gift voucher. ## 2.2.3. Intervention and control The intervention involved written contextual guidance on healthy eating behaviors obtained from Change4Life webpages, which prompted participants to download and use the Change4Life Food Scanner app to make healthier food choices and be a “sugar smart shopper.” Details of the app’s features and BCTs have been previously published [26]. Briefly, the app encourages healthier food and drink choices by providing nutritional feedback of barcode scanned items through various visual methods. Sugar, salt, and saturated fat content is depicted in sugar cubes, salt sachets and fat slabs, alongside grams. Information, when available, is provided per 100 g/ml and per portion. The control condition consisted of usual practice (no contextual information or guidance was provided regarding healthy eating behaviors and no reference was made to Change4Life). ## 2.3. Study procedures and measures Upon consenting, participants completed sociodemographic measures which consisted of child age and sex, child and parent height and weight, location, parent ethnicity, parent education and household size and income. Data on household income was used to group participants on level of economic deprivation based on the Index of Multiple Deprivation [29]. Participants were then randomized into an intervention or control arm. All participants completed 3 days food diaries via myfood24® [30], and psychosocial and health economic measures via online surveys (Qualtrics, Provo, UT, United States) [31] at baseline and 3 months follow up. Only after completion of baseline measures did participants in the intervention arm receive intervention exposure. Those in the intervention arm completed app engagement measures fortnightly over 12 weeks. At 3 months follow-up, participants completed 3 days food diaries using myfood24®, psychosocial measures and health economic measures as previously described. In addition, participants provided study and app feedback through open- and closed-ended questions. The duration of the study was bounded by time constraints of the project. Details of the study, including feasibility, acceptability, and clinical efficacy outcomes, will be published elsewhere (manuscript in preparation). This paper reports the feasibility of collecting economic outcomes of the intervention for the purposes of cost-effectiveness analysis. ## 2.4. Economic study and statistical methods A cost-consequence analysis was conducted. Cost-consequence methods have been recommended for the evaluation of digital products [32]. These consisted of healthcare resource use and associated costs, school absence, workplace absenteeism, and HRQoL measures. Statistical analysis was carried out on STATA/SE 15.1. This study undertook a healthcare and societal perspective to address the generalizable issues of feasibility pertaining to both. Questions were adapted from a number of surveys identified from the Database of Instruments for Resource Use Measurement (DIRUM), [33] except for HRQoL measures. Permissions were obtained from the copyright holders of original surveys. Despite economic impacts of the Food Scanner app being reflected as distal outcomes within the conceptual model, these were investigated within this study to assess the feasibility of using such measures within a future cost-utility and/or cost-effectiveness analysis of the Food Scanner app. In addition, as this is a feasibility study, and therefore not powered to detect significant differences, descriptive statistics were conducted only, and inferential statistics were not. Reported comparisons need to be interpreted with caution in all cases, and mean differences are reported trends only. ## 2.4.1. Study and intervention costs The majority of study costs were related to the completion of food diaries using myfood24®. Costs relating to the production of physical resources were not factored into cost estimates as they were considered sunken costs (a cost spent that cannot be reversed). With regards to opportunity costs associated with the distribution of physical resources, this was also not considered given that distribution of trial promotion material was no longer actioned by schools and community centers due to COVID-19 lockdown measures. This also meant that the trial incurred cost losses incurred by printing and postage services of materials that were not distributed to parents due to lockdown measures. Separate to trial data, a Freedom of Information request was submitted by the study team to PHE in October 2020 enquiring about the total costs of the Change4Life campaign, as well as development and maintenance costs of the Change4Life Food Scanner app. This was submitted to estimate intervention costs as data was not available publicly. Access to such data would allow us to conduct more accurate cost-effectiveness analyses going forward and would allow the estimation of the mean cost per user [15]. A response was received in December 2020 outlining total marketing costs associated with the Change4Life campaign. In addition, to gain insight into the cost per download, the Change4Life Food Scanner app webpages were consulted for number of downloads for both Google Play and the Apple App store [34, 35]. ## 2.4.2. Health related quality of life Participants completed the Child Health Utility 9 Dimension (CHU9D) instrument, a short validated pediatric HRQoL instrument [36, 37] which was used a measurement of health outcomes within this study. This is a preference-based measure designed for self-completion by 7–17 years-olds and proxy completion for younger age groups [38]. Given that parents were the ones participating in the trial, the parent proxy version was utilized. The instrument consists of nine dimensions: worried, sad, pain, tired, annoyed, schoolwork/homework, sleep, daily routine, and ability to join in activities. Each dimension consists of five response options ranging from the least severe option (e.g., my child does not feel worried/sad/tired today) to most severe (e.g., my child feels very worried/sad/tired today). Parents are asked to decide which option represents their child best on the day of completion. Utility values (value or preference that the population gives to a particular health state) were calculated through the use of UK adult preference weights (i.e., utility values were based on UK adult preferences) [39, 40]. Utility values were then used to calculate quality adjusted life years (QALYs) using the trapezium rule (area under the curve; a measure of effect) [41]. The CHU9D was used to assess the feasibility of collecting HRQoL measures when evaluating a dietary mobile app. ## 2.4.3. Child healthcare use Current evidence indicates increased healthcare use and hospital admissions [42] and costs amongst children with overweight and obesity [43]. As such, this study tested the feasibility of collecting self-reported healthcare resource usage as a basis for measuring healthcare costs. Participants were asked to report healthcare services used in the last 3 months including number of visits and total length of time per contact [44]; These questions were included in order to assess incremental effects of the Food Scanner app on short term health resource use. Healthcare resource costs, including general practitioner (GP), nurse, dental, hospital inpatient and hospital outpatient were estimated using 2021 PSSRU unit costs [45]. The National Schedule of NHS Costs (year $\frac{2019}{2020}$) was used to estimate accident and emergency costs [46]. See Table 1 for healthcare cost data and assumptions. **TABLE 1** | Resource | Cost (£) | Unit | Assumption | | --- | --- | --- | --- | | GP consultation | 3.7 | Min | GP costs were estimated at £3.70 per minute of patient contact, including qualification costs. This excluded direct care staff costs as the majority of the trial ran during the COVID pandemic, and the majority of GP consultations had become via telephone. | | Nurse | 0.73 | Min | Dental costs were estimated at 73.3p per minute of patient contact (based on £44 per hour). Costs included qualifications. | | Hospital inpatient | 827.0 | Visit | Inpatient costs are not calculated by time. Costs were available for non-elective short and long stays. Given that only one respondent had an inpatient stay which lasted less than 24 h, it was considered a short stay. | | Hospital outpatient | 137.0 | Visit | Outpatient attendance was not available by minutes or hours, but rather having occurred or not, despite this information being collected from participants. Given that no further details were collected regarding the nature of the outpatient visit, a weighted average cost of all outpatient attendances was selected. | | Accident and emergency | 182.0 | Visit | Accident and emergency costs were sourced through the National Schedule of NHS Costs 2019–2020 for NHS trusts and NHS foundation trusts. Data was not collected on the reason for the A&E visit, and whether participants were admitted, if they had any investigations or treatments. Therefore, a weighed mean average of all A&E visits was selected, accounting to £182 per unit. | | Non-routine dental | 3.28 | Min | Dental costs were estimated at £3.28 per minute of patient contact (based on £197 per hour of patient contact). Data on the nature of the appointment was not collected therefore whether any dental procedures were carried out can not be ascertained. | ## 2.4.4. Productivity and personal financial losses Societal perspectives include costs which matter to society, such as workplace productivity losses and personal financial losses. Outcome measures considered school absenteeism in the past 3 months due to a health problem [47] and workplace absenteeism in the past 3 months due to child’s health [48]. Productivity losses were estimated by multiplying days off work due to child health by median daily rate of £108.20, based on the Sheffield median weekly income [49]. Increases in grocery shopping expenditure can be an unintended consequence of dietary interventions [50, 51] given that healthier foods are more costly than less healthier alternatives [52, 53]. In order to determine whether a full investigation into grocery expenditure is warranted in a full-scale trial, participants in the intervention arm were asked at 3 months follow up, “using the Food Scanner app has led me to spend… a lot less/slightly less/the same/slightly more/a lot more… on groceries.” ## 2.4.5. Sensitivity analysis and handling of missing data It is not unusual for cost data to be right skewed or follow a gamma distribution, as opposed to a normal distribution. This is due to the majority of the population being in good health, therefore incurring minimal healthcare costs [54]. Standard deviation z-scores were explored for healthcare and workplace absenteeism cost data (i.e., productivity costs). Extreme data points, interpreted as those five standard deviations from the mean, were removed from the analysis, as part of a sensitivity analysis. In addition to complete case analysis, multiple imputation (MI) was also conducted as part of a sensitivity measure. It allowed us to explore the feasibility of using such approaches when evaluating the economic impacts of a dietary app, especially when retention rates could impact on the completeness of data. Multiple imputation methods were adopted using Monte Carlo simulation techniques [55]. The Gaussian normal regression imputation method was conducted, where data was assumed missing at random (MAR). Sociodemographic data with complete cases were selected as auxiliary variables for MI purposes. These included: condition, child age, child sex, ethnicity, location, education, household income and household size. Therefore, participants with missing sociodemographic data were removed from the dataset for multiple imputation purposes ($$n = 12$$). These respondents did not report any school absences, workplace absenteeism or healthcare resource use that could lead to noticeable changes in total costs and mean differences. Variables considered for MI included QALYs (calculated from CHU9D outcomes), healthcare resource costs, workplace absenteeism due to child’s health, and school absenteeism, all at baseline and 3 months follow up. All these variables had between 35 and $50\%$ missing data. The percentage of missing cases per variable determined the number of imputations per variable [56]. Additional imputations were conducted in cases where the Fraction of Missing Information (FMI) percentage was above the number of imputations. A single result per case was calculated based on the average value of imputations per variable. Multiple imputation was favored over other missing data handling techniques as it considers the variance between and within variables and reduces chances of biased estimates which often arise in other methods [57]. ## 3.1. Participants A total of 176 participants were assessed for eligibility through a screening questionnaire. Of which, 50 were excluded from further participation in the study. Reasons included not meeting the inclusion criteria, not providing an email address to forward trial material, and not fully completing the consent form. As such, 126 ($72\%$) participants were randomized to the intervention ($$n = 62$$) or control arm ($$n = 64$$). In the intervention arm, 40 ($65\%$) completed baseline measures and therefore received the allocated intervention; whilst 22 ($35\%$) participants did not engage in the study material. In the control arm, 39 ($61\%$) participants completed baseline measures and 25 ($39\%$) did not engage in the study material. At 3 months follow up, data was analyzed from 29 ($47\%$) participants in the intervention and 35 ($55\%$) in the control arm (see Figure 2). **FIGURE 2:** *Consort flow chart of the Change4Life Food Scanner app pilot and feasibility trial.* Table 2 outlines the baseline characteristics of the study sample. Overall, the sample consisted of parents of children with an average age of 6.81 (SD = 2.04) and a similar distribution of male and females. The parent sample was predominantly White British ($71\%$). The majority of parents had completed higher education ($69\%$). Data on household income suggested that $32\%$ of the sample were in the least deprived quintile, whilst $13\%$ were in the most deprived. Most of the sample had a household size of four or smaller ($83\%$). **TABLE 2** | Unnamed: 0 | Unnamed: 1 | All | Intervention | Control | | --- | --- | --- | --- | --- | | N | – | 126 | 62 | 64 | | Missing cases | – | 12a | 7b | 5c | | Child age (years) | Mean (SD) | 6.81 (2.04) | 6.77 (1.77) | 6.85 (2.28) | | Child sex | N (%) Female | 60 (51.7) | 26 (46.4) | 34 (56.7) | | | N (%) Male | 56 (48.3) | 30 (53.6) | 26 (43.3) | | Parent ethnicity | N (%) White British | 81 (71.1) | 41 (75.4) | 40 (67.8) | | | N (%) White other | 9 (7.9) | 5 (9.1) | 4 (6.8) | | | N (%) Asian | 11 (9.6) | 4 (7.3) | 7 (11.9) | | | N (%) Mixed White and Black | 4 (3.5) | 3 (5.5) | 1 (1.7) | | | N (%) Other | 9 (7.9) | 2 (3.6) | 7 (11.9) | | Parent education | N (%) Higher educationd | 79 (69.3) | 39 (70.9) | 40 (67.8) | | | N (%) Other | 35 (30.7) | 16 (29.1) | 19 (32.2) | | Household income (quintiles) | N (%) Q1—most deprived | 16 (12.7) | 10 (16.1) | 6 (9.4) | | | N (%) Q2 | 5 (4) | 2 (3.2) | 3 (4.7) | | | N (%) Q3 | 16 (12.7) | 6 (9.7) | 10 (15.6) | | | N (%) Q4 | 28 (22.2) | 14 (22.6) | 14 (21.9) | | | N (%) Q5—least deprived | 40 (31.7) | 18 (29.0) | 22 (34.4) | | | N (%) Unknowne | 21 (16.7) | 12 (19.4) | 9 (14.1) | | Household size | N (%) 2 | 10 (8.8) | 6 (10.9) | 4 (6.8) | | | N (%) 3 | 32 (28.1) | 9 (10.9) | 23 (39.0) | | | N (%) 4 | 53 (46.5) | 33 (60) | 20 (33.9) | | | N (%) 5 | 14 (12.3) | 4 (7.3) | 10 (16.9) | | | N (%) Other | 5 (4.4) | 3 (5.4) | 2 (3.4) | ## 3.2. Study costs The total cost of the feasibility study was £4666.29 in year 2020 (Table 3). The average cost was calculated at £36.05 [2020] per participant ($$n = 126$$). The cost almost doubles to £70.98 [2020] per participant when numbers are based on study completers ($$n = 64$$). **TABLE 3** | Item | Cost | | --- | --- | | Myfood24® —2 years access+participant entries | £1810 | | Incentives—gift vouchers (intervention) | £1015 | | Incentives—gift vouchers (control) | £1050 | | Incentives—withdrawal survey voucher | £25 | | Incentives—prize draw (Virgin Experience Days Gift card)+shipping | £154.99 | | Mobile sim card | £44.90 | | Social media advertising | £419 | | Call for Participants advertising | £24 | | Print and postage services | £123.40 | | Total | £4666.29 | ## 3.3. Intervention related costs Data from Google play shows that the Change4Life Food Scanner app has achieved over 500,000 downloads to date [34]. This information is not available on the Apple app store. Outcomes from the FOI request noted that PHE agrees to a fixed rate for services, but no further information or breakdown of costs was provided regarding development and maintenance costs. The FOI request was therefore unsuccessful in gaining the information necessary for a comprehensive cost-consequence analysis. On the other hand, PHE confirmed they had run two Change4Life campaigns in 2017 encouraging healthy eating for children and families, to the value of £3.5 million in paid media activity. As part of these campaigns, consumers were encouraged to download the “Be Food Smart” app (as the Food Scanner app was then called) to find out how much sugar, fat and salt were in a range of popular products, and to help consumers choose healthier options. PHE further confirmed that they do not hold any information on the Return on Investment for the Change4Life campaign, or the Food Scanner app. As we were unable to retrieve specific app-related costs, cost per download could not be quantified. When investigating the financial consequences of using the app, 20 out of 28 participants ($71\%$) reported that using the Food Scanner app led them to spend the same amount on groceries. Whereas seven participants ($25\%$) reported that using the app led them to spend slightly more on groceries. Only one participant reported spending less on groceries after using the app ($3.6\%$). ## 3.4. Health related quality of life A total of 78 ($62\%$) participants completed CHU9D measures at baseline, and 63 ($50\%$) completed these measures at follow up. One participant was removed from analysis at 3 months follow up due to missing data. This resulted in 62 complete cases across baseline and follow up. Very few problems were reported in children’s HRQoL (see Supplementary Table 1). Similar mean scores were found between baseline and follow-up across all dimensions for both intervention and control groups. Finally, there was a greater range, in the direction of worse HRQoL at follow-up, in comparison to baseline, for the intervention group only. Table 4 outlines mean differences (SD) between baseline and follow-up across conditions. The mean difference (SD) for the total CHU9D score at follow-up was –0.464 (4.558) for the intervention arm and –0.588 (4.054) for the control arm. When CHU9D scores were converted into utilities, the mean difference at follow-up was 0.007 (0.104) for the intervention arm, and 0.014 (0.089) for the control arm. This resulted in 0.222 QALYs for children in the intervention arm (SD = 0.019, $95\%$ CI: 0.215; 0.230) and 0.226 QALYs (SD = 0.016, $95\%$ CI: 0.220; 0.232) in the control arm over the 3 months period of the study. This amounted to a mean reduction in QALYs between groups over the trial period of –0.004 (SD = 0.024, $95\%$ CI: –0.005; 0.012). **TABLE 4** | Costs and consequences | Intervention | Control | | --- | --- | --- | | Child healthcare costs (£) | Child healthcare costs (£) | Child healthcare costs (£) | | N | 26 | 32 | | Mean difference (SD) between baseline and follow-up | –52.560 (213.59) | –21.790 (87.91) | | 95% CI | –138.83; 33.71 | –53.48; 9.90 | | Health related quality of life scorea | Health related quality of life scorea | Health related quality of life scorea | | N | 28 | 34 | | Mean difference (SD) between baseline and follow up | –0.464 (4.558) | –0.588 (4.054) | | 95% CI | –2.232; 1.303 | –2.003; 0.826 | | Utility score | Utility score | Utility score | | N | 28 | 34 | | Mean difference (SD) between baseline and follow up | 0.007 (0.104) | 0.014 (0.089) | | 95% CI | –0.0336; 0.0471 | –0.0169; 0.045 | | Quality adjusted life years | Quality adjusted life years | Quality adjusted life years | | N | 28 | 34 | | Mean (SD) between baseline and follow up | 0.222 (0.019) | 0.226 (0.016) | | 95% CI | 0.215; 0.230 | 0.220; 0.232 | | School absenteeism | School absenteeism | School absenteeism | | N | 29 | 32 | | Mean difference (SD) between baseline and follow-up | –0.362 (1.253) | –0.547 (1.364) | | 95% CI | –0.839; 0.114 | –1.039; –0.055 | | Workplace productivity due to child’s health (£) | Workplace productivity due to child’s health (£) | Workplace productivity due to child’s health (£) | | N | 27 | 34 | | Mean difference (SD) between baseline and follow-up | –80.148 (235.516) | –15.912 (54.148) | | 95% CI | –173.315; 13.019 | –34.805; 2.981 | ## 3.5. Child healthcare use Parents reported more frequent healthcare resource use at baseline compared to follow-up within both study arms (see Table 5). GP services were most frequently reported. There was greater healthcare resource use and associated costs at baseline compared to follow-up in both study arms. There was a £1684.30 decrease in healthcare costs at follow-up in the intervention arm, and £782.31 decrease in the control arm over the 3 months study period. As outlined in Table 4, mean difference (SD) between baseline and follow-up child health-care costs was –£52.56 ($95\%$ CI: –£138.83; £33.71) for the intervention arm ($$n = 26$$) and –£21.79 ($95\%$ CI: –£53.48; £9.90) for the control arm ($$n = 32$$). This amounted to a mean reduction between groups over the data collection period of –£30.77 (SD = 230.97; $95\%$ CI: –£113.80; £52.26). **TABLE 5** | Healthcare resource | Intervention | Intervention.1 | Control | Control.1 | | --- | --- | --- | --- | --- | | | Baseline (n = 38) | Follow up (n = 28) | Baseline (n = 37) | Follow up (n = 33) | | Healthcare resource use (minutes)† | Healthcare resource use (minutes)† | Healthcare resource use (minutes)† | Healthcare resource use (minutes)† | Healthcare resource use (minutes)† | | GP | 85 | 20 | 75 | 32 | | Nurse | 0 | 0 | 15 | 5 | | Hospital inpatient | 840 | 0 | 0 | 0 | | Hospital outpatient | 55 | 25 | 45 | 40 | | A&E | 60 | 0 | 625 | 0 | | Non-routine dental | 80 | 90 | 51 | 15 | | Total | 1120 | 135 | 811 | 92 | | Healthcare resource use (visits)† | Healthcare resource use (visits)† | Healthcare resource use (visits)† | Healthcare resource use (visits)† | Healthcare resource use (visits)† | | Hospital inpatient | 1 | 0 | 0 | 0 | | Hospital outpatient | 2 | 2 | 2 | 2 | | A&E | 1 | 0 | 2 | 0 | | Healthcare resource costs (£)§ | Healthcare resource costs (£)§ | Healthcare resource costs (£)§ | Healthcare resource costs (£)§ | Healthcare resource costs (£)§ | | GP | 388.5 | 74 | 277.5 | 192.4 | | Nurse | 0 | 0 | 18.33 | 0 | | Hospital inpatient | 827 | 0 | 0 | 0 | | Hospital outpatient | 274 | 274 | 274 | 274 | | A&E | 182 | 0 | 364 | 0 | | Non-routine dental | 656 | 295.2 | 364.08 | 49.2 | | Total | 2327.50 | 643.20 | 1297.91 | 515.60 | ## 3.6. Productivity and personal financial losses Total days off school due to ill health, and consequential parent time off work, over the past 3 months was reported (see Table 6). Over the trial period, there was a reduction of 20 days off work in the intervention arm, and a reduction of 6 days off work in the control arm. Baseline absenteeism cost amounted to £2272.20 within the intervention arm, and £649.20 within the control arm. At 3 months follow up, workplace absenteeism costs amounted to £108.20 in the intervention arm and £0 in the control arm. **TABLE 6** | Absenteeism and associated costs | Intervention | Intervention.1 | Control | Control.1 | | --- | --- | --- | --- | --- | | | Baseline (n = 40) | Follow up (n = 27) | Baseline (n = 38) | Follow up (n = 35) | | Child total days off school due to ill health | 14.5 | 4 | 19.5 | 0 | | Parent total time off work due to child health | 21 | 1 | 6 | 0 | | Parent productivity costs (£)† | 2272.20 | 108.20 | 649.20 | 0 | Based on complete case analysis, mean difference between baseline and follow-up school absenteeism was –0.362 ($95\%$ CI: –0.839; 0.114) per child for the intervention arm ($$n = 29$$) and –0.547 ($95\%$ CI: –1.039; –0.055) for the control arm ($$n = 32$$). This amounted to a mean difference reduction of –£80.15 ($95\%$ CI: –£173.315; £13.019) in workplace productivity losses within the intervention arm and –£15.91 ($95\%$ CI: –£34.81; £2.98) in the control arm per participant. This resulted in a mean difference reduction of –£64.24 (SD = 241.66, $95\%$ CI: –£147.54; £19.07) between study arms at follow up. ## 3.7. Sensitivity analysis Two data points were removed from the analysis due to z-scores greater than five. Mean differences (SD) between baseline and follow-up child healthcare costs were –£14.28 ($95\%$ CI: –£50.89; £22.33) for the intervention arm ($$n = 25$$) and –£21.84 ($95\%$ CI: –£53.55; £9.87) for the control arm ($$n = 32$$). This amounted to a mean difference between groups over the data collection period of £7.56 (SD = 124.91; $95\%$ CI: –£39.66; £54.70). There was a mean reduction (SD) between baseline and follow-up workplace productivity costs of –£41.62 ($95\%$ CI: –£92.70; £9.47) for the intervention arm ($$n = 26$$) and –£15.88 ($95\%$ CI: –£34.74; £2.98) for the control arm ($$n = 34$$). This amounted to a mean difference between groups over the data collection period of –£25.73 (SD = 137.54; $95\%$ CI: –£73.98; £22.51). The number of missing observations that were accounted for within multiple imputation ranged between 39 and 42 at baseline, and 54–55 at 3 months follow up. The dataset comprised of 114 complete observations after multiple imputation (intervention: $$n = 55$$; control: $$n = 59$$). Supplementary Table 2 provides a breakdown of totals and means of multiple imputation outcomes. Mean differences between baseline and follow-up of multiple imputation cost and consequence outcomes are outlined in Supplementary Table 3. In summary, mean differences between study conditions over the study period led to a mean decrease in healthcare resource costs by –£12.95 (SD = 163.92, $95\%$ CI: –£55.49; £29.59), workplace productivity cost reduction of –£36.72 (SD = 174.12, $95\%$ CI: –£81.74; £8.31), and a mean reduction in QALYs by –0.005 (SD = 0.018, $95\%$ CI: 0.000; 0.009, see Supplementary Table 3). ## 4. Discussion The current pilot study investigated the feasibility of collecting and evaluating cost-effectiveness measures to help inform the development of a full-scale trial evaluating the Change4Life Food Scanner app. This is the first study, to our knowledge, to assess the cost and associated consequences of a UK Government dietary app. All analyses are preliminary and should be interpreted with caution. Complete case analysis suggested a reduction in healthcare resource costs, school absence and workplace productivity losses, and a modest increase in utilities, at follow-up, for both intervention and control arms. When mean differences were compared between groups, there was a greater reduction in both healthcare expenditures and productivity losses in the intervention arm, alongside a modest reduction in QALYs. Similar findings were apparent within multiple imputation. These findings suggest that the Food Scanner app may have the potential to be cost-saving from a healthcare and societal perspective, however, a larger sample size is needed to test for significance between-groups, alongside a longer follow-up period to ascertain intervention effects on distal outcomes. The time horizon of the study was considerably short for the outcomes under investigation. Overweight and obesity alongside healthcare and societal consequences are long-term trajectory issues that cannot be validly predicted from this 3 months feasibility study. The presence of a long-term economic model would provide the basis for making predictions about the long-term impact of short-term changes observed in this study and a full-scale trial. Therefore, we cannot ascertain whether the Food Scanner app will have any impacts on HRQoL, healthcare and societal costs in the long-term, as suggested within the conceptual model. A full-scale trial with a 24 months follow-up period may be necessary to allow for any short- (e.g., diet) and medium-term (e.g., body weight and HRQoL) impacts of the intervention to be captured. Economic evaluations alongside trials involve an analysis of trial costs. Costs of running the feasibility study amounted to £36.05 per participant, based on the number of consenting participants. However, costs per participant almost doubled when the average is based on study completers. Alongside sample size calculations, such costing will provide an estimate on the funding requirements of a full-scale trial. Calculation of study costs could be used to inform a full pre-trial model analysis to calculate the expected net benefit of a full trial design and whether this is positive or negative. However, to achieve this, intervention costs estimates would be needed alongside a long-term impact model. The latest Medical Research Council (MRC) guidance on the evaluation of complex interventions has suggested that economic modeling could be adopted within feasibility studies to verify whether the predicted benefits of the intervention justify both intervention costs and that of any future research (i.e., expected value of perfect information analysis) [58]. This could help determine whether the implementation of a full-scale trial is beneficial. The current study was unable to account for costs relating to the development and maintenance of the Change4Life Food Scanner app and attempts to access this information were unsuccessful. This was partly due to the costs of the app being intertwined with the costs of running the broader Change4Life campaign. In addition, there is a lack of information in the public domain regarding total number of previous and current app installs. There is a misconception that apps are a low-cost approach to achieving public health outcomes [12]. Whilst the cost per download is low, and some apps are available for free to the user, the costs of development and ongoing maintenance, as well as the program or campaign in which they are embedded, are substantial [14]. For example, Kalita et al. [ 18] evaluated a multicomponent intervention that included a dietary app component. App development costs (expert estimation) was estimated at £324,000, for an app duration of 10 years, in addition to 5 years of development time. Maintenance costs were assumed to be $25\%$ of app development costs, amounting to £16,200. On the other hand, Tully et al. [ 24] estimated app development costs at approximately €11,000, whilst maintenance costs were estimated at approximately €2000 (15–$20\%$ of app development costs). Additional costs were also flagged, such as cloud data storage). Alongside substantial app costs, there is difficulty in demonstrating intervention effects. This includes short-term intervention effects, which are both small and difficult to measure, as well as long-term effects, due to difficulty in providing validated approaches to predicting long term outcomes [59]. Therefore, economic evaluation is imperative to gain estimates of long-term outcomes that otherwise would not be possible. Given the difficulties in external evaluation, and more importantly in light of accepted frameworks for evaluation of complex interventions in complex settings [58], economic evaluations and long-term modeling should be embedded within programs. However, further transparency and research is needed exploring app development and maintenance costs by intervention complexity and features in order to guide evaluations. Such research may consider the inclusion of app developers as key stakeholders within discussions whereby a map of the app development journey can be mapped out alongside cost estimates. However, it is also likely that the size of app development companies and location may impact on cost of services. Such data will help guide the estimation of app-related costs in the absence of data and should be utilized alongside a series of sensitivity analyses. App promotion is a necessary driver to maximize app uptake and therefore has the potential to increase cost-effectiveness of app-based interventions [14]. Given that the Food Scanner app was initially released as part of a multi-media national campaign comprising of billboard and TV-based advertisements, as well as resources for schools [25], calculations of app-related costs may become entangled with Change4Life promotion material and general campaign costs. Cost-effectiveness of app promotion has been previously investigated within evaluations. A conceptual model was produced to reflect the likely population of New Zealand that would download a promoted weight loss app and use it at least once. Results suggested that smartphone app promotion costs amounted to NZ $2,883,000 over 1 year, resulting in small health gains and borderline cost-effectiveness at a population level. However, the model did not factor in app use by those not exposed to the mass media campaign, as well as duration and quality of app engagement [60, 61]. In the case of the Food Scanner app, costs associated with the Change4Life campaign in general were available only. Using these cost-estimates within cost-effectiveness analysis of the Food Scanner app risks overestimating costs involved in relation to the intervention received. Given that the Food Scanner app is freely available on the app market, individuals may engage with the app without having been exposed to, or engaged with, any of the other campaign material. Although the Food Scanner app can be considered as a standalone intervention, it is ultimately a component within a larger complex intervention (or campaign) operating in a complex obesity system. Ideally, complex interventions alongside their components should be evaluated individually to gain insight into the active ingredients leading to changes in behavior [58, 62]. Healthcare resource use, and associated costs, was reported throughout the trial period. Results suggested a greater reduction in healthcare expenditure within the intervention arm. We cannot ascertain whether such changes were due to intervention exposure given the short-term follow-up of the intervention, as any impacts on healthcare use are more likely to be distal as suggested within the conceptual model. In addition, the running of the trial was impacted by the coronavirus pandemic. The pandemic resulted in decreased population A&E attendance [63], and decreased outpatient services [64], therefore caution must be taken when interpreting results. Number of missing data for healthcare resource use measures were similar to other outcomes obtained within the trial. Although these measures were considered feasible, assumptions were made when costing the use of healthcare resources, given the ample costing options available on the National Schedule of NHS Costs 2019–2020 for NHS trusts and NHS foundation trusts, especially for A&E and inpatient services [46]. The CHU9D instrument was considered a feasible HRQoL measure for the purposes of the trial. Given the current study was only 3 months, we did not expect to see any considerable change in CHU9D outcomes, as was evidenced within our findings. Results suggested some worsening of HRQoL outcomes, though minimal, within the intervention group at follow-up. Given that COVID-19 was a study confounder, the pandemic may have impacted negatively on child outcomes and mental health [65]. On the other hand, the lack of variability in CHU9D responses could suggest that the CHU9D is not sensitive enough to detect changes in HRQoL in a predominantly healthy sample. For example, a systematic review investigating utility values for childhood obesity interventions found very small but significant differences by child weight status [66]. A longer study follow-up period, with a larger sample size, would help provide clarity regarding the CHU9D’s suitability, particularly if the intervention were to result in improvements in dietary choices. There was a reduction in productivity losses at follow up, in both condition arms. These results are aligned with school absence data. Our measures did not account for whether time off work was taken as paid (annual leave) or unpaid leave. This ought to be considered in future revisions of trial measures, as it may risk overestimating productivity losses. Future revisions of this measure should also consider workplace absenteeism for both parents as opposed to the participating parent only, to account for differences in how responsibilities are divided within households. A recent review on the use of productivity loss instruments has recommended the use of the institute for Medical Technology Assessment MTA Productivity Cost Questionnaire to capture absenteeism, presenteeism and unpaid work over a 4 weeks recall period [67]; which has been previously advised for increased recall precision [68]. In addition, given that recruitment specifically took place in Yorkshire and the Humber, differences in median weekly wages by geographic region was not incorporated within costing assumptions. However, this may be necessary within a full-scale trial should recruitment be expanded to the UK more generally. Dietary interventions may risk unintended economic consequences, which may act as a barrier to continued engagement or dietary behavior change [51]. Approximately a quarter of the sample in the intervention arm reported having spent slightly more on groceries due to their use of the Food Scanner app. This is similar to previous research that aimed to improve the healthiness of children’s lunchboxes, however, resulted in a non-significant increase in the cost of packed lunches at follow-up [16]. Given that a small proportion of individuals within the intervention arm reported increased grocery expenditures due to the 3 months trial, future measures within a full-scale trial ought to quantify these findings, for example through the collection of shopping receipts. This method has previously been used to monitor food purchasing behaviors [69]. Sensitivity analyses were conducted within the trial. Removal of outliers, or extreme data points, for cost data resulted in smaller mean differences between intervention and control arms over the trial period, in comparison to complete case analysis. Results suggested greater productivity losses within the intervention arm, as was the case within complete case analysis. However, after sensitivity analysis greater healthcare resource costs were found within the control arm, which was not the case within complete case analysis. Excluding outliers has demonstrated an impact on cost data. A future trial protocol should consider how outliers are to be interpreted and how extreme cost items should be handled. Previous research has adopted bootstrapping techniques, which reduces the impact of highly skewed data and extreme data points [70]. Alternatively, the 95th percentile of the overall sample’s baseline and follow-up costs have also been used to determine cost outliers [71]. The current evaluation has considered a broad range of economic measures which were considered feasible and explored multiple imputation methods for missing data handling. However, the study did have several limitations. Opportunity costs for lost time for using the Food Scanner app was not accounted for. Given that data on time spent engaging with the app was collected, opportunity costs could have potentially been quantified. However, there would have been uncertainty regarding appropriate costing units. Another limitation involved the considerable amount of missing data, amounting to approximately $50\%$ due to the high dropout rate early in the trial (before randomization exposure). Despite this, the sample size was still within the suggested range for pilot and feasibility studies [72, 73]. However, there were considerable differences in baseline reported outcomes for healthcare resource use and parent time off work due to child health between study arms. We cannot ascertain whether differences in baseline characteristics may be driving differences in outcomes at follow up, as opposed to the intervention. It is necessary that participant retention methods are considered for a full-scale trial, alongside efforts to over-recruit participants to account for a high drop out. This pilot and feasibility study exploring the economic and health impacts of the Change4Life Food Scanner app adds to the modest yet growing literature on the cost-effectiveness of mHealth dietary interventions. This is currently an under researched area, given the development and evaluation of dietary interventions has only started to emerge over the past decade. As such, the consideration of appropriate economic outcome measures, in addition to clinical outcomes, is necessary within feasibility studies before they are implemented in large-scale trials. Our results suggested that outcomes under investigation were feasible, though may require some minor revisions to best capture accurate data. The use of an RCT study design was also considered feasible to investigate the study question. However, given the nature of complex interventions within complex food systems [74], such designs may need to be supplemented with qualitative data collection to help explain the relationships between intervention exposure and outcomes of interest [75]. In addition, in cases where missing data cannot be prevented, multiple imputation methods were considered a successful approach to handle missing data whilst considering both within- and between-participant variability. However, further research is warranted into the effectiveness of dietary smartphone apps, dietary app uptake, duration of use and the variability of costs associated with the development and ongoing maintenance of dietary apps. ## Data availability statement The raw data supporting the conclusions of this article will be made available by the authors upon reasonable request, without undue reservation. ## Ethics statement The studies involving human participants were reviewed and approved by the University of Sheffield Research Ethics Committee [026380] in August 2019. The patients/participants provided their written informed consent to participate in this study. ## Author contributions SM conceived and managed the project, undertook data collection, data analysis, and data interpretation. All authors participated in the design of the study, critically reviewed the manuscript, reviewed, and accepted the final version of the manuscript. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2023.1125542/full#supplementary-material ## References 1. 1.NHS Digital. National Child Measurement Programme, England, Provisional 2021/22 School Year Outputs. Leeds: NHS Digital (2022).. (2022) 2. Bass R, Eneli I. **Severe childhood obesity: an under-recognised and growing health problem.**. (2015) **91** 639-45. DOI: 10.1136/postgradmedj-2014-133033 3. 3.Public Health England. Health Matters: Obesity and the Food Environment. London: Public Health England (2017).. (2017) 4. Cawley J. **The economics of childhood obesity.**. (2010) **29** 364-71. DOI: 10.1377/hlthaff.2009.0721 5. Trasande L, Chatterjee S. **The impact of obesity on health service utilization and costs in childhood.**. (2009) **17** 1749-54. DOI: 10.1038/oby.2009.67 6. Ritchie H, Roser M.. (2017) 7. 7.Ofcom. Online Nation 2021 Report. London: Ofcom (2021).. (2021) 8. Krebs P, Duncan D. **Health app use among US mobile phone owners: a national survey.**. (2015) **3**. DOI: 10.2196/mhealth.4924 9. Marcolino M, Oliveira J, D’Agostino M, Ribeiro A, Alkmim M, Novillo-Ortiz D. **The impact of mHealth interventions: systematic review of systematic reviews.**. (2018) **6**. DOI: 10.2196/mhealth.8873 10. Tate E, Spruijt-Metz D, O’Reilly G, Jordan-Marsh M, Gotsis M, Pentz M. **mHealth approaches to child obesity prevention: successes, unique challenges, and next directions.**. (2013) **3** 406-15. DOI: 10.1007/s13142-013-0222-3 11. Barlow S, Ohlemeyer C. **Parent reasons for nonreturn to a pediatric weight management program.**. (2006) **45** 355-60. DOI: 10.1177/000992280604500408 12. Iribarren S, Cato K, Falzon L, Stone P. **What is the economic evidence for mHealth? a systematic review of economic evaluations of mHealth solutions.**. (2017) **12**. DOI: 10.1371/journal.pone.0170581 13. McNamee P, Murray E, Kelly M, Bojke L, Chilcott J, Fischer A. **Designing and undertaking a health economics study of digital health interventions.**. (2016) **51** 852-60. DOI: 10.1016/j.amepre.2016.05.007 14. Michie S, Yardley L, West R, Patrick K, Greaves F. **Developing and evaluating digital interventions to promote behavior change in health and health care: recommendations resulting from an international workshop.**. (2017) **19**. DOI: 10.2196/jmir.7126 15. Gomes M, Murray E, Raftery J. **Economic evaluation of digital health interventions: methodological issues and recommendations for practice.**. (2022) **40** 367-78. DOI: 10.1007/s40273-022-01130-0 16. Sutherland R, Nathan N, Brown A, Yoong S, Finch M, Lecathelinais C. **A randomized controlled trial to assess the potential efficacy, feasibility and acceptability of an m-health intervention targeting parents of school aged children to improve the nutritional quality of foods packed in the lunchbox ‘SWAP IT’.**. (2019) **16**. DOI: 10.1186/s12966-019-0812-7 17. Brown A, Sutherland R, Reeves P, Nathan N, Wolfenden L. **Cost and cost effectiveness of a pilot m-Health intervention targeting parents of school-aged children to improve the nutritional quality of foods packed in the lunchbox.**. (2021) **13**. DOI: 10.3390/nu13114136 18. Kalita N, Cooper K, Baird J, Woods-Townsend K, Godfrey K, Cooper C. **Cost-effectiveness of a dietary and physical activity intervention in adolescents: a prototype modelling study based on the Engaging Adolescents in Changing Behaviour (EACH-B) programme.**. (2022) **12**. DOI: 10.1136/bmjopen-2021-052611 19. 19.National Institute for Clinical Excellence. Guide to the Methods of Technology Appraisal. London: National Institute for Clinical Excellence (2013).. (2013) 20. Law L, Kelly J, Savill H, Wallen M, Hickman I, Erku D. **Cost-effectiveness of telehealth-delivered diet and exercise interventions: a systematic review.**. (2022). DOI: 10.1177/1357633X211070721 21. Kipping R, Langford R, Brockman R, Wells S, Metcalfe C, Papadaki A. **Child-care self-assessment to improve physical activity, oral health and nutrition for 2-to 4-year-olds: a feasibility cluster RCT.**. (2019) **7** 1-164. DOI: 10.3310/phr07130 22. Simpson S, Matthews L, Pugmire J, McConnachie A, McIntosh E, Coulman E. **An app-, web-and social support-based weight loss intervention for adults with obesity: the ‘HelpMeDoIt!’feasibility randomised controlled trial.**. (2020) **6**. DOI: 10.1186/s40814-020-00656-4 23. Simpson S, Matthews L, Pugmire J, McConnachie A, McIntosh E, Coulman E. **An app-, web-and social support-based weight loss intervention for adults with obesity: the HelpMeDoIt! feasibility RCT.**. (2020) **8** 1-14. DOI: 10.3310/phr08030 24. Tully L, Sorensen J, O’Malley G. **Pediatric weight management through mhealth compared to face-to-face care: cost analysis of a randomized control trial.**. (2021) **9**. DOI: 10.2196/31621 25. Bradley J, Gardner G, Rowland M, Fay M, Mann K, Holmes R. **Impact of a health marketing campaign on sugars intake by children aged 5–11 years and parental views on reducing children’s consumption.**. (2020) **20**. DOI: 10.1186/s12889-020-8422-5 26. Mahdi S, Michalik-Denny E, Buckland N. **An assessment of behavior change techniques in two versions of a dietary mobile application: the Change4Life food scanner.**. (2022) **10**. DOI: 10.3389/fpubh.2022.803152 27. Mahdi S, Buckland N, Chilcott J.. (2022) 28. Eldridge S, Chan C, Campbell M, Bond C, Hopewell S, Thabane L. **CONSORT 2010 statement: extension to randomised pilot and feasibility trials.**. (2016) **2**. DOI: 10.1186/s40814-016-0105-8 29. 29.Department for Work and Pensions. Income Distribution. London: Department for Work and Pensions (2022).. (2022) 30. 30.myfood. The Best Way to Measure and Manage Nutrition. (2022). Available online at: https://www.myfood24.org/ (accessed February 9, 2023).. (2022) 31. 31.Qualtrics. Make Every Interaction an Experience That Matters. (2020). Available online at: https://www.qualtrics.com/uk/ (accessed February 9, 2023).. (2020) 32. 32.Office for Health Improvement and Disparities. Cost Consequence Analysis: Health Economic Studies. (2020). Available online at: https://www.gov.uk/guidance/cost-consequence-analysis-health-economic-studies (accessed February 9, 2023).. (2020) 33. 33.Database of Instruments for Resource Use Measurement. (2023). Available online at: http://www.dirum.org/ (accessed February 9, 2023).. (2023) 34. 34.Google Play. NHS Food Scanner. Department of Health and Social Care (Digital). (2022). Available online at: https://play.google.com/store/apps/details?id=com.phe.c4lfoodsmart (accessed February 9, 2023).. (2022) 35. 35.Apple App Store. NHS Food Scanner. Department of Health and Social Care (Digital). (2022). Available online at: https://apps.apple.com/gb/app/nhs-food-scanner/id1182946415 (accessed February 9, 2023).. (2022) 36. Stevens K. **Working with children to develop dimensions for a preference-based, generic, pediatric, health-related quality-of-life measure.**. (2010) **20** 340-51. DOI: 10.1177/1049732309358328 37. Ratcliffe J, Huynh E, Stevens K, Brazier J, Sawyer M, Flynn T. **Nothing about us without us? a comparison of adolescent and adult health-state values for the child health utility-9D using profile case best–worst scaling.**. (2016) **25** 486-96. DOI: 10.1002/hec.3165 38. 38.The University of Sheffield. CHU9D – Measuring Health and Calculating QALYs for Children and Adolescents. (2023). Available online at: https://licensing.sheffield.ac.uk/product/CHU-9D (accessed February 9, 2023).. (2023) 39. Stevens K. **Valuation of the child health utility 9D index.**. (2012) **30** 729-47. DOI: 10.2165/11599120-000000000-00000 40. Stevens K.. (2008). DOI: 10.1037/t71525-000 41. Whitehead S, Ali S. **Health outcomes in economic evaluation: the QALY and utilities.**. (2010) **96** 5-21. DOI: 10.1093/bmb/ldq033 42. Jones Nielsen J, Laverty A, Millett C, Mainous IA, Majeed A, Saxena S. **Rising obesity-related hospital admissions among children and young people in England: national time trends study.**. (2013) **8**. DOI: 10.1371/journal.pone.0065764 43. Breitfelder A, Wenig C, Wolfenstetter S, Rzehak P, Menn P, John J. **Relative weight-related costs of healthcare use by children—results from the two German birth cohorts, GINI-plus and LISA-plus.**. (2011) **9** 302-15. DOI: 10.1016/j.ehb.2011.02.001 44. Cottrell D, Wright-Hughes A, Collinson M, Boston P, Eisler I, Fortune S. **Effectiveness of systemic family therapy versus treatment as usual for young people after self-harm: a pragmatic, phase 3, multicentre, randomised controlled trial.**. (2018) **5** 203-16. DOI: 10.1016/S2215-0366(18)30058-0 45. Jones K, Burns A.. (2021) 46. 46.NHS England. 2019/20 National Cost Collection Data Publication. (2021). Available online at: https://www.england.nhs.uk/publication/2019-20-national-cost-collection-data-publication/ (accessed February 9, 2023).. (2021) 47. Powell C, Kolamunnage-Dona R, Lowe J, Boland A, Petrou S, Doull I. **MAGNEsium trial in children (MAGNETIC): a randomised, placebo-controlled trial and economic evaluation of nebulised magnesium sulphate in acute severe asthma in children.**. (2013) **17** v-vi, 1-216. DOI: 10.3310/hta17450 48. Beecham J, Knapp M.. (2001) **Vol. 2** 200-24 49. 49.Office for National Statistics. Employee Earnings in the UK: 2020. (2020). Available online at: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/earningsandworkinghours/bulletins/annualsurveyofhoursandearnings/2020 (accessed February 9, 2023).. (2020) 50. Jensen J, Poulsen S. **The new Nordic diet–consumer expenditures and economic incentives estimated from a controlled intervention.**. (2013) **13**. DOI: 10.1186/1471-2458-13-1114 51. Saulle R, Semyonov L, La Torre G. **Cost and cost-effectiveness of the mediterranean diet: results of a systematic review.**. (2013) **5** 4566-86. DOI: 10.3390/nu5114566 52. Rao M, Afshin A, Singh G, Mozaffarian D. **Do healthier foods and diet patterns cost more than less healthy options? a systematic review and meta-analysis.**. (2013) **3**. DOI: 10.1136/bmjopen-2013-004277 53. Kern D, Auchincloss A, Stehr M, Roux A, Moore L, Kanter G. **Neighborhood prices of healthier and unhealthier foods and associations with diet quality: evidence from the multi-ethnic study of atherosclerosis.**. (2017) **14**. DOI: 10.3390/ijerph14111394 54. Thompson S, Barber J. **How should cost data in pragmatic randomised trials be analysed?**. (2000) **320** 1197-200. DOI: 10.1136/bmj.320.7243.1197 55. Rubin D.. (1987). DOI: 10.1002/9780470316696 56. White I, Royston P, Wood A. **Multiple imputation using chained equations: issues and guidance for practice.**. (2011) **30** 377-99. DOI: 10.1002/sim.4067 57. Jakobsen J, Gluud C, Wetterslev J, Winkel P. **When and how should multiple imputation be used for handling missing data in randomised clinical trials–a practical guide with flowcharts.**. (2017) **17**. DOI: 10.1186/s12874-017-0442-1 58. Skivington K, Matthews L, Simpson S, Craig P, Baird J, Blazeby J. **A new framework for developing and evaluating complex interventions: update of medical research council guidance.**. (2021) **374**. DOI: 10.1136/bmj.n2061 59. Mahdi S, Marr C, Buckland N, Chilcott J. **Methods for the economic evaluation of obesity prevention dietary interventions in children: a systematic review and critical appraisal of the evidence.**. (2022) **23**. DOI: 10.1111/obr.13457 60. Cleghorn C, Wilson N, Nair N, Kvizhinadze G, Nghiem N, McLeod M. **Health benefits and cost-effectiveness from promoting smartphone apps for weight loss: multistate life table modeling.**. (2019) **7**. DOI: 10.2196/11118 61. Jones A, Grout L, Wilson N, Nghiem N, Cleghorn C. **The cost-effectiveness of a mass media campaign to promote smartphone apps for weight loss: updated modeling study.**. (2022) **6**. DOI: 10.2196/29291 62. Craig P, Dieppe P, Macintyre S, Michie S, Nazareth I, Petticrew M. **Developing and evaluating complex interventions: the new medical research council guidance.**. (2008) **337**. DOI: 10.1136/bmj.a1655 63. McConkey R, Wyatt S.. (2020) 64. Bottle A, Neale F, Foley K, Viner R, Kenny S, Aylin P. **Impact of COVID-19 on outpatient appointments in children and young people in England: an observational study.**. (2022) **12**. DOI: 10.1136/bmjopen-2022-060961 65. Thomas H, Runions K, Lester L, Lombardi K, Epstein M, Mandzufas J. **Western Australian adolescent emotional wellbeing during the COVID-19 pandemic in 2020.**. (2022) **16**. DOI: 10.1186/s13034-021-00433-y 66. Brown V, Tan E, Hayes A, Petrou S, Moodie M. **Utility values for childhood obesity interventions: a systematic review and meta-analysis of the evidence for use in economic evaluation.**. (2018) **19** 905-16. DOI: 10.1111/obr.12672 67. Hubens K, Krol M, Coast J, Drummond M, Brouwer W, Uyl-de Groot C. **Measurement instruments of productivity loss of paid and unpaid work: a systematic review and assessment of suitability for health economic evaluations from a societal perspective.**. (2021) **24** 1686-99. DOI: 10.1016/j.jval.2021.05.002 68. Severens J, Mulder J, Laheij R, Verbeek A. **Precision and accuracy in measuring absence from work as a basis for calculating productivity costs in The Netherlands.**. (2000) **51** 243-9. DOI: 10.1016/S0277-9536(99)00452-9 69. Monsivais P, Perrigue M, Adams S, Drewnowski A. **Measuring diet cost at the individual level: a comparison of three methods.**. (2013) **67** 1220-5. DOI: 10.1038/ejcn.2013.176 70. Reilly C, Butler J, Culler S, Gary R, Higgins M, Schindler P. **An economic evaluation of a self-care intervention in persons with heart failure and diabetes.**. (2015) **21** 730-7. DOI: 10.1016/j.cardfail.2015.06.382 71. Smith D, O’Keeffe-Rosetti M, Leo M, Mayhew M, Benes L, Bonifay A. **Economic evaluation: a randomized pragmatic trial of a primary care-based cognitive behavioral intervention for adults receiving long-term opioids for chronic pain.**. (2022) **60** 423-31. DOI: 10.1097/MLR.0000000000001713 72. Sim J, Lewis M. **The size of a pilot study for a clinical trial should be calculated in relation to considerations of precision and efficiency.**. (2012) **65** 301-8. DOI: 10.1016/j.jclinepi.2011.07.011 73. Julious S. **Sample size of 12 per group rule of thumb for a pilot study.**. (2005) **4** 287-91. DOI: 10.1002/pst.185 74. Butland B, Jebb S, Kopelman P, McPherson K, Thomas S, Mardell J. (2007) 75. Ariss S, Nasr N.. (2022)
--- title: Geographic disparities and temporal changes of COVID-19 hospitalization risks in North Dakota authors: - Md Marufuzzaman Khan - Nirmalendu Deb Nath - Matthew Schmidt - Grace Njau - Agricola Odoi journal: Frontiers in Public Health year: 2023 pmcid: PMC10061029 doi: 10.3389/fpubh.2023.1062177 license: CC BY 4.0 --- # Geographic disparities and temporal changes of COVID-19 hospitalization risks in North Dakota ## Abstract ### Background Although the burden of the coronavirus disease 2019 (COVID-19) has been different across communities in the US, little is known about the disparities in COVID-19 burden in North Dakota (ND) and yet this information is important for guiding planning and provision of health services. Therefore, the objective of this study was to identify geographic disparities of COVID-19 hospitalization risks in ND. ### Methods Data on COVID-19 hospitalizations from March 2020 to September 2021 were obtained from the ND Department of Health. Monthly hospitalization risks were computed and temporal changes in hospitalization risks were assessed graphically. County-level age-adjusted and spatial empirical Bayes (SEB) smoothed hospitalization risks were computed. Geographic distributions of both unsmoothed and smoothed hospitalization risks were visualized using choropleth maps. Clusters of counties with high hospitalization risks were identified using Kulldorff's circular and Tango's flexible spatial scan statistics and displayed on maps. ### Results There was a total of 4,938 COVID-19 hospitalizations during the study period. Overall, hospitalization risks were relatively stable from January to July and spiked in the fall. The highest COVID-19 hospitalization risk was observed in November 2020 (153 hospitalizations per 100,000 persons) while the lowest was in March 2020 (4 hospitalizations per 100,000 persons). Counties in the western and central parts of the state tended to have consistently high age-adjusted hospitalization risks, while low age-adjusted hospitalization risks were observed in the east. Significant high hospitalization risk clusters were identified in the north-west and south-central parts of the state. ### Conclusions The findings confirm that geographic disparities in COVID-19 hospitalization risks exist in ND. Specific attention is required to address counties with high hospitalization risks, especially those located in the north-west and south-central parts of ND. Future studies will investigate determinants of the identified disparities in hospitalization risks. ## 1. Introduction The first COVID-19 case in the US was detected in January 2020, and as of August 30, 2022, the US has reported the highest number of confirmed cases globally [94,110,810], with 1,039,055 deaths [1, 2]. The disease was reported in every US state although its incidence and severity varied geographically. This might be due to geographic differences in behavioral and demographic factors such as smoking history, co-morbidities, and environmental pollutants which have been shown to play a role in COVID-19 incidence, hospitalization, and mortality risks (3–6). Geographic differences in vaccination rates are another factor that might have impacted geographic disparities in hospitalization rates. According to a report published by the Centers for Disease Control and Prevention (CDC), COVID-19 hospitalization rates were 29.2 times higher in unvaccinated individuals than those who were fully vaccinated [7]. National surveillance data demonstrated that overall hospitalization rates increased in the US due to the emergence of COVID-19 [1, 8]. Therefore, regular monitoring of hospitalization rates, clinical features, and disposition of hospitalized patients is essential to understanding the epidemiology of COVID-19 in the US, and is also helpful for guiding, planning and prioritizing healthcare resource utilization. Just like the other US states, North Dakota, a Midwest US state sharing borders with Canada, was affected by the COVID-19 pandemic directly, as well as the disease's health, social and economic ramifications. Unfortunately, little is known about the disparities in COVID-19 burden in North Dakota. A study conducted among the North Dakota tribal people of Spirit Lake reported incidence rates of 520–600 cases per 100,000/person-week, which was 1.5 times higher than the state average during the same time period [9]. Knowledge of the burden and geographic disparities of COVID-19 risks in North *Dakota is* important in guiding the planning and provision of health services and ensuring that the healthcare system is not overwhelmed by high numbers of patients in some geographic locations. Therefore, the objective of this study was to identify geographic disparities of COVID-19 hospitalization risks in North Dakota. Two statistically rigorous approaches are used to identify statistically significant clusters of hospitalization risks. Geographic distribution of identified clusters are presented in maps. ## 2.1. Study area and data sources The study area encompassed the entire state of North Dakota (Figure 1). As of 2020, North Dakota had the fifth lowest population among all states of the United States of America and had a population of ~0.8 million. The state has 53 counties of which 38 have population densities lower than seven persons per square mile [10, 11]. Cass county is the most populous (179,937 residents), while Slope county is the least populous with only 788 residents. The age distribution of the population is 0–19 years old ($26.2\%$), 20–44 years old ($35.4\%$), 45–64 years old ($23\%$), and ≥65 years old ($15.3\%$). The overall racial composition of North *Dakota is* ~$86.9\%$ White, $5.6\%$ Native American, $3.4\%$ Black, and all other races comprise $4.1\%$ of the population. By ethnicity, the majority ($95.9\%$) of the population is non-Hispanic [12]. **Figure 1:** *Map of North Dakota showing geographic distribution of urban and rural counties.* Data on COVID-19 hospitalized cases covering the time period from March 2020 to September 2021 was collected by the North Dakota Department of Health and Human Services (NDHHS) through COVID-19 case interviews and hospital reporting. A COVID-19 hospitalized case was defined as a person who had a first-time diagnosis of COVID-19 confirmed by laboratory tests and was admitted to a hospital. Only first-time diagnosed and hospitalized cases were included in the study because re-infected cases have a lower risk of hospitalization compared to first-time infections [13]. Additional case data provided by HHS included county of residence and age. Data on county-level total population and population of different age categories were extracted from the 2016–2020 American Community Survey 5-years average estimate [14]. Cartographic boundary file for county-level geographic analysis was downloaded from the United States Census Bureau TIGER Geodatabase [15]. Data on urban and rural classification were obtained from the US Census Bureau [16]. ## 2.2. Data preparation and visualization All descriptive statistical analyses were performed in SAS 9.4 [17]. Age was categorized into ≤ 19 years, 20–44 years, 45–64 years, and 65 years or older. Monthly hospitalization risks were computed and expressed as the number of hospitalized cases per 100,000 persons. The study period was divided into peak and non-peak periods as follows: if there were ≥25 hospitalized cases per 100,000 persons in a month, it was identified as a month of the peak period, otherwise it was classified as a non-peak period. Two peak and two non-peak periods were identified. The first peak period started in August 2020 and lasted through December 2020, and the second peak period spanned from August 2021 to September 2021. The first and second non-peak periods were March 2020 to July 2020 and January 2021 to July 2021, respectively. Temporal changes in monthly COVID-19 hospitalized cases per 100,000 persons were assessed graphically in Microsoft Excel 2022 [18]. Difference between average hospitalization risks in peak and non-peak periods were compared using two sample t-Test. For each of the four time periods, COVID-19 hospitalized cases were aggregated to the county level. Direct age-adjusted county-level hospitalization risks were calculated using the county-level total population as the denominator and the 2010 US population as the standard [19]. Spatial Empirical Bayes (SEB) smoothed age-adjusted hospitalization risks were then calculated in GeoDa 1.14 [20] using 1st order Queen contiguity spatial weight. Based on the US Census Bureau definition, North Dakota counties were classified into the following three categoris: [1] Urban (<$50\%$ of county population live in rural areas); [2] Mostly rural (50–$99.9\%$ of county population live in rural areas); [3] Completely rural ($100\%$ of county population live in rural areas) (Figure 1) [13]. ## 2.3.1. Kulldorff's circular spatial scan statistics Kulldorff's circular spatial scan statistics (CSSS), implemented in SaTScan 9.6 [21], was used to identify circular clusters of age-adjusted high hospitalization risks. A discrete Poisson probability model specifying circular non-overlapping purely spatial high-risk clusters was used. The maximum circular window size was set at $24\%$ of the population at risk based on the population of Cass County, which has the largest population in North Dakota. This ensures that all counties have a chance of being included in a cluster regardless of their population size. To identify statistically significant clusters, 999 Monte Carlo replications and likelihood ratio test were used specifying a critical p-value of 0.05. ## 2.3.2. Tango's flexible spatial scan statistics Both circular and irregularly shaped high-risk clusters of age-adjusted hospitalization risks were investigated using Tango's flexible spatial scan statistics (FSSS) implemented in FleXScan 3.1.2 [22, 23]. Poisson probability models with a restricted log likelihood (LLR) ratio (specifying an alpha of 0.2) and a maximum cluster size of 15 counties were specified to preclude potential inclusion of counties with non-elevated hospitalization risks. For statistical inference, 999 Monte Carlo replications were used, and statistical significance was assessed using a critical p-value of 0.05. ## 2.4. Cartographic displays QGIS [24] was used to display the geographic distribution of both smoothed and non-smoothed COVID-19 hospitalization risks and the location of spatial clusters. Jenk's optimization classification scheme was used to determine critical intervals for choropleth maps. ## 3.1. Spatial and temporal patterns There was a total of 4,938 COVID-19 hospitalizations during the study period. Age-adjusted COVID-19 hospitalization risks varied across counties ranging from 0 to 365 hospitalizations per 100,000 persons in March to July 2020 and January to September 2021. However, higher hospitalization risks were observed from August to December 2020 and ranged from 0 to 1,300 hospitalizations per 100,000 persons (Figures 2–4). At the beginning of the study period (March to July 2020), a few rural counties had high age-adjusted hospitalization risks (>56 hospitalizations per 100,000 persons), while almost half of the counties had high age-adjusted hospitalization risks during the rest of the study period (>316 hospitalizations per 100,000 persons in August to December 2020, >56 hospitalizations per 100,000 persons in January-September 2021). These included rural and urban counties (Figures 1–3). Counties in the western and central parts of the state tended to have consistently high age-adjusted hospitalization risks, while low age-adjusted hospitalization risks were observed in the east (Figures 2, 3). **Figure 2:** *County-level unsmoothed age-adjusted COVID-19 hospitalization risks in North Dakota, March 2020–September 2021.* **Figure 3:** *County-level spatial empirical Bayes (SEB) smoothed age-adjusted COVID-19 hospitalization risks in North Dakota, March 2020–September 2021.* Overall, COVID-19 hospitalization risks were relatively stable from January to July and spiked in the fall (Figure 4). A sharp increase in hospitalization risk was observed from August to November 2020, and a sharp decrease was observed thereafter in December 2020. A similar pattern of increase was observed in August 2021. Average hospitalization risk in peak period was significantly ($p \leq 0.05$) higher compared to non-peak period. The highest COVID-19 hospitalization risk was observed in November 2020 (153 hospitalizations per 100,000 persons) while the lowest was in March 2020 (4 hospitalizations per 100,000 persons) (Figure 4). **Figure 4:** *Temporal patterns of COVID-19 hospitalization risks in North Dakota, March 2020–September 2021.* ## 3.2. Clusters of high COVID-19 hospitalization risks Figure 5 shows age-adjusted high COVID-19 hospitalization risk clusters identified in North Dakota using the Tango's FSSS. Consistent with high age-adjusted hospitalization risks observed in the western and central parts of the state, significant high hospitalization risk clusters were identified in these areas (Figures 2, 3, 5). There was increase in both the numbers of counties involved in the clusters and sizes of the populations affected in peak periods compared to non-peak periods (Table 1 and Figure 5). **Figure 5:** *Spatial clusters of age-adjusted high COVID-19 hospitalization risks identified in North Dakota using Tango's flexible spatial scan statistics, March 2020–September 2021.* TABLE_PLACEHOLDER:Table 1 Two significant high hospitalization risk clusters were identified in peak periods of August to December 2020 and August to September 2021, one in the north-west and the other in the south-central part of the state. In addition, a high-risk cluster was identified in the north-central part of the state in August to December 2020 and included several counties (Rolette, Towner, Benson, Eddy) that were not part of any cluster identified in other peak and non-peak periods. On the other hand, a high hospitalization risk cluster involving only Cass county was identified in eastern North Dakota during the first non-peak period in March to July 2020. Another high hospitalization risk cluster (Sioux county) identified at the same time in the south-central part of the state had the highest hospitalization risk ratio (HRR = 6.68; $$p \leq 0.001$$) (Table 1 and Figure 5). During the second non-peak period in January to July 2021, a high-hospitalization risk cluster was identified in the western part of the state (Figure 5). The cluster identified in the second non-peak period included more counties and a larger population than that of the first non-peak period. Interestingly, Kulldorff's CSSS had almost similar findings to Tango's FSSS (Table 2; Figures 5, 6). However, an additional high-risk cluster was identified in Kulldorff's CSSS in the south-central part of the state during January–July 2021 (Figure 6). Williams county was consistently part of a high hospitalization risk cluster during peak and non-peak periods from August 2020 to the end of the study period (Figures 1, 5, 6). Several counties in the south-central part of the state (Morton, Grant, Sioux, Emmons) were part of high hospitalization risk clusters during both peak periods. However, some counties in central North Dakota (Rolette, Benson, Eddy, Ward) that were part of high-hospitalization risk clusters during the first peak period in August to December 2020 were not part of any cluster during the second peak period in August to September 2021. The opposite trend of transitioning from non-significant to statistically significant high hospitalization risk clusters was observed in McKenzie, Mercer, and Stark counties and these were mostly located in the western part of the state. These findings were consistent between Tango's FSSS and Kulldorff's CSSS methods (Figures 1, 5, 6). Burleigh county, where the state capital is located, was in part of high hospitalization risk cluster during the peak period but not during the non-peak period. During both non-peak periods, only Sioux county was consistently identified in high hospitalization risk clusters in the CSSS method (Figures 1, 6). ## 4. Discussion This study investigated county-level geographic disparities and temporal changes of age-adjusted COVID-19 hospitalization risks in North Dakota. Although substantial disparities were reported in infectious disease morbidities and mortalities among North Dakotans, little is known about disparities of COVID-19 burden in North Dakota [25]. The findings of the current study help to fill this gap and are useful in guiding evidence-based health planning and resource allocation in combating the COVID-19 problem. Since North *Dakota is* one of the most rural states, findings also help to understand the burden of COVID-19 in rural populations of the US [26]. The increase in age-adjusted hospitalization risk in fall 2020 and a decrease thereafter in early spring 2021 was consistent with the overall COVID-19 hospitalization trend observed in the US [27]. The decline in COVID-19 hospitalization risks in spring 2021 could be due to the arrival of Food and Drug Administration (FDA) emergency authorized vaccines [28] and starting of vaccinations in December 2020 [29]. In addition, there were restrictive policies such as social distancing, mandatory masking, and limited mobilities imposed during 2020 and early 2021, which resulted in low transmission of COVID-19 [30]. However, the rise of new delta variant, relaxed COVID-19 restriction policies, and waning immunity could be responsible for the observed surge in July 2021 [30]. Although there was an overall increase in hospitalization risk starting from fall 2020 across counties in North Dakota, counties in the western and central parts of the state had higher hospitalization risks than the eastern part of the state. This is probably due to geographical differences in vaccination coverage. According to a report by NDHHS, percentages of populations having at least one dose of COVID-19 vaccine were substantially higher in counties of the eastern part of the state compared to the rest of the state [31]. The reasons for the low vaccination rates in the western and central North Dakota could be lack of sufficient vaccine administration facilities and limited accessibility due to its rough terrain [32]. Geographic barriers could also have prevented COVID-19 patients from getting supportive outpatient care which might have increased hospitalization risks in those areas. A Kaiser Family Foundation report stated that rural populations are less likely to get vaccine compared to urban populations due to lack of understanding of COVID-19 severity and vaccine effectiveness [13]. However, a report published by the Office of the Assistant Secretary for Planning and Evaluation (ASPE) showed that there was a high percentage of unvaccinated individuals in North Dakota who were willing to get vaccinated despite living in rural areas [33]. High hospitalization risk clusters identified in south-central (Sioux) and east-central areas (Benson, Rolette) of the state could be due to racial distributions of populations of those areas. The majority of the population of those counties are American Indians (AI) [34, 35], who have the highest hospitalization risks among all minority populations in the US due to limited access to healthcare services and underlying health disparities [36, 37]. In addition, the percentage of the population with health insurance and median household income among these populations were lower than the state average [38, 39]. However, the percentage of the population that had received at least one dose of vaccine in 2021 was higher in Rolette and Benson counties than that of Sioux county [31]. This could explain the fact that Sioux county was part of high hospitalization risk clusters in both peak periods, while Rolette and Benson counties were not included in any high hospitalization risk cluster in the second peak. Suffice it to say that vaccines may have been effective in reducing hospitalization risks in Rolette and Benson counties [40]. Since all three counties have similar racial distribution, median income, and insurance coverage, the low vaccination rate in Sioux county could be due to structural barriers such as limited healthcare accessibility. A report published by ASPE supports this hypothesis showing that unvaccinated individuals of Sioux county were interested in getting vaccines [33]. This implies that there were no behavior-related barriers to vaccination such as lack of trust or misperception [41, 42]. Moreover, culturally inclusive programs implemented in north and east central counties might have played an important role in managing COVID-19 in those areas. A tribally managed CDC-guided comprehensive COVID-19 case management program was implemented among populations of Spirit Lake during the first peak period in late September 2020. Since this program was effective in controlling COVID-19 risks [9], similar programs could help to manage COVID-19 in other tribal areas and public health communities. Similar to AI people, non-Hispanic Black people are more likely to be hospitalized due to COVID-19 than non-Hispanic White people due to underlying health disparities (37, 43–45). Counties in the western part of the state have relatively high percentages of non-Hispanic Black populations which could have contributed to the high hospitalization risks observed in those areas. However, the eastern part of the state also has a relatively high percentage of non-Hispanic Black population but low hospitalization risk. The reason for this remains unclear. High vaccination coverage and better geographic accessibility to healthcare facilities might have reduced hospitalization risks in the eastern part of North Dakota. ## 4.1. Strengths and limitations Use of two statistically rigorous spatial epidemiological approaches to investigate geographic disparities in age-adjusted COVID-19 hospitalization risk in North *Dakota is* a key strength of this study. Both FSSS and CSSS methods are robust, adjust for multiple comparisons, and are free of pre-selection bias. Additionally, the FSSS method identifies both circular and non-circular windows. Moreover, this is the first study investigating geographic disparities of COVID-19 hospitalization burden in North Dakota. Study findings are crucial for NDHHS targeting control efforts aimed at reducing disparities and improving health for all North Dakotans. However, this study is not without limitations. Confirmed COVID-19 hospitalized cases might be under-reported, especially at the beginning of the study period, because availability of COVID-19 testing varied across the state, and testing requirements changed over time. ## 5. Conclusion This study has confirmed that geographic disparities in COVID-19 hospitalization risks exist in North Dakota. Specific attention is required to address counties with high hospitalization risks. Study findings are useful for guiding COVID-19 response geared at reducing disparities and improving COVID-19 outcomes. ## Data availability statement The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author. ## Ethics statement This study was reviewed and approved by the University of Tennessee Institutional Review Board (IRB). The IRB number is UTK IRB-22-07032-XM. All study methods were carried out in accordance with relevant guidelines and regulations. The study used data provided by the North Dakota Department of Health and Human Services. The identity of human subjects cannot be ascertained directly or through identifiers linked to the subjects. The investigators did not contact and re-identify subjects. Since the study used secondary data, no human participants were recruited by the investigators, and therefore, the University of Tennessee IRB granted a waiver for consent to participate. ## Author contributions MK and AO conceptualized research idea and analyzed data. MK, ND, and AO wrote the manuscript. MS and GN were involved in manuscript review. All authors read and approved the final manuscript. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2023.1062177/full#supplementary-material ## References 1. 1.Centers for Disease Control and Prevention. CDC COVID Data Tracker. Atlanta, GA (2022).. *CDC COVID Data Tracker* (2022) 2. 2.World Health Organization. WHO Coronavirus (COVID-19) Dashboard with Vaccination Data. Geneva: WHO (2022).. *WHO Coronavirus (COVID-19) Dashboard with Vaccination Data* (2022) 3. Bansal M. **Cardiovascular disease and COVID-19**. *Diabetes Metab Syndr Clin Res Rev.* (2020) **14** 247-50. DOI: 10.1016/j.dsx.2020.03.013 4. Coccia M. **How do low wind speeds and high levels of air pollution support the spread of COVID-19?**. *Atmos Pollut Res.* (2021) **12** 437-45. DOI: 10.1016/j.apr.2020.10.002 5. Igoe M, Das P, Lenhart S, Lloyd AL, Luong L, Tian D. **Geographic disparities and predictors of COVID-19 hospitalization risks in the St. Louis Area, Missouri (USA)**. *BMC Public Health.* (2022) **22** 321. DOI: 10.1186/s12889-022-12716-w 6. Zheng Z, Peng F, Xu B, Zhao J, Liu H, Peng J. **Risk factors of critical & mortal COVID-19 cases: A systematic literature review and meta-analysis**. *J Infect.* (2020) **81** e16-25. DOI: 10.1016/j.jinf.2020.04.021 7. 7.Centers for Disease Control and Prevention. National Center for Immunization and Respiratory Diseases (NCIRD) Home. Atlanta, GA (2022).. *National Center for Immunization and Respiratory Diseases (NCIRD) Home* (2022) 8. **COVID data tracker weekly review**. *Sci Res* (2021) 9. Matthias J, Charboneau T, Schaffer C, Rusten J, Whitmer S, Paz JD. **COVID-19 case investigation and contact tracing program — Spirit Lake Tribe, North Dakota, September–November 2020**. *MMWR Surveill Summ* (2021) **70** 533-4. DOI: 10.15585/mmwr.mm7014a4 10. 10.Rural Health Information Hub. Health and Healthcare in Frontier Areas Overview. Grand Forks, ND (2020).. *Health and Healthcare in Frontier Areas Overview* (2020) 11. 11.Center for Rural Health. North Dakota Frontier Counties. Grand Forks, ND (2020).. *North Dakota Frontier Counties* (2020) 12. 12.U.S. Census Bureau. U.S. Census Bureau QuickFacts. Suitland, MD (2022).. *U.S. Census Bureau QuickFacts* (2022) 13. Ratcliffe M, Burd C, Holder K, Fields A. *Defining Rural at the U.S. Censusu Bureau* (2016) 14. 14.US Census Bureau. American Community Survey (ACS). Suitland, MD (2025).. *American Community Survey (ACS)* (2025) 15. United States Census Bureau. **TIGER/Line Shapefiles**. (2019) 16. United States Census Bureau. *Urban and Rural* (2022) 17. SAS Institute inc. *SAS version 9.4* (2017) 18. Microsoft Corporation. *Microsoft Excel* (2022) 19. Li C, Ford ES, Zhao G, Wen X-J, Gotway CA. **Age adjustment of diabetes prevalencese of 2010 US Census data**. *J Diabetes.* (2014) **6** 451-61. DOI: 10.1111/1753-0407.12122 20. 20.The University of Chicago. GeoDa: Introduction to Spatial Data Analysis. Chicago, IL (2022).. *GeoDa: Introduction to Spatial Data Analysis* (2022) 21. Kulldorff M. *SaTScan - Software for the spatial, Temporal, and Space-time Scan Statistics* (2018) 22. Tango T, Takahashi K, Yokoyama T. *FleXScan: Software for the Flexible Scan Statistics* (2005) 23. Tango T, Takahashi K. **A flexibly shaped spatial scan statistic for detecting clusters**. *Int J Health Geogr.* (2005) **4** 11. DOI: 10.1186/1476-072X-4-11 24. 24.QGIS.org. QGIS Geographic Information System. (2021). Available online at: http://www.qgis.org (accessed September 1, 2022).. *QGIS Geographic Information System.* (2021) 25. 25.North Dakota Department of Health. North Dakota 2017 Health Disparities Report. Bismarck, ND (2017).. *North Dakota 2017 Health Disparities Report* (2017) 26. 26.World Population Review. Most Rural States. Walnut, CA. (2022).. *Most Rural States* (2022) 27. 27.Centers for Disease Control and Prevention. CDC COVID Data Tracker: Hospital Admissions. Atlanta, GA (2022).. *CDC COVID Data Tracker: Hospital Admissions* (2022) 28. 28.U.S. Department of Health & Human Services. COVID-19 Vaccines. Washington, DC (2022).. *COVID-19 Vaccines* (2022) 29. Wehbi N. *North Dakota Department of Health Recognizes One Year Anniversary of First COVID-19 Vaccine Administered in the State* (2021) 30. Maragakis L. *Coronavirus Second Wave, Third Wave and Beyond: What Causes a COVID Surge* (2021) 31. 31.North Dakota Department of Health. COVID19 Vaccine Dashboard. Bismarck, ND (2022).. *COVID19 Vaccine Dashboard* (2022) 32. 32.World Atlas. North Dakota Maps & Facts - World Atlas. Saint-Laurent, QC (2021).. *North Dakota Maps & Facts - World Atlas* (2021) 33. Beleche T, Kolbe A, Bush L, Sommers B. *Unvaccinated for COVID-19 but Willing: Demographic Factors, Geographic Patterns, and Changes Over Time Key Points* (2021) 34. 34.County Health Rankings & Roadmaps. Percentage of American Indian & Alaska Native in North Dakota. Madison, WI (2022).. *Percentage of American Indian & Alaska Native in North Dakota* (2022) 35. Barrientos M, Soria C. **North Dakota American Indian and Alaska Native Population Percentage by County**. *Index Mundi.* (2022) 36. Weeks R. *New data shows COVID-19's disproportionate impact on American Indian, Alaska Native tribes* (2021) 37. 37.Centers for Disease Control and Prevention. Risk for COVID-19 Infection, Hospitalization, and Death By Race/Ethnicity. Atlanta, GA (2022).. *Risk for COVID-19 Infection, Hospitalization, and Death By Race/Ethnicity* (2022) 38. 38.County Health Rankings & Roadmaps. Median Household Income in North Dakota. Madison, WI (2022).. *Median Household Income in North Dakota* (2022) 39. 39.County Health Rankings & Roadmaps. Uninsured Adults in North Dakota. Madison, WI (2022).. *Uninsured Adults in North Dakota* (2022) 40. 40.Centers for Disease Control and Prevention. CDC COVID Data Tracker: Vaccine Effectiveness. Atlanta, GA (2022).. *CDC COVID Data Tracker: Vaccine Effectiveness* (2022) 41. Beleche T, Kolbe A, Bush L, Sommers B. *COVID-19 Vaccine Hesitancy: Demographic Factors, Geographic Patterns, and Changes Over Time* (2021) 42. Gonzales A, Lee EC, Grigorescu V, Smith SR. **Lew N, Sommers BD**. *Overview of Barriers and Facilitators in COVID-19 Vaccine Outreach.* (2021) 43. Gupta R, Agrawal R, Bukhari Z, Jabbar A, Wang D, Diks J. **Higher comorbidities and early death in hospitalized African-American patients with Covid-19**. *BMC Infect Dis.* (2021) **21** 1-11. DOI: 10.1186/s12879-021-05782-9 44. Khan MM, Roberson S, Reid K, Jordan M, Odoi A. **Prevalence and predictors of stroke among individuals with prediabetes and diabetes in Florida**. *BMC Public Health.* (2022) **22** 243. DOI: 10.1186/s12889-022-12666-3 45. Khan MM, Odoi A, Odoi EW. **Geographic disparities in COVID-19 testing and outcomes in Florida**. *BMC Public Health* (2023) **23** 79. DOI: 10.1186/S12889-022-14450-9
--- title: The specific mitochondrial unfolded protein response in fast- and slow-twitch muscles of high-fat diet-induced insulin-resistant rats authors: - Can Li - Nan Li - Ziyi Zhang - Yu Song - Jialin Li - Zhe Wang - Hai Bo - Yong Zhang journal: Frontiers in Endocrinology year: 2023 pmcid: PMC10061072 doi: 10.3389/fendo.2023.1127524 license: CC BY 4.0 --- # The specific mitochondrial unfolded protein response in fast- and slow-twitch muscles of high-fat diet-induced insulin-resistant rats ## Abstract ### Introduction Skeletal muscle insulin resistance (IR) plays an important role in the pathogenesis of type 2 diabetes mellitus. Skeletal muscle is a heterogeneous tissue composed of different muscle fiber types that contribute distinctly to IR development. Glucose transport shows more protection in slow-twitch muscles than in fast-twitch muscles during IR development, while the mechanisms involved remain unclear. Therefore, we investigated the role of the mitochondrial unfolded protein response (UPRmt) in the distinct resistance of two types of muscle in IR. ### Methods Male Wistar rats were divided into high-fat diet (HFD) feeding and control groups. We measured glucose transport, mitochondrial respiration, UPRmt and histone methylation modification of UPRmt-related proteins to examine the UPRmt in the slow fiber-enriched soleus (Sol) and fast fiber-enriched tibialis anterior (TA) under HFD conditions. ### Results Our results indicate that 18 weeks of HFD can cause systemic IR, while the disturbance of Glut4-dependent glucose transport only occurred in fast-twitch muscle. The expression levels of UPRmt markers, including ATF5, HSP60 and ClpP, and the UPRmt-related mitokine MOTS-c were significantly higher in slow-twitch muscle than in fast-twitch muscle under HFD conditions. Mitochondrial respiratory function is maintained only in slow-twitch muscle. Additionally, in the Sol, histone methylation at the ATF5 promoter region was significantly higher than that in the TA after HFD feeding. ### Conclusion The expression of proteins involved in glucose transport in slow-twitch muscle remains almost unaltered after HFD intervention, whereas a significant decline of these proteins was observed in fast-twitch muscle. Specific activation of the UPRmt in slow-twitch muscle, accompanied by higher mitochondrial respiratory function and MOTS-c expression, may contribute to the higher resistance to HFD in slow-twitch muscle. Notably, the different histone modifications of UPRmt regulators may underlie the specific activation of the UPRmt in different muscle types. However, future work applying genetic or pharmacological approaches should further uncover the relationship between the UPRmt and insulin resistance. ## Introduction As the largest metabolic tissue in the body, skeletal muscle accounts for approximately $40\%$ of body weight and has the ability to utilize large amounts of blood sugar in the body [1]. At rest, skeletal muscle consumes over $20\%$ of the body’s energy expenditure [2], while during exercise, skeletal muscle is responsible for at least $95\%$ of the additional energy consumption [3]. In addition, the consumption rate of postprandial blood glucose in skeletal muscle is more than $80\%$~$90\%$ [4]. Therefore, skeletal muscle is important for systemic metabolic function and has the potential to serve as a therapeutic target for metabolic diseases. As a heterogeneous tissue, mammalian skeletal muscle is composed of different types of muscle fibers, and different muscle fibers have specific metabolic characteristics [5]. Generally, slow-twitch muscle composed of type I and IIa fibers has a relatively slow contraction rate, high glucose/lipid oxidation ratio and high densities of mitochondria and capillaries. Fast-twitch muscle consists of type IIx and IIb fibers, which are characterized by a fast contraction speed, relatively high glycolysis rate and few mitochondria [5]. These disparities may influence the response of muscles to certain physiological and pathological challenges, such as exercise [6], aging [7] and insulin resistance [8]. Mitochondria from slow-twitch muscles have an approximately twofold higher H2O2 scavenging capacity than mitochondria from fast-twitch muscles [9]. Slow-twitch muscles also show higher activities of antioxidant enzymes than fast-twitch muscles [10]. Thus, slow-twitch muscles usually receive more protection than fast-twitch muscles in oxidative stress-related diseases [11]. Stuart et al. [ 12] showed positive correlations between the proportion of type I fibers in muscle and whole-body insulin sensitivity. In obese and type 2 diabetes (T2D) individuals, the glucose-handling capacity of slow oxidative muscle fibers was significantly higher than that of fast glycolytic muscle fibers [8]. In summary, glucose transport shows more protection in slow-twitch muscles than in fast-twitch muscles during insulin resistance (IR) development, while the mechanisms involved remain unclear. Mitochondria are one of the most important organelles in mammalian skeletal muscle cells, and mitochondrial homeostasis is essential for the normal function of skeletal muscle. Mitochondrial dysfunction may lead to metabolic disorders, loss of muscle mass and contractile dysfunction. Therefore, mitochondria have developed a series of mechanisms to maintain their homeostasis, including antioxidant machinery, fission and fusion, mitochondrial biogenesis, mitophagy [13] and the mitochondrial unfolded protein response (UPRmt) [14]. Evidence suggests that insulin sensitivity in skeletal muscle is closely related to mitochondrial homeostasis. A previous study showed that the inhibition of mitochondrial oxidative stress partly preserved insulin sensitivity in human and rodent muscles [15]. Aberrant mitochondrial fission is associated with mitochondrial dysfunction and insulin resistance in skeletal muscle [16]. Additionally, mitochondrial respiration is decreased in the skeletal muscle of individuals with T2D [17]. In isolated insulin-resistant rat skeletal muscle cells, the restoration of insulin-stimulated glucose uptake induced by melatonin is accompanied by preserved mitochondrial respiration [18]. Overall, approaches to enhance mitochondrial homeostasis may improve insulin sensitivity in skeletal muscle. The UPRmt is an adaptive transcriptional response that was initially described as a mechanism for cells to maintain mitochondrial protein homeostasis during mitochondrial dysfunction, as these organelles are constantly importing and processing proteins in an unfolded state. Hoogenraad et al. [ 19] described the pathway in rat hepatoma cells in which mitochondrial proteostasis was disturbed by ethidium bromide (EB) treatment, characterized by the accumulation of unfolded proteins in mitochondria. Zhao et al. [ 20] found similar results in mitochondria that were mildly stressed by overexpression of ΔOTC, a mutated ornithine transcarbamylase that cannot properly fold within mitochondria. Both of their studies confirmed the restoration of mitochondrial homeostasis and cellular status after UPRmt activation. The UPRmt is a mitochondrial stress response by which mitochondria initiate the transcriptional activation programs of mitochondrial chaperone proteins and proteases encoded by nuclear DNA to maintain proteostasis [21]. As a stress-triggered process, UPRmt activation ultimately leads to mitochondrial functional recovery [22], metabolic adaptations [22], and innate immunity [23] at the cellular level. Previous studies have shown that activation of the UPRmt in multiple tissues exhibits protective roles in a variety of diseases. Wang et al. [ 24] reported that UPRmt induced by doxycycline has protective effects on cardiac infarction, but this protection is absent in Atf5-/- mice. This research confirmed the protective effect of ATF5-dependent UPRmt in myocardial disease. Another study obtained similar results, confirming that the UPRmt could be activated in the hearts of mice subjected to chronic hemodynamic overload, and further boosting UPRmt with nicotinamide riboside in cardiomyocytes in vitro or in hearts in vivo significantly ameliorated the decline in mitochondrial respiration induced by these stresses [25]. In addition, Gariani et al. [ 26] demonstrated that in long-term high-fat high-sucrose-fed animals, nicotinamide riboside could increase sirtuin-mediated UPRmt activation, triggering an adaptive process to increase hepatic β-oxidation and mitochondrial complex content and activity. A recent study demonstrated that UPRmt markers were elevated in acute insulin resistance in skeletal muscle induced by a high-fat diet [27]. This research indicated that mitochondrial damage from a high-fat diet (HFD) induced the UPRmt as a stress response in skeletal muscle. As mentioned above, different types of muscle fibers contribute distinctly to the development of insulin resistance. It is unclear whether the UPRmt is involved in this muscle type-dependent metabolic adaptation. Therefore, the purpose of this study was to investigate the role of UPRmt in the distinct resistance of two types of muscle in IR. The soleus (Sol) and tibialis anterior (TA) were selected to represent slow- and fast-twitch muscles, respectively. ## Animals and sample collection Twenty-four male Wistar rats (8 weeks old) were purchased from Beijing Vital River Laboratory Animal Technology Co. Ltd. and then migrated into air-conditioned polypropylene cages under a 12-h light/dark cycle in our SPF-grade facility. The room temperature was maintained at 24~27°C and 40-$45\%$ relative humidity. After 1 week of acclimatization, rats were randomly divided into two groups: [1] the control group ($$n = 12$$) was fed a normal chow diet (14 kcal% from fat, mainly consisting of corn oil, 66 kcal% from carbohydrate and 20 kcal% from protein), and [2] the HFD group ($$n = 12$$) was fed a high-fat diet (60 kcal% from fat, mainly consisting of lard, 20 kcal% from carbohydrate and 20 kcal% from protein) for 18 weeks. The chow diet (HFK, #1032) and the high-fat diet (HFK, #H10060) were purchased from Beijing HFK Bioscience Co., Ltd. All rats had free access to food and drinking water. At the end of the final week, 8 rats from each group were selected for FBG and OGTT tests after 6 h of fasting. Six hours after those tests, these rats were euthanized under anesthesia induced by sevoflurane followed by intraperitoneal injection of pentobarbital sodium. Skeletal muscle tissues and blood samples were harvested immediately for subsequent experiments. Simultaneously, 4 rats from each group were selected for use only in the hyperinsulinemic–euglycemic clamp test and were then killed by euthanasia. Due to their predominant muscle fiber type in whole muscle, the Sol muscle is representative of slow oxidative muscle, and the TA muscle is representative of fast glycolytic muscle. All animals received proper care, and all animal experiments were approved and supervised by the Animal Ethics Committee of the Tianjin University of Sport in accordance with the National Research Council’s Guide for the Care and Use of Laboratory Animals. ## Fasting insulin test, fasting blood glucose test and oral glucose tolerance test The FIN test was used to measure the fasting blood insulin concentration of the rats. This test was performed by using the blood sample collected from the last step. The fasting insulin levels in the plasma were determined using an assay kit (CUSABIO, FIN Elisa kit) according to the manufacturer’s instructions. Before the FBG and OGTT, the rats were fasted for 6 h. An FBG test was used to assess the fasting blood glucose level. Blood samples were collected by pricking the rat tail tip with a needle. Blood glucose test strips (Roche Accu-Check Active) were used to determine the FBG of each rat. Homeostasis model assessment of insulin resistance (HOMA-IR) was calculated based on the equation HOMA-IR=serum insulin (mmol/L)×blood glucose (mmol/L)/22.5 [28]. The OGTT was used to evaluate the glucose tolerance of the rats. A $50\%$ glucose Solution was infused by intragastric administration at a dose of 2 g of glucose per 1 kg of body weight, and blood glucose was measured every half hour after intragastric administration. This procedure lasted for 2.5 h. GraphPad Prism 8 was used to establish the OGTT curve. Blood glucose values at each time point were recorded, and continuous curves were drawn. The area under the curve (AUC) was calculated to evaluate the insulin sensitivity of rats. ## Hyperinsulinemic-euglycemic clamp test Four rats were selected from each group for the hyperinsulinemic–euglycemic clamp test. Anesthesia for selected rats was induced by sevoflurane, followed by intraperitoneal injection of pentobarbital sodium after 6 h of fasting. The right jugular and left carotid were exposed for the following cannulation: a t-branch for glucose and insulin infusion was catheterized into the right jugular, and an indwelling needle was catheterized into the left carotid for blood sample collection. Recombinant human insulin (Humulin R) was infused by a peristaltic pump at a fixed flow rate of 5 mU/kg/min; glucose was infused by another peristaltic pump, and the infusion rate (GIR) was adjusted promptly to maintain blood glucose at ~5.0 mM. Carotid blood was collected for the blood glucose test every 5 min for 2 h. Insulin sensitivity was evaluated by the GIR during the last 30 min of the experiment and quantified by the AUC of the data collected from this duration. To avoid the unknown influences exerted by a constant infusion of exogenesis insulin lasting for 2 h, these rats were then killed by euthanasia after the clamp test and no longer used for other experiments. ## Mitochondrial isolation from skeletal muscles The mitochondrial isolation procedure was based on the sucrose step density gradient centrifugation method [29]. The isolation procedure is briefly described as follows: 300 mg of skeletal muscle tissue was taken from each muscle collected in the last step for mitochondrial isolation. The muscle tissue was first cut into very small pieces and then transferred into a Dounce homogenizer for gentle homogenization. This whole step was performed in ice-cold MS homogenization buffer (210 mM mannitol, 70 mM sucrose, 5 mM Tris-HCl, 1 mM EDTA, pH 7.5) as quickly as possible. The suspension was centrifuged at 1000 × g and 4°C for 10 min, and then the supernatant was transferred into another tube for a 2nd centrifugation at 12,000 × g and 4°C for 10 min. The precipitate was crude skeletal muscle mitochondria. The final pellet was resuspended in a volume and buffer appropriate for subsequent work. The mitochondrial suspension was carefully applied on top of the sucrose step density gradient Solution (15 mL of 1.0 M sucrose gradient Solution over 15 mL of 1.5 M sucrose gradient Solution, 10 mM Tris-HCl, 1 mM EDTA, pH 7.5) in Ultra-Clear centrifuge tubes. The tubes were centrifuged at 50,000 × g for 20 min. The purified mitochondria formed a layer at 1.0 M/1.5 M sucrose interface. The final dilution and resuspension of the purified mitochondria from the last step were performed according to previous research [29]. Finally, purified and intact mitochondria were prepared for other experiments. ## Mitochondrial respiration Skeletal muscle mitochondrial respiratory function was assessed by an Oroboros Oxygraph-2k (Oroboros Instruments, 10023-01) as previously described [30]. In brief, the MiR05-Kit (Oroboros Instruments, 60101-01) served as the mitochondrial respiration medium. Two milliliters of medium was initially injected into the system, and the O2 calibration procedure for adequate O2 dissolution was immediately performed. Then, 100 μg of mitochondria was added to the reaction system after O2 calibration, and a mixture of malate (final conc. 2 mM) and glutamate (final conc. 10 mM) was added to the system when the O2 consumption curve became steady. This phase of O2 consumption is state 2 respiration. When the curve plateaued, ADP (final conc. 0.1 mM) was added to the system; the O2 consumption in this phase represents state 3 respiration (ST3). The final steady curve of O2 consumption followed by ADP exhaustion represents state 4 respiration (ST4). The respiratory control rate (RCR) was calculated as ST3/ST4. ## Western blotting and real-time qPCR Western blotting (WB) was used to determine the expression of recognized glucose transport markers, UPRmt markers and MOTS-c in this study. The experiments were performed under the standard WB procedure, briefly as below. A 100-mg muscle sample cut into very small pieces or ~5 mg of isolated mitochondria was transferred into a Dounce homogenizer (200-μl EP tube for mitochondria) with 1 ml (0.05 ml for mitochondria) of RIPA lysis buffer (Beyotime, P0013B) containing 10 μl (1 μl for mitochondria) of protease and phosphatase inhibitor cocktail (Thermo Scientific™, #78445) for thorough homogenization (ultrasonication for mitochondria). The supernatant was then collected after centrifugation for protein concentration determination by using a BCA protein assay kit (Beyotime, P0012). Denaturation was performed with protein loading buffer (Solarbio, P1040). Denatured proteins were separated by SDS−PAGE electrophoresis and transferred to 0.22-μm PVDF membranes (Millipore, Billerica). Immobilon Western Chemiluminescent HRP Substrate (Millipore, WBKLS0500) was used for target protein detection after blocking and primary/secondary antibody incubations of PVDF membranes. Adobe Photoshop CC was used to quantify the luminance intensity of the protein bands. The following primary antibodies were used: Insulin *Receptor beta* (Abcam, ab69508); IRS-1 (CST, #2382); Phospho-IRS-1 Ser307 (CST,#2381); AS160 (CST, 2670); Phospho-AS160 T642 (CST, 8881); Glut4 (Abcam, ab33780); CHOP (CST, 2895); ATF5 (Abcam, ab184923); HSP60 (CST, 4870S); ClpP (Abcam, ab124822); COX4 (CST, 4850S);β-tubulin (CST, 2146S) and Mots-c (FabGennix, MOTSC-101AP). Real-time qPCR was chosen to assess the relative fold change in MOTS-c. Total RNA was extracted from ~50 mg of tissue by using the Dynabeads mRNA Purification Kit (Invitrogen™, #61006), and the operation was performed according to the manufacturer’s instructions. RNA was reverse transcribed using a High-Capacity RNA-to-cDNA™ kit (Thermo Scientific™, #4368813), and quantitative real-time PCR was performed on a 96-well PCR device (7500 Real-Time PCR System, Applied Biosystems™) by using SYBR Green Select Master Mix (Applied Biosystems™, #4472919). All procedures were performed following the manufacturers’ instructions. β-Actin was used to normalize the expression levels of target genes, and the relative expression of MOTS-c was analyzed using the 2−ΔΔCt method. The specific sequence information for the primers used in this study is as follows: MOTS-c Forward, 5′-GACACCTTGCCTAGCCACAC-3′; MOTS-c Reverse, 5′-TGGCTGGCACGAAATTTACCA-3′; β-Actin Forward, 5′-CCGTAAAGACCTCTATGCCAACA-3′; β-Actin Reverse, 5′-TATCCATTCTCAAGAGCAGCGAAAG-3′. ## Chromatin immunoprecipitation In this study, the two kinds of histone modifications on the ATF5 promoter were detected by ChIP. ChIP experiments and the subsequent purification of cross-linked DNA were performed using a SimpleChIP® Enzymatic Chromatin IP Kit (CST, 9003), and all procedures were performed according to the manufacturer’s instructions. Anti-H3K4me3 (CST, 9751) and anti-H3K27me3 (CST, 9733) were used to measure the corresponding histone methylation at the ATF5 promoter region, and rabbit mAb IgG (CST, 3900) was used as the negative control. The purified DNA samples from immunoprecipitation were further quantified by RT-qPCR (7500 Real-Time PCR System, Applied Biosystems™). Values were normalized to each individual input control. The primers were specifically designed for the ATF5 gene promoter region, and the sequences were as follows: ATF5 promoter Forward, 5′- AGGAGGACCATAGGCATTG-3′; ATF5 promoter Reverse, 5′- GCTAGACAGGCATTCTACCAC -3′. ## Statistical analysis The values in this study are presented as the mean ± SD. The data were analyzed using independent T tests, two-way ANOVA and two-way ANOVA with repeated measures, followed by a post hoc Bonferroni’s multiple comparison tests, as appropriate. Analyses of the main effects (muscle type) and interaction effects (muscle type×diet protocol) were also conducted. Descriptive and analytical statistics were examined with SPSS v26.0 software. Results with p values < 0.05 were considered statistically significant. ## A high-fat diet causes systemic insulin resistance After 18 weeks of HFD feeding, rats in the HFD group showed a significant increase in blood insulin concentration (6.98 ± 2.08 vs. 15.54 ± 4.59, $p \leq 0.01$) and fasting blood glucose (5.75 ± 0.58 vs. 8.29 ± 0.6, $p \leq 0.05$) compared with those in the control group (Figures 1A, B). Consistent with this, the HOMA-IR index was significantly increased in HFD rats compared with the controls (1.67 ± 0.46 vs. 5.37 ± 1.59, $p \leq 0.01$) (Figure 1C). In the hyperinsulinemic-euglycemic clamp test, HFD rats showed a significantly decreased GIR (20.56 ± 3.31 vs. 12.71 ± 1.71, $p \leq 0.01$) (Figure 1E). The AUC of the OGTT curve reflected that the glucose tolerance of HFD rats was much lower than that of the control group (97.03 ± 2.89 vs. 68.44 ± 2.06, $p \leq 0.01$) (Figure 1D). These results indicate that long-term (18 weeks) HFD consumption could induce whole-body insulin resistance in rats. **Figure 1:** *Whole-body insulin resistance induced by HFD. Each graph represents (A) fasting blood insulin concentration; (B) fasting blood glucose; (C) HOMA-IR; (D) the original OGTT curve and its AUC; and (E) glucose infusion rate. (*p<0.05; *p<0.01; n=8 in each group in graphs (A–D), n=4 in each group in graph E).* ## A high-fat diet causes impairment of glucose transport specifically in fast-twitch muscle The Sol and TA were chosen for investigating the process of HFD-induced adaptation/deterioration of different muscles. To evaluate the impairment of insulin signaling and glucose transport in slow- and fast-twitch muscles in response to HFD, we tested the protein levels of insulin receptor-β (IR-β), p-IRS1, IRS1, Glut4, AS160 and p-AS160, and the results were as follows: first, the results of the two-way ANOVA of IR-β expression showed a significant main effect of muscle type ($p \leq 0.05$) but no main effect of dietary intervention. The IR-β content was significantly higher in Sol than that in TA muscle (Figures 2A, B). However, the expression of p-IRS1, the active form of IRS1, and the p-IRS1/IRS ratio showed a significant decrease ($p \leq 0.05$) in both Sol and TA in response to the HFD (Figures 2A–C). Moreover, there was no significant interaction effect between muscle type and dietary intervention. Second, we found that the expression of Glut4 in TA muscle was markedly lower in the HFD group than in the C group ($p \leq 0.01$) (Figures 2A, D). Similarly, in comparison to the C group, the expression of p-AS160 (T642) in the HFD group was significantly downregulated 0.40-fold ($p \leq 0.05$) in the TA (Figures 2A, D). Consistent with this, the ratio of p-AS160/AS160 in the TA also decreased 0.36-fold ($p \leq 0.05$) in the control group compared to the HFD group (Figures 2A, E). However, the expression of Glut4, AS160 and p-AS160 showed no significant changes in the Sol (Figure 2). The results of the two-way ANOVA of Glut4 expression showed a significant main effect of muscle type ($p \leq 0.05$) and a main effect of dietary intervention ($p \leq 0.05$). Moreover, there was a significant interaction effect between muscle type and dietary intervention ($p \leq 0.05$). These results revealed that the impairments in glucose transport induced by a HFD occur specifically in fast-twitch muscles. **Figure 2:** *Insulin and glucose transport in fast- and slow-twitch muscles after HFD intervention. (A) The original western blotting results of phosphorylated IRS1 (S307), IRS1, IR-β, phosphorylated AS160 (T642), AS160 and Glut4 in muscle lysates. (B) Quantification of relative expression of p-IRS1 (S307), IRS1 and IR-β protein in each group. (C) The ratio of the relative expression of p-IRS1 and total IRS1. (D) Quantification of relative expression of the p-AS160 (T642), AS160 and Glut4 protein in each group. (E) The ratio of the relative expression of p-IRS1/IRS1. (*p<0.05; **p<0.01; and #p<0.05 when comparison was conducted between muscles; n=8 for each group).* ## A high-fat diet causes mitochondrial respiratory dysfunction in fast-twitch muscle To investigate the alterations in mitochondrial respiratory function in different muscles under HFD conditions, mitochondrial respiration was measured by an Oroboros Oxygraph-2k. The results showed no significant changes in mitochondrial respiration in Sol muscle after HFD feeding. In TA mitochondria, a significant decrease in ST3 and an increase in ST4 were observed in HFD rats compared with control rats ($p \leq 0.05$), and the RCR (ST3/ST4) was significantly decreased in this comparison ($p \leq 0.05$) (Figure 3). Two-way ANOVA revealed a significant main effect of muscle type ($p \leq 0.05$) for ST3 and RCR; these two parameters were significantly higher in the TA than in Sol muscle. In summary, mitochondrial respiratory function was decreased only in the TA but not in the Sol muscle after long-term HFD feeding. **Figure 3:** *Mitochondrial respiratory functions in fast- and slow-twitch muscles after HFD intervention. Traces of mitochondrial O2 consumption (A), Mitochondrial State 4 (B) and State 3 (C) respiration and the respiratory control rate (D) in the Sol and TA under stimulation with the respiratory chain substrate (malate+glutamate) are shown. (*p<0.05; n=8 for each group).* ## ATF5-dependent UPRmt is preferentially activated by HFD in slow-twitch muscle Western blotting was used to measure the expression of the identified UPRmt markers. ATF5 and CHOP are located in multiple regions in the cell; thus, the expression levels of these proteins were measured in muscle homogenates. HSP60 and ClpP are located in the mitochondrial matrix, so these proteins should be measured in isolated mitochondria. The results showed that the expression level of ATF5 was upregulated by 0.84-fold ($p \leq 0.05$) in HFD rats compared to the controls in Sol muscle and was upregulated by 0.43-fold ($p \leq 0.05$) in TA muscle (Figures 4A, C). While two-way ANOVA revealed a significant interaction effect between muscle types and dietary intervention for the expression of ATF5. No significant changes in the expression level of CHOP were observed in either Sol or TA muscle in HFD rats compared to the controls (Figures 4A, B). Two-way ANOVA revealed a significant main effect of muscle type ($p \leq 0.05$), the expression of CHOP in Sol was significantly higher than that in TA muscle. Moreover, for the expression of CHOP, no significant interaction effect between muscle types and dietary intervention was observed. Regarding Sol mitochondrial proteins, HFD rats showed markedly upregulated expression of HSP60 by 4.68-fold ($p \leq 0.01$) and of ClpP by 1.33-fold ($p \leq 0.01$) compared with control rats (Figures 4A, D, E). In TA mitochondria, only the expression of HSP60 was upregulated 1.44-fold ($p \leq 0.05$) in HFD rats compared to control rats (Figures 4A, D). No significant changes in ClpP were observed in TA mitochondria after HFD consumption. Notably, two-way ANOVA revealed no significant main effect of muscle type for the expression of HSP60 and ClpP, but it indicated a significant interaction effect between muscle type and dietary intervention for both HSP60 and ClpP levels ($p \leq 0.05$). These results indicated that the activation of the UPRmt in Sol muscle was more profound than that in TA muscle after HFD intervention. **Figure 4:** *The relative expression of UPRmt markers in Sol and TA muscles. (A) The original western blots of CHOP and ATF5 from muscle lysates; HSP60 and ClpP from isolated mitochondria. (B, C) Quantification of the relative expression of CHOP and ATF5 in Sol and TA in each group. (D, E) Quantification of the relative expression of HSP60 and ClpP in Sol and TA in each group. (*p<0.05; **p<0.01; and #p<0.05 when comparison was conducted between muscles; n=8 for each group).* ## The expression of MOTS-c decreased after HFD feeding specifically in fast TA muscle MOTS-c is a mitochondria-derived peptide that can participate in regulating glucose metabolism. Our results showed that the expression of MOTS-c increased by 0.37 times ($p \leq 0.05$) in Sol muscle after HFD feeding. Consistent with the protein expression, compared with the control group, the mRNA level of MOTS-c in the Sol muscle of HFD-fed rats was significantly increased by 0.62 times ($p \leq 0.05$). In the TA, there was no significant difference in the expression of MOTS-c protein or mRNA after HFD feeding (Figure 5). In this section, two-way ANOVA revealed a significant main effect of muscle type ($p \leq 0.05$) for the protein and mRNA levels of MOTS-c. Moreover, it showed a significant interaction effect of muscle type and dietary intervention for both the protein and mRNA levels of MOTS-c ($p \leq 0.05$). **Figure 5:** *The relative protein and mRNA expression levels of MOTS-c in slow- and fast-twitch muscles among all groups. (A) The protein expression level of MOTS-c in the Sol and TA muscles among the groups. (B) Relative fold change in MOTS-c mRNA levels in the Sol and TA muscles among the groups. (*p<0.05 and #p<0.05 when comparison was conducted between muscles; n=8 for each group).* ## Epigenetic modulation, which is associated with transcription promotion, occurred only in slow-twitch muscle after HFD feeding Histone methylations, including H3K4me3 and H3K27me3, have been proven to be involved in regulating the UPRmt [31, 32], in which H3K4me3 has the effect of activating transcription, whereas H3K27me3 inhibits transcription. First, we tested the transcription levels of ATF5 in the Sol and TA muscles of the HFD group and control group. We found that the mRNA level of ATF5 in the Sol of HFD rats significantly increased by 1.54 times compared with that of the control group ($p \leq 0.01$) (Figure 6A). Furthermore, the ChIP assay results showed that compared with that in the control rats, the enrichment of H3K4me3 in the promoter region of ATF5 in Sol was significantly upregulated by 0.5-fold in HFD rats ($p \leq 0.01$) (Figure 6B). The enrichment of H3K27me3 in the promoter region of ATF5 in the TA muscle of HFD-fed rats was significantly increased by 0.77-fold ($p \leq 0.01$) (Figure 6B). Notably, for these two opposite epigenetic modulations, the findings showed both a significant main effect of muscle type ($p \leq 0.05$) and a significant interaction effect between muscle type and dietary intervention ($p \leq 0.05$). In summary, both the higher enrichment efficiency of H3K4me3 at the ATF5 promoter region in Sol muscle and the higher enrichment efficiency of H3K27me3 at the ATF5 promoter region in TA muscle may contribute to the more profoundly upregulated ATF5 in Sol muscle than in TA muscle. **Figure 6:** *The transcription level and histone methylation at the promoter region of ATF5. (A) The transcription level of ATF5 in Sol and TA muscles among the groups; (B) The H3K4me3 and H3K27me3 enrichment rate of the ATF5 promoter in Sol and TA muscles among the groups. **p<0.01; and #p<0.05 when comparison was conducted between muscles; n=8 for each group; IP, immunoprecipitation; NC, negative control).* ## Discussion As a heterogeneous tissue, skeletal muscle differs with respect to its different muscle fiber types in response to physiological and pathological stresses. In the present study, a HFD-induced insulin resistance model was established, and we found that the impairment in glucose transport in fast-twitch muscle was more pronounced than that in slow-twitch muscle. The main novelty of this paper is to reveal that UPRmt is mainly activated in the Sol after HFD feeding, not in the TA. Along with the activation of the UPRmt, the maintenance of mitochondrial respiration function and the upregulated expression of MOTS-c, a mitokine involved in regulating glucose metabolism, were observed only in the Sol. Further ChIP experiments revealed that the higher histone methylation H3K4me3 at the ATF5 promoter region in Sol and H3K27me3 at the ATF5 promoter region in TA may contribute to the more pronounced activation of the UPRmt in Sol muscle. After 18 weeks of HFD feeding, we tested the status of glucose metabolism in all of the rats. Following a previous study that discussed the establishment of an insulin resistance rat model induced by long-term high-fat diet feeding [33], we selected the HOMA-IR [28], OGTT and hyperinsulinemic-euglycemic clamp test [34] to evaluate systemic glucose metabolism in rats. Our results indicated that the HFD rats developed systemic insulin resistance. Different types of muscle fibers contribute differently to adaptation or deterioration under metabolic stress, such as a HFD [35]. Through further investigation of glucose metabolism in slow- and fast-twitch skeletal muscles, our results showed that the AS160-Glut4 axis, the key player in the regulation of glucose transport [36], was significantly decreased only in TA muscle after HFD feeding. These results imply that fast-twitch muscle is more susceptible to HFD than slow-twitch muscle and is more likely to suffer impairment in glucose metabolism. Meanwhile, slow-twitch muscles may be more tolerant to HFD. In line with our results, a previous study indicated that short-chain fatty acyl CoA dehydrogenase activity was elevated after 8 weeks of HFD feeding in Sol muscle but was not changed in TA muscle [37]. For HFD versus control diet-fed rats, the glucose uptake in insulin-stimulated single fibers was significantly ($p \leq 0.05$) lower for type II but not type I fibers [38]. A series of studies conducted by Cartee [35, 39] found that 2 weeks of HFD feeding could cause a significant decrease in Glut4 expression in type IIb fibers, as well as insulin-stimulated glucose uptake in multiple fast type II fibers but not type I fibers. In addition, the insulin-stimulated elevation of p-AS160 (S704) was blunted in type IIx and IIbx fibers in insulin-resistant rats [39]. Consistently, in this study, the disparities in the expression of proteins involved in glucose transport show that the glucose transport of slow-twitch muscle remains almost unaltered after HFD intervention, but glucose transport of fast-twitch muscle was significantly impaired. As a stress-triggered mitochondrial protection response, the UPRmt has gained much attention in recent decades. The canonical UPRmt transcriptional axis is activated upon mitochondrial protein misfolding/aggregation in the mitochondrial matrix. In addition, the UPRmt sirtuin axis and the UPRmt estrogen receptor alpha axis are likely highly complementary to the canonical UPRmt transcriptional axis in securing mitochondrial health [40]. In the present study, we mainly focused on the canonical UPRmt transcriptional axis. The UPRmt transcriptional response involves activating mitochondrial molecular chaperones and quality-control proteases [20]. The UPRmt in C. elegans is coordinated by multiple factors, including the transcription factor ATFS-1. Haynes et al. [ 41] confirmed that regulation of the UPRmt is conserved from worms to mammals and that ATF5, the homolog of ATFS-1, is required for UPRmt activation in mammalian cells. In the present study, the data showed that in both Sol and TA muscles, the expression levels of ATF5 and the chaperone HSP60 were increased after HFD feeding. However, the relative fold change in these two proteins was more profound in the Sol than in the TA. The expression of protease ClpP increased only in the Sol. These results indicate that the HFD-induced UPRmt is mainly activated in slow-twitch muscle. Consistent with our results, a recent study demonstrated that the UPRmt could be activated in mouse skeletal muscle by short-term HFD feeding [27]. Similarly, in mouse epididymal white adipose tissue (eWAT), the UPRmt was activated after unsaturated fish oil diet (UFD) feeding [42]. The reasons for the HFD-induced UPRmt have not yet been confirmed, while some potential mechanisms may be involved in this process. First, it has been shown that a long-term HFD can lead to proteostatic perturbation in skeletal muscle. A very recent study by Fletcher et al. [ 43] showed that immunoproteasome and total proteasome function are significantly reduced in obese muscle. In addition, it is possible that a HFD could induce the elevation of ROS, which may ultimately activate the UPRmt [20, 44]. Additionally, accumulated fatty acids may lead to mitochondrial uncoupling and create mitochondrial stress to induce the UPRmt. In support of this view, a recent study showed that overexpression of uncoupling proteins in neurons of C. elegans can induce the UPRmt [45]. A HFD is known to induce the expression of uncoupling proteins in skeletal muscle, and the upregulation is more pronounced in slow-twitch muscle fibers [46]. This may support the muscle type-dependent activation of the UPRmt by a HFD in the present study. Normal mitochondrial function is thought to be crucial for ensuring glucose metabolism [16]. As one of the mitochondrial quality-control system processes, the UPRmt is considered to be able to maintain mitochondrial homeostasis and restore mitochondrial function [47]. In the current study, we found that both in the control and the HFD groups, the malate/glutamate-dependent mitochondrial respiration of fast-twitch muscle is higher than that of slow-twitch muscle. This result is consistent with previous studies [7, 48], the results of which showed that TA has significantly higher O2 consumption than Sol and first dorsal interosseus muscle. Notably, preserved mitochondrial respiration was observed only in Sol muscle under HFD conditions. In the TA muscle, mitochondrial respiration significantly declined after HFD intervention. HFD feeding has been reported to impair mitochondrial respiratory function in multiple tissues, including skeletal muscle, and to further induce insulin resistance [49, 50]. However, this pathological process has been proven to be muscle fiber type dependent. Consistent with our study, Pinho et al. [ 51] suggested that the alterations in mitochondrial respiration after HFD feeding occur in a fiber type-dependent manner. In contrast to the soleus muscle, palmitate oxidation in the epitrochlearis (mixed muscle type but mainly fast glycolytic) muscle is significantly lower and increased less than that in the Sol muscle after 8 weeks of HFD feeding [51]. Mitochondrial respiratory function relies on OXPHOS status, and evidence suggests that the UPRmt is capable of restoring mitochondrial OXPHOS. Nargund et al. [ 52] demonstrated that the UPRmt induced by ATFS-1 promotes OXPHOS recovery during mitochondrial stress. They found that ATFS-1 associates with both nDNA and mtDNA under mitochondrial stress and ultimately leads to respiratory recovery by orchestrating OXPHOS component assembly and increasing proteostatic capacity [52]. Therefore, compared with that in fast-twitch muscle, the higher activation of the UPRmt in slow-twitch muscle helps to maintain mitochondrial respiration, thus protecting glucose metabolism. In addition to restoring mitochondrial function, the UPRmt also regulates metabolism by elevating mitochondrial-derived peptide (MDP) production [53]. As an identified MDP, MOTS-c is a stress-induced mitochondrial-derived 16-amino-acid peptide encoded by the 12S rRNA sORF in mtDNA. In our study, the expression of MOTS-c in the Sol of HFD rats was significantly higher than that of control rats. In the TA muscle, no significant changes in MOTS-c were observed in the HFD and control groups. The UPRmt is positively correlated with MOTS-c, which is considered a retrograde signal for mitochondria to communicate with the nucleus in response to mitochondrial stress [54]. In the present study, we also found that the expression of MOTS-c showed a muscle type-specific pattern in line with UPRmt activation. The metabolic regulatory role of MOTS-c was first identified in the process of gene screening for metabolic regulators of human cells, and that study showed that MOTS-c directly targets skeletal muscle and regulates insulin sensitivity in mice [55]. Recently, Reynolds et al. [ 56] indicated that exercise induced the endogenous MOTS-c level in skeletal muscle, which contributes to elevated lipid utilization capacity. Growing evidence suggests that MOTS-c plays a role in coordinating cellular glucose, mitochondrial, and fatty acid metabolism [55, 57]. According to recent research, MOTS-c exhibits a fiber type-specific expression pattern because it is positively associated with slow oxidative fibers. The authors hold the position based on their data that the production of MOTS-c in muscle cells is increased with aging, and it is tempting to further speculate that this phenomenon is tied to the greater expression of MOTS-c in slow-type fibers and the age-related fast-to-slow fiber type transition [58]. Taken together, the findings show that compared with that in fast-twitch muscle, the higher level of MOTS-c in slow-twitch muscle, which is positively correlated with the UPRmt, may help to maintain glucose metabolism. Emerging evidence shows that epigenetic mechanisms are key factors to be considered in the coordinated regulation of skeletal muscle fiber types and metabolic patterns. Based on the results mentioned before, we found that the activation of the UPRmt was different in slow and fast-twitch muscles under the same HFD stress. This disparity may be attributed to the difference in epigenetic status in slow- and fast-twitch muscles. According to fundamental studies, epigenetic modulation plays a profound role in regulating the activation of the UPRmt [32, 59]. Therefore, to test whether the different statuses of epigenetic modulation of slow- and fast-twitch muscles contribute to the fiber type-specific activation of the UPRmt, we studied the epigenetic modulations of ATF5. Chromatin remodeling has been shown to play a central role in UPRmt activation. Tian Y et al. [ 59] demonstrated that activation of the UPRmt requires the dimethylation of lysine 3 of histone 3 (H3K9) in the presence of met-2 and lin-65, which leads to a compacted and overall silenced chromatin state, while simultaneously, some chromatin portions remain loose, favoring the binding of UPRmt regulators such as DVE-1. On the other hand, UPRmt activation also requires the conserved demethylases JMJD-3.1 and JMJD-1.2 [32], which reduce chromatin compaction by removing methylation from H3K9 and H3K27, respectively [60, 61]. Generally, H3K4me3 is regarded as a transcriptionally positive histone modulation, while H3K27me3 is a transcriptionally inhibitory histone modulation [62]. Our data indicated that the enrichment rates of H3K4me3 and H3K27me3 on the ATF5 promoter in Sol and TA muscles were significantly opposite after HFD feeding. Sol had higher H3K4me3 modulation of the ATF5 promoter region than TA, while TA had higher H3K27me3 modulation of the ATF5 promoter after HFD intervention. This is quite similar to the preferentially activated UPRmt in Sol. Emerging evidence indicates that the distribution of active histone modifications, such as H3K4me3, largely differs between fast- and slow-twitch muscles. A recent study described epigenetic profiling between fast/glycolytic and slow/oxidative muscles. That study revealed that the epigenome in Sol muscle was quite different from that in extensor digitorum longus (EDL) muscle, and they accounted for the different myocellular characteristics. The study identified transcription factors with motifs enriched in H3K4me3 peaks, such as MEF2C, PPARA, SOX6 and NFATC2, as they are known to influence differentiation and lipid metabolism in slow/oxidative muscle [63]. Other studies have demonstrated that slow-twitch muscles have a unique epigenetic system that regulates gene expression, which might be closely associated with contractile and metabolic properties [64, 65]. In summary, this differentiated epigenetic modulation of ATF5 may underlie the mechanism involved in muscle fiber-type-specific UPRmt activation. ## Conclusions In this study, the disparities in the expression of proteins involved in glucose transport show that the glucose transport of slow-twitch muscle remains almost unaltered after HFD intervention, but glucose transport of fast-twitch muscle was significantly impaired. This phenomenon may result from preferential activation of the UPRmt in slow-twitch muscle compared to fast-twitch muscle. On the one hand, due to the mitochondrial repair performed by the UPRmt, mitochondrial respiratory function in slow-twitch muscle is preserved. On the other hand, the higher level of MOTS-c, a UPRmt-related mitokine, may contribute to the maintenance of glucose metabolism in slow-twitch muscle. Notably, this muscle-type-dependent UPRmt activation is probably caused by different histone modifications of the UPRmt regulator (Figure 7). However, future work applying genetic or pharmacological approaches should further uncover the relationship between the UPRmt and insulin resistance. **Figure 7:** *HFD induced the specific activation of the UPRmt in slow- and fast-twitch muscles. (↑, upregulation; ↓, downregulation; →, no change).* ## Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. ## Ethics statement The animal study was reviewed and approved by Ethics Committee of Tianjin University of Sport. ## Author contributions Conceptualization: CL, HB and YZ. Methodology: CL, NL and ZZ. Investigation: CL and NL. Writing-original draft preparation: CL. Literature research: CL, JL, ZW and YS. Writing-review and editing: CL, HB and YZ. Supervision, review and editing: HB and YZ. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## References 1. Rolfe DF, Brown GC. **Cellular energy utilization and molecular origin of standard metabolic rate in mammals**. *Physiol Rev* (1997) **77**. DOI: 10.1152/physrev.1997.77.3.731 2. Gallagher D, Belmonte D, Deurenberg P, Wang Z, Krasnow N, Pi-Sunyer FX. **Organ-tissue mass measurement allows modeling of REE and metabolically active tissue mass**. *Am J Physiol* (1998) **275**. DOI: 10.1152/ajpendo.1998.275.2.E249 3. Baskin KK, Winders BR, Olson EN. **Muscle as a "mediator" of systemic metabolism**. *Cell Metab* (2015) **21**. DOI: 10.1016/j.cmet.2014.12.021 4. DeFronzo RA, Tripathy D. **Skeletal muscle insulin resistance is the primary defect in type 2 diabetes**. *Diabetes Care* (2009) **32 Suppl 2**. DOI: 10.2337/dc09-S302 5. Schiaffino S, Reggiani C. **Fiber types in mammalian skeletal muscles**. *Physiol Rev* (2011) **91**. DOI: 10.1152/physrev.00031.2010 6. Yan Z, Okutsu M, Akhtar YN, Lira VA. **Regulation of exercise-induced fiber type transformation, mitochondrial biogenesis, and angiogenesis in skeletal muscle**. *J Appl Physiol (1985).* (2011) **110**. DOI: 10.1152/japplphysiol.00993.2010 7. Crupi AN, Nunnelee JS, Taylor DJ, Thomas A, Vit JP, Riera CE. **Oxidative muscles have better mitochondrial homeostasis than glycolytic muscles throughout life and maintain mitochondrial function during aging**. *Aging (Albany NY).* (2018) **10**. DOI: 10.18632/aging.101643 8. Albers PH, Pedersen AJ, Birk JB, Kristensen DE, Vind BF, Baba O. **Human muscle fiber type-specific insulin signaling: impact of obesity and type 2 diabetes**. *Diabetes.* (2015) **64**. DOI: 10.2337/db14-0590 9. Anderson EJ, Neufer PD. **Type II skeletal myofibers possess unique properties that potentiate mitochondrial H(2)O(2) generation**. *Am J Physiol Cell Physiol* (2006) **290**. DOI: 10.1152/ajpcell.00402.2005 10. Criswell D, Powers S, Dodd S, Lawler J, Edwards W, Renshler K. **High intensity training-induced changes in skeletal muscle antioxidant enzyme activity**. *Med Sci Sports Exerc.* (1993) **25**. DOI: 10.1249/00005768-199310000-00009 11. Talbot J, Maves L. **Skeletal muscle fiber type: using insights from muscle developmental biology to dissect targets for susceptibility and resistance to muscle disease**. *Wiley Interdiscip Rev Dev Biol* (2016) **5**. DOI: 10.1002/wdev.230 12. Stuart CA, McCurry MP, Marino A, South MA, Howell ME, Layne AS. **Slow-twitch fiber proportion in skeletal muscle correlates with insulin responsiveness**. *J Clin Endocrinol Metab* (2013) **98**. DOI: 10.1210/jc.2012-3876 13. Sligar J, DeBruin DA, Saner NJ, Philp AM, Philp A. **The importance of mitochondrial quality control for maintaining skeletal muscle function across health span**. *Am J Physiol Cell Physiol* (2022) **322**. DOI: 10.1152/ajpcell.00388.2021 14. Munoz-Carvajal F, Sanhueza M. **The mitochondrial unfolded protein response: A hinge between healthy and pathological aging**. *Front Aging Neurosci* (2020) **12**. DOI: 10.3389/fnagi.2020.581849 15. Anderson EJ, Lustig ME, Boyle KE, Woodlief TL, Kane DA, Lin CT. **Mitochondrial H2O2 emission and cellular redox state link excess fat intake to insulin resistance in both rodents and humans**. *J Clin Invest.* (2009) **119**. DOI: 10.1172/JCI37048 16. Jheng HF, Tsai PJ, Guo SM, Kuo LH, Chang CS, Su IJ. **Mitochondrial fission contributes to mitochondrial dysfunction and insulin resistance in skeletal muscle**. *Mol Cell Biol* (2012) **32**. DOI: 10.1128/MCB.05603-11 17. Mogensen M, Sahlin K, Fernstrom M, Glintborg D, Vind BF, Beck-Nielsen H. **Mitochondrial respiration is decreased in skeletal muscle of patients with type 2 diabetes**. *Diabetes.* (2007) **56**. DOI: 10.2337/db06-0981 18. Teodoro BG, Baraldi FG, Sampaio IH, Bomfim LH, Queiroz AL, Passos MA. **Melatonin prevents mitochondrial dysfunction and insulin resistance in rat skeletal muscle**. *J Pineal Res* (2014) **57**. DOI: 10.1111/jpi.12157 19. Martinus RD, Garth GP, Webster TL, Cartwright P, Naylor DJ, Hoj PB. **Selective induction of mitochondrial chaperones in response to loss of the mitochondrial genome**. *Eur J Biochem* (1996) **240** 98-103. DOI: 10.1111/j.1432-1033.1996.0098h.x 20. Zhao Q, Wang J, Levichkin IV, Stasinopoulos S, Ryan MT, Hoogenraad NJ. **A mitochondrial specific stress response in mammalian cells**. *EMBO J* (2002) **21**. DOI: 10.1093/emboj/cdf445 21. Melber A, Haynes CM. **UPR(mt) regulation and output: a stress response mediated by mitochondrial-nuclear communication**. *Cell Res* (2018) **28**. DOI: 10.1038/cr.2018.16 22. Naresh NU, Haynes CM. **Signaling and regulation of the mitochondrial unfolded protein response**. *Cold Spring Harb Perspect Biol* (2019) **11**. DOI: 10.1101/cshperspect.a033944 23. Pellegrino MW, Nargund AM, Kirienko NV, Gillis R, Fiorese CJ, Haynes CM. **Mitochondrial UPR-regulated innate immunity provides resistance to pathogen infection**. *Nature.* (2014) **516**. DOI: 10.1038/nature13818 24. Wang YT, Lim Y, McCall MN, Huang KT, Haynes CM, Nehrke K. **Cardioprotection by the mitochondrial unfolded protein response requires ATF5**. *Am J Physiol Heart Circ Physiol* (2019) **317**. DOI: 10.1152/ajpheart.00244.2019 25. Smyrnias I, Gray SP, Okonko DO, Sawyer G, Zoccarato A, Catibog N. **Cardioprotective effect of the mitochondrial unfolded protein response during chronic pressure overload**. *J Am Coll Cardiol* (2019) **73**. DOI: 10.1016/j.jacc.2018.12.087 26. Gariani K, Menzies KJ, Ryu D, Wegner CJ, Wang X, Ropelle ER. **Eliciting the mitochondrial unfolded protein response by nicotinamide adenine dinucleotide repletion reverses fatty liver disease in mice**. *Hepatology.* (2016) **63**. DOI: 10.1002/hep.28245 27. Lee H, Ha TY, Jung CH, Nirmala FS, Park SY, Huh YH. **Mitochondrial dysfunction in skeletal muscle contributes to the development of acute insulin resistance in mice**. *J Cachexia Sarcopenia Muscle.* (2021) **12**. DOI: 10.1002/jcsm.12794 28. Antunes LC, Elkfury JL, Jornada MN, Foletto KC, Bertoluci MC. **Validation of HOMA-IR in a model of insulin-resistance induced by a high-fat diet in wistar rats**. *Arch Endocrinol Metab* (2016) **60**. DOI: 10.1590/2359-3997000000169 29. Clayton DA, Shadel GS. **Purification of mitochondria by sucrose step density gradient centrifugation**. *Cold Spring Harb Protoc* (2014) **2014** pdb prot080028. DOI: 10.1101/pdb.prot080028 30. Torres MJ, Kew KA, Ryan TE, Pennington ER, Lin CT, Buddo KA. **17beta-estradiol directly lowers mitochondrial membrane microviscosity and improves bioenergetic function in skeletal muscle**. *Cell Metab* (2018) **27** 167-79.e7. DOI: 10.1016/j.cmet.2017.10.003 31. Feng W, Yonezawa M, Ye J, Jenuwein T, Grummt I. **PHF8 activates transcription of rRNA genes through H3K4me3 binding and H3K9me1/2 demethylation**. *Nat Struct Mol Biol* (2010) **17**. DOI: 10.1038/nsmb.1778 32. Merkwirth C, Jovaisaite V, Durieux J, Matilainen O, Jordan SD, Quiros PM. **Two conserved histone demethylases regulate mitochondrial stress-induced longevity**. *Cell.* (2016) **165**. DOI: 10.1016/j.cell.2016.04.012 33. Chalkley SM, Hettiarachchi M, Chisholm DJ, Kraegen EW. **Long-term high-fat feeding leads to severe insulin resistance but not diabetes in wistar rats**. *Am J Physiol Endocrinol Metab* (2002) **282**. DOI: 10.1152/ajpendo.00173.2001 34. Morris EM, Meers GME, Ruegsegger GN, Wankhade UD, Robinson T, Koch LG. **Intrinsic high aerobic capacity in Male rats protects against diet-induced insulin resistance**. *Endocrinology.* (2019) **160**. DOI: 10.1210/en.2019-00118 35. Pataky MW, Wang H, Yu CS, Arias EB, Ploutz-Snyder RJ, Zheng X. **High-fat diet-induced insulin resistance in single skeletal muscle fibers is fiber type selective**. *Sci Rep* (2017) **7** 13642. DOI: 10.1038/s41598-017-12682-z 36. Sharma M, Dey CS. **AKT ISOFORMS-AS160-GLUT4: The defining axis of insulin resistance**. *Rev Endocr Metab Disord* (2021) **22**. DOI: 10.1007/s11154-021-09652-2 37. Shortreed KE, Krause MP, Huang JH, Dhanani D, Moradi J, Ceddia RB. **Muscle-specific adaptations, impaired oxidative capacity and maintenance of contractile function characterize diet-induced obese mouse skeletal muscle**. *PloS One* (2009) **4**. DOI: 10.1371/journal.pone.0007293 38. Turner N, Kowalski GM, Leslie SJ, Risis S, Yang C, Lee-Young RS. **Distinct patterns of tissue-specific lipid accumulation during the induction of insulin resistance in mice by high-fat feeding**. *Diabetologia.* (2013) **56**. DOI: 10.1007/s00125-013-2913-1 39. Pataky MW, Van Acker SL, Dhingra R, Freeburg MM, Arias EB, Oki K. **Fiber type-specific effects of acute exercise on insulin-stimulated AS160 phosphorylation in insulin-resistant rat skeletal muscle**. *Am J Physiol Endocrinol Metab* (2019) **317**. DOI: 10.1152/ajpendo.00304.2019 40. Munch C. **The different axes of the mammalian mitochondrial unfolded protein response**. *BMC Biol* (2018) **16** 81. DOI: 10.1186/s12915-018-0548-x 41. Fiorese CJ, Schulz AM, Lin YF, Rosin N, Pellegrino MW, Haynes CM. **The transcription factor ATF5 mediates a mammalian mitochondrial UPR**. *Curr Biol* (2016) **26**. DOI: 10.1016/j.cub.2016.06.002 42. Bhaskaran S, Unnikrishnan A, Ranjit R, Qaisar R, Pharaoh G, Matyi S. **A fish oil diet induces mitochondrial uncoupling and mitochondrial unfolded protein response in epididymal white adipose tissue of mice**. *Free Radic Biol Med* (2017) **108**. DOI: 10.1016/j.freeradbiomed.2017.04.028 43. Fletcher E, Wiggs M, Greathouse KL, Morgan G, Gordon PM. **Impaired proteostasis in obese skeletal muscle relates to altered immunoproteasome activity**. *Appl Physiol Nutr Metab* (2022) **47**. DOI: 10.1139/apnm-2021-0764 44. Runkel ED, Liu S, Baumeister R, Schulze E. **Surveillance-activated defenses block the ROS-induced mitochondrial unfolded protein response**. *PloS Genet* (2013) **9** e1003346. DOI: 10.1371/journal.pgen.1003346 45. Shao LW, Niu R, Liu Y. **Neuropeptide signals cell non-autonomous mitochondrial unfolded protein response**. *Cell Res* (2016) **26**. DOI: 10.1038/cr.2016.118 46. Schrauwen P, Hoppeler H, Billeter R, Bakker AH, Pendergast DR. **Fiber type dependent upregulation of human skeletal muscle UCP2 and UCP3 mRNA expression by high-fat diet**. *Int J Obes Relat Metab Disord* (2001) **25**. DOI: 10.1038/sj.ijo.0801566 47. Fiorese CJ, Haynes CM. **Integrating the UPR(mt) into the mitochondrial maintenance network**. *Crit Rev Biochem Mol Biol* (2017) **52**. DOI: 10.1080/10409238.2017.1291577 48. Conley KE, Amara CE, Jubrias SA, Marcinek DJ. **Mitochondrial function, fibre types and ageing: new insights from human muscle in vivo**. *Exp Physiol* (2007) **92**. DOI: 10.1113/expphysiol.2006.034330 49. Liu R, Jin P, Yu L, Wang Y, Han L, Shi T. **Impaired mitochondrial dynamics and bioenergetics in diabetic skeletal muscle**. *PloS One* (2014) **9**. DOI: 10.1371/journal.pone.0092810 50. Heyne E, Schrepper A, Doenst T, Schenkl C, Kreuzer K, Schwarzer M. **High-fat diet affects skeletal muscle mitochondria comparable to pressure overload-induced heart failure**. *J Cell Mol Med* (2020) **24**. DOI: 10.1111/jcmm.15325 51. Pinho RA, Sepa-Kishi DM, Bikopoulos G, Wu MV, Uthayakumar A, Mohasses A. **High-fat diet induces skeletal muscle oxidative stress in a fiber type-dependent manner in rats**. *Free Radic Biol Med* (2017) **110**. DOI: 10.1016/j.freeradbiomed.2017.07.005 52. Nargund AM, Fiorese CJ, Pellegrino MW, Deng P, Haynes CM. **Mitochondrial and nuclear accumulation of the transcription factor ATFS-1 promotes OXPHOS recovery during the UPR(mt)**. *Mol Cell* (2015) **58**. DOI: 10.1016/j.molcel.2015.02.008 53. Durieux J, Wolff S, Dillin A. **The cell-non-autonomous nature of electron transport chain-mediated longevity**. *Cell.* (2011) **144** 79-91. DOI: 10.1016/j.cell.2010.12.016 54. Lee C, Kim KH, Cohen P. **MOTS-c: A novel mitochondrial-derived peptide regulating muscle and fat metabolism**. *Free Radic Biol Med* (2016) **100**. DOI: 10.1016/j.freeradbiomed.2016.05.015 55. Lee C, Zeng J, Drew BG, Sallam T, Martin-Montalvo A, Wan J. **The mitochondrial-derived peptide MOTS-c promotes metabolic homeostasis and reduces obesity and insulin resistance**. *Cell Metab* (2015) **21**. DOI: 10.1016/j.cmet.2015.02.009 56. Reynolds JC, Lai RW, Woodhead JST, Joly JH, Mitchell CJ, Cameron-Smith D. **MOTS-c is an exercise-induced mitochondrial-encoded regulator of age-dependent physical decline and muscle homeostasis**. *Nat Commun* (2021) **12** 470. DOI: 10.1038/s41467-020-20790-0 57. Li S, Wang M, Ma J, Pang X, Yuan J, Pan Y. **MOTS-c and exercise restore cardiac function by activating of NRG1-ErbB signaling in diabetic rats**. *Front Endocrinol (Lausanne).* (2022) **13**. DOI: 10.3389/fendo.2022.812032 58. D'Souza RF, Woodhead JST, Hedges CP, Zeng N, Wan J, Kumagai H. **Increased expression of the mitochondrial derived peptide, MOTS-c, in skeletal muscle of healthy aging men is associated with myofiber composition**. *Aging (Albany NY).* (2020) **12**. DOI: 10.18632/aging.102944 59. Tian Y, Garcia G, Bian Q, Steffen KK, Joe L, Wolff S. **Mitochondrial stress induces chromatin reorganization to promote longevity and UPR(mt)**. *Cell.* (2016) **165**. DOI: 10.1016/j.cell.2016.04.011 60. Sobue S, Inoue C, Hori F, Qiao S, Murate T, Ichihara M. **Molecular hydrogen modulates gene expression**. *Biochem Biophys Res Commun* (2017) **493**. DOI: 10.1016/j.bbrc.2017.09.024 61. Richards BJ, Slavin M, Oliveira AN, Sanfrancesco VC, Hood DA. **Mitochondrial protein import and UPR(mt) in skeletal muscle remodeling and adaptation**. *Semin Cell Dev Biol* (2022) **143**. DOI: 10.1016/j.semcdb.2022.01.002 62. Woo H, Ha SD, Lee SB, Buratowski S, Kim TSJE, Medicine M. **Modulation of gene expression dynamics by co-transcriptional histone methylations**. *Exp Mol Med* (2017) **49**. DOI: 10.1038/emm.2017.19 63. Bengtsen M, Winje IM, Eftestol E, Landskron J, Sun C, Nygard K. **Comparing the epigenetic landscape in myonuclei purified with a PCM1 antibody from a fast/glycolytic and a slow/oxidative muscle**. *PloS Genet* (2021) **17**. DOI: 10.1371/journal.pgen.1009907 64. Kawano F, Nimura K, Ishino S, Nakai N, Nakata K, Ohira Y. **Differences in histone modifications between slow- and fast-twitch muscle of adult rats and following overload, denervation, or valproic acid administration**. *J Appl Physiol (1985).* (2015) **119**. DOI: 10.1152/japplphysiol.00289.2015 65. Kawano F. **Histone modification: A mechanism for regulating skeletal muscle characteristics and adaptive changes**. *Appl Sci* (2021) **11** 3905. DOI: 10.3390/app11093905
--- title: Acupuncture improved hepatic steatosis in HFD-induced NAFLD rats by regulating intestinal microbiota authors: - Haiying Wang - Qiang Wang - Cuimei Liang - Liang Pan - Hui Hu - Hongjuan Fang journal: Frontiers in Microbiology year: 2023 pmcid: PMC10061080 doi: 10.3389/fmicb.2023.1131092 license: CC BY 4.0 --- # Acupuncture improved hepatic steatosis in HFD-induced NAFLD rats by regulating intestinal microbiota ## Abstract ### Background Intestinal dysbiosis has been increasingly implicated in the pathogenesis of non-alcoholic fatty liver disease (NAFLD). Acupuncture has been shown to have beneficial effects on NAFLD, but the mechanism is not yet clear. This study explores the potential beneficial effects of acupuncture on intestinal microbiota in NAFLD. ### Methods An NAFLD model in Sprague Dawley rats was established using a high-fat diet (HFD) for 10 weeks. NAFLD rats were randomly divided into control, model, and acupuncture groups. Following acupuncture treatment over 6 weeks, automated biochemical analysis was used to measure serum lipid metabolism parameters, including levels of alanine transferase, aspartate transferase, alkaline phosphatase, total cholesterol, triglycerides, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol. The level of serum inflammatory factors interleukin (IL)-6, IL-10, and tumor necrosis factor-alpha (TNF-α) were measured by enzyme-linked immunosorbent assay. The characteristics of steatosis were evaluated using quantitative computed tomography, hematoxylin and eosin staining, and Oil Red O staining in the liver, while the intestinal microbiota was determined using 16S rRNA gene sequencing. ### Results Acupuncture decreased the systemic inflammatory response, ameliorated dyslipidemia, and improved liver function indexes in NAFLD model rats. Tomography and staining indicated that acupuncture reduced steatosis and infiltration of inflammatory cells in the liver. 16S rRNA analysis showed that acupuncture reduced the Firmicutes to Bacteroidetes (F/B) ratio, increased the abundance of microbiota, including Bacteroidales_S24-7_group, Prevotellaceae, Bacteroidaceae, Blautia, norank_f_Bacteroidales_S24-7_group, Bacteroides, and Prevotella_9, and decreased the abundance of Ruminococcaceae_UCG-014. Correlation analysis suggested a close correlation between lipid metabolism, inflammation factors, hepatic steatosis, and the changed intestinal microbiota. ### Conclusion Acupuncture can significantly improve lipid metabolism and the systemic inflammatory response in HFD-induced NAFLD rats, potentially by regulating intestinal microbiota composition. ## Introduction Non-alcoholic fatty liver disease (NAFLD) is recognized as a major public health threat, and its incidence continues to grow annually, reaching $25.2\%$ worldwide (Asrani et al., 2019). As the top contributor to rapid increases in mortality due to liver-related diseases (Paik et al., 2020), NAFLD has become the primary indication for end-stage liver disease (Estes et al., 2018), primary hepatocellular carcinoma (Wong et al., 2014), and liver transplantation (Noureddin et al., 2018). In a longitudinal cohort study with a 14.2-year follow-up, researchers found that even the liver steatosis simplex at an early stage (triglyceride accumulated in the cytoplasm of non-fat cells) increased the mortality risk by $71\%$, and this risk is positively related to NAFLD severity (Simon et al., 2021). NAFLD is closely related to metabolic syndromes such as obesity, insulin resistance, hyperlipidemia, and hypertension (Ballestri et al., 2016). Epidemiology studies with data from 20 different countries confirmed that NAFLD prevalence is twice as high among those with type 2 diabetes compared with that of the general population (Younossi et al., 2019). Although many drug trials are currently evaluating therapies for NAFLD, none have been approved for clinical use. Hence, there is an urgent need to identify safe and efficient interventions. As proven by increasing evidence, NAFLD occurrence and development are driven by gut–liver axis unbalance and metabolites of intestinal microbiota (Sharpton et al., 2019). A study found that when the feces of obese women with NAFLD were planted into the intestinal tract of rats fed with a general diet, the liver triglyceride content of the rats increased in 14 days (Hoyles et al., 2018). Le Roy et al. reported that when germ-free mice underwent implantation of feces from hyperglycemic mice and were fed a high-fat diet (HFD) for 16 weeks, bullous steatosis was found in their liver cells (Le Roy et al., 2013). This indicates the critical role played by intestinal microbiota in NAFLD occurrence and development. Furthermore, when NAFLD mice induced by an HFD were treated with probiotics such as *Lactobacillus lactis* and Pediococcus for 8 weeks, NAFLD symptoms improved (Yu et al., 2021). Given sizable animal models and human studies demonstrating the role of intestinal microbiota in NAFLD development, it is important to determine whether specific bacterial strains or community composition drives disease progression. As an alternative therapy with a history spanning several thousand years, acupuncture plays a key role in traditional Chinese medicine. Several studies confirmed that acupuncture could effectively alleviate hepatic steatosis and improve glucolipid metabolism and insulin resistance (Dong et al., 2020; Han et al., 2020). Acupuncturing various acupoints on HFD-induced NAFLD rats was found to effectively suppress the response of inflammatory factors such as tumor necrosis factor-alpha (TNF-α) and interleukin (IL)-6 (Yu et al., 2017), thus suppressing inflammation. Acupuncture intervention by Zhang et al. on HFD-induced obesity mouse models effectively alleviated oxidative stress and liver cell apoptosis (Zhang et al., 2020), effectively inhibiting NAFLD progression. Various studies confirmed that acupuncture therapy is valid and safe for alleviating NAFLD (Chen et al., 2021), yet there are few reports on whether acupuncture can improve NAFLD by regulating intestinal microbiota. To better understand the mechanism of acupuncture therapy for NAFLD, we established a NAFLD rat model by HFD feeding. Observations of intestinal microbiota changes in NAFLD rats following acupuncture treatment could support research on the potential mechanism of acupuncture in managing NAFLD. ## Experimental animals We purchased 30 healthy male Sprague–Dawley (SD) rats (age: 5–6 weeks old, body mass: 220 ± 20 g) from the Experimental Animal Centre of the Chinese People’s Liberation Army Academy of Military Medical Sciences, Beijing, China. All rats were allowed to feed and drink freely, with feed and water replaced once every other day. A standard specific pathogen-free environment was established with 22 ± 2°C ambient temperature, 50–$60\%$ relative humidity, and 12 h of alternating lighting and darkness. The Animal Experiments and Experimental Animal Welfare Committee of the Chinese People’s Liberation Army Center of Disease Control and Prevention approved the experimental protocol, and the study process followed all ethical review guidelines. ## Establishment of NAFLD rat model The rats were randomly divided into control ($$n = 11$$) and model ($$n = 19$$) groups after a week of adjustable feeding. The control group was fed with a normal chow diet (NCD: $24\%$ corn flour, $20\%$ bran, $20\%$ bean cake, $20\%$ four, $6\%$ cellulose, $5\%$ fish meal, $3\%$ bone meal, and $2\%$ salt; Experiment Animal Center of Military Medical Science Academy), while the model group was fed with an HFD ($2.5\%$ cholesterol, $0.3\%$ sodium cholate, $20\%$ saccharose, $20\%$ lard, and $57.2\%$ basal feed; Hua Fukang Biotechnology Co., Ltd., Beijing, China). Thereafter, three rats each were randomly selected from the control and model groups for a liver and spleen quantitative computed tomography (QCT) scan. Hematoxylin and eosin (H&E) and Oil Red O staining were performed on the liver tissue to measure liver steatosis and confirm the NAFLD rat model. ## Acupuncture intervention We randomly divided 16 NAFLD rats into the model ($$n = 8$$) and acupuncture ($$n = 8$$) groups. Both groups were fed the HFD, while the control group ($$n = 8$$) was fed the NCD. The experimental procedure is illustrated in Figure 1. **Figure 1:** *Groups and experimental procedure.* Acupoints with the bilateral “Daimai” (GB 26) were selected. The rats of the acupuncture group were placed and fixed in self-made conical rat bags. Sterile disposable acupuncture needles (Zhongyan Taihe Medicine Company, Ltd., Beijing, China; 0.30 mm × 25 mm) were inserted vertically into the bilateral GB 26 acupoints at a depth of 4–5 mm. The GB 26 acupoints were connected with the positive and negative poles of an electric acupuncture apparatus (Yingdi Electronic Medical Appliances Co., Ltd., Changzhou, China), with the following parameters: dilatational wave, frequency 2 Hz/15 Hz, and strength 1.5 mA. Treatments were performed on alternate weekdays (Monday/Wednesday/Friday) over 6 weeks. Each treatment lasted 20 min. During acupuncture, rats of the model group were fixed at the same time for 20 min without treatment, and the rats in the control group received no intervention. ## Serum biochemical marker assay All rats were anesthetized by intraperitoneal injection with $10\%$ chloral hydrate (Sinopharm Chemical Reagent Co., Ltd., Shanghai, China) after fasting for 12 h. Blood samples were collected from the abdominal aorta and centrifuged at 4°C for 10 min at 3000 rpm. Next, the serum samples were refrigerated at −80°C until further analysis. The levels of serum lipid parameters, including total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL), and low-density lipoprotein cholesterol (LDL), as well as serum liver function parameters of alanine transferase (ALT), aspartate transferase (AST), and alkaline phosphatase (ALP) were measured using the MEK7222K Automated Hematology Analyzer (Nihon Kohden, Tokyo, Japan). Specific enzyme-linked immunosorbent assay kits were used to measure the levels of serum inflammatory factors, including IL-6, TNF-α, and IL-10, as per the manufacturer’s instructions (R&D Systems, Minneapolis, MN, USA). ## QCT scan Five rats were randomly selected from each group, and after anesthesia, the limbs of each rat were fixed horizontally on a plate. QCT was performed using a Toshiba Aquilion 80-slice CT Scanner (Toshiba, Tokyo, Japan) with a Mindways calibration phantom (Mindways Software, Austin, TX, USA). The scan covered the diaphragmatic dome to the symphysis pubis, avoiding the hepatic portal blood vessels and intra-abdominal fat tissues and keeping a distance from the liver edges. Liver fat content was determined as follows: the right posterior lobe of the hepatic portal level, regions of interest (ROI) 40 ± 0.5 mm2, spleen depth divided equally into three shares, measuring the middle-level center ROI 10 ± 0.2 mm2. Scan parameters: 0.985 pitch, 120 cm bed height, 120 kV, 250 mA, 1.0-mm thickness, 500 mm2 field of view, and standard reconstruction. The original images were uploaded to the CT workstation and analyzed using Mindways software version 4.2. The QCT attenuation values were measured using the Hounsfeld Unit (HU) for both the liver and spleen, and hepatic steatosis was negatively correlated to liver attenuation values (Lee and Park, 2014). Liver CT (CTL) attenuation values were used to measure the liver fat content. Since the CT attenuation value of the spleen remained relatively stable, the ratio of the CT attenuation values of liver to spleen (CTL/S) was measured to quantitatively evaluate the severity of hepatic steatosis, being mild 0.7–1.0, moderate 0.5–0.7, and severe ≤0.5 (Zeng et al., 2008). All QCT operations were carried out by a radiologist recruited separately and blinded to the sample characteristics. ## H&E staining Following blood sampling, all the overnight fasted rats were sacrificed after being anesthetized, and whole fresh liver tissue was obtained and weighed for every rat. The liver samples were then fixed with $10\%$ formaldehyde, embedded with paraffin, and cut into 2-μm thick sections. Some sections underwent H&E staining using standard techniques to observe morphological changes under a light microscope (IX81; Olympus, Tokyo, Japan). ## Oil Red O staining The sections were subjected to Oil Red O according to the manufacturer’s instructions (Sinopharm Chemical Reagent Co., Ltd., Shanghai, China), and the distribution of liver lipid droplets was observed under a microscope (IX81; Olympus, Tokyo, Japan). Olympus Image-Pro Plus 6.0 was used to quantitatively analyze the stained regions. ## Fecal 16S rRNA gene sequencing Before rats were sacrificed, fecal samples were collected. Microbial community genomic DNA was extracted from fecal samples using an E.Z.N.A.® soil DNA Kit (Omega Bio-tek, Norcross, GA, USA) according to the manufacturer’s instructions. The DNA extract was checked using $1\%$ agarose gel electrophoresis. The final concentration and purity of microbial DNA were measured using a NanoDrop 2000 UV–vis spectrophotometer (Thermo Scientific, Wilmington, NC, USA). The hypervariable region V3–V4 of the bacterial 16S rRNA gene was amplified using primer pairs 338F (5′-ACTCCT ACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGT WTCTAAT-3′) using an ABI GeneAmp® 9,700 PCR thermocycler (Applied Biosystems, Foster City, CA, USA). PCR amplification conditions were as follows: denaturation at 95°C for 3 min, 27 cycles of denaturation at 95°C for 30 s, annealing at 55°C for 30 s, extension at 72°C for 45 s, single extension at 72°C for 10 min, and a final extension at 72°C for 10 min. An AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) was used for PCR purification. A Quantus™ Fluorometer (Promega, Madison, WI, USA) was used for quantitative testing. Purified amplicons were pooled in equimolar concentrations, and paired-end sequencing was carried out using an Illumina MiSeq PE 300 platform (Illumina, San Diego, CA, USA). ## Microbial analysis of fecal 16S rRNA gene sequencing The raw sequencing reads were demultiplexed, filtered, and merged using FLASH software version 1.2.7 (Magoč and Salzberg, 2011). Operational taxonomic units (OTUs) with $97\%$ similarity cutoff were assigned to the same OTUs using Uparse, and chimeric sequences were identified and deleted using the UCHIME algorithm. Representative sequences for each OTU were analyzed using RDP Classifier at a confidence threshold of 0.7 with the 16S rRNA database (Silva v128). Relative OTU abundances were normalized using a standard sequence number corresponding to the sample with the least sequences and then used for diversity analysis. Rank-abundance curves were used to assess species richness and evenness. Alpha diversity analysis was conducted using Shannon and Sobs indices, and the rarefaction curve of the Shannon index was evaluated. Community composition was visualized as bar plots for phylum, family, and genus levels. Partial least squares discriminant analysis (PLS-DA) was conducted to determine species similarity and distinction. Distance-based redundancy analysis (db-RDA) was used to evaluate the correlation between physiological data (lipid metabolism parameters, inflammatory factors, and hepatic steatosis indicators) and gut microbiota. Spearman correlation was illustrated via heatmap. ## Statistical analysis SPSS 20.0 (SPSS Inc., Chicago, IL, USA) was used to analyze the data, and the statistical description of variables is shown as the mean ± standard deviation. Normally distributed data were tested by one-way analysis of variance (ANOVA) followed by least significant difference (LSD) analysis; non-normally distributed data were analyzed using the nonparametric Kruskal–Wallis test. Changes in the composition of intestinal microflora were evaluated using the Kruskal–Wallis H-test, Wilcoxon rank-sum test, or Mann–Whitney U-test. Correlation analysis was performed using Spearman correlation. $p \leq 0.05$ was considered statistically significant. ## Acupuncture improved metabolic disorders and inflammatory response in NAFLD rats Compared with the control group, the model group exhibited significant increases in serum TC, TG (both $p \leq 0.01$, Figures 2A,B), LDL ($p \leq 0.001$, Figure 2D), ALT, ALP (both $p \leq 0.01$, Figures 2E,G), AST ($p \leq 0.05$, Figure 2F) levels and a significant decrease in HDL levels ($p \leq 0.01$, Figure 2C). The acupuncture group revealed significant decreases in serum TC, TG, AST, and ALP (all $p \leq 0.05$, Figures 2A,B,F,G), LDL ($p \leq 0.001$, Figure 2D), and ALT ($p \leq 0.01$, Figure 2E) levels and a significant increase in HDL level ($p \leq 0.05$, Figure 2C) relative to the model group. The results suggest that acupuncture improved metabolic disorders in HFD-induced NAFLD rats. **Figure 2:** *Impact of acupuncture on serum lipid metabolism and inflammation in non-alcoholic fatty liver disease (NAFLD) rats. Serum lipid metabolism parameters (A–G), Serum inflammatory factors (H–J). (A) Serum total cholesterol (TC). (B) Serum triglycerides (TG). (C) Serum high-density lipoprotein cholesterol (HDL). (D) Serum low-density lipoprotein cholesterol (LDL). (E) Serum alanine transferase (ALT). (F) Serum aspartate transferase (AST). (G) Serum alkaline phosphatase (ALP). (H) Serum interleukin (IL)-6. (I) Serum tumor necrosis factor-alpha (TNF-α). (J) Serum interleukin (IL)-10. Data are shown as mean ± SD. *p < 0.05, **p < 0.01, ***p < 0.001, Control vs. Model; #p < 0.05, ##p < 0.01, ###p < 0.001, Model vs. Acupuncture.* The model group had significantly higher serum IL-6 and TNF-α levels (both $p \leq 0.01$, Figures 2H,I) and significantly lower IL-10 levels ($p \leq 0.01$, Figure 2J) than those of the control group. The acupuncture group showed significant decreases in IL-6 and TNF-α levels (both $p \leq 0.05$, Figures 2H,I) and a significant increase in IL-10 levels ($p \leq 0.05$, Figure 2J) compared with those of the model group. These results demonstrate that acupuncture suppresses the expression of proinflammatory factors IL-6 and TNF-α and increases that of the protective inflammatory factor IL-10. ## Acupuncture ameliorated hepatic steatosis in NAFLD rats CTL attenuation values were analyzed using QCT (Figure 3A), yielding CTL values of the control and model groups of 54.9 and 26.6 HU (<40), respectively, indicating a decrease in the model group compared with that of the control group ($p \leq 0.01$, Figure 3B). The CTL value of the acupuncture group was 51.5 HU, indicating an increase compared with that of the model group ($p \leq 0.05$, Figure 3B). As shown in Figure 3C, in the control group, no rats exhibited hepatic steatosis (CTL/S > 1); in the model group, three rats exhibited moderate liver steatosis (CTL/S: 0.5–0.7), and two rats exhibited severe hepatic steatosis (CTL/S: ≤ 0.5). In the acupuncture group, four rats exhibited mild hepatic steatosis (CTL/S: 0.7–1), and one rat exhibited moderate hepatic steatosis (CTL/S: 0.5–0.7). The CTL/S of the model group was significantly decreased compared with that of the control group ($p \leq 0.001$, Figure 3C), while the CTL/S of the acupuncture group was significantly increased compared with that of the model group ($p \leq 0.001$, Figure 3C). These results demonstrate that acupuncture significantly alleviates liver fat content, thus slowing the progress of hepatic steatosis. **Figure 3:** *Impact of acupuncture on hepatic steatosis in non-alcoholic fatty liver disease (NAFLD) rats. (A) Liver and spleen CT value measurements of liver fat content. QCT films of the liver and spleen of the control, model, and acupuncture groups; green circles indicate regions of interest (ROI) in liver and spleen measurements used to indicate the CT values of the liver and spleen. (B) Liver CT (CTL) values (HU). (C) Liver to spleen CT ratio (CTL/S). (D) Fresh liver tissues. (E) Liver weight (g). (F) Oil Red O staining of rat livers (×100). (G) Quantification of the red area/total area by Oil Red O staining. (H) H&E staining of liver tissue (×100). Data are shown as mean ± SD. *p < 0.05, ***p < 0.001, Control vs. Model, #p < 0.05, ##p < 0.01, Model vs. Acupuncture.* Figure 3D shows fresh liver tissues. The liver color of the control group appeared dark red without obvious lipid particle accumulation. However, the liver of the model group appeared enlarged with an obvious yellowish-white color and visible lipid particle accumulation. The liver appearance and color of the acupuncture group were closer to those of the control group, with a visible reduction in lipid particles compared with the model group (Figure 3D). The liver weight of the model group was significantly increased compared with that of the control group ($p \leq 0.001$, Figure 3E), while that of the acupuncture group was significantly decreased relative to that of the model group ($p \leq 0.01$, Figure 3E). Oil Red O staining confirmed that the hepatocyte nuclei in the control group appeared blue without obvious lipid deposition, while diffuse red lipid droplets within adjacent cells merged, and the lipid droplets compressed the nuclei in the model group (Figure 3F). Statistical analysis confirmed significantly increased lipid deposition in the model group ($p \leq 0.05$, Figure 3G) and significantly decreased deposition in the acupuncture group ($p \leq 0.05$, Figure 3G). The results of H&E staining are shown in Figure 3H. The control group did not display discernible hepatic steatosis, hepatocyte ballooning, lobular inflammation, or liver lobule structure disorder; however, the model group exhibited clear hepatic steatosis, hepatocyte ballooning, lobular inflammation, and liver lobule structure disorder. Compared with the model group, the acupuncture group demonstrated visible improvement in hepatic steatosis, hepatocyte ballooning, lobular inflammation, and liver lobule structure disorder. ## Effects of acupuncture on community abundance and diversity of intestinal microbiota in NAFLD rats 16S rRNA sequencing was used to analyze intestinal microbiota in NAFLD rats. In total, 1,139,259 optimized sequences were obtained from 24 samples. The number of optimized-sequence bases (bp) was 469,868,757, the average number of sequences per sample was 47,469 bp, and the average sequence length was 412 bp (min_length: 216, max_length: 518). Rank-abundance curves were used to determine species diversity, including species richness and community evenness. The width and smoothness of curves obtained for the control, model, and acupuncture groups were similar. The results indicated no significant changes in species diversity among the three groups (Figure 4A). The rarefaction curve of the Shannon index was flat, indicating that the sequenced data were sufficient and community diversity was high (Figure 4B). Alpha diversity of Sobs and Shannon indices on the OTU level was also analyzed to determine community richness and diversity, respectively. Intestinal microbiota showed no statistically significant differences in community richness (Figures 4C,D) or diversity (Figures 4E,F) among the control, model, and acupuncture groups. These results indicate that neither the HFD diet nor acupuncture induce significant changes in microbiota community richness or diversity in NAFLD rats. **Figure 4:** *Sample sequencing results and species diversity analysis. (A) Rank-abundance curves indicating species diversity, including richness and evenness. Species richness is represented by the width of the curve. The larger the curve range on the horizontal axis, the higher the species richness; the smoother the curve, the more community evenness. (B) Rarefaction curve of Shannon index at the OTU level. (C) Alpha diversity of Sobs index at the OTU level indicating community richness. (D) Statistical analysis of the Sobs index at the OTU level. (E) Alpha diversity of the Shannon index at the OTU level indicating community diversity. (F) Statistical analysis of the Shannon index at the OTU level.* ## Effect of acupuncture on intestinal microbiota composition in NAFLD rats PLS-DA revealed that intestinal microbiota characteristics were distinct across control, model, and acupuncture groups at the OTU level (Figure 5A). The distribution of control group intestinal microbiota was closer to that of the acupuncture group, while both were distinct from that of the model group. To determine specific differences among the three groups, a bar plot was used to show the relative abundance of intestinal microbiota at phylum, family, and genus levels. The dominant microbiota phyla were Firmicutes and Bacteroidetes, with Actinobacteria, Proteobacteria, and Saccharibacteria also being abundant (Figure 5B). Statistical analysis showed that the relative abundance of Firmicutes in the model group was significantly higher than that of the control group ($p \leq 0.05$, Figure 5C), and the abundance of Firmicutes was not affected by acupuncture (Figure 5C). The relative abundance of Bacteroidetes in the model group was significantly decreased compared with that in the control group ($p \leq 0.01$, Figure 5D). The relative abundance of Bacteroidetes in the acupuncture group was significantly increased compared with that in the model group ($p \leq 0.001$, Figure 5D). The Firmicutes to Bacteroidetes (F/B) ratio of the model group was significantly higher compared with that of the control group ($p \leq 0.01$, Figure 5E), while the F/B ratio of the acupuncture group was significantly lower than that of the model group ($p \leq 0.01$, Figure 5E). **Figure 5:** *Regulating composition of the intestinal microbiota of non-alcoholic fatty liver disease (NAFLD) rats. (A) Partial least squares discriminant analysis (PLS-DA) at the OTU level. (B) Community bar plot analysis at the phylum level. “Others” represents a combination of all species with less than 0.01 abundance among all the samples. (C) Statistical difference analysis of Firmicutes among the control, model, and acupuncture groups. (D) Statistical difference analysis of Bacteroidetes among the three groups. (E) Statistical difference analysis of Firmicutes to Bacteroidetes (F/B) ratio. Data are shown as mean ± SD (n = 8 per group). *p < 0.05, **p < 0.01, Control vs. Model; ##p < 0.01, ###p < 0.001, Model vs. Acupuncture.* The three most dominant microbiota families were Lachnospiraceae, Erysipelotrichaceae, and Peptostreptococcaceae, and no significant differences were observed among the three groups (Figures 6A,B). The relative abundances of Bacteroidales_S24-7_group and Bacteroidaceae in the model group were significantly decreased compared with those in the control group (both $p \leq 0.01$, Figures 6C,D). The relative abundances of Bacteroidales_S24-7_group ($p \leq 0.05$, Figure 6C) and Bacteroidaceae ($p \leq 0.01$, Figure 6D) in the acupuncture group were significantly increased compared with those in the model group. Compared with the control group, the relative abundance of Prevotellaceae was decreased in the model group, but this difference was not statistically significant (Figure 6E). Compared with the model group, the relative abundance of Prevotellaceae in the acupuncture group significantly increased ($p \leq 0.05$, Figure 6E). **Figure 6:** *Impact of acupuncture on the composition of intestinal microbiota at the family level. (A) Community bar plot analysis. (B) Statistical difference analysis of the top three dominant microbiota families (Lachnospiraceae, Erysipelotrichaceae, and Peptostreptococcaceae) among all groups. (C) Bacteroidales_S24-7_group. (D) Bacteroidaceae. (E) Prevotellaceae. Data are shown as mean ± SD (n = 8 per group). **p < 0.01, Control vs. Model; #p < 0.05, ##p < 0.01, Model vs. Acupuncture.* ## Db-RDA at the genus level Overall, the bacterial community characteristics of the control and acupuncture groups were more similar than when each was compared with the model group (Figures 8A–F). The arrows derived via db-RDA for the correlation between the bacterial community and the lipid metabolism parameters ALT, AST (both $$p \leq 0.001$$, Figure 8A), and ALP ($$p \leq 0.002$$, Figure 8A); TC ($$p \leq 0.013$$, Figure 8B); TG ($$p \leq 0.001$$, Figure 8B); HDL ($$p \leq 0.001$$, Figure 8C); and LDL ($$p \leq 0.002$$, Figure 8C) presented significant distinctions. The inflammatory factors IL-6 ($$p \leq 0.001$$, Figure 8D), TNF-α ($$p \leq 0.005$$, Figure 8D), and IL-10 ($$p \leq 0.008$$, Figure 8D) were significantly correlated with the bacterial community. The hepatic steatosis markers CTL ($$p \leq 0.001$$, Figure 8E) and CTL/S ($$p \leq 0.002$$, Figure 8F) also presented close correlations with the bacterial community. **Figure 8:** *Distance-based redundancy analysis (db-RDA) on genus level of lipid metabolism parameters, inflammatory factors, and hepatic steatosis with intestinal microbiota. (A) Serum ALT, AST, and ALP. (B) Serum TC and TG. (C) Serum HDL and LDL. (D) Serum IL-6, TNF-α, and IL-10. (E) Liver CT (CTL) values. (F) CT values of liver to spleen ratio (CTL/S). The length of the red arrows represents the degree of the effect of serum lipid metabolism parameters, inflammation factors, and hepatic steatosis on the intestinal microbiota. The longer the arrow, the greater the correlation.* The above results indicate that lipid metabolism parameters, inflammatory factors, and hepatic steatosis are associated with the intestinal microbiota of NAFLD rats, yet the specific correlations need further elucidation. ## Spearman correlation analysis at the phylum and genus levels To further demonstrate the relationship of specific species at the phylum and genus levels with lipid metabolism parameters, inflammatory factors, and hepatic steatosis, Spearman correlation analysis was performed using heatmaps. Spearman correlations at the phylum level are shown in Figure 9. Figure 9A shows the correlations between lipid metabolism parameters with Firmicutes and Bacteroidetes. Serum LDL ($r = 0.52$, $$p \leq 0.028$$) exhibited significant positive correlations with p-Firmicutes. The lipid metabolism parameters of ALT (r = −0.61, $$p \leq 0.007$$), TC (r = −0.72, $$p \leq 0.001$$), TG (r = −0.54, $$p \leq 0.020$$) and LDL (r = −0.67, $$p \leq 0.002$$) exhibited significant negative correlations with Bacteroidetes, and HDL ($r = 0.52$, $$p \leq 0.028$$) exhibited significant positive correlations with Bacteroidetes. Figure 9B shows the correlations between inflammatory factors and Firmicutes and Bacteroidetes. IL-6 ($r = 0.59$, $$p \leq 0.010$$) was positively correlated with Firmicutes. IL-6 (r = −0.74, $$p \leq 0.000$$) and TNF-α (r = −0.70, $$p \leq 0.001$$) were significantly negatively correlated with Bacteroidetes, while IL-10 ($r = 0.66$, $$p \leq 0.003$$) presented a significant positive correlation with Bacteroidetes. Figure 9C shows the correlations between hepatic steatosis and Firmicutes and Bacteroidetes. CTL (r = −0.60, $$p \leq 0.018$$) and CTL/S (r = −0.63, $$p \leq 0.012$$) exhibited significant negative correlations with Firmicutes, whereas CTL ($r = 0.68$, $$p \leq 0.005$$) and CTL/S ($r = 0.58$, $$p \leq 0.024$$) were significantly positively correlated with Bacteroidetes. **Figure 9:** *Spearman correlation heatmaps at the phylum level. (A) Correlation heatmap of the serum lipid metabolism parameters ALT, AST, ALP, TC, TG, HDL, and LDL with the intestinal microbiota. (B) Correlation heatmap of the inflammatory factors IL-6, TNF-α, and IL-10 with the intestinal microbiota. (C) Correlation heatmap of the hepatic steatosis indicators CTL and CTL/S with the intestinal microbiota. Red indicates a positive correlation, and blue indicates a negative correlation; the darker the color, the higher the correlation. Significance threshold, *p < 0.05, **p < 0.01, ***p < 0.001.* Spearman correlations at the genus level are shown in Figure 10. Figure 10A shows the correlations between lipid metabolism parameters and the top abundant genera of the intestinal microbiota. ALP and unclassified_f_Lachnospiraceae ($r = 0.49$, $$p \leq 0.038$$), as well as TC and Bifidobacterium ($r = 0.50$, $$p \leq 0.037$$), presented significant positive correlations. Significant negative correlations were found for the following relationships: norank_f_Bacteroidales_S24-7_group with ALT (r = −0.47, $$p \leq 0.047$$), TC (r = −0.68, $$p \leq 0.002$$), and LDL (r = −0.61, $$p \leq 0.007$$); Bacteroides with ALT (r = −0.74, $$p \leq 0.000$$), AST (r = −0.51, $$p \leq 0.030$$), TC (r = −0.56, $$p \leq 0.015$$), LDL (r = −0.56, $$p \leq 0.015$$), and TG (r = −0.50, $$p \leq 0.033$$); Blautia and Prevotella_9 with AST (r = −0.50/−0.53, $$p \leq 0.035$$/0.022) and ALP (r = −0.51/−0.66, $$p \leq 0.030$$/0.003); and Phascolarctobacterium with ALP (r = −0.49, $$p \leq 0.038$$) exhibited a significant negative correlation. Figure 10B shows Spearman correlation between inflammatory factors and primary genera. Clostridium_sensu_stricto_1 ($r = 0.49$, $$p \leq 0.039$$) and Lactobacillus ($r = 0.57$, $$p \leq 0.014$$) were positively correlated to TNF-α, and Bacteroides (r = −0.61, $$p \leq 0.007$$) were negatively correlated to TNF-α. *The* genera norank_f_Bacteroidales_S24-7_group ($r = 0.47$, $$p \leq 0.048$$), Bacteroides ($r = 0.64$, $$p \leq 0.004$$), and Prevotella_9 ($r = 0.49$, $$p \leq 0.039$$) were positively correlated to IL-10, and Lactobacillus (r = −0.53, $$p \leq 0.024$$) exhibited negative correlations with IL-10; norank_f_Bacteroidales_S24-7_group (r = −0.51, $$p \leq 0.029$$) and Bacteroides (r = −0.58, $$p \leq 0.011$$) showed negative correlations with IL-6. Figure 10C shows the correlations between hepatic steatosis and genera. Bacteroides were positively correlated with CTL ($r = 0.61$, $$p \leq 0.016$$). **Figure 10:** *Spearman correlation heatmaps at the genus level. (A) Correlation heatmap of the serum lipid metabolism parameters ALT, AST, ALP, TC, TG, HDL, and LDL with the intestinal microbiota. (B) Correlation heatmap of the inflammatory factors IL-6, TNF-α, and IL-10 with the intestinal microbiota. (C) Correlation heatmap of the hepatic steatosis indicators CTL and CTL/S with the intestinal microbiota. Red indicates a positive correlation, and blue indicates a negative correlation; the darker the color, the higher the correlation. Significant threshold: *p < 0.05, **p < 0.01, ***p < 0.001.* The results above further support the notion that intestinal microbiota at the phylum and genus level is closely related to serum lipid metabolism parameters, inflammatory factors, and hepatic steatosis in NAFLD rats treated with acupuncture. ## Discussion We developed an HFD-induced NAFLD rat model to evaluate the effects of 6-week acupuncture treatment on fatty livers. Our results demonstrate that acupuncture effectively improved several indicators, such as serum lipids, liver function, and serum inflammatory factors. QCT was used to quantitatively evaluate liver fat content, while liver histopathology staining was performed to evaluate the lipidosis of liver tissues. Compared with serum markers and liver biopsy, the QCT method was selected as it is non-invasive, simple, and reliable (Marzuillo et al., 2015). The results show that acupuncture effectively improved hepatic steatosis and lipidosis, inhibited the inflammatory response, and slowed NAFLD progression. Previous clinical and experimental studies (Jiang et al., 2011; Li et al., 2021b) have demonstrated that acupuncture has beneficial effects on lipid metabolism and type 2 diabetes complicated by NAFLD (Li et al., 2021a). The present study further proved that acupuncture could effectively improve lipid metabolism and alleviate hepatic steatosis in NAFLD rats, which is consistent with previously published findings (Zeng et al., 2014; Ma et al., 2020; Wang et al., 2022). However, we did not investigate the mechanism of action of acupuncture treatment in NAFLD, which should be explored in future studies. The gastrointestinal tract interacts closely with the liver; the two are connected through the portal vein to form the intestine–liver axis. Therefore, disorders of intestinal microbiota and their related biology are seen as important regulatory factors in NAFLD pathophysiology (Tripathi et al., 2018). This study found that acupuncture could change the composition of the intestinal microbiota of NAFLD rats. The GB 26 acupoint was chosen in this study, owing to its anatomical position on the abdomen near the intestine, which may have contributed to its role in regulating intestinal microbiota. Current studies on NAFLD and intestinal microbiota focus on the composition of microflora and their metabolic mechanisms (He et al., 2021), and few studies have explored acupuncture’s role in regulating intestinal microbiota. A study by Xie et al. found that acupuncture could improve intestinal microbiota diversity (Xie et al., 2020). Another clinical study on intestinal microbiota and persistent NAFLD found declining microorganism diversity in NAFLD patients (Kim et al., 2019). We found no significant differences in microbiota diversity among the model, control, and acupuncture groups. This may be explained by the fact that differences among individual microorganisms are greater than those among communities, or our findings may have been influenced by sample size. Current studies on community diversity are inconsistent, indicating the need for further research on microbiota species diversity relating to NAFLD using larger sample sizes. In the human intestinal microbiome, Firmicutes and Bacteroidetes are dominant, comprising over $90\%$ of the total community (Eckburg et al., 2005). Therefore, the F/B ratio often serves as a marker of intestinal microbiome performance (Hildebrandt et al., 2009). Clinical studies found that the F/B ratio of NAFLD patients was positively correlated to steatosis (Jasirwan et al., 2021). The F/B ratio increased in HFD-induced NAFLD mice and decreased following bilberry anthocyanin intervention (Nakano et al., 2020), supporting the trend observed in this study. Furthermore, although acupuncture did not significantly influence the relative abundance of Firmicutes, it significantly increased the abundance of probiotic Bacteroidetes, demonstrating an effective improvement in NAFLD intestinal microbiota by acupuncture. Bacteroidales_S24-7_group bacteria can produce butyrate, which may slow NAFLD progression (Endo et al., 2013; Zhou et al., 2017). Our study found that the top three dominant microbiota families among the control, model, and acupuncture groups were Lachnospiraceae, Erysipelotrichaceae, and Peptostreptococcaceae, without significant differences; however, acupuncture significantly increased the abundance of Bacteroidales_S24-7_group, Prevotellaceae, and Bacteroidaceae, indicating an improved probiotics ratio in NAFLD rats after treatment. The genus *Blautia is* considered a probiotic that may improve metabolic disorders as it produces butyrate, which benefits intestinal health (Liu et al., 2021). A study from Japan found that increased Blautia abundance had a significant negative correlation with visceral fat accumulation (Ozato et al., 2019). HFD-fed mice administered fermented celery (Apium graveolens L.) juice intervention exhibited a significant increase in Ruminococcaceae_UCG-014 abundance (Zhao et al., 2021). Bacteroides may potentially treat metabolic disorders such as diabetes and obesity (Yang et al., 2017). Glucose metabolism improvement induced by dietary fibers may be related to Prevotella abundance increase, yet some studies also link Prevotella to adverse physiological effects such as insulin resistance (Pedersen et al., 2016). The current study showed that, at the genus level, acupuncture could significantly improve the relative abundance of the probiotics Blautia, Prevotella_9, norank_f_Bacteroidales_S24-7_group, and Bacteroides and decrease the abundance of Ruminococcaceae_UCG-014; therefore, we hypothesized that acupuncture could improve hepatic steatosis and lipidosis, as well as inhibit the inflammatory response by regulating composition of some intestinal microbiota, so as to slow NAFLD progression. As revealed by the db-RDA, serum lipid metabolism parameters, hepatic steatosis, and inflammatory factors were closely correlated to the bacterial community. The microbiota community distances among the three groups were distinct, and species in the acupuncture group were more similar to those in the control group than those in the model group. This further demonstrated that acupuncture potentially regulates lipid metabolism, inflammatory responses, and hepatic steatosis by influencing intestinal microbiota. However, this correlation analysis is only from the general level, and more specific correlations should be performed. Using Spearman correlation, we found that lipid metabolism and inflammation were mostly related to changes in the phylum Bacteroidetes, while hepatic steatosis was related to both Firmicutes and Bacteroidetes. At the genus level, lipid metabolism and inflammation were mostly correlated with norank_f_Bacteroidales_S24-7_group and Bacteroides, as well as Lactobacillus, Clostridium_sensu_stricto_1g_Blautia, and Prevotella_9. Hepatic steatosis was mostly correlated with Bacteroides. These correlations indicate that NAFLD markers that were changed by acupuncture are closely related to the intestinal microbiota. However, fecal bacteria transplantation experiments are needed to clarify the influence of changed intestinal microbiota on NAFLD. Correlations with each marker vary across genera, and each species within each genus exerts different functions in the intestine (Walker et al., 2014). There are two main limitations of this study. First, present studies on acupuncture in treating NAFLD are still insufficient, with a critical lack of data on relevant genera and correlations between NAFLD-related markers and intestinal microbiota. Additionally, larger clinical and experimental studies are required to verify the effectiveness of acupuncture and to investigate its potential mechanisms. Therefore, more advanced sequencing methods, such as metagenomics, are required to further analyze the influence of acupuncture on intestinal microbiota in NAFLD and clarify the roles of specific genera. ## Conclusion Acupuncture can improve hepatic steatosis in HFD-induced NAFLD rats. We demonstrated that acupuncture might have a beneficial effect on NAFLD by improving intestinal microbiota. Our findings also support the validity of selecting acupoint GB 26 in this study. Further studies are recommended to validate the regulatory role of acupuncture on the intestinal microbiota of NAFLD rats by fecal bacteria transplantation and investigate the role of intestinal microbiota metabolites. ## Data availability statement The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material. ## Ethics statement The animal study was reviewed and approved by Research Animal Care Committee of the Institute of Chinese People’s Liberation Army Center of Disease Control and Prevention. ## Author contributions HF and HH designed and guided the experiment study, they contributed equally to this work and shared the corresponding authors. HW carried out the experiment procedures, analyzed the data, and completed the manuscript. QW guided the experimental modeling, manuscript writing, and assisted in statistical analysis. CL and LP carried out the experimental modeling and acupuncture intervention, and all the authors participated in manuscript revisions. All authors contributed to the article and approved the submitted version. ## Funding This study was supported by grants from the Beijing Administration of Traditional Chinese Medicine, Chinese Medicine “3 + 3” Inheritance Project “JiasanYang Famous Research Studio,” Beijing Municipal Administration of Hospitals Incubating Program (PX2023019) and Beijing Natural Science Foundation [7232047]. ## Conflict of interest QW was employed by Chinese People’s Liberation Army Center of Disease Control and Prevention. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer CL declared a shared parent affiliation with the author HF to the handling editor at the time of review. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## References 1. Asrani S. K., Devarbhavi H., Eaton J., Kamath P. S.. **Burden of liver diseases in the world**. *J. Hepatol.* (2019) **70** 151-171. DOI: 10.1016/j.jhep.2018.09.014 2. Ballestri S., Nascimbeni F., Romagnoli D., Lonardo A.. **The independent predictors of non-alcoholic steatohepatitis and its individual histological features: insulin resistance, serum uric acid, metabolic syndrome, alanine aminotransferase and serum total cholesterol are a clue to pathogenesis and candidate targets for treatment**. *Hepatol. Res.* (2016) **46** 1074-1087. DOI: 10.1111/hepr.12656 3. Chen P., Zhong X., Dai Y., Tan M., Zhang G., Ke X.. **The efficacy and safety of acupuncture in non-alcoholic fatty liver disease: a systematic review and meta-analysis of randomized controlled trials**. *Medicine (Baltimore)* (2021) **100** e27050. DOI: 10.1097/md.0000000000027050 4. Dong C., Zhang C. R., Xue B. Y., Miu W. F., Fang N. Y., Li K.. **Electroacupuncture combined with lifestyle control on obese nonalcoholic fatty liver disease: a randomized controlled trial**. *Zhongguo Zhen Jiu* (2020) **40** 129-134. DOI: 10.13703/j.0255-2930.20190201-k00034 5. Eckburg P. B., Bik E. M., Bernstein C. N., Purdom E., Dethlefsen L., Sargent M.. **Diversity of the human intestinal microbial flora**. *Science* (2005) **308** 1635-1638. DOI: 10.1126/science.1110591 6. Endo H., Niioka M., Kobayashi N., Tanaka M., Watanabe T.. **Butyrate-producing probiotics reduce nonalcoholic fatty liver disease progression in rats: new insight into the probiotics for the gut-liver axis**. *PLoS One* (2013) **8** e63388. DOI: 10.1371/journal.pone.0063388 7. Estes C., Razavi H., Loomba R., Younossi Z., Sanyal A. J.. **Modeling the epidemic of nonalcoholic fatty liver disease demonstrates an exponential increase in burden of disease**. *Hepatology* (2018) **67** 123-133. DOI: 10.1002/hep.29466 8. Han J., Guo X., Meng X. J., Zhang J., Yamaguchi R., Motoo Y.. **Acupuncture improved lipid metabolism by regulating intestinal absorption in mice**. *World J. Gastroenterol.* (2020) **26** 5118-5129. DOI: 10.3748/wjg.v26.i34.5118 9. He L. H., Yao D. H., Wang L. Y., Zhang L., Bai X. L.. **Gut microbiome-mediated alteration of immunity, inflammation, and metabolism involved in the regulation of non-alcoholic fatty liver disease**. *Front. Microbiol.* (2021) **12** 761836. DOI: 10.3389/fmicb.2021.761836 10. Hildebrandt M. A., Hoffmann C., Sherrill-Mix S. A., Keilbaugh S. A., Hamady M., Chen Y. Y.. **High-fat diet determines the composition of the murine gut microbiome independently of obesity**. *Gastroenterology* (2009) **137** 1716-1724.e1711-1712. DOI: 10.1053/j.gastro.2009.08.042 11. Hoyles L., Fernández-Real J. M., Federici M., Serino M., Abbott J., Charpentier J.. **Molecular phenomics and metagenomics of hepatic steatosis in non-diabetic obese women**. *Nat. Med.* (2018) **24** 1070-1080. DOI: 10.1038/s41591-018-0061-3 12. Jasirwan C. O. M., Muradi A., Hasan I., Simadibrata M., Rinaldi I.. **Correlation of gut Firmicutes/Bacteroidetes ratio with fibrosis and steatosis stratified by body mass index in patients with non-alcoholic fatty liver disease**. *Biosci. Microbiota Food Health* (2021) **40** 50-58. DOI: 10.12938/bmfh.2020-046 13. Jiang Y. L., Ning Y., Liu Y. Y., Wang Y., Zhang Z., Yin L. M.. **Effects of preventive acupuncture on streptozotocin-induced hyperglycemia in rats**. *J. Endocrinol. Investig.* (2011) **34** e355-e361. DOI: 10.3275/7859 14. Kim H. N., Joo E. J., Cheong H. S., Kim Y., Kim H. L., Shin H.. **Gut microbiota and risk of persistent nonalcoholic fatty liver diseases**. *J. Clin. Med.* (2019) **8** 1089. DOI: 10.3390/jcm8081089 15. Le Roy T., Llopis M., Lepage P., Bruneau A., Rabot S., Bevilacqua C.. **Intestinal microbiota determines development of non-alcoholic fatty liver disease in mice**. *Gut* (2013) **62** 1787-1794. DOI: 10.1136/gutjnl-2012-303816 16. Lee S. S., Park S. H.. **Radiologic evaluation of nonalcoholic fatty liver disease**. *World J. Gastroenterol.* (2014) **20** 7392-7402. DOI: 10.3748/wjg.v20.i23.7392 17. Li X., Jia H. X., Yin D. Q., Zhang Z. J.. **Acupuncture for metabolic syndrome: systematic review and meta-analysis**. *Acupunct. Med.* (2021b) **39** 253-263. DOI: 10.1177/0964528420960485 18. Li M., Yao L., Huang H., Wang G., Yu B., Zheng H.. **Acupuncture for type 2 diabetes mellitus with nonalcoholic fatty liver disease: a protocol for systematic review and meta-analysis**. *Medicine (Baltimore)* (2021a) **100** e26043. DOI: 10.1097/md.0000000000026043 19. Liu X., Mao B., Gu J., Wu J., Cui S., Wang G.. **Blautia-a new functional genus with potential probiotic properties?**. *Gut Microbes* (2021) **13** 1-21. DOI: 10.1080/19490976.2021.1875796 20. Ma B., Li P., An H., Song Z.. **Electroacupuncture attenuates liver inflammation in nonalcoholic fatty liver disease rats**. *Inflammation* (2020) **43** 2372-2378. DOI: 10.1007/s10753-020-01306-w 21. Magoč T., Salzberg S. L.. **FLASH: fast length adjustment of short reads to improve genome assemblies**. *Bioinformatics* (2011) **27** 2957-2963. DOI: 10.1093/bioinformatics/btr507 22. Marzuillo P., Grandone A., Perrone L., Miraglia Del Giudice E.. **Controversy in the diagnosis of pediatric non-alcoholic fatty liver disease**. *World J. Gastroenterol.* (2015) **21** 6444-6450. DOI: 10.3748/wjg.v21.i21.6444 23. Nakano H., Wu S., Sakao K., Hara T., He J., Garcia S.. **Bilberry anthocyanins ameliorate NAFLD by improving dyslipidemia and gut microbiome dysbiosis**. *Nutrients* (2020) **12** 3252. DOI: 10.3390/nu12113252 24. Noureddin M., Vipani A., Bresee C., Todo T., Kim I. K., Alkhouri N.. **NASH leading cause of liver transplant in women: updated analysis of indications for liver transplant and ethnic and gender variances**. *Am. J. Gastroenterol.* (2018) **113** 1649-1659. DOI: 10.1038/s41395-018-0088-6 25. Ozato N., Saito S., Yamaguchi T., Katashima M., Tokuda I., Sawada K.. **Blautia genus associated with visceral fat accumulation in adults 20-76 years of age**. *NPJ Biofilms Microbiomes* (2019) **5** 28. DOI: 10.1038/s41522-019-0101-x 26. Paik J. M., Golabi P., Younossi Y., Mishra A., Younossi Z. M.. **Changes in the global burden of chronic liver diseases from 2012 to 2017: the growing impact of NAFLD**. *Hepatology* (2020) **72** 1605-1616. DOI: 10.1002/hep.31173 27. Pedersen H. K., Gudmundsdottir V., Nielsen H. B., Hyotylainen T., Nielsen T., Jensen B. A.. **Human gut microbes impact host serum metabolome and insulin sensitivity**. *Nature* (2016) **535** 376-381. DOI: 10.1038/nature18646 28. Sharpton S. R., Ajmera V., Loomba R.. **Emerging role of the gut microbiome in nonalcoholic fatty liver disease: from composition to function**. *Clin. Gastroenterol. Hepatol.* (2019) **17** 296-306. DOI: 10.1016/j.cgh.2018.08.065 29. Simon T. G., Roelstraete B., Khalili H., Hagström H., Ludvigsson J. F.. **Mortality in biopsy-confirmed nonalcoholic fatty liver disease: results from a nationwide cohort**. *Gut* (2021) **70** 1375-1382. DOI: 10.1136/gutjnl-2020-322786 30. Tripathi A., Debelius J., Brenner D. A., Karin M., Loomba R., Schnabl B.. **The gut-liver axis and the intersection with the microbiome**. *Nat. Rev. Gastroenterol. Hepatol.* (2018) **15** 397-411. DOI: 10.1038/s41575-018-0011-z 31. Walker A. W., Duncan S. H., Louis P., Flint H. J.. **Phylogeny, culturing, and metagenomics of the human gut microbiota**. *Trends Microbiol.* (2014) **22** 267-274. DOI: 10.1016/j.tim.2014.03.001 32. Wang G., Li M., Yu S., Guan M., Ma S., Zhong Z.. **Tandem mass tag-based proteomics analysis of type 2 diabetes mellitus with non-alcoholic fatty liver disease in mice treated with acupuncture**. *Biosci. Rep.* (2022) **42** BSR20212248. DOI: 10.1042/bsr20212248 33. Wong R. J., Cheung R., Ahmed A.. **Nonalcoholic steatohepatitis is the most rapidly growing indication for liver transplantation in patients with hepatocellular carcinoma in the U.S**. *Hepatology* (2014) **59** 2188-2195. DOI: 10.1002/hep.26986 34. Xie L. L., Zhao Y. L., Yang J., Cheng H., Zhong Z. D., Liu Y. R.. **Electroacupuncture prevents osteoarthritis of high-fat diet-induced obese rats**. *Biomed. Res. Int.* (2020) **2020** 9380965. DOI: 10.1155/2020/9380965 35. Yang J. Y., Lee Y. S., Kim Y., Lee S. H., Ryu S., Fukuda S.. **Gut commensal Bacteroides acidifaciens prevents obesity and improves insulin sensitivity in mice**. *Mucosal Immunol.* (2017) **10** 104-116. DOI: 10.1038/mi.2016.42 36. Younossi Z. M., Golabi P., de Avila L., Paik J. M., Srishord M., Fukui N.. **The global epidemiology of NAFLD and NASH in patients with type 2 diabetes: a systematic review and meta-analysis**. *J. Hepatol.* (2019) **71** 793-801. DOI: 10.1016/j.jhep.2019.06.021 37. Yu M., Li G., Tang C. L., Gao R. Q., Feng Q. T., Cao J.. **Effect of Electroacupunctrue stimulation at Fenglong (ST 40) on expression of SREBP-1 c in non-alcoholic fatty liver disease rats**. *Zhen Ci Yan Jiu* (2017) **42** 308-314. PMID: 29072011 38. Yu J. S., Youn G. S., Choi J., Kim C. H., Kim B. Y., Yang S. J.. **Lactobacillus lactis and Pediococcus pentosaceus-driven reprogramming of gut microbiome and metabolome ameliorates the progression of non-alcoholic fatty liver disease**. *Clin. Transl. Med.* (2021) **11** e634. DOI: 10.1002/ctm2.634 39. Zeng M. D., Fan J. G., Lu L. G., Li Y. M., Chen C. W., Wang B. Y.. **Guidelines for the diagnosis and treatment of nonalcoholic fatty liver diseases**. *J. Dig. Dis.* (2008) **9** 108-112. DOI: 10.1111/j.1751-2980.2008.00331.x 40. Zeng Z. H., Zeng M. H., Huang X. K., Chen R., Yu H.. **Effect of electroacupuncture stimulation of back-shu points on expression of TNF-alpha and lipid peroxidation reaction in the liver tissue in non-alcoholic fatty liver disease rats**. *Zhen Ci Yan Jiu* (2014) **39** 288-292. PMID: 25219124 41. Zhang S. Y., Li L. L., Hu X., Tang H. T.. **Effect of acupuncture on oxidative stress and apoptosis-related proteins in obese mice induced by high-fat diet**. *Zhongguo Zhen Jiu* (2020) **40** 983-988. DOI: 10.13703/j.0255-2930.20190821-0006 42. Zhao D., Cao J., Jin H., Shan Y., Fang J., Liu F.. **Beneficial impacts of fermented celery (Apium graveolens L.) juice on obesity prevention and gut microbiota modulation in high-fat diet fed mice**. *Food Funct.* (2021) **12** 9151-9164. DOI: 10.1039/d1fo00560j 43. Zhou D., Pan Q., Xin F. Z., Zhang R. N., He C. X., Chen G. Y.. **Sodium butyrate attenuates high-fat diet-induced steatohepatitis in mice by improving gut microbiota and gastrointestinal barrier**. *World J. Gastroenterol.* (2017) **23** 60-75. DOI: 10.3748/wjg.v23.i1.60
--- title: Changes of intestinal microbiota in the giant salamander (Andrias davidianus) during growth based on high-throughput sequencing authors: - Mingcheng Cai - Huan Deng - Hanchang Sun - Wantong Si - Xiaoying Li - Jing Hu - Mengjun Huang - Wenqiao Fan journal: Frontiers in Microbiology year: 2023 pmcid: PMC10061097 doi: 10.3389/fmicb.2023.1052824 license: CC BY 4.0 --- # Changes of intestinal microbiota in the giant salamander (Andrias davidianus) during growth based on high-throughput sequencing ## Abstract Despite an increasing appreciation of the importance of host–microbe interaction in healthy growth, information on gut microbiota changes of the Chinese giant salamander (Andrias davidianus) during growth is still lacking. Moreover, it is interesting to identify gut microbial structure for further monitoring A. davidianus health. This study explored the composition and functional characteristics of gut bacteria in different growth periods, including tadpole stage (ADT), gills internalization stage (ADG), 1 year age (ADY), 2 year age (ADE), and 3 year age (ADS), using high-throughput sequencing. The results showed that significant differences were observed in microbial community composition and abundance among different growth groups. The diversity and abundance of intestinal flora gradually reduced from larvae to adult stages. Overall, the gut microbial communities were mainly composed of Fusobacteriota, Firmicutes, Bacteroidota, and Proteobacteria. More specifically, the *Cetobacterium genus* was the most dominant, followed by Lactobacillus and Candidatus Amphibiichlamydia. Interestingly, Candidatus Amphibiichlamydia, a special species related to amphibian diseases, could be a promising indicator for healthy monitoring during A. davidianus growth. These results could be an important reference for future research on the relationship between the host and microbiota and also provide basic data for the artificial feeding of A. davidianus. ## Introduction The Chinese giant salamander (Andrias davidianus), widely distributed in China, is the largest and most primitive urodele amphibian alive worldwide. It has been listed as a national class II protected species in China and the Appendix I of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES), 2008 (Zhang et al., 2003; Jiang et al., 2015; Turvey et al., 2021). With the breakthrough of artificial breeding technology, the A. davidianus aquaculture industry is rapidly developing. Giant salamander aquaculture could not only meet the consumption needs of modern life but also promote economic revitalization in remote mountains, which is listed as a key project for agricultural industrialization. In China, there were ~2 million A. davidianus artificially reproduced each year (Meng et al., 2014). The breeding modes of this species mainly included entire captive, bionic captive, and primordial ecological breeding (Liang, 2007). Under artificial breeding conditions, A. davidianus generally could grow to the adult stage in 2–3 years. However, the morbidity of infectious diseases is increasing due to intensive culture and artificial changes in the living environment. Infectious diseases mainly included bacterial, viral, parasitic, and other diseases, which could cause heavy economic losses (Jiang et al., 2015; Gui et al., 2018). At present, it is difficult to evaluate whether A. davidianus grows healthily and to choose reasonable measures to prevent relative diseases at the early stage. Hence, it is important to understand the health status and select an accurate monitoring index to guide the breeding of A. davidianus. The gut microbiota plays important roles in host metabolism and immunity, making it an effective indicator for monitoring the response of animal organisms to environmental changes (Tilg and Kaser, 2011). The gut microbes are diverse, mainly live in the second half of the digestive tract, and consist of nearly 200 common species and about 1,000 uncommon species (Ley et al., 2008). Factors such as host diets, genetic background, and immune status could influence microbiota composition (Turnbaugh et al., 2009; Benson et al., 2010). The population and abundance of gut microbes in different habitats vary greatly, and the status also is reflected in the giant salamander under different temperatures and ages (Zhang et al., 2018; Zhu et al., 2021). There is a close relationship between intestinal flora and host health (Wu et al., 2019), which mainly reflected that microbes are mainly colonized in the intestinal tract and play key roles in the digestive system (Gerritsen et al., 2011). The complex microbes constitute a microbial community, and the balance contributes to maintaining the host’s gut function, including energy uptake, metabolite production, immune system development, and gastrointestinal diseases (He, 2012). According to statistics, <$1\%$ of microorganisms in nature could be obtained by the traditional media method in vitro. High-throughput sequencing technology is widely used to detect the gut microecosystem considering the complex and culture-independent feature (Turnbaugh et al., 2010; Siezen and Kleerebezem, 2011). However, there are insufficient profiles and changes of gut microbiota in the Chinese giant salamander, which results in a lack of comprehensive understanding of the relationship between the microbiome and host considering diverse species. This study mainly focused on the monitoring indices of A. davidianus during the breeding process. Intestinal microbes of healthy A. davidianus in different growth periods were collected for 16S rRNA sequencing to understand the changes in intestinal microflora from larvae to adult stages. The results contributed to establishing a gut microbiota database for A. davidianus and provided much information for the construction of an accurate monitoring system and standardized artificial breeding of A. davidianus. ## Animals breeding and sample collection A total of 80 healthy A. davidianus were cultured in the Experimental Animal Center at Chongqing University of Arts and Sciences. The growth data of A. davidianus, such as body length and weight, were recorded during whole growth periods. According to the growth characteristics, the guts of A. davidianus were collected from five growth stages, which include tadpole stage (ADT), gills internalization stage (ADG), 1 year age (ADY), 2 year age (ADE), and 3 year age (ADS). Three A. davidianus were randomly selected for euthanasia in each group, and the gut was excised from the abdominal cavity. The separated guts were transferred to a sterilized kraft paper and knotted with cotton rope to decrease the cross-pollution in the different intestinal segments. The tissue samples were then immersed in a $4\%$ paraformaldehyde solution for fixation. The contents at the end of the rectum were immediately collected and stored at −80°C until further high-throughput sequencing analysis. ## Histomorphological observation of guts The tissue samples of the small intestine, large intestine, and rectum collected at different growth stages were sectionized using the conventional paraffin ultra-thin sectioning method (Schmitt et al., 2019). This process mainly includes embedding, sectioning, and HE staining steps. After completion, the morphological changes of intestinal tissue at each stage were observed with a microscope and photographed using a microscopic imaging system. ## DNA extraction To analyze the composition of the bacterial community, genomic DNA was extracted from the aforementioned gut content samples using the CTAB method (Wilson, 2001). DNA extraction operation was quickly performed after these gut samples were fully mixed. Agarose gel ($0.8\%$ w/v) electrophoresis was then performed to evaluate the purity and concentration of the extracted DNA. According to the concentration detected, an appropriate amount of DNA was taken and diluted to 1 ng/μL with sterile water. ## 16S rRNA amplification and sequencing Using diluted genomic DNA as a template, specific primers with barcodes were designed according to the selection of sequencing regions. The 16S rDNA target region (V3/V4) was amplified by PCR with primers 338F: 5′-ACTCCTACGGGAGGCAGCA and 806R: GGACTACHV GGGTWTCTAAT-3′. More detailed parameters of the PCR reaction were performed as described previously (Song et al., 2019). PCR products were detected by electrophoresis with agarose gel ($2\%$ w/v). The PCR products were purified by magnetic beads, quantified by a microplate reader, and then mixed in equal amounts according to concentration. After full mixing, PCR products were detected by electrophoresis with agarose gel ($2\%$ w/v). DNA fragments from the agarose gels were recovered using a QIAquick Gel Extraction Kit. The purified PCR products were used for constructing the sequencing library using TruSeq® DNA PCR-Free Sample Preparation Kit. Prior to sequencing, the sequencing libraries were subjected to quantification using Qubit and Q-PCR. The qualified libraries were subjected to high-throughput sequencing using an Illumina NovaSeq 6000. ## Bioinformatics and statistical analysis The paired-end sequences from high-throughput sequencing were assigned to the corresponding samples according to the primer and barcode information. After amputation of barcode and primer sequences, the reads of each sample were spliced to obtain raw tags using FLASH (version 1.2.7; Magoč and Salzberg, 2011) and were then strictly filtrated to obtain clean tags (Bokulich et al., 2013). The quality of raw tags was evaluated using Quantitative Insights into Microbial Ecology (QIIME) software (version 1.9.1; Caporaso et al., 2010). For raw tag interception, the threshold value of continuous low-quality bases is 19, and the base length is 3. These tags would be filtered when the base length is <$75\%$ of whole tags with continuous high quality. Finally, the effective tags were obtained when raw tags, such as ambiguous bases, chimeras, and mismatched primers in the reads, were filtered by initial quality screening. The obtained high-quality effective tags for all samples were clustered in operational taxonomic units (OTUs) based on $97\%$ identity using the UPARSE algorithm (version 7.0.1001.). Prior to homogenization, the representative sequences for OTUs were annotated and classified based on the Mothur method and SSU rRNA database and were phylogenetically analyzed by MUSCLE (version 3.8.31; Quast et al., 2013). Alpha diversity analysis was performed using QIIME (version 1.9.1) to calculate the values of observed-OTUs, Chao1, Shannon, Simpson, and so on. Beta diversity was used to identify similarities and differences between different samples using the same QIIME software. In addition, rarefaction curves were used to evaluate the rationality of sequencing depth. Linear discriminant analysis effect size (LEfSe) was generated to identify significantly differential biomarkers among groups. Based on the microbiota composition, the functional enrichment of the KEGG pathway was further predicted by PICRUSt2 (version 2.3.0-b; Langille et al., 2013). R software (version 2.15.3) was applied to statistical analysis and plotting. The criterion of significance was conducted at p-values of <0.05, and the data were expressed as means ± SD. ## Growth and histomorphology changes in Andrias davidianus Five sampling time points were chosen, namely, tadpole stage (ADT, 90 days), gills internalization stage (ADG, 180 days), 1 year age (ADY, 365 days), 2 year age (ADE, 730 days), and 3 year age (ADS, 1,095 days). We measured the body weights and lengths of the Chinese giant salamander and collected their gut samples during growth. Average weights of A. davidianus were 5.03 ± 0.21, 65.17 ± 11.28, 175.50 ± 29.86, 1640.07 ± 86.51, and 2450.93 ± 125.61 g for ADT, ADG, ADY, ADE, and ADS, respectively. The total lengths of 80 individuals were 4.13 ± 0.31, 29.10 ± 1.15, 40.87 ± 3.59, 67.63 ± 2.90, and 75.37 ± 3.37 cm for the aforementioned stages. The histological changes of the A. davidianus gut at different growth periods were observed using a microscope after staining the tissue section with hematoxylin–eosin (HE). In the small intestine, both muscular thickness and villi length gradually increased with the increase in age, as shown in ADY, ADE, and ADS stages (Figures 1A–C). Lymphoid follicular accumulation began to appear in the submucosa in the ADS stage (Figure 1D). **Figure 1:** *Morphological changes of the small intestine in Andrias davidianus. (A) The small intestine in ADY. (B) The small intestine in ADE. (C) The small intestine in ADS. (D) Lymphoid follicular cluster of the small intestine in ADS (as shown in red circle). The sections were observed using high power fields of 500-fold magnification with several measurements at different positions in each sample.* Similar changes were observed in the large intestine in the three stages. The number of goblet cells gradually increased in the villous epithelium with the growth of the giant salamander (Figures 2A–C). Gland-like structures were seen in the lamina propria of villi in the ADS sample (Figure 2D). In addition, there were a large number of lymphocytes aggregated in this stage. **Figure 2:** *Morphological changes of the large intestine in A. davidianus. (A) The large intestine in ADY. (B) The large intestine in ADE. (C) The large intestine in ADS. (D) Glandular structures of the large intestine in ADS (as shown in red circle). The sections were observed using high power fields of 500-fold magnification with several measurements at different positions in each sample.* For rectum samples, lymph nodes were observed in the submucosa during the ADG period (Figure 3A). Partial epithelial cell necrosis occurred in the villous epithelium in the ADY stage (Figure 3B). The intestinal adenoid structure was observed in the villi during the ADE stage (Figure 3C). The entire intestinal wall thickens during ADS, and the villi become shorter compared to those in other periods (Figure 3D). **Figure 3:** *Morphological changes of the rectum in A. davidianus. (A) Rectum in ADG. (B) Rectum in ADY. (C) Rectum in ADE. (D) Adenoid structure of rectum in ADE. The sections were observed using high power fields of 500-fold magnification with several measurements at different positions in each sample.* ## Quality assessment and OTU classification of intestinal microbiota We initially performed a quality screening for high-throughput sequencing data of intestinal microbiota to eliminate erroneous and questionable sequences, which contributed to verifying sequence reliability. A total of 2,824,829 high-quality reads were produced from the data with an average of 62,774 reads per sample (40,308–69,489) and an average length of 252–256 bp (Table 1). **Table 1** | Sample name | Raw PE (#) | Clean tags (#) | Effective tags (#) | AvgLen (nt) | Effective % | | --- | --- | --- | --- | --- | --- | | ADT.1 | 101308 | 94073 | 69301 | 255 | 68.41 | | ADT.2 | 100768 | 80775 | 63758 | 254 | 63.27 | | ADT.3 | 107905 | 90226 | 68762 | 254 | 63.72 | | ADT.4 | 80030 | 65983 | 54363 | 256 | 67.93 | | ADT.5 | 76151 | 52868 | 45326 | 256 | 59.52 | | ADT.6 | 60969 | 46926 | 40308 | 254 | 66.11 | | ADT.7 | 82525 | 76883 | 58506 | 254 | 70.89 | | ADT.8 | 100953 | 80845 | 61795 | 255 | 61.21 | | ADT.9 | 111270 | 99848 | 68616 | 254 | 61.67 | | ADG.1 | 98953 | 90992 | 65897 | 253 | 66.59 | | ADG.2 | 106166 | 99808 | 64081 | 253 | 60.36 | | ADG.3 | 101276 | 85942 | 61871 | 253 | 61.09 | | ADG.4 | 99766 | 93940 | 62464 | 253 | 62.61 | | ADG.5 | 99933 | 95332 | 61622 | 253 | 61.66 | | ADG.6 | 100406 | 97713 | 61628 | 253 | 61.38 | | ADG.7 | 104978 | 103333 | 67312 | 253 | 64.12 | | ADG.8 | 60540 | 59720 | 48885 | 253 | 80.75 | | ADG.9 | 99331 | 97963 | 60526 | 253 | 60.93 | | ADY.1 | 101088 | 99812 | 67299 | 253 | 66.57 | | ADY.2 | 106987 | 101732 | 68073 | 253 | 63.63 | | ADY.3 | 98537 | 97225 | 63723 | 253 | 64.67 | | ADY.4 | 106643 | 103450 | 67171 | 253 | 62.99 | | ADY.5 | 108714 | 105960 | 64198 | 253 | 59.05 | | ADY.6 | 99380 | 94419 | 63398 | 253 | 63.79 | | ADY.7 | 102852 | 90903 | 65584 | 253 | 63.77 | | ADY.8 | 97701 | 88630 | 64194 | 253 | 65.7 | | ADY.9 | 80541 | 77103 | 55467 | 253 | 68.87 | | ADE.1 | 95742 | 94082 | 60347 | 252 | 63.03 | | ADE.2 | 100077 | 98453 | 63331 | 252 | 63.28 | | ADE.3 | 101997 | 100245 | 60176 | 252 | 59.0 | | ADE.4 | 108372 | 106630 | 69489 | 252 | 64.12 | | ADE.5 | 100517 | 98780 | 61618 | 252 | 61.3 | | ADE.6 | 108086 | 106241 | 64997 | 252 | 60.13 | | ADE.7 | 106566 | 105123 | 68622 | 252 | 64.39 | | ADE.8 | 103684 | 102285 | 65134 | 252 | 62.82 | | ADE.9 | 104706 | 102917 | 62668 | 252 | 59.85 | | ADS.1 | 100827 | 99503 | 60355 | 252 | 59.86 | | ADS.2 | 104600 | 103237 | 65316 | 252 | 62.44 | | ADS.3 | 98551 | 97027 | 62298 | 252 | 63.21 | | ADS.4 | 104411 | 102606 | 67620 | 252 | 64.76 | | ADS.5 | 98451 | 96720 | 63422 | 252 | 64.42 | | ADS.6 | 111531 | 109772 | 65997 | 252 | 59.17 | | ADS.7 | 101881 | 100197 | 67766 | 252 | 66.51 | | ADS.8 | 113859 | 112026 | 69235 | 252 | 60.81 | | ADS.9 | 99731 | 98082 | 62310 | 252 | 62.48 | The qualified reads were composed of 5,772, 1,033, 1,066, 479, and 393 OTUs in ADT, ADG, ADY, ADE, and ADS based on $97\%$ nucleotide-sequence identity, respectively (Figure 4A). The curves of rarefaction and rank abundance per sample were relatively flat and displayed saturate tendency, which suggested that the depth and evenness of sequences meet the requirements for sequencing and further analysis (Figures 4B–D). **Figure 4:** *Venn diagrams and feasibility analysis. (A) Venn diagrams of the distribution of OTUs in five groups. (B) Rank abundance curves for groups. (C) Rank abundance curves for samples. (D) Rarefaction curves for samples.* ## Analysis of microbial community diversity The alpha diversity of gut microbiota samples showed the goods coverage estimates varied from $99.3\%$ to $99.9\%$ for all samples, which indicated excellent coverage (Table 2). The average Chao1 indices for experimental groups ADT, ADG, ADY, ADE, and ADS were 1594.56, 488.91, 512.81, 354.01, and 262.45, and the corresponding ACE indices were 1589.90, 492.12, 517.26, 350.47, and 264.35, respectively. Moreover, the averages of Shannon indices for these five groups were 7.43, 5.60, 5.55, 4.58, and 3.03, respectively. The Chao1, ACE, Shannon, and Simpson indices for these five groups displayed gradually downward trends, which indicated that the abundance and diversity of the intestinal microbial community reduced as growth. Remarkably, the three diversity indices (ACE, Chao1, and Shannon) of the initial ADT group were much higher than those of other groups. In contrast, significant differences in gut microbiota abundance and diversity were observed between the ADT and other groups. The α-diversity indices revealed a significant difference in the diversity and richness of gut microbiota among different growth groups. Both the weighted and the unweighted principal coordinate analysis (PCoA) plots revealed that the samples in most groups were clustered separately except for some similarity between ADG and ADY, which indicated that the differences existed in gut microbiota for most of the comparative samples (Figure 5). ## Bacterial community composition in groups Our results showed that the bacterial community comprised 66 phyla, 175 classes, 373 orders, 536 families, 894 genera, and 418 species. The Fusobacteriota, Firmicutes, Bacteroidota, Proteobacteria, Chlamydiae, Desulfobacterota, Verrucomicrobiota, unidentified_Bacteria, Kapabacteria, and Actinobacteriota were the top 10 phyla for all samples. These phyla constituted the core of the microbiota and accounted for $85.4\%$–$99.3\%$ of the taxonomic groups identified. Especially, the Firmicutes represented $59.04\%$ of the totals in ADE, and the Fusobacteriota accounted for $57.53\%$ in the ADS group, respectively. The ADT groups were primarily composed of Firmicutes ($24.53\%$), Bacteroidota ($21.52\%$), and Proteobacteria ($17.52\%$). The Fusobacteriota, Firmicutes, and Bacteroidota were dominant phyla for the ADG and ADY groups, representing $19.60\%$, $28.19\%$, and $26.87\%$ of the totals for the ADG group and $24.18\%$, $33.21\%$, and $18.60\%$ for the ADY group. The dominant phyla for ADE and ADS groups were Fusobacteriota ($13.73\%$ and $57.53\%$), Firmicutes ($59.04\%$ and $19.02\%$), and Proteobacteria ($14.46\%$ and $20.27\%$; Figure 6A). **Figure 6:** *Relative abundance of the gut microbiota of A. davidianus. (A) Microbial community bar plot of phyla. (B) Microbial community bar plot of genera.* The top 10 genera were Cetobacterium, Lactobacillus, Candidatus Amphibiichlamydia, Hydrogenoanaerobacterium, Akkermansia, Methylobacterium–Methylorubrum, Bacteroides, Desulfovibrio, Bilophila, and Parabacteroides, which accounted for $17.53\%$–$57.96\%$ of the taxonomic groups identified (Figure 6B). The genus Cetobacterium had a significantly higher abundance than other genera, which comprised $3.93\%$, $19.49\%$, $24.13\%$, $13.72\%$, and $57.52\%$ of the overall bacterial composition in ADT, ADG, ADY, ADE, and ADS groups, respectively. The levels of this genus in the ADS group were significantly higher than in the other groups. Bacteroides was the second most abundant at $2.90\%$, $3.59\%$, $6.13\%$, $2.02\%$, and $0.20\%$ for ADT, ADG, ADY, ADE, and ADS groups, respectively. The other dominant genera were Hydrogenoanaerobacterium and Candidatus Amphibiichlamydia, which represented $5.57\%$ (ADG) and $5.41\%$ (ADY) of the overall bacterial composition, respectively. In the ADS group, the most numerous genus was Cetobacterium at $57.52\%$, whereas other genera except these top 10 were observed to be predominant for other groups at $51.77\%$–$82.47\%$ of the overall bacterial composition, respectively. The relative abundance of these bacteria was also displayed through a heatmap produced by clustering analysis. The distribution of bacterial genera in each sample could also be observed in the heatmap (Supplementary Figure S1). ## Microbial profile and core microbiota of intestinal microflora Analysis of similarities (ANOSIM) was used to evaluate whether differences between groups (two or more groups) were significantly greater than those within groups (Table 3). The results showed significant and remarkable differences among different groups (R > 0, $$p \leq 0.001$$), except the ADG–ADY ($$p \leq 0.127$$). Although there was no significant difference, the R-values for the ADG–ADY comparison were greater than zero (0.106), indicating potential differences between the ADG and ADY groups. **Table 3** | Group | R-value | Value of p | | --- | --- | --- | | ADG-ADY | 0.106 | 0.127 | | ADS-ADY | 0.8059 | 0.001 | | ADS-ADG | 0.9702 | 0.001 | | ADT-ADY | 0.4043 | 0.001 | | ADT-ADG | 0.3707 | 0.001 | | ADT-ADS | 0.6934 | 0.001 | | ADE-ADY | 0.8323 | 0.001 | | ADE-ADG | 0.8803 | 0.001 | | ADE-ADS | 1.0 | 0.001 | | ADE-ADT | 0.6636 | 0.001 | To understand the ecosystem of five samples, linear discriminant analysis effect size (LEfSe) analysis was used to uncover the complex system of microbial communities. Meanwhile, linear discriminant analysis was used to identify the differences in all samples (LDA threshold is 4; Figure 7). The results illustrated that there were 43 bacterial clades, consisting of three classes, 15 orders, and 18 families, which were crucial bacterial branches distinguishing giant salamander samples. According to Figure 7A, Lactobacillus (4.4) and Sediminibacterium (4.1) were significantly enriched in ADT. Hydrogenoanaerobacterium (4.5), Akkermansia (4.4), Parabacteroides (4.2), and Bilophila (4.2) were significantly enriched bacterium in ADG. In ADY, three realms showed significant enrichment, which were Candidatus Amphibiichlamydia (4.5), Bacteroides (4.5), and Desulfovibrio (4.4), respectively. Hafnia Obesumbacterium (4.5) and Alistipes (4.0) were significant in ADE. More bacteria had significant abundance in ADS, such as Cetobacterium (5.4) and Clostridium-sensu-stricto-1 (4.3). In the cladogram, these circles represented different taxonomic levels from phylum to genus, and each small circle represented a classification at that level (Figure 7B). The relative abundance of microbes is proportional to the diameter size of the circle. The significant differences were marked with different colors consistent with corresponding levels except for the yellow which represented no significant difference. These differential microbial groups that play important roles were visually displayed at different levels in the cladogram. **Figure 7:** *Beta diversity of microbial communities in guts of A. davidianus at different growth stages. (A) Histogram of linear discriminatory analyses (LDA) score distribution. (B) Cladogram of LDA effect size (LEfSe) of microbial taxa (LDA > 4).* ## Function prediction The Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) bioinformatics software package was used to predict the metagenomic function of marker genes based on the KEGG database. As shown in Figure 8, the genes obtained from 16S rRNA sequencing were mainly enriched into 41 KEGG pathways and were classified into seven categories. There were 4, 3, 4, 6, 12, 8, and 4 pathways involved in cellular processes, environmental information processing, genetic information processing, human diseases, metabolism, organismal systems, and unclassified categories, respectively. The membrane transport pathway had the most annotated genes including 8,313,360 genes, which belong to environmental information processing. Moreover, the metabolism category had the most pathways and relatively abundant genes, such as carbohydrate metabolism, amino acid metabolism, and energy metabolism had 73,944,375, 66,615,423, and 40,419,909 genes, respectively. Other enriched pathways, such as replication and repair [55,545,160] and poorly characterized [35,422,177], belonged to genetic information processing and unclassified categories, respectively. **Figure 8:** *Heatmap of the KEGG pathways annotated by PICRUSt.* ## Discussion The intestinal tract is the most important digestive and absorption organ of animals. A large number of bacteria were colonized in the intestines and played indispensable roles in maintaining the overall health of the host (Singh et al., 2017). In our histomorphological results, numerous wrinkles in the lining and gradual increases in villi length were observed in A. davidianus intestine during growth periods, which contributed to the colonization of microorganisms. The intestinal floras were different among different individuals or the same one in different conditions, which were related to ages, diets, health status, and ranging profile (Korpela et al., 2021; Liu et al., 2021; Spencer et al., 2021; Sztandarski et al., 2022). High-throughput sequencing was used to investigate the gut and lung prokaryotic community profiles of adult Chinese giant salamanders at age 3 (Wu et al., 2019). In addition, the previous gut microbiota report of A. davidianus provided much microbial information related to age changes from age 1 to 4 (Zhang et al., 2018). However, studies on earlier ages, histomorphological observation, developmental effects, and disease-related pathogen identification are still lacking. These intestinal microbes are comprised of both beneficial and harmful members. The balance maintenance of intestinal flora and an increase in probiotics proportion could effectively keep the host healthy (Aziz et al., 2013). Therefore, the acquisition of composition and abundance of intestinal flora is well helpful to disease prevention and treatment (Feng et al., 2018). Gut microbiota plays an important role in the growth and development of host animals with huge abundance and complex structures. We found that the alpha diversity indices, such as OTU number, Shannon, Simpson, Chao1, and ACE, were decreased as A. davidianus grew, which indicated the species diversity and abundance of intestinal flora reduced. These values in the ADT stage were significantly higher than in other periods, which may be deduced that the number of beneficial bacteria increased while conditional pathogens were reduced. The phenomena of lymphoid follicles accumulation and adenoid structure that existed in submucosa first observed in ADG also contributed to the elimination of harmful microbes in guts. A previous study also revealed that phylogeny plays the most important role in the formation of microbial communities, rather than food and environment (Bai et al., 2021). Previous studies verified that aging is a multifactorial process and would influence many principal physiological systems, including the gastrointestinal system (Mabbott et al., 2015). In addition, histomorphological changes in the gut at different ages or stages were found in mice (Yang et al., 2019). In humans, both the composition and stability of gut microbiota were reported to change with age (Li et al., 2016). However, the relationship between gut microbiota and gut histomorphological needs further studies. The proportion of Fusobacteriota in the ADS group was significantly higher than in other groups, and there was a gradually increased tendency during growth periods except for ADE. More detailed analysis showed that Cetobacterium was the dominant genus in this phylum. On the contrary, the levels of Bacteroidota, unidentified_Bacteria, and Kapabacteria displayed a gradual decline. Fusobacteriota, Firmicutes, and Actinobacteriota are the dominant intestinal phyla for all animals. These similarities suggested that these microorganisms are important participants of host functions, such as normal digestion, absorption, and immune responses. Cetobacterium, an anaerobic bacteria belonging to the core microbiota of fish gut, was also the most dominant genus in A. davidianus. Lactobacillus, known as beneficial bacteria, was common in the gastrointestinal tract of most aquatic animals (Liu et al., 2021). They could convert large amounts of hexose substrates into pyruvate and then generate the final lactate via the glycolytic pathway (Dempsey and Corr, 2022). More importantly, the abundance of Candidatus *Amphibiichlamydia genus* in early growth periods (ADT, ADG, and ADY) was much higher than in later phases (ADE and ADS). We speculated that it may be the result of A. davidianus development, as these salamanders develop, their small intestine increases in complexity, and the wrinkles and partmentalization may play key roles in the abundance changes of Candidatus Amphibiichlamydia. This species has attracted much attention as a special pathogen for amphibian diseases (Martel et al., 2013), which may be a promising potential marker for the prediction of A. davidianus diseases. Although none of the A. davidianus tadpoles showed signs of clinical disease, more experiments related to pathogenic conditions need to be performed considering a high prevalence of $71\%$ in bullfrogs (Lithobates catesbeianus). In beta diversity, the result of PCoA showed that there was a very similar species composition between ADG and ADY, which indicated a closer relationship in the two samples compared to others. This result was also verified by statistical analysis of ANOSIM and LEfSe, further indicating the differences of intergroup were greater than intragroup. These results were consistent with previous high-throughput sequencing results in zebrafish gut microbiota (Xiao et al., 2021). Host development overwhelmed environmental effects in governing fish gut microbial community succession from larvae to adult fish stages due to host genetics, immunology, and gut nutrient niches. This study is another example of reduced abundance and diversity of the intestinal microbial community as growth. Maintaining a healthy gut is key to disease prevention in animals. Metabolic activities of microorganisms would generate many important nutrients, such as short-chain fatty acids, vitamins, and amino acids, which would affect host health. The succinate and secondary bile acids produced by *Parabacteroides distasonis* played key roles in the modulation of host metabolism, and disturbance of intestinal flora was closely related to the occurrence of obesity, diabetes, and hyperlipidemia (Wang et al., 2019). The enriched KEGG pathways of annotated genes were also oriented to multiple functions of intestinal microorganisms in the giant salamander. Different growth and developed stages could have differential intestinal structures, which may influence intestine flora. The intestinal flora, in turn, also could affect the intestinal development and immune function of A. davidianus. Keeping the balance of intestinal flora would effectively help the host maintain health status. ## Conclusion This study investigated the changes in the intestinal microbial community in A. davidianus from larvae to adult stages. The results revealed that the diversity and abundance of gut microbiota had significant alterations that were characterized by declining levels of growth. Most of the top 10 phyla and genera had significant differences among different groups, except for a similar microbial community in ADG–ADY. *These* genes annotated in intestinal microbes were mainly enriched into KEGG pathways including cellular processes and environmental information processing, which played important roles in growth metabolism, nutrient absorption, and immune regulation. In addition, Candidatus Amphibiichlamydia, a special species for amphibians, was a promising potential indicator of gut microbiota stability. These findings expanded our current understanding of the succession of gut microbiota across A. davidianus growth and also provided new insights into the breeding monitoring of other aquatic animals. ## Data availability statement The data presented in the study are deposited in the NCBI repository, accession number PRJNA903076. The data is publicly available with accession number of PRJNA903076 and website: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA903076. ## Ethics statement The animal study was reviewed and approved by the Ethics Committee of Chongqing University of Arts and Sciences. ## Author contributions MC and HD carried out biochemical assays, performed 16S rRNA amplicon analysis, and drafted and revised the manuscript. HS, MH, and WF participated in the design of the study, analyzed the data, and revised the manuscript. XL, WS, and JH conceived the study and revised the manuscript. All authors contributed to the article and approved the submitted version. ## Funding This study was supported by the Science and Technology Research Project of Chongqing Municipal Education Commission (grant no. KJQN201801301), the Foundation and Advanced Research Project of Chongqing Science and Technology Commission (grant nos. cstc2020jscx-msxmX0055 and cstc2019jscx-gksbX0147), and Chongqing Talents Programme (grant no. CQYC20200309221). ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2023.1052824/full#supplementary-material ## References 1. Aziz Q., Doré J., Emmanuel A., Guarner F., Quigley E. M.. **Gut microbiota and gastrointestinal health: current concepts and future directions**. *Neurogastroenterol. Motil.* (2013) **25** 4-15. DOI: 10.1111/nmo.12046 2. Bai S., Zhang P., Zhang C., Du J., Du X., Zhu C.. **Comparative study of the gut microbiota among four different marine mammals in an aquarium**. *Front. Microbiol.* (2021) **12** 769012. DOI: 10.3389/fmicb.2021.769012 3. Benson A. K., Kelly S. A., Legge R., Ma F., Low S. J., Kim J.. **Individuality in gut microbiota composition is a complex polygenic trait shaped by multiple environmental and host genetic factors**. *Proc. Natl. Acad. Sci. U. S. A.* (2010) **107** 18933-18938. DOI: 10.1073/pnas.1007028107 4. Bokulich N. A., Subramanian S., Faith J. J., Gevers D., Gordon J. I., Knight R.. **Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing**. *Nat. Methods* (2013) **10** 57-59. DOI: 10.1038/nmeth.2276 5. Caporaso J. G., Kuczynski J., Stombaugh J., Bittinger K., Bushman F. D., Costello E. K.. **QIIME allows analysis of high-throughput community sequencing data**. *Nat. Methods* (2010) **7** 335-336. DOI: 10.1038/nmeth.f.303 6. Dempsey E., Corr S. C.. **Lactobacillus spp. for gastrointestinal health: current and future perspectives**. *Front. Immunol.* (2022) **13** 840245. DOI: 10.3389/fimmu.2022.840245 7. Feng W., Ao H., Peng C.. **Gut microbiota, short-chain fatty acids, and herbal medicines**. *Front. Pharmacol.* (2018) **9** 1354. DOI: 10.3389/fphar.2018.01354 8. Gerritsen J., Smidt H., Rijkers G. T., de Vos W. M.. **Intestinal microbiota in human health and disease: the impact of probiotics**. *Genes Nutr.* (2011) **6** 209-240. DOI: 10.1007/s12263-011-0229-7 9. Gui L., Chinchar V. G., Zhang Q.. **Molecular basis of pathogenesis of emerging viruses infecting aquatic animals**. *Aquacult. Fisheries* (2018) **3** 1-5. DOI: 10.1016/j.aaf.2017.12.003 10. He G. Z.. **Entamoeba histolytica: cloning, expression and evaluation of the efficacy of a recombinant amebiasis cysteine proteinase gene (ACP1) antigen in minipig**. *Exp. Parasitol.* (2012) **130** 126-129. DOI: 10.1016/j.exppara.2011.11.007 11. Jiang N., Fan Y., Zhou Y., Liu W., Ma J., Meng Y.. **Characterization of Chinese giant salamander iridovirus tissue tropism and inflammatory response after infection**. *Dis. Aquat. Org.* (2015) **114** 229-237. DOI: 10.3354/dao02868 12. Korpela K., Kallio S., Salonen A., Hero M., Kukkonen A. K., Miettinen P. J.. **Gut microbiota develop towards an adult profile in a sex-specific manner during puberty**. *Sci. Rep.* (2021) **11** 23297. DOI: 10.1038/s41598-021-02375-z 13. Langille M. G. I., Zaneveld J., Caporaso J. G., McDonald D., Knights D., Reyes J. A.. **Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences**. *Nat. Biotechnol.* (2013) **31** 814-821. DOI: 10.1038/nbt.2676 14. Ley R. E., Hamady M., Lozupone C., Turnbaugh P. J., Ramey R. R., Bircher J. S.. **Evolution of mammals and their gut microbes**. *Science* (2008) **320** 1647-1651. DOI: 10.1126/science.1155725 15. Li H., Qi Y., Jasper H.. **Preventing age-related decline of gut compartmentalization limits microbiota Dysbiosis and extends lifespan**. *Cell Host Microbe* (2016) **19** 240-253. DOI: 10.1016/j.chom.2016.01.008 16. Liang G.. **Chinese Giant salamander captive breeding models in Shaanxi Province and primary assessment**. *J. Econ. Anim.* (2007) **11** 234-237. DOI: 10.13326/j.jea.2007.04.015 17. Liu C., Zhao L. P., Shen Y. Q.. **A systematic review of advances in intestinal microflora of fish**. *Fish Physiol. Biochem.* (2021) **47** 2041-2053. DOI: 10.1007/s10695-021-01027-3 18. Mabbott N. A., Kobayashi A., Sehgal A., Bradford B. M., Pattison M., Donaldson D. S.. **Aging and the mucosal immune system in the intestine**. *Biogerontology* (2015) **16** 133-145. DOI: 10.1007/s10522-014-9498-z 19. Magoč T., Salzberg S. L.. **FLASH: fast length adjustment of short reads to improve genome assemblies**. *Bioinformatics* (2011) **27** 2957-2963. DOI: 10.1093/bioinformatics/btr507 20. Martel A., Adriaensen C., Sharifian-Fard M., Vandewoestyne M., Deforce D., Favoreel H.. **The novel 'Candidatus Amphibiichlamydia ranarum' is highly prevalent in invasive exotic bullfrogs (**. *Environ. Microbiol. Rep.* (2013) **5** 105-108. DOI: 10.1111/j.1758-2229.2012.00359.x 21. Meng Y., Ma J., Jiang N., Zeng L. B., Xiao H. B.. **Pathological and microbiological findings from mortality of the Chinese giant salamander (**. *Arch. Virol.* (2014) **159** 1403-1412. DOI: 10.1007/s00705-013-1962-6 22. Quast C., Pruesse E., Yilmaz P., Gerken J., Schweer T., Yarza P.. **The SILVA ribosomal RNA gene database project: improved data processing and web-based tools**. *Nucleic Acids Res.* (2013) **41** D590-D596. DOI: 10.1093/nar/gks1219 23. Schmitt V. H., Schmitt C., Hollemann D., Weinheimer O., Mamilos A., Kirkpatrick C. J.. **Tissue expansion of lung bronchi due to tissue processing for histology – a comparative analysis of paraffin versus frozen sections in a pig model**. *Pathol. Res. Pract.* (2019) **215** 152396. DOI: 10.1016/j.prp.2019.03.024 24. Siezen R. J., Kleerebezem M.. **The human gut microbiome: are we our enterotypes?**. *Microb. Biotechnol.* (2011) **4** 550-553. DOI: 10.1111/j.1751-7915.2011.00290.x 25. Singh R. K., Chang H. W., Yan D., Lee K. M., Ucmak D., Wong K.. **Influence of diet on the gut microbiome and implications for human health**. *J. Transl. Med.* (2017) **15** 73. DOI: 10.1186/s12967-017-1175-y 26. Song M., Zeng J., Jia T., Gao H., Zhang R., Jiang J.. **Effects of sialylated lactulose on the mouse intestinal microbiome using Illumina high-throughput sequencing**. *Appl. Microbiol. Biot.* (2019) **103** 9067-9076. DOI: 10.1007/s00253-019-10169-7 27. Spencer C. N., McQuade J. L., Gopalakrishnan V., McCulloch J. A., Vetizou M., Cogdill A. P.. **Dietary fiber and probiotics influence the gut microbiome and melanoma immunotherapy response**. *Science* (2021) **374** 1632-1640. DOI: 10.1126/science.aaz7015 28. Sztandarski P., Marchewka J., Konieczka P., Zdanowska-Sąsiadek Ż., Damaziak K., Riber A. B.. **Gut microbiota activity in chickens from two genetic lines and with outdoor-preferring, moderate-preferring, and indoor-preferring ranging profiles**. *Poultry Sci.* (2022) **101** 102039. DOI: 10.1016/j.psj.2022.102039 29. Tilg H., Kaser A.. **Gut microbiome, obesity, and metabolic dysfunction**. *J. Clin. Invest.* (2011) **121** 2126-2132. DOI: 10.1172/jci58109 30. Turnbaugh P. J., Quince C., Faith J. J., McHardy A. C., Yatsunenko T., Niazi F.. **Organismal, genetic, and transcriptional variation in the deeply sequenced gut microbiomes of identical twins**. *Proc. Natl. Acad. Sci. U. S. A.* (2010) **107** 7503-7508. DOI: 10.1073/pnas.1002355107 31. Turnbaugh P. J., Ridaura V. K., Faith J. J., Rey F. E., Knight R., Gordon J. I.. **The effect of diet on the human gut microbiome: a metagenomic analysis in humanized gnotobiotic mice**. *Sci. Transl. Med.* (2009) **1** 6ra14. DOI: 10.1126/scitranslmed.3000322 32. Turvey S. T., Chen S., Tapley B., Liang Z., Wei G., Yang J.. **From dirty to delicacy? Changing exploitation in China threatens the world's largest amphibians**. *People Nat.* (2021) **3** 446-456. DOI: 10.1002/pan3.10185 33. Wang K., Liao M., Zhou N., Bao L., Ma K., Zheng Z.. **Parabacteroides distasonis alleviates obesity and metabolic dysfunctions via production of succinate and secondary bile acids**. *Cell Rep.* (2019) **26** 222-235.e5. DOI: 10.1016/j.celrep.2018.12.028 34. Wilson K.. **Preparation of genomic DNA from bacteria**. *Curr. Protoc. Mol. Biol.* (2001) **56** 2.4.1-2.4.5. DOI: 10.1002/0471142727.mb0204s56 35. Wu Z., Gatesoupe F.-J., Zhang Q., Wang X., Feng Y., Wang S.. **High-throughput sequencing reveals the gut and lung prokaryotic community profiles of the Chinese giant salamander (Andrias davidianus)**. *Mol. Biol. Rep.* (2019) **46** 5143-5154. DOI: 10.1007/s11033-019-04972-8 36. Xiao F., Zhu W., Yu Y., He Z., Wu B., Wang C.. **Host development overwhelms environmental dispersal in governing the ecological succession of zebrafish gut microbiota**. *NPJ Biofilms Microbi.* (2021) **7** 5. DOI: 10.1038/s41522-020-00176-2 37. Yang M., Liu Y., Xie H., Wen Z., Zhang Y., Wu C.. **Gut microbiota composition and structure of the Ob/Ob and Db/Db mice**. *Int. J. Endocrinol.* (2019) **2019** 1394097-1394099. DOI: 10.1155/2019/1394097 38. Zhang P., Chen Y. Q., Liu Y. F., Zhou H., Qu L. H.. **The complete mitochondrial genome of the Chinese giant salamander, Andrias davidianus (Amphibia: Caudata)**. *Gene* (2003) **311** 93-98. DOI: 10.1016/s0378-1119(03)00560-2 39. Zhang M., Gaughan S., Chang Q., Chen H., Lu G., Wang X.. **Age-related changes in the gut microbiota of the Chinese giant salamander (Andrias davidianus)**. *Microbiology* (2018) **8** e778. DOI: 10.1002/mbo3.778 40. Zhu L., Zhu W., Zhao T., Chen H., Zhao C., Xu L.. **Environmental temperatures affect the gastrointestinal microbes of the Chinese Giant salamander**. *Front. Microbiol.* (2021) **12** 543767. DOI: 10.3389/fmicb.2021.543767
--- title: 'Impacts of short-term air pollution exposure on appendicitis admissions: Evidence from one of the most polluted cities in mainland China' authors: - Yanhu Ji - Xuefeng Su - Fengying Zhang - Zepeng Huang - Xiaowei Zhang - Yueliang Chen - Ziyi Song - Liping Li journal: Frontiers in Public Health year: 2023 pmcid: PMC10061118 doi: 10.3389/fpubh.2023.1144310 license: CC BY 4.0 --- # Impacts of short-term air pollution exposure on appendicitis admissions: Evidence from one of the most polluted cities in mainland China ## Abstract ### Background Emerging evidence indicates that air pollutants contribute to the development and progression of gastrointestinal diseases. However, there is scarce evidence of an association with appendicitis in mainland China. ### Methods In this study, Linfen city, one of the most polluted cities in mainland China, was selected as the study site to explore whether air pollutants could affect appendicitis admissions and to identify susceptible populations. Daily data on appendicitis admissions and three principal air pollutants, including inhalable particulate matter (PM10), nitrogen dioxide (NO2), and sulfur dioxide (SO2) were collected in Linfen, China. The impacts of air pollutants on appendicitis were studied by using a generalized additive model (GAM) combined with the quasi-Poisson function. Stratified analyses were also performed by sex, age, and season. ### Results We observed a positive association between air pollution and appendicitis admissions. For a 10 μg/m3 increase in pollutants at lag01, the corresponding relative risks (RRs) and $95\%$ confidence intervals ($95\%$ CIs) were 1.0179 (1.0129–1.0230) for PM10, 1.0236 (1.0184–1.0288) for SO2, and 1.0979 (1.0704–1.1262) for NO2. Males and people aged 21–39 years were more susceptible to air pollutants. Regarding seasons, the effects seemed to be stronger during the cold season, but there was no statistically significant difference between the seasonal groups. ### Conclusions Our findings indicated that short-term air pollution exposure was significantly correlated with appendicitis admissions, and active air pollution interventions should be implemented to reduce appendicitis hospitalizations, especially for males and people aged 21–39 years. ## Introduction Appendicitis is an inflammation caused by a blockage of the cavity of the appendix tube for various reasons or a secondary bacterial infection [1, 2]. Currently, the standard treatment for appendicitis is appendectomy, but the incidence of complications among patients is 5–$28\%$ [3, 4]. In the twenty-first century, the pooled incidence of appendicitis worldwide ranges from 100 to 151 cases per 100 thousand person-years [5]. In the United States, 1 in 15 people suffers from appendicitis, and appendicitis-related hospitalizations cost an average of $3 billion a year [6, 7]. In China, appendicitis was one of the top five most economically burdensome diseases in 2013 [8], and the incidence of this condition has increased [7]. Given the increasing incidence and financial burden of appendicitis, identifying the risk factors associated with this illness is of great importance. Air pollution seriously affects human health and constitutes a serious global public health problem. It has been reported that $90\%$ of the global population lives in areas where air pollution levels exceed World Health Organization (WHO) guidelines, causing ~7 million deaths each year [9]. A substantial number of epidemiological studies have reported that air pollution exposure is correlated with mortality and cardiovascular, respiratory and psychiatric diseases [10, 11], but few studies have examined its relationship with appendicitis. Experimental studies have shown that air pollutants can change intestinal immunity, increase intestinal permeability and affect intestinal microbial composition (12–14), which may be related to the occurrence and development of appendicitis. Moreover, the associations observed between air pollutants and appendicitis admissions have been inconsistent in published studies. A case-crossover study conducted by Kaplan et al. in Calgary, Canada, reported that exposure to ozone (O3) and NO2 in summer was the primary risk factor for appendicitis admission [15]. Subsequently, Kaplan et al. conducted a survey in 12 Canadian cities and found that the daily average maximum O3 level was significantly associated with perforated appendicitis admissions [16]. In Taiwan, adverse effects of air pollutants (O3, NO2, and PM10) on daily appendicitis hospitalizations were also observed on cool days [17]. However, other studies found no relationship with appendicitis admissions [18, 19]. Therefore, it is necessary to conduct more studies in different regions to further clarify the association between air pollutants and appendicitis admissions. Moreover, although the impacts of air pollutants on appendicitis have attracted increasing attention (15–18, 20), no such studies have been conducted in mainland China. With the intensification of vehicle exhaust emissions and rapid urbanization and industrialization, most of China's inland cities have faced serious air pollution situations. In addition, the concentration and composition of air pollutants vary considerably between different countries and regions [10, 21, 22], and the results of studies on the adverse effects of pollutants on appendicitis may not have been fully understood. In this study, Linfen city, a heavily polluted city in mainland China, was selected to examine the impacts of air pollutants on appendicitis admissions and to identify susceptible populations. ## Study area Linfen (35°23′~36°57′ N, 110°22′~112°34′ E) is in southwestern Shanxi Province, China, and has a temperate continental climate (Figure 1). By 2021, Linfen city included 1 municipal district, 14 county seats and 2 county-level cities, with a total area of 20,302 square kilometers. According to national air quality data from December 2018 and January–December 2018 released by the Ministry of Ecology and Environment of China, Linfen ranked last among 169 key cities in China, making it the city with the worst air quality in China (https://www.mee.gov.cn/). Linfen's severe air pollution comes primarily from coal mining, vehicle emissions and industrial pollution [23]. In addition, *Linfen is* in a basin surrounded by mountains, where pollutants gather above the city, and special topographical features further exacerbate its air pollution levels. **Figure 1:** *Geographical location of Linfen.* ## Data collection Data on daily appendicitis admissions were collected from Linfen People's Hospital, which has 1,800 beds and performs an average of nearly 44,000 operations a year. As the largest comprehensive grade A hospital in Linfen city, it attracts the most patients with appendicitis to be hospitalized for treatment. We collected records from the hospital information system for all appendicitis admissions from January 1, 2016 to December 31, 2018. Data variables collected included date of admission, sex, age and home address. Appendicitis was coded and classified by experienced professionals according to the 10th revision of the International Classification of Diseases (ICD-10 codes K35.9, K35.0, and K35.1) [15]. Cases with ICD-10 codes of unspecified, chronic and recurrent appendicitis were all excluded. Since air quality monitoring stations are only located in urban areas, we further excluded appendicitis cases of people who lived outside the urban area of Linfen based on their home addresses. Daily air pollution concentrations (NO2, SO2, and PM10) during the same period were gathered from the Linfen Ecological Environment Bureau, which has seven national air quality monitoring stations. Daily averages from the seven monitoring stations were employed as a proxy for general air pollution levels. Meteorological data, including daily average temperature and relative humidity, were provided by the China Meteorological Data Service Network. ## Statistical analysis We summarized the daily appendicitis hospitalizations and environmental variable data by date to form a time series dataset. Data characteristics are described as the means ± standard deviations (SDs) and quartiles. The correlation coefficients between environmental variables were estimated by Spearman correlation analysis. The data on daily appendicitis admissions were qualitative data, and the pattern generally followed an overdispersed Poisson distribution [24]. Therefore, a generalized additive model (GAM) with the quasi-Poisson function was used to evaluate the impacts of ambient air pollution on appendicitis admissions. Based on previously published studies and the minimum Akaike information criterion, a natural spline (ns) function with 7 degrees of freedom (df) per year was used to adjust for the long-term and seasonal trends of calendar days [25, 26]. The potential confounding impacts of mean temperature (MT) and relative humidity (RH) were both adjusted by 3 df [27, 28]. The day of the week effect (categorical variable) and holiday effect (dummy variable) were also adjusted for in the basic model. The fitted final model is as follows (Equation 1): The variables are explained as follows: *After a* core model that contained all the adjusted variables was created, air pollutants were added to the model separately. Studies have shown that the cumulative effect in the single-pollutant model may be underestimated. Therefore, we not only evaluated the single-day lag of lag0-lag5 but also applied the multiday moving average lag of lag01-lag05 to investigate the impacts of pollutants on appendicitis admissions [27, 29]. Further stratified analyses were performed to investigate the modification effects of sex, age (≤ 20, 21–39, and ≥40) [16] and season (warm and cold) [11]. Differences between groups were examined by the following formula [30] (Equation 2). In the formula, Q1 and Q2 are the estimates of the subgroups. SE1 and SE2 represent their respective standard errors. For example, when we performed a sex-stratified analysis, Q1 and SE1 indicated the estimated values and standard errors for males, respectively, while Q2 and SE2 were the corresponding values for females. The robustness of the model was tested using several sensitivity analyses. First, we constructed two- and three-pollutant models to assess the confounding effects. Second, we varied the 6–9 df for temporal trends. Third, we also changed the df (3–5) for the two meteorological factors. The R software (4.2.1) was used for all statistical analyses in this study. When the contaminant concentration increased by 10 μg/m3, the corresponding RR and $95\%$ CI of appendicitis hospitalizations were expressed as the results. ## Results Table 1 summarizes the descriptive characteristics of appendicitis admissions and environmental variables. In this study, 1,427 hospitalizations for appendicitis were included. Among these cases, $82.9\%$ (1,183 cases) were males and $77.8\%$ (1,110 cases) were 21–39 years old. Regarding air pollutants, the daily average concentrations were 117.42 μg/m3 (ranging from 16 to 658 μg/m3) for PM10, 67.62 μg/m3 (ranging from 4 to 858 μg/m3) for SO2 and 36.04 μg/m3 (ranging from 6 to 124 μg/m3) for NO2. Additionally, the daily average temperature and relative humidity were 14.87°C and $52.58\%$, respectively. The time series plots of pollutants are displayed in Supplementary Figure 1. The concentrations of air pollutants reached their peak in winter but showed a yearly downward trend. **Table 1** | Variables | Sum | Mean (SD) | Min | P10 | P25 | P75 | P90 | Max | | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Appendicitis cases | 1427 | 1.30 (0.95) | 0 | 0.0 | 1.0 | 2.0 | 3.0 | 5.0 | | Male | 1183 | 1.08 (0.96) | 0 | 0.0 | 0.0 | 2.0 | 2.0 | 5.0 | | Female | 244 | 0.22 (0.47) | 0 | 0.0 | 0.0 | 0.0 | 1.0 | 3.0 | | ≤ 20 years | 174 | 0.15 (0.38) | 0 | 0.0 | 0.0 | 0.0 | 1.0 | 2.0 | | 21–39 years | 1110 | 1.01 (0.98) | 0 | 0.0 | 0.0 | 2.0 | 2.0 | 5.0 | | ≥40 years | 143 | 0.13 (0.35) | 0 | 0.0 | 0.0 | 0.0 | 1.0 | 3.0 | | Warm season (April to September) | 492 | 0.45 (0.65) | 0 | 0.0 | 0.0 | 1.0 | 1.0 | 4.0 | | Cold season (October to March) | 935 | 0.85 (1.11) | 0 | 0.0 | 0.0 | 2.0 | 2.0 | 5.0 | | PM10 (μg/m3) | - | 117.42 (77.82) | 16 | 49.5 | 68.0 | 138.0 | 208.0 | 658.0 | | SO2 (μg/m3) | - | 67.62 (95.21) | 4 | 13.0 | 19.0 | 67.0 | 154.0 | 858.0 | | NO2 (μg/m3) | - | 36.04 (18.38) | 6 | 16.0 | 23.0 | 45.0 | 62.0 | 124.0 | | Mean temperature (°C) | - | 14.87 (10.50) | −10.0 | 0.3 | 5.6 | 24.0 | 28.6 | 33.3 | | Relative humidity (%) | - | 52.58 (17.79) | 10.0 | 29.0 | 39.0 | 65.0 | 77.0 | 98.0 | The Spearman correlation coefficients of the environmental variables are shown in Table 2. These pollutants were strongly correlated with one another, including PM10 and SO2 (rs = 0.66, $P \leq 0.001$), PM10 and NO2 (rs = 0.65, $P \leq 0.001$), and SO2 and NO2 (rs = 0.66, $P \leq 0.001$). Meteorological factors, including the mean temperature and relative humidity were negatively correlated with pollutants. However, there was a weak positive correlation between the two meteorological factors ($P \leq 0.001$). **Table 2** | Unnamed: 0 | PM10 | SO2 | NO2 | Mean temperature | | --- | --- | --- | --- | --- | | SO2 | 0.66*** | | | | | NO2 | 0.65*** | 0.66*** | | | | Mean temperature | −0.42*** | −0.53*** | −0.54*** | | | Relative humidity | −0.093** | −0.23*** | −0.036 | 0.23*** | Table 3 shows the RRs and $95\%$ CIs of appendicitis admissions per 10 μg/m3 increase in pollutants at various lag days. The results indicated that short-term air pollution exposure was significantly associated with hospitalizations for appendicitis. In the single-day lag models, the most significant estimates all occurred on the current day (lag0), and the effect values were 1.0170 (1.0146–1.0194) for PM10, 1.0230 (1.0187–1.0273) for SO2, and 1.0648 (1.0509–1.0790) for NO2. In moving average exposure models, these three pollutants all maintained a significant positive association with appendicitis admissions from lag01 to lag05. The most significant effects on hospitalizations for appendicitis were all observed at lag01. For every 10 μg/m3 increase in pollutants at lag01, the corresponding effects were 1.0179 (1.0129–1.0230) for PM10, 1.0236 (1.0184–1.0288) for SO2, and 1.0979 (1.0704–1.1262) for NO2. **Table 3** | Lag | PM10 | SO2 | NO2 | | --- | --- | --- | --- | | 0 | 1.0170 (1.0146–1.0194)* | 1.0230 (1.0187–1.0273)* | 1.0648 (1.0509–1.0790)* | | 1 | 1.0095 (1.0050–1.0141)* | 1.0133 (1.0086–1.0181)* | 1.0392 (1.0224–1.0562)* | | 2 | 1.0021 (0.9976–1.0066) | 1.0069 (1.0021–1.0117)* | 1.0130 (0.9942–1.0321) | | 3 | 1.0001 (0.9944–1.0032) | 1.0020 (0.9974–1.0067) | 0.9926 (0.9728–1.0128) | | 4 | 1.0004 (0.9961–1.0048) | 0.9987 (0.9941–1.0033) | 0.9812 (0.9616–1.0013) | | 5 | 1.0006 (0.9962–1.0049) | 0.9975 (0.9929–1.0021) | 0.9858 (0.9666–1.0054) | | 1 | 1.0179 (1.0129–1.0230)* | 1.0236 (1.0184–1.0288)* | 1.0979 (1.0704–1.1262)* | | 2 | 1.0158 (1.0102–1.0213)* | 1.0235 (1.0176–1.0295)* | 1.0926 (1.0601–1.1261)* | | 3 | 1.0137 (1.0076–1.0199)* | 1.0226 (1.0160–1.0292)* | 1.0915 (1.0698–1.1136)* | | 4 | 1.0133 (1.0066–1.0200)* | 1.0207 (1.0135–1.0280)* | 1.0797 (1.0431–1.1175)* | | 5 | 1.0131 (1.0059–1.0204)* | 1.0192 (1.0113–1.0271)* | 1.0689 (1.0292–1.1102)* | The overall and sex-specific analyses for appendicitis per 10 μg/m3 increase in pollutants are summarized in Figure 2. We only found adverse effects of pollutants in the male group, with the strongest effects of 1.0197 (1.0140–1.0254) for PM10 at lag0, 1.0248 (1.0155–1.0341) for SO2 at lag04 and 1.1097 (1.0674–1.1537) for NO2 at lag03. However, no significant effect was found in the female group (Supplementary Table 1). **Figure 2:** *Overall and sex-specific analyses of appendicitis admissions per 10 μg/m3 increase in pollutant concentrations.* Figure 3 shows the results of the age-specific analysis. Significant adverse effects were observed only in the 21-39 age group, and all occurred at lag0-lag1 and lag01-lag05. The most significant effects of PM10, NO2, and SO2 were 1.0230 (1.0169–1.0292) at lag0, 1.1178 (1.0786–1.1583) at lag02, and 1.0257 (1.0184–1.0329) at lag01, respectively (Supplementary Table 1). **Figure 3:** *Age-specific analysis of appendicitis admissions per 10 μg/m3 increase in pollutant concentrations.* In terms of seasonal stratification, the effects of the cold season seemed to be stronger than those of the warm season, but there was no statistical significance between the groups (Supplementary Table 2). Table 4 displays the results of appendicitis admissions after adjusting for other pollutants. For SO2 and NO2, the effects decreased when other pollutants were added to the model, but the associations with appendicitis remained statistically significant in the multipollutant models. For PM10, the effect value was still statistically significant when only NO2 was adjusted for in the model. However, when only SO2 was adjusted for or both NO2 and SO2 were adjusted for in the model, the association between PM10 and appendicitis became statistically non-significant. In addition, when we further adjusted the df of the temporal trends (6–9), daily average temperature (3–5) and relative humidity (3–5), the associations between the three pollutants and appendicitis admissions remained statistically significant, indicating that our results were robust (Supplementary Tables 3, 4). **Table 4** | Model | RR | 95% CI | | --- | --- | --- | | PM 10 | PM 10 | PM 10 | | Single pollutant model | 1.0179 | 1.0129–1.0230* | | +NO2 | 1.0091 | 1.0032–1.0149* | | +SO2 | 1.0044 | 0.9978–1.0109 | | +NO2+SO2 | 1.0019 | 0.9952–1.0086 | | SO 2 | SO 2 | SO 2 | | Single pollutant model | 1.0236 | 1.0184–1.0288* | | +PM10 | 1.0204 | 1.0133–1.0274* | | +NO2 | 1.0166 | 1.0099–1.0232* | | +PM10+NO2 | 1.0154 | 1.0078–1.0231* | | NO 2 | NO 2 | NO 2 | | Single pollutant model | 1.0979 | 1.0704–1.1262* | | +PM10 | 1.0697 | 1.0433–1.0969* | | +SO2 | 1.0487 | 1.0197–1.0786* | | +PM10+SO2 | 1.0466 | 1.0166–1.0775* | ## Discussion To date, this may be the first study conducted in mainland China that investigates the impacts of short-term air pollution exposure on appendicitis by using a time series approach. This hospital-based study indicated that short-term exposure to PM10, SO2, and NO2 was significantly associated with daily appendicitis hospitalizations. Following the construction of single- and multipollutant models, the results indicated that gaseous pollutants seemed to have a more pronounced effect on appendicitis than PM10. Furthermore, males and people aged 21–39 years seemed to be more susceptible to air pollutants. This study adds to the epidemiological evidence of the effects of air pollution on gastrointestinal disorders. In 2021, based on the scientific research of the past 15 years, the WHO issued the unprecedentedly strict “Global Air Quality Guidelines” (AQGs), which put forwards greater requirements for atmospheric concentration indicators and determined the 24-h mean concentration standards of PM10, SO2, and NO2 as 45, 40, and 25 μg/m3, respectively [31]. In our study, the daily average concentrations of PM10, SO2, and NO2 were 117.42, 67.62, and 36.04 μg/m3, respectively, which were all considerably higher than the WHO air quality standards. Using the WHO 24-h concentration standards as a guide, during the 1,096 days of the study period, PM10 exceeded the standard on 1,017 days, SO2 on 492 days, and NO2 on 757 days, indicating that the air pollution in Linfen was very serious. According to China's air quality standards, the daily average concentrations of PM10 and SO2 were both higher than the level I standard (50 μg/m3), while NO2 concentration was lower than the level I standard (80 μg/m3). Furthermore, the average daily concentrations of all three pollutants were below China's level II air quality standards (150 μg/m3 for PM10 and SO2, 80 μg/m3 for NO2). Nevertheless, the concentration of PM10 exceeded China's level II air quality standards on 228 days, SO2 on 116 days and NO2 on 35 days. Moreover, during the study period, we observed that the daily maximum mean concentration of SO2 reached 858 μg/m3, which was 21 times the WHO air quality level (40 μg/m3), indicating a very serious level of SO2 pollution in Linfen city. Linfen's air pollution is so bad that the city has been repeatedly rated as one of the most polluted cities in China by the Ministry of Ecology and Environment. Linfen is rich in coal resources, and the higher concentration of air pollution in winter may be related to coal burning during the heating season. In addition, a large number of companies with high levels of energy consumption and pollution are clustered in the Linfen Basin, which is also the most important reason for Linfen's serious pollution [23]. In view of these considerations, it is of great importance to study the impacts of air pollutants on appendicitis admissions in Linfen, a city with serious air pollution in mainland China. There is relatively little epidemiological evidence of the impacts of air pollution on appendicitis. Studies have mainly been conducted in developed countries, and the results of previous studies are still controversial. Therefore, we collected hospitalization data of appendicitis patients in Linfen city, a city with severe air pollution in mainland China, and conducted this study to look for a possible association with air pollution. Our results found a positive association between air pollutants and appendicitis admissions, which is supported by several published studies. A cross-sectional study in Tunisia compared the impacts of environmental factors on perforated and non-perforated appendicitis and reported that short-term exposure to PM10 (2-day lag mean concentration) was significantly associated with perforated appendicitis (RR 1.066, 1.007–1.130) [20]. Kaplan et al. performed a case-crossover study involving 5,191 hospitalized patients with appendicitis over the age of 18 in Calgary, Canada, and found that short-term air pollution exposure increased the incidence of appendicitis [15]. In addition, the effects were most pronounced for SO2 (OR 1.30, $95\%$ CI 1.03–1.63), NO2 (OR 1.76, $95\%$ CI 1.20–2.58), and PM10 (OR 1.20, $95\%$ CI 1.05–1.38) in summer (July–August) [15]. Another case-crossover study also reported a significant adverse effect of NO2 on hospitalization for appendicitis in Taiwan, with PM10 having a significant effect only during the cold season (below 23°C), while SO2 was not found to have a significant effect in either the single- or two-pollutant model in the study [17]. Other studies have also shown that PM10, SO2, or NO2 are not associated with appendicitis [18, 19]. The reasons for these differences may be the use of different study designs, the selection of the appendicitis population (e.g., ICD codes, perforated, or non-perforated appendicitis), and the concentration and composition of air pollution in different regions. Additionally, studies have demonstrated that other air pollutants (e.g., O3 and CO) are associated with the onset of appendicitis. Kaolan et al. showed that the impacts of O3 (OR 1.32, $95\%$ CI 1.10–1.57) and CO (OR 1.35, $95\%$ CI 1.01–1.80) in summer were most pronounced with the incidence of appendicitis [15]. A study in Taiwan reported that exposure to O3 was correlated with the frequency of appendicitis hospitalizations [17]. The impact of ambient O3 on appendicitis was also confirmed by a multicity case-crossover study. In that study, Kaplan et al. found that higher levels of environmental O3 exposure were correlated with perforated appendicitis [16]. However, the effects of O3 and CO were not analyzed in this study because these data were not available; studies that include these pollutants are urgently needed. Studies have shown that males are more likely to develop appendicitis than females, with lifetime incidences of 8.6 and $6.7\%$, respectively [32]. In this study, the number of appendicitis hospitalizations was also much higher among males than among females (1,183 vs. 244). In addition, consistent with previous studies, our study showed that males were more likely to be affected by air pollutants than females [16, 32, 33]. This finding may be because outdoor work is predominantly performed by males and thus these individuals are more exposed to air pollutants. It is recommended that males be well protected against air pollutants when working outdoors to effectively reduce the number of hospitalizations for appendicitis. Appendicitis is more likely to occur in younger people [32, 34, 35]. In our study, most hospitalized patients with appendicitis were 21–39 years old, and they were more susceptible to pollutants. However, one study reported that people aged over 64 years were more susceptible to NO2 than younger adults (age 18–35 years) in terms of appendicitis [15], which was different from our findings. However, the specific reasons for this outcome are still unknown, and further studies are needed to clarify these age-specific effects. Studies in South Korea, Canada and elsewhere have shown that appendicitis is more likely to occur in summer, which is possibly due to dietary habits [36]. During the summer months, people may increase their intake of low-fiber foods and sugar when they go outside, possibly leading to constipation and appendicitis [37]. In addition, people may open their windows or go outside on warm days, which further increases their exposure to air pollutants [15]. In this study, the impacts of air pollutants seemed to be stronger in the cold season, but the difference between the cold and warm seasons was not statistically significant. The differences in these studies may be due to the climatic characteristics of different regions and different concentrations and components of air pollution. Furthermore, no such studies have been conducted in mainland China, which limits our further in-depth comparative analysis. In the two- and three- pollutant models, after adding other contaminants to the SO2 and NO2 models, the effect values decreased somewhat but remained significant. After adding NO2 to the PM10 model, the effect value was also statistically significant, but when only SO2 was adjusted for or both SO2 and NO2 were adjusted for, the effects became non-significant. This trend suggests that gaseous pollutants may play a greater role in inducing appendicitis than PM10. Prevention and control of air pollutants, especially gaseous pollutants, should be strengthened in the future. The specific mechanism of how air pollutants affect the onset of appendicitis is not yet clear, but the association between the two is somewhat biologically plausible. One study reported that inhaling or ingesting air pollutants may cause an inflammatory response in humans [15]. Particles are deposited in the nasopharyngeal chambers, cleared by cilia and swallowed, so they may directly affect the gastrointestinal tract [38]. Air pollutants can activate immune cells and lead to cytotoxicity in intestinal epithelial cells [39]. Air pollutants can also directly affect the gut microbiota, which may lead to digestive diseases [40]. Animal studies have shown that exposing the intestinal tracts of mice to particulate matter alters the colon microbiota structure and increases the levels of interleukin-8 (IL-8) and IL-17 [14]. In addition, exposing mice to particulate matter results in changes in gut microbial composition and metabolic processes, which may also contribute to the inflammatory response [12]. Several limitations in this study should not be ignored. First, this study had an ecological design and had some limitations regarding causal inference. Second, the daily cases of hospitalization for appendicitis were only from one hospital in Linfen city, and the conclusions of the study may not be widely generalizable and may not represent other cities and regions with different characteristics. Although only one hospital was included in this study, it was still very representative. In 2021, the number of outpatient and emergency patients in Linfen People's Hospital reached 1,031,200, and the number of discharged patients reached 65,000. This is the largest comprehensive grade A hospital in Linfen with multiple specialties, advanced equipment and a strong technical force. Most patients with appendicitis seek treatment at this hospital. Third, consistent with many published time series studies, this study also used monitoring station data to replace individual exposure data, which inevitably has the problem of exposure measurement errors. In recent years, some more advanced modeling methods, such as land use regression models and satellite inversion, have been applied to assess precise individual exposures, which can improve the spatial resolution of exposure assessment to some extent. In a follow-up study, we will try to apply a more accurate individual exposure assessment method to explore the impacts of air pollutants on appendicitis. Fourth, some individual factors, such as physical condition, occupation, diet, marital status, education and income, may have influenced admissions for appendicitis, but we did not have access to these data [32, 41, 42]. Additionally, the onset date of appendicitis may have differed from the date of hospitalization, and studies have reported that up to $20\%$ of patients with appendicitis may have delayed admission to the hospital [43]. Further studies that fully consider these limitations are urgently needed. ## Conclusions In conclusion, our findings indicated that short-term air pollutant (PM10, SO2, and NO2) exposure was significantly correlated with the number of hospitalizations for appendicitis in heavily polluted Linfen city. Males and people aged 21–39 were more susceptible to air pollution. Our study adds to the epidemiological evidence of an association of air pollutants with appendicitis, providing a reference for governments and health authorities to develop targeted air pollution interventions. ## Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. ## Ethics statement Prior to data collection, the Ethics Committee of Shantou University had approved this study. Overall aggregated data were used in our study and no information about individual patient privacy was involved in the analysis. ## Author contributions YJ and FZ: conceptualization, data curation, investigation, and writing—original draft. ZH and XZ: validation and formal analysis. YC and ZS: conceptualization and writing—review and editing. XS and LL: writing—review and editing and supervision. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2023.1144310/full#supplementary-material ## References 1. Keller CA, Dudley RM, Huycke EM, Chow RB, Ali A. **Stump appendicitis**. *Radiol Case Rep.* (2022) **17** 2534-6. DOI: 10.1016/j.radcr.2022.04.034 2. D'Souza N, Nugent K. **Appendicitis**. *Am Fam Physician.* (2016) **93** 142-3. PMID: 26926413 3. Podda M, Pisanu A, Sartelli M, Coccolini F, Damaskos D, Augustin G. **Diagnosis of acute appendicitis based on clinical scores: is it a myth or reality?**. *Acta Biomed* (2021) **92** e2021231. DOI: 10.23750/abm.v92i4.11666 4. Masoomi H, Nguyen NT, Dolich MO, Mills S, Carmichael JC, Stamos MJ. **Laparoscopic appendectomy trends and outcomes in the United States: data from the Nationwide Inpatient Sample (NIS), 2004–2011**. *Am Surg.* (2014) **80** 1074-7. DOI: 10.1177/000313481408001035 5. Song MY, Ullah S, Yang HY, Ahmed MR, Saleh AA, Liu BR. **Long-term effects of appendectomy in humans: is it the optimal management of appendicitis?**. *Expert Rev Gastroenterol Hepatol* (2021) **15** 657-64. DOI: 10.1080/17474124.2021.1868298 6. Davies GM, Dasbach EJ, Teutsch S. **The burden of appendicitis-related hospitalizations in the United States in 1997**. *Surg Infect.* (2004) **5** 160-5. DOI: 10.1089/sur.2004.5.160 7. Ferris M, Quan S, Kaplan BS, Molodecky N, Ball CG, Chernoff GW. **The global incidence of appendicitis: a systematic review of population-based studies**. *Ann Surg* (2017) **266** 237-41. DOI: 10.1097/SLA.0000000000002188 8. Song X, Lan L, Zhou T, Yin J, Meng Q. **Economic burden of major diseases in China in 2013**. *Front Public Health.* (2021) **9** 649624. DOI: 10.3389/fpubh.2021.649624 9. Orru H, Ebi KL, Forsberg B. **The interplay of climate change and air pollution on health**. *Curr Environ Health Rep.* (2017) **4** 504-13. DOI: 10.1007/s40572-017-0168-6 10. Cohen AJ, Brauer M, Burnett R, Anderson HR, Frostad J, Estep K. **Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015**. *Lancet* (2017) **389** 1907-18. DOI: 10.1016/S0140-6736(17)30505-6 11. Ji Y, Liu B, Song J, Cheng J, Wang H, Su H. **Association between traffic-related air pollution and anxiety hospitalizations in a coastal Chinese city: are there potentially susceptible groups?**. *Environ Res* (2022) **209** 112832. DOI: 10.1016/j.envres.2022.112832 12. Salim SY, Jovel J, Wine E, Kaplan GG, Vincent R, Thiesen A. **Exposure to ingested airborne pollutant particulate matter increases mucosal exposure to bacteria and induces early onset of inflammation in neonatal IL-10-deficient mice**. *Inflamm Bowel Dis* (2014) **20** 1129-38. DOI: 10.1097/MIB.0000000000000066 13. Salim SY, Kaplan GG, Madsen KL. **Air pollution effects on the gut microbiota: a link between exposure and inflammatory disease**. *Gut Microbes.* (2014) **5** 2154es. DOI: 10.4161/gmic.27251 14. Kish L, Hotte N, Kaplan GG, Vincent R, Tso R, Gänzle M. **Environmental particulate matter induces murine intestinal inflammatory responses and alters the gut microbiome**. *PLoS ONE* (2013) **8** e62220. DOI: 10.1371/journal.pone.0062220 15. Kaplan GG, Dixon E, Panaccione R, Fong A, Chen L, Szyszkowicz M. **Effect of ambient air pollution on the incidence of appendicitis**. *CMAJ* (2009) **181** 591-7. DOI: 10.1503/cmaj.082068 16. Kaplan GG, Tanyingoh D, Dixon E, Johnson M, Wheeler AJ, Myers RP. **Ambient ozone concentrations and the risk of perforated and nonperforated appendicitis: a multicity case-crossover study**. *Environ Health Perspect* (2013) **121** 939-43. DOI: 10.1289/ehp.1206085 17. Chen CC, Yang CY. **Effects of ambient air pollution exposure on frequency of hospital admissions for appendicitis in Taipei, Taiwan**. *J Toxicol Environ Health A.* (2018) **81** 854-60. DOI: 10.1080/15287394.2018.1498276 18. McGowan JA, Hider RN, Chacko E, Town GI. **Particulate air pollution and hospital admissions in Christchurch, New Zealand**. *Aust N Z J Public Health.* (2002) **26** 23-9. DOI: 10.1111/j.1467-842X.2002.tb00266.x 19. Pönkä A, Virtanen M. **Low-level air pollution and hospital admissions for cardiac and cerebrovascular diseases in Helsinki**. *Am J Public Health* (1996) **86** 1273-80. DOI: 10.2105/AJPH.86.9.1273 20. Aroui H, Kalboussi H, Ghali AE, Kacem I, Maoua M, Maatoug J. **The effect of environmental factors on the incidence of perforated appendicitis**. *Ann Ital Chir* (2018) **89** 431-7. PMID: 30049910 21. Liu C, Cai J, Chen R, Sera F, Guo Y, Tong S. **Coarse particulate air pollution and daily mortality: a global study in 205 cities**. *Am J Respir Crit Care Med* (2022) **206** 999-1007. DOI: 10.1164/rccm.202111-2657OC 22. Wang YS, Chang LC, Chang FJ. **Explore regional PM25 features and compositions causing health effects in Taiwan**. *Environ Manage.* (2021) **67** 176-91. DOI: 10.1007/s00267-020-01391-5 23. Liu L, Ma X, Wen W, Sun C, Jiao J. **Characteristics and potential sources of wintertime air pollution in Linfen, China**. *Environ Monit Assess.* (2021) **193** 252. DOI: 10.1007/s10661-021-09036-8 24. Du N, Ji A-L, Liu X-L, Tan C-L, Huang X-L, Xiao H. **Association between short-term ambient nitrogen dioxide and type 2 diabetes outpatient visits: a large hospital-based study**. *Environ Res* (2022) **215** 114395. DOI: 10.1016/j.envres.2022.114395 25. Meng Y, Lu Y, Xiang H, Liu S. **Short-term effects of ambient air pollution on the incidence of influenza in Wuhan, China: a time-series analysis**. *Environ Res* (2021) **192** 110327. DOI: 10.1016/j.envres.2020.110327 26. Zhou Y-M, Fan Y-N, Yao C-Y, Xu C, Liu X-L, Li X. **Association between short-term ambient air pollution and outpatient visits of anxiety: a hospital-based study in northwestern China**. *Environ Res* (2021) **197** 111071. DOI: 10.1016/j.envres.2021.111071 27. Zhang C, Ding R, Xiao C, Xu Y, Cheng H, Zhu F. **Association between air pollution and cardiovascular mortality in Hefei, China: a time-series analysis**. *Environ Pollut* (2017) **229** 790-7. DOI: 10.1016/j.envpol.2017.06.022 28. Li D, Ji A, Lin Z, Tan C, Huang X, Xiao H. **Short-term ambient air pollution exposure and adult primary insomnia outpatient visits in Chongqing, China: a time-series analysis**. *Environ Res* (2022) **212** 113188. DOI: 10.1016/j.envres.2022.113188 29. Bell ML, Samet JM, Dominici F. **Time-series studies of particulate matter**. *Annu Rev Public Health.* (2004) **25** 247-80. DOI: 10.1146/annurev.publhealth.25.102802.124329 30. Zhong P, Huang S, Zhang X, Wu S, Zhu Y, Li Y. **Individual-level modifiers of the acute effects of air pollution on mortality in Wuhan, China**. *Glob Health Res Policy.* (2018) **3** 27. DOI: 10.1186/s41256-018-0080-0 31. Carvalho H. **New WHO global air quality guidelines: more pressure on nations to reduce air pollution levels**. *Lancet Planet Health.* (2021) **5** e760-1. DOI: 10.1016/S2542-5196(21)00287-4 32. Golz RA, Flum DR, Sanchez SE, Liu X, Donovan C, Drake FT. **Geographic association between incidence of acute appendicitis and socioeconomic status**. *JAMA Surg.* (2020) **155** 330-8. DOI: 10.1001/jamasurg.2019.6030 33. Walter K. **Acute appendicitis**. *JAMA.* (2021) **326** 2339. DOI: 10.1001/jama.2021.20410 34. Addiss DG, Shaffer N, Fowler BS, Tauxe RV. **The epidemiology of appendicitis and appendectomy in the United States**. *Am J Epidemiol* (1990) **132** 910-25. DOI: 10.1093/oxfordjournals.aje.a115734 35. Luckmann R, Davis P. **The epidemiology of acute appendicitis in California: racial, gender, and seasonal variation**. *Epidemiology* (1991) **2** 323-30. DOI: 10.1097/00001648-199109000-00003 36. Wei P-L, Chen C-S, Keller JJ, Lin H-C. **Monthly variation in acute appendicitis incidence: a 10-year nationwide population-based study**. *J Surg Res.* (2012) **178** 670-6. DOI: 10.1016/j.jss.2012.06.034 37. Ilves I, Fagerström A, Herzig K-H, Juvonen P, Miettinen P, Paajanen H. **Seasonal variations of acute appendicitis and nonspecific abdominal pain in Finland**. *World J Gastroenterol.* (2014) **20** 4037-42. DOI: 10.3748/wjg.v20.i14.4037 38. Kaplan GG, Szyszkowicz M, Fichna J, Rowe BH, Porada E, Vincent R. **Non-specific abdominal pain and air pollution: a novel association**. *PLoS ONE* (2012) **7** e47669. DOI: 10.1371/journal.pone.0047669 39. Beamish LA, Osornio-Vargas AR, Wine E. **Air pollution: an environmental factor contributing to intestinal disease**. *J Crohns Colitis.* (2011) **5** 279-86. DOI: 10.1016/j.crohns.2011.02.017 40. Nell S, Suerbaum S, Josenhans C. **The impact of the microbiota on the pathogenesis of IBD: lessons from mouse infection models**. *Nat Rev Microbiol.* (2010) **8** 564-77. DOI: 10.1038/nrmicro2403 41. Naderan M, Babaki AES, Shoar S, Mahmoodzadeh H, Nasiri S, Khorgami Z. **Risk factors for the development of complicated appendicitis in adults**. *Ulus Cerrahi Derg.* (2016) **32** 37-42. DOI: 10.5152/UCD.2015.3031 42. Kleif J, Vilandt J, Gögenur I. **Recovery and convalescence after laparoscopic surgery for appendicitis: a longitudinal cohort study**. *J Surg Res.* (2016) **205** 407-18. DOI: 10.1016/j.jss.2016.06.083 43. Bickell NA, Aufses AH Jr. *J Am Coll Surg.* (2006) **202** 401-6. DOI: 10.1016/j.jamcollsurg.2005.11.016
--- title: Endometrial stromal cell miR-19b-3p release is reduced during decidualization implying a role in decidual-trophoblast cross-talk authors: - Ellen Menkhorst - Teresa So - Kate Rainczuk - Siena Barton - Wei Zhou - Tracey Edgell - Evdokia Dimitriadis journal: Frontiers in Endocrinology year: 2023 pmcid: PMC10061138 doi: 10.3389/fendo.2023.1149786 license: CC BY 4.0 --- # Endometrial stromal cell miR-19b-3p release is reduced during decidualization implying a role in decidual-trophoblast cross-talk ## Abstract ### Introduction A healthy pregnancy requires successful blastocyst implantation into an adequately prepared or ‘receptive’ endometrium. Decidualization of uterine endometrial stromal fibroblast cells (hESF) is critical for the establishment of a healthy pregnancy. microRNAs (miRs) are critical regulators of cellular function that can be released by a donor cell to influence the physiological state of recipient cells. We aimed to determine how decidualization affects hESF miR release and investigated the function of one decidualization regulated miR, miR-19b-3p, previously shown to be associated with recurrent pregnancy loss. ### Method miR release by hESF was determined by miR microarray on culture media from hESF decidualized in vitro for 3 and 14 days by treatment with oestradiol and medroxyprogesterone acetate. Cellular and whole endometrial/decidual tissue miR expression was quantified by qPCR and localized by in situ hybridization. The function of miR-19b-3p in HTR8/Svneo trophoblast cells was investigated using real time cell analysis (xCELLigence) and gene expression qPCR. ### Results From our miR screen we found that essentially all hESF miR release was reduced following in vitro decidualization, significantly so for miR-17-5p, miR-21-3p, miR-34c-3p, miR-106b-5p, miR-138-5p, miR-296-5p, miR-323a-3p, miR-342-3p, miR-491-5p, miR-503-5p and miR-542-5p. qPCR demonstrated that miR-19b-3p, 181a-2-3p and miR-409-5p likewise showed a significant reduction in culture media following decidualization but no change was found in cellular miR expression following decidualization. In situ hybridization localized miR-19b-3p to epithelial and stromal cells in the endometrium and qPCR identified that miR-19b-3p was significantly elevated in the cycling endometrium of patients with a history of early pregnancy loss compared to normally fertile controls. Functionally, overexpression of miR-19b-3p significantly reduced HTR8/Svneo trophoblast proliferation and increased HOXA9 expression. ### Discussion Our data demonstrates that decidualization represses miR release by hESFs and overexpression of miR-19b-3p was found in endometrial tissue from patients with a history of early pregnancy loss. miR-19b-3p impaired HTR8/Svneo proliferation implying a role in trophoblast function. Overall we speculate that miR release by hESF may regulate other cell types within the decidua and that appropriate release of miRs by decidualized hESF is essential for healthy implantation and placentation. ## Introduction A healthy pregnancy requires successful blastocyst implantation into an adequately prepared or ‘receptive’ endometrium. Decidualization of human uterine human uterine endometrial stromal fibroblast (hESF) is critical for the establishment of a healthy pregnancy [1, 2]; impaired decidualization is associated with poor pregnancy outcomes including recurrent early pregnancy loss and preeclampsia (3–5). Decidualization is initiated post-ovulation by corpus luteum-secreted progesterone and involves the reprogramming of hESF, including significant phenotypic and functional changes: hESF become rounded, highly secretory and with altered extellular matrix expression [1]. In women, decidualization begins each menstrual cycle regardless of the presence of a functional blastocyst [1]. Decidual cells interact with the implanting blastocyst to facilitate implantation and placentation: they regulate extravillous trophoblast (EVT) proliferation, migration and invasion (6–8), shield the conceptus from environmental stress signals [1], regulate the recruitment and differentiation of the uterine-resident immune cell population (9–11) and are thought to ‘sense’ the quality of the conceptus, facilitating rejection of incompetent embryos [12, 13]. microRNAs (miRs) are critical regulators of cellular function and have been most intensively investigated in cancer, where they regulate metastasis, angiogenesis and inflammation [14, 15]. miRs can also act as ‘hormones’ – donor cells (which release the miR) can influence the physiological state of recipient cells (cells which take up the miR) over short (cell to neighboring cell) and long (effects on a different organ) distances [15, 16]. In pregnancy, miRs are produced by cells within the decidua (decidual cells, leucocytes and endothelial cells) [17] and placental villous trophoblast [18]. miR expression is altered in the decidua of early pregnancy loss compared to healthy pregnancies [18]. Less is known about miRs during endometrial remodelling. In vitro, decidual cellular miRs regulate decidualization [19], however little known about how secreted endometrial miRs may regulate other cells within the decidua including trophoblast. We aimed to determine how decidualization affected hESF miR release and determine the expression and function of one hESF released miR, miR-19b-3p, previously associated with recurrent early pregnancy loss [20]. ## Primary tissue collection This study followed the NHMRC guidelines for ethical conduct in human research. Ethics approvals for this study were provided by The Royal Women’s Hospital and Monash Health Human Research and Ethics Committees (#90317B, #06014C and #03066B). Written and informed consent was obtained from each participant. Endometrial biopsies were collected by dilatation and curettage ($$n = 26$$ women; Table 1). Five biopsies were used for decidualization experiments (1 with history of early pregnancy loss), 3 for in situ (2 with history of early pregnancy loss) and 18 for RNA extraction (12 fertile, 6 with a history of early pregnancy loss). The women had no hormonal treatment for ≥ 3 months before tissue collection. **Table 1** | Unnamed: 0 | Normally fertile (n=17) | Pregnancy Loss (n=9) | | --- | --- | --- | | Maternal age (years) a | 35.5+/-1.5 (26-46) | 37.3+/-1.3 (33-41) | | Gravidity b | 2.3+/-0.2 (1-3) | 3.7+/-1.2 (2-6) | | Parity median c | 2.0 (0-3) | 0.0 (0-1) | | Number of previous lossesd | 0 | 3.3+/-0.9 (2-5) | First trimester products of conception were collected following elective termination of pregnancy by evacuation for psychosocial reasons ($$n = 4$$; amenorrhea 6-11 weeks). Term placental villous and decidual tissue was donated by healthy women following spontaneous labor at term (>37 weeks; $$n = 4$$). Serum was collected from women aged >18 years ($$n = 5$$/group) attending an IVF clinic, who had successful pregnancies following IVF and those who had repeated pregnancy loss following IVF. Serum was collected from women undergoing oocyte collection, two days after induction of ovulation by human chorionic gonadotrophin. Subsequent details of outcomes of embryo transfer in the same cycle were recorded. ## Cell culture All cells were cultured at 37°C in a $5\%$ CO2 humidified culture incubator. hESF were maintained in DMEM/F12 (Gibco, Thermo Fisher Scientific, Inc.) plus $10\%$ charcoal stripped Fetal *Bovine serum* (FBS; Gibco, Thermo Fisher Scientific, Inc.) and $1\%$ antibiotics (penicillin, streptomycin, amphoceterin B; Gibco, Thermo Fisher Scientific, Inc.). HTR8/SVneo cells (CRL-3271) were from the ATCC and cultured with RPMI (Gibco, Thermo Fisher Scientific, Inc.) plus $10\%$ heat inactivated FBS (Gibco, Thermo Fisher Scientific, Inc.). ## Decidualization hESF were isolated using collagenase digestion and filtration as previously described [21], resulting in a $97\%$ stromal fibroblast population [22]. hESF were decidualized as previously described [21] by treatment for 14 days with oestradiol (E, 10-8M; Sigma) and medroxyprogesterone acetate (MPA, 10-7M; Sigma) in DMEM/F12 containing $2\%$ charcoal stripped FBS and $1\%$ antibiotics. The media was refreshed every 2-3 days, on a Monday, Wednesday and Friday. Cells and culture media were collected on Day 3 and Day 14, both after 72h of culture. Cells were pelleted by centrifugation at 500xg then snap-frozen. Culture media was centrifuged at 500xg for 5 minutes to pellet cell debris then the supernatant snap-frozen. ## Prolactin ELISA PRL secretion by decidualized hESF (culture media collected on days 3 and 14) was quantified by ELISA as per the manufacturer’s instructions (DuoSet kit #DY682, R&D systems) [23]. ## RNA isolation Decidual culture media: RNA was isolated from 200uL culture media collected on Day 3 and Day 14 of culture and media only control using the RNeasy Micro Kit (Qiagen) according to the manufacturer’s instructions. hESF & HTR8/Svneo cells, endometrial and decidual tissue: RNA extraction was performed as previously described using Tri Reagent according to the manufacturer's instructions (Sigma-Aldrich, Merck). Serum: RNA extraction (from 250uL serum) was performed using the TRIzol LS reagent (Ambion, Life Technologies) as per the manufacturer’s instructions. Genomic DNA was removed from isolated RNA using the DNAfree kit (Ambion; Thermo Fisher Scientific, Inc.) according to the manufacturer’s protocol. A spectrophotometer (Nanodrop Technologies; Thermo Fisher Scientific, Inc.), was used at an absorbance ratio of $\frac{260}{280}$ nm to analyze RNA sample concentration, yield and purity. ## microRNA array cDNA synthesis was performed using the miRCURY LNA™ Universal RT microRNA PCR system (Qiagen) and microRNA PCR Human Panel (I) as previously described [24]. cDNA products diluted 60-fold were plated on the microRNA PCR Human Panel (I) plate and qPCR was performed using a 7900HT thermocycler (Applied Biosystems) using the recommended parameters (Qiagen). Raw CT values were normalized (ΔCT) to the average of the control wells (UniSP3) on the plate, then ΔΔCT calculated by normalizing the ΔCT to the average of the day 3 samples for each gene (Supplementary Table 1). A media only control was run to enable exclusion of miRs present in the treatment media. ## miR RT-qPCR cDNA was synthesized from 10ng total RNA using the TaqMan reverse transcription kit (Applied Biosystems; Thermo Fisher Scientific, Inc), and specific TaqMan miR primer sets (cat no. # 4427975; miR-19b-3p #000396; miR-181a-2-3p #002317; miR-409-5p #002331; rnU6, #001973; Applied Biosystems; Thermo Fisher Scientific, Inc.) on the Veriti 7 fast block real-time qPCR system (Applied Biosystems). miR qPCR was performed in triplicate (final reaction volume, 10 μl) in 384-well micro- optical plates (Applied Biosystems; Thermo Fisher Scientific, Inc.) on the ABI 7900HT fast block or Viia 7 qPCR systems (Applied Biosystems; Thermo Fisher Scientific, Inc.). A template-free negative control and RNase-free water only was added for each run. The qPCR conditions were: 95°C for 10 min and 40 cycles of 95°C for 15s followed by 60°C for 1 min. Relative expression levels were calculated as per the manufacturer’s instructions using the comparative cycle threshold method (ΔΔCT). ## mRNA RT-qPCR Total RNA (250ng) was reverse transcribed using Superscript III (Invitrogen) (0.5 µL per reaction) as previously described [25]. qPCR was performed as previously described [25] using Power SYBR Green master mix (Applied Biosystems) on the Veriti 7 fast block real-time qPCR system (Applied Biosystems). Primer sequences are as follows: 18s Fwd: 5`GATCCATTGGAGGGCAAGTCT3`, Rev: 5`CCAAGATCCACCTACGAGCTT3`; Fwd: HOXA9 5`TACGTGGACTCGTTCCTGCT3`, Rev: 5`CGTCGCCTTGGACTGGAAG3`; PTEN Fwd: 5`TCCATCCTGCAGAAGAAGCC3`, Rev: 5`AGGATATTGTGCAACTCTGCAA3`; (Sigma-Aldrich). A template-free negative control in the presence of primers and RNase-free water only negative controls were added for each run. The qPCR conditions were: 95°C for 10 min and 40 cycles of 95°C for 15s followed by 60°C for 1 min. Relative expression levels (normalized to 18s ribosomal RNA) were calculated as per the manufacturer’s instructions using the comparative cycle threshold method (ΔΔCT). ## In situ hybridization In situ hybridization was performed as previously described [26]. Briefly, 4 μm thickness endometrial sections were deparaffinized and rehydrated in xylene, neat ethanol, $96\%$ ethanol, and $70\%$ ethanol and then placed in PBS Proteinase K (15 μg/mL) digestion was performed at 37°C for 15 min. Following PBS wash, 100 nM miR-19b-3p detection probe (#339111, YD00619863-BCG; Qiagen) or scramble control probe (cat no. # 339111 YD00699004-BCG) was applied to sections and placed in a 60°C incubator for 1 h. Slides were then washed in 5x sodium-saline citrate (SSC), 1x SSC and 0.2x SSC buffers at 60°C for 5 min, and 0.2x SSC at room temperature (RT) for 5 min, then placed in PBS. Blocking solution of $10\%$ CAS block (008120, Thermo), $2\%$ sheep serum, $1\%$ bovine serum albumin (BSA) in PBS-Tween (T) was applied to sections and incubated at RT for 15 min. After incubation, sections were treated with anti-DIG-fluorescein 1:50 in $0.5\%$ BSA/PBS at RT for 1 h. Following additional washes in PBS-T, sections were counterstained with DAPI to indicate the cell nuclei (blue). Sections were visualized using Olympus BX63 fluorescence microscope and cellSense software. All images were taken under the same exposure and settings. ## Real time cell analysis The real-time cell analyser (RTCA) MP xCELLigence instrument (ACEA Biosciences; Agilent Technologies GmbH) was used to interrogate the effect of miR-19b-3p on HTR8/Svneo adhesion and proliferation. HTR8/Svneo were transfected with 100nM miR-19b-3p mimic (cat no. 339173 YM00470545-ADB) or negative control (cat no. 339173 YM00479902-ADB) using Lipofectamine RNAiMAX (13778100, Thermo Fisher) and Opti-MEM medium (11524456, Fisher) following manufacturer’s instructions for 72 h. After transfection cells were seeded into E-plate 96 (ACEA Biosciences; Agilent Technologies GmbH) at ~10,000 cells/well in RPMI supplemented with $5\%$ FCS. Data was collected ever 15 minutes for a total of 96h. ## Statistical analysis Statistical analyses were performed using GraphPad Prism 9.5.0. Paired t-tests, one-way ANOVA and repeated measures ANOVA were performed. All data is presented as mean ± SEM. $P \leq 0.05$ was considered statistically significant. ## hESF miR release is repressed by decidualization We identified 98 miRs released by hESF into the culture media (Figure 1A; Supplementary Table 1). The most highly expressed miRs were miR-125b-5p, -23a-3p and let-7b-5p. miR release into the culture media was highly repressed following in vitro hESF decidualization (Figure 1A): 11 miRs showed a significant reduction at Day 14 of in vitro decidualization (Figure 1B). Decidualization was confirmed by PRL secretion (Figure 1C). **Figure 1:** *miR release was reduced following hESF decidualization. (A) Fold-change of all miRs identified in hESF culture media by microarray from Day 3 to Day 14. (B) miRs with significantly reduced levels in hESF culture media between Day 3 and Day 14. (C). Prolactin (PRL) secretion by hESF on Day 3 and Day 14 of decidualization. (D–F). qPCR of miR-19b-3p (D), miR-181a-2-3p (E) and miR-409-5p (F) in hESF cells and culture media on Day 3 and Day 14 of decidualization. (G–I). qPCR of miR-19b-3p (G), miR-181a-2-3p (H) and miR-409-5p (I) in whole tissue biopsies collected during the proliferative (prolif) and late secretory (LSec) stages of the menstrual cycle and 1st trimester and term decidua. Data shows mean ± SEM; *P<0.05; **P<0.01; ***P<0.001; (C–F), paired t-test; (G–I), one-way ANOVA.* To confirm the array data, we investigated the expression of 3 different miRs in hESF matched cellular and culture media RNA (Figures 1D–F). Interestingly, although the cellular levels of miRs-19b-3p, -181a-2-3p and -409-5p were not altered by decidualization, miR concentration in culture media was significantly reduced (Figures 1D–F). In contrast when we investigated whether decidualization altered miR-19b-3p, -181a-2-3p or -409-5p expression in whole endometrial tissue biopsies (non-decidualized:proliferative endometrium; decidualized: late secretory endometrium, 1st trimester or term decidua) (Figures 1G–I) we found no difference in expression between non-decidualized and decidualized tissue, although miR-181a-2-3p was significantly elevated in term decidua compared to late secretory endometrium (Figure 1H) and miR-409-5p was significantly elevated in 1st trimester decidua compared to late secretory endometrium (Figure 1I). ## Endometrial miR-19b-3p is increased in women with a history of early pregnancy loss In situ hybridization of cycling endometrial tissue biopsies localized miR-19b-3p to most cell types in the endometrium, although endometrial glandular epithelial cell expression was variable even within adjacent glands (Figure 2A). Using qPCR, we found that expression miR-19b-3p in endometrial tissue biopsies was significantly increased in patients with a history of early pregnancy loss compared to fertile controls (Figure 2B). This increase was not found in serum from women undergoing IVF with a history of repeated early pregnancy loss (Figure 2C). **Figure 2:** *miR-19b-3p expression is elevated in endometrium from women with a history of early pregnancy loss. (A) In situ hybridization of miR-19b-3p in endometrium. Localization of miR-19b-3p indicated by green fluorescent staining. DAPI (blue) counterstaining identifies nuclei. (B) qPCR of miR-19b-3p in endometrium from fertile patients and patients with a history of early pregnancy loss (EPL). (C) qPCR of miR-19b-3p in serum from fertile patients and patients with a history of early pregnancy loss. g, glandular epithelium; l, luminal epithelium; s, stroma; Data shows mean ± SEM; **P<0.01; (B, C), paired t-test.* ## miR-19b-3p reduces HTR8/Svneo trophoblast proliferation As impaired decidualization is associated with recurrent pregnancy loss and miR-19b-3p release was suppressed by decidualization, we investigated the effect of miR-19b-3p on trophoblast function using the HTR8/Svneo cell line. HTR8/Svneo transfected with miR-19b-3p mimic showed elevated miR-19b-3p expression in the cell pellet (Figure 3A), suggesting that miR-19b-3p is taken up from the media. Using a Real-Time Cell Analysis system (xCELLigence) we found there was no effect of miR-19b-3p on HTR8/Svneo adhesion (Figure 3B) but after 60h miR-19b-3p significantly inhibited HTR8/Svneo proliferation compared to control (Figure 3C). We investigated whether transfection with the miR-19b-3p affected HTR8/Svneo production of predicted miR-19b-3p targets [27, 28]: miR-19b-3p increased HOXA9 mRNA but had no effect on PTEN mRNA (Figure 3D). **Figure 3:** *miR-19b-3p overexpression in HTR8/Svneo cells impaired proliferation. (A) Treatment with miR-19b-3p mimic (○) significantly increased miR-19b-3p levels in HTR8/Svneo culture media (CM) and cell pellet compared to scramble control (●). (B) miR-19b-3p mimic had no effect on HTR8/Svneo adhesion (n=3/group). (C) miR-19b-3p mimic significantly reduced HTR8/Svneo proliferation after 60h (n=3/group). (D) miR-19b-3p mimic significantly increased HOXA9 expression but had no effect on PTEN. Alignment of miR-19b-3p and the 3`UTR of HOXA9 and PTEN is also shown. Data shows mean ± SEM; *P<0.05; **P<0.01; ***P<0.001; (A–C), repeated measures ANOVA; (D), paired t-test.* ## Discussion Here we showed for the first time that decidualization was associated with a global repression of miR release by hESFs. We found that endometrial tissue collected from women with a history of early pregnancy loss had significantly higher mir-19b-3p production conmpared to fertile controls and transfection of miR-19b-3p mimic to HTR8/Svneo trophoblast cells significantly impaired cell proliferation and increased HOXA9 mRNA production. Our observation that global miR release was reduced in decidualized hESF is striking. Released miRs can be transferred to another cell, triggering actions in target cells [15]. Certainly, decidualized cell secretions promote decidualization of surrounding stromal cells [1], regulate uterine-resident lympocyte recruitment and differentiation [29] and promote trophoblast invasion [8, 30, 31]. We hypothesize hESF-released miRs would be taken up by surrounding cells (eg. other decidual cells, trophoblast, immune and endothelial cells) and our data suggests that decidualization may release these other cells from hESF mediated control. We did not investigate the mechanism by which this repression in miR release occurs, however extracellular vesicle production is increased following decidualization with cAMP [32], suggesting that there may be a change in other methods of release (eg argonaute proteins). Whether argonaute proteins in hESF are regulated by decidualization has not been investigated. It was somewhat surprising that we didn’t see a change in cellular miRs using this in vitro model as have been seen in other models that investigated only cellular miRs following in vitro decidualization, including miR-181a (downregulated 3-fold) and miR-409-5p (upregulated 2.3-fold) [17]. We saw a non-significant trend to increased miR-409-5p cellular expression and a significant increase in production in the 1st trimester decidua compared to late secretory phase endometrium. To exclude the direct effect of oestradiol or MPA on miR release in this study we collected cells and culture media 3 days after initiating the decidualization treatments. Although there is negligible PRL secretion on day 3, it is possible that alterations to miR production are initiated early in decidualization and we may have seen an effect on miR production if we compared hESF before and after decidualization hormone treatment as is done in other studies. Collectively, previous studies and our results suggest that dysregulated miR-19b-3p production may be involved in the etiology of recurrent pregnancy loss. We found that miR-19b-3p was significantly elevated in the cycling endometrium of patients with a history of early pregnancy loss and Tian et al. showed that miR-19b-3p is decreased in the placental villous of patients with a history of recurrent early pregnancy loss [20]. Furthermore, miR-19b-3p is dysregulated in monocytes from patients with antiphospholipid syndrome [33], an acquired thrombophilia diagnosed in 15-$20\%$ of patients with recurrent early pregnancy loss [5]. The function of miR-19b-3p appears highly cell type specific. In trophoblast miR-19b-3p overexpression prevents syncytialization of primary human cytotrophoblasts [34], decreased PTEN production in JEG-3 [20] and here we found miR-19b-3p impaired HTR8/Svneo trophoblast proliferation. In other tissues miR-19b-3p mostly promotes proliferation (35–39). The inhibition of proliferation by miR-19b-3p mimic seen here may be due to the increase in HOXA9 production also stimulated by the miR-19b-3p mimic: HOXA9 inhibits HTR8/Svneo proliferation [40], migration and invasion [41]. A role for miR-19b-3p in inflammation is also proposed, however again the function of miR-19b-3p in regulating inflammatory responses is not clear. miR-19b-3p increases apoptosis and intracellular reactive oxygen species in endothelial cells [42], enhances Th1/M1 inflammatory responses (43–45) and inhibits Treg differentiation [43], but may also promote M2 polarization [46, 47]. That miR-19b-3p may be pro-inflammatory in pregnancy is suggested by elevated levels in maternal plasma of pregnancies with gestational diabetes mellitus [48, 49], and preterm birth [50], both inflammatory conditions. Loss of miR-19b-3p in the endometrium during decidualization therefore may be crucial to promote trophoblast differentiation and maternal tolerance. In conclusion, we found that in vitro decidualization was associated with reduced miR release and that overexpression of miR-19b-3p was found in endometrial tissue from patients with history of early pregnancy loss. Finally, we found that miR-19b-3p impaired HTR8/Svneo proliferation implying a role for this decidual-released miR in trophoblast function. Overall we speculate that decidualization may act to reduce endometrial stromal cell regulation of other cell types within the decidua, particularly trophoblast, enabling healthy implantation and placentation during early pregnancy. ## Data availability statement The original contributions presented in the study are included in the article/ Supplementary Material, Further inquiries can be directed to the corresponding author/s. ## Ethics statement The studies involving human participants were reviewed and approved by Royal Women’s Hospital Research Ethics Committee Monash Health Human Research Ethics Committee. The patients/participants provided their written informed consent to participate in this study. ## Author contributions EM conception, design, experiments, wrote manuscript. TS experiments. KR experiments. SB experiments. WZ experiments, edited manuscript. TE design, samples, edited manuscript. ED design, edited manuscript. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2023.1149786/full#supplementary-material ## References 1. Evans J, Salamonsen LA, Winship A, Menkhorst E, Nie G, Gargett CE. **Fertile ground: human endometrial programming and lessons in health and disease**. *Nat Rev Endocrinol* (2016) **12**. DOI: 10.1038/nrendo.2016.116 2. Mori M, Bogdan A, Balassa T, Csabai T, Szekeres-Bartho J. **The decidua - the maternal bed embracing the embryo-maintains the pregnancy**. *Sem Immunopath* (2016) **38**. DOI: 10.1007/s00281-016-0574-0 3. Founds S, Conley YP, Lyons-Weiler JF, Jeyabalan A, Allen Hogge W, Conrad KP. **Altered global gene expression in first trimester placentas of women destined to develop preeclampsia**. *Placenta* (2009) **30** 15-24. DOI: 10.1016/j.placenta.2008.09.015 4. Lucas ES, Dyer NP, Murakami K, Lee YH, Chan YW, Grimaldi G. **Loss of endometrial plasticity in recurrent pregnancy loss**. *Stem Cells* (2016) **34**. DOI: 10.1002/stem.2222 5. Dimitriadis E, Menkhorst E, Saito S, Kutteh WH, Brosens JJ. **Recurrent pregnancy loss**. *Nat Rev Dis Primers* (2020) **6** 98. DOI: 10.1038/s41572-020-00228-z 6. Graham CH, Lysiak JJ, McCrae KR, Lala PK. **Localization of transforming growth factor-β at the human fetal-maternal interface: Role in trophoblast growth and Differentiation1**. *Biol Reproduction.* (1992) **46**. DOI: 10.1095/biolreprod46.4.561 7. Xu G, Guimond M-J, Chakraborty C, Lala PK. **Control of proliferation, migration, and invasiveness of human extravillous trophoblast by decorin, a decidual Product1**. *Biol Reprod* (2002) **67**. DOI: 10.1095/biolreprod67.2.681 8. Menkhorst EM, Van Sinderen M, Correia J, Dimitriadis E. **Trophoblast function is altered by decidual factors in gestational-dependant manner**. *Placenta* (2019) **80** 8-11. DOI: 10.1016/j.placenta.2019.03.013 9. Keskin D, Allan DSJ, Rybalov B, Andzelm MM, Stern JNH, Kopcow HD. **TGFβ promotes conversion of CD16+ peripheral blood NK cells into CD16- NK cells with similarities to decidual NK cells**. *PNAS* (2007) **104**. DOI: 10.1073/pnas.0611098104 10. Xu X, Wang Q, Deng B, Wang H, Dong Z, Qu X. **Monocyte chemoattractant protein-1 secreted by decidual stromal cells inhibits NK cells cytotoxicity by up-regulating expresssion of SOCS3**. *PloS One* (2012) **7**. DOI: 10.1371/journal.pone.0041869 11. Wheeler K, Jena MK, Pradhan BS, Nayak N, Das S, Hsu C-D. **VEGF may contribute to macrophage recruitment and M2 polarization in the decidua**. *PloS One* (2018) **13**. DOI: 10.1371/journal.pone.0191040 12. Salker M, Teklenburg G, Molokhia M, Lavery S, Trew G, Aojanepong T. **Natural selection of human embryos: impaired decidualization of endometrium disables embryo-maternal interactions and causes recurrent pregnancy loss**. *PloS One* (2010) **5**. DOI: 10.1371/journal.pone.0010287 13. Brosens JJ, Salker MS, Teklenburg G, Nautiyal J, Salter S, Lucas ES. **Uterine selection of human embryos at implantation**. *Sci Rep* (2014) **4**. DOI: 10.1038/srep03894 14. Chew C, Conos S, Unal B, Tergaonkar V. **Noncoding RNAs: master regulators of inflammatory signaling**. *Trends Mol Med* (2018) **24** 66-84. DOI: 10.1016/j.molmed.2017.11.003 15. Fabbri M. **MicroRNAs and miRceptors: a new mechanism of action for intercellular communication**. *Philos Trans R Soc Lond B Biol Sci* (2018) **373**. DOI: 10.1098/rstb.2016.0486 16. Park Y. **MicroRNA exocytosis by vesicle fusion in neuroendocrine cells**. *Front Endocrinol* (2017) **8**. DOI: 10.3389/fendo.2017.00355 17. Estella C, Herrer I, Moreno-Moya JM, Quiñonero A, Martínez S, Pellicer A. **miRNA signature and dicer requirement during human endometrial stromal decidualization**. *PloS One* (2012) **7**. DOI: 10.1371/journal.pone.0041080 18. Wang JM, Gu Y, Zhang Y, Yang Q, Zhang X, Yin L. **Deep-sequencing identification of differentially expressed miRNAs in decidua and villus of recurrent miscarriage patients**. *Arch Gynecol Obstet* (2016) **293**. DOI: 10.1007/s00404-016-4038-5 19. Hong L, Yu T, Xu H, Hou N, Cheng Q, Lai L. **Down-regulation of miR-378a-3p induces decidual cell apoptosis: a possible mechanism for early pregnancy loss**. *Hum Reprod* (2018) **33** 11-22. DOI: 10.1093/humrep/dex347 20. Tian S, Yu J, Zhang Y, Bian Y, Ma J, Yan J. **Overexpression of PTEN regulated by miR-19b and miR-494 in the villous of recurrent spontaneous abortion patients**. *J Reprod Immunol* (2020) **140** 103133. DOI: 10.1016/j.jri.2020.103133 21. Menkhorst EM, Van Sinderen ML, Rainczuk K, Cuman C, Winship A, Dimitriadis E. **Invasive trophoblast promote stromal fibroblast decidualization**. *Sci Rep* (2017) **7** 8690. DOI: 10.1038/s41598-017-05947-0 22. Dimitriadis E, Robb L, Salamonsen LA. **Interleukin 11 advances progesterone-induced decidualization of human endometrial stromal cells**. *Mol Hum Reprod* (2002) **8**. DOI: 10.1093/molehr/8.7.636 23. Grbac E, So T, Varshney S, Williamson N, Dimitriadis E, Menkhorst E. **Prednisolone alters endometrial decidual cells and affects decidual-trophoblast interactions**. *Front Cell Dev Biol* (2021) **9**. DOI: 10.3389/fcell.2021.647496 24. Cuman C, Van Sinderen M, Gantier MP, Rainczuk K, Sorby K, Rombauts L. **Human blastocyst secreted microRNA regulate endometrial epithelial cell adhesion**. *EBioMedicine* (2015) **2**. DOI: 10.1016/j.ebiom.2015.09.003 25. Menkhorst E, Zhou W, Santos L, Delforce S, So T, Rainczuk K. **Galectin-7 impairs placentation and causes preeclampsia features in mice**. *Hypertension* (2020) **76**. DOI: 10.1161/HYPERTENSIONAHA.120.15313 26. Barton S, Zhou W, Santos L, Menkhorst E, Yang G, Tinn Teh W. **miR-23b-3p regulates human endometrial epithlial cell adhesion implying a role in implantation**. *Reproduction* (2023) **165**. DOI: 10.1530/REP-22-0338 27. Liu W, Wang X. **Prediction of functional microRNA targets by integrative modeling of microRNA binding and target expression data**. *Genome Biol* (2019) **20** 18. DOI: 10.1186/s13059-019-1629-z 28. Chen Y, Wang X. **miRDB: an online database for prediction of functional microRNA targets**. *Nucleic Acids Res* (2020) **48** D127-d131. DOI: 10.1093/nar/gkz757 29. Vinketova K, Mourdjeva M, Oreshkova T. **Human decidual stromal cells as a component of the implantation niche and a modulator of maternal immunity**. *J Pregnancy* (2016) **2016** 8689436. DOI: 10.1155/2016/8689436 30. Menkhorst EM, Lane N, Winship AL, Li P, Yap J, Meehan K. **Decidual-secreted factors alter invasive trophoblast membrane and secreted proteins implying a role for decidual cell regulation of placentation**. *PloS One* (2012) **7**. DOI: 10.1371/journal.pone.0031418 31. Menkhorst E, Winship A, Van Sinderen M, Dimitriadis E. **Human extravillous trophoblast invasion: intrinsic and extrinsic regulation**. *Reprod Fertil Dev* (2016) **28**. DOI: 10.1071/RD14208 32. Ma Q, Beal JR, Bhurke A, Kannan A, Yu J, Taylor RN. **Extracellular vesicles secreted by human uterine stromal cells regulate decidualization, angiogenesis, and trophoblast differentiation**. *PNAS* (2022) **119**. DOI: 10.1073/pnas.2200252119 33. Juárez-Vicuña Y, Guzmán-Martín CA, Martínez-Martínez LA, Hernández-Díazcouder A, Huesca-Gómez C, Gamboa R. **miR-19b-3p and miR-20a-5p are associated with the levels of antiphospholipid antibodies in patients with antiphospholipid syndrome**. *Rheumatol Int* (2021) **41**. DOI: 10.1007/s00296-021-04864-w 34. Kumar P, Luo Y, Tudela C, Alexander JM, Mendelson CR. **The c-myc-regulated microRNA-17~92 (miR-17~92) and miR-106a~363 clusters target hCYP19A1 and hGCM1 to inhibit human trophoblast differentiation**. *Mol Cell Biol* (2013) **33**. DOI: 10.1128/MCB.01228-12 35. Jiang T, Ye L, Han Z, Liu Y, Yang Y, Peng Z. **miR-19b-3p promotes colon cancer proliferation and oxaliplatin-based chemoresistance by targeting SMAD4: validation by bioinformatics and experimental analyses**. *J Exp Clin Cancer Res* (2017) **36**. DOI: 10.1186/s13046-017-0602-5 36. Daguia Zambe JC, Zhai Y, Zhou Z, Du X, Wei Y, Ma F. **miR-19b-3p induces cell proliferation and reduces heterochromatin-mediated senescence through PLZF in goat male germline stem cells**. *J Cell Physiol* (2018) **233**. DOI: 10.1002/jcp.26231 37. Xiaoling G, Shuaibin L, Kailu L. **MicroRNA-19b-3p promotes cell proliferation and osteogenic differentiation of BMSCs by interacting with lncRNA H19**. *BMC Med Genet* (2020) **21**. DOI: 10.1186/s12881-020-0948-y 38. Wang Q, Dong Y, Wang H. **microRNA-19b-3p-containing extracellular vesicles derived from macrophages promote the development of atherosclerosis by targeting JAZF1**. *J Cell Mol Med* (2022) **26** 48-59. DOI: 10.1111/jcmm.16938 39. Li ZL, Li D, Yin GQ. **MiR-19b-3p promotes tumor progression of non-small cell lung cancer**. *Histol Histopathol* (2022) **37**. DOI: 10.14670/HH-18-448 40. Shi Z, Liu B, Li Y, Liu F, Yuan X, Wang Y. **MicroRNA-652-3p promotes the proliferation and invasion of the trophoblast HTR-8/SVneo cell line by targeting homeobox A9 to modulate the expression of ephrin receptor B4**. *Clin Exp Pharmacol Physiol* (2019) **46**. DOI: 10.1111/1440-1681.13080 41. Liu X, Liu X, Liu W, Luo M, Tao H, Wu D. **HOXA9 transcriptionally regulates the EPHB4 receptor to modulate trophoblast migration and invasion**. *Placenta* (2017) **51** 38-48. DOI: 10.1016/j.placenta.2017.01.127 42. Xue Y, Wei Z, Ding H, Wang Q, Zhou Z, Zheng S. **MicroRNA-19b/221/222 induces endothelial cell dysfunction**. *Atherosclerosis* (2015) **241**. DOI: 10.1016/j.atherosclerosis.2015.06.031 43. Jiang S, Li C, Olive V, Lykken E, Feng F, Sevilla J. **Molecular dissection of the miR-17-92 cluster's critical dual roles in promoting Th1 responses and preventing inducible treg differentiation**. *Blood* (2011) **118**. DOI: 10.1182/blood-2011-05-355644 44. Yin L, Song C, Zheng J, Fu Y, Qian S, Jiang Y. **Elevated expression of miR-19b enhances CD8+ T cell function by targeting PTEN in HIV invected long term non-progressors with sustained viral suppression**. *Front Immunol* (2019) **9**. DOI: 10.3389/fimmu.2018.03140 45. Lv L-L, Feng Y, Wu M, Wang B, Li Z-L, Zhong X. **Exosomal miRNA-19b-3p of tubular epithelial cells promotes M1 macrophage activation in kidney injury**. *Cell Death Differ* (2020) **27**. DOI: 10.1038/s41418-019-0349-y 46. Chen J, Zhang K, Zhi Y, Wu Y, Chen B, Bai J. **Tumor-derived exosomal miR-19b-3p facilitates M2 macrophage polarization and exosomal LINC00273 secretion to promote lung adenocarcinoma metastasis**. *Clin Transl Med* (2021) **11**. DOI: 10.1002/ctm2.478 47. Jiahui C, Jiadai Z, Nan Z, Rui Z, Lipin H, Jian H. **miR-19b-3p/PKNOX1 regulates viral myocarditis by regulating macrophage polarization**. *Front Genet* (2022) **13**. DOI: 10.3389/fgene.2022.902453 48. Zhu Y, Tian F, Li H, Zhou Y, Lu J, Ge Q. **Profiling maternal plasma microRNA expression in early pregnancy to predict gestational diabetes mellitus**. *Int J Gynecology Obstetrics* (2015) **130** 49-53. DOI: 10.1016/j.ijgo.2015.01.010 49. Li J, Gan B, Lu L, Chen L, Yan J. **Expression of microRNAs in patients with gestational diabetes mellitus: a systematic review and meta-analysis**. *Acta Diabetol* (2022). DOI: 10.1007/s00592-022-02005-8 50. Cook J, Bennett PR, Kim SH, Teoh TG, Sykes L, Kindinger LM. **First trimester circulating MicroRNA biomarkers predictive of subsequent preterm delivery and cervical shortening**. *Sci Rep* (2019) **9**. DOI: 10.1038/s41598-019-42166-1
--- title: Integrative network-based analysis on multiple Gene Expression Omnibus datasets identifies novel immune molecular markers implicated in non-alcoholic steatohepatitis authors: - Jun-jie Zhang - Yan Shen - Xiao-yuan Chen - Man-lei Jiang - Feng-hua Yuan - Shui-lian Xie - Jie Zhang - Fei Xu journal: Frontiers in Endocrinology year: 2023 pmcid: PMC10061151 doi: 10.3389/fendo.2023.1115890 license: CC BY 4.0 --- # Integrative network-based analysis on multiple Gene Expression Omnibus datasets identifies novel immune molecular markers implicated in non-alcoholic steatohepatitis ## Abstract ### Introduction Non-alcoholic steatohepatitis (NASH), an advanced subtype of non-alcoholic fatty liver disease (NAFLD), has becoming the most important aetiology for end-stage liver disease, such as cirrhosis and hepatocellular carcinoma. This study were designed to explore novel genes associated with NASH. ### Methods Here, five independent Gene Expression Omnibus (GEO) datasets were combined into a single cohort and analyzed using network biology approaches. ### Results 11 modules identified by weighted gene co-expression network analysis (WGCNA) showed significant association with the status of NASH. Further characterization of four gene modules of interest demonstrated that molecular pathology of NASH involves the upregulation of hub genes related to immune response, cholesterol and lipid metabolic process, extracellular matrix organization, and the downregulation of hub genes related to cellular amino acid catabolic, respectively. After DEGs enrichment analysis and module preservation analysis, the Turquoise module associated with immune response displayed a remarkably correlation with NASH status. *Hub* genes with high degree of connectivity in the module, including CD53, LCP1, LAPTM5, NCKAP1L, C3AR1, PLEK, FCER1G, HLA-DRA and SRGN were further verified in clinical samples and mouse model of NASH. Moreover, single-cell RNA-seq analysis showed that those key genes were expressed by distinct immune cells such as microphages, natural killer, dendritic, T and B cells. Finally, the potential transcription factors of Turquoise module were characterized, including NFKB1, STAT3, RFX5, ILF3, ELF1, SPI1, ETS1 and CEBPA, the expression of which increased with NASH progression. ### Discussion In conclusion, our integrative analysis will contribute to the understanding of NASH and may enable the development of potential biomarkers for NASH therapy. ## Introduction Non-alcoholic fatty liver disease (NAFLD) is likely to become the most common chronic liver disease, affecting about $25\%$ in the adult population [1]. It is characterized by excessive accumulation of hepatic triacylglycerol (TG) and encompasses a spectrum of liver pathologies ranging from isolated steatosis (non-alcoholic fatty liver, NAFL) to non-alcoholic steatohepatitis (NASH), a more severe form of fatty liver disease featured by lobular inflammatory infiltrates, hepatocyte ballooning and fibrosis [2]. Up to $30\%$ of the patients with NAFLD will process to NASH [3], which may eventually progress to cirrhosis, hepatocellular carcinoma (HCC) and liver failure [4]. Moreover, NASH is considered the hepatic manifestation of metabolic syndrome, commonly alongside serious extrahepatic diseases, such as dyslipidemia, hypertension, obesity and type 2 diabetes mellitus (T2DM) [5, 6], and multiple pathogenic pathways are involved in NASH progression. Previous studies have contributed greatly to our understanding of genetic and environmental risk factors in the pathogenesis of NAFLD. Genome-wide association studies (GWAS) have revealed genetic variants in several loci (PNPLA3, TM6SF2, GCKR, MTARC1 and HSD17B13) that promote NAFLD risks in humans (7–11), which highlights the dysregulation of gene expression and/or function as an important players in the development and progression of NASH. Integrating multi-omics approaches including genomics, transcriptomics, proteomics and metabolomics have provided additional insights (12–15), which may not be elucidated by genomics analysis alone. In addition, previous bioinformatics analyses in cross-sectional studies have facilitated the exploration of potential biomarkers related to NAFLD/NASH (16–19). However, for complex disease trait, the comprehensive molecular characterization of NASH are still not entirely deciphered. As a consequence, no effective pharmacological therapies targeting NASH are presently available. Hence, further exploration into the molecular pathogenesis of NASH and diagnostic biomarkers are essential to build novel approaches for management of NASH. Network biology approaches have proven effective for uncovering new perturbed pathways underlying molecular pathology [18, 20, 21]. Contrary to traditional differential expression analysis methods based on gene expression profiling, network-based approaches investigate the correlation among changing genes from a systematic perspective. *Weighted* gene co-expression network analysis (WGCNA) has become a frequently used method for multigene analysis, which establishes gene sets (modules) from observed gene expression data using unsupervised hierarchical clustering. WGCNA is widely used for exploring the relationship between diverse gene sets and clinical features [22, 23], providing insights into functions of co-expression gene modules and detecting hub genes related to the clinical characteristics of various diseases [24, 25]. In the present work, we aimed to identify deregulated modules, hub genes and transcription factors (TFs) associated with NASH by integrating transcriptomic data with biological network analysis between normal liver tissues and NASH tissues. We obtained five liver transcriptome datasets from the Gene Expression Omnibus (GEO) database [26]. We first generated MergeCohort by merging five pre-processed datasets. Based on the combining expression matrix, differentially expressed gene (DEG) analysis was performed to identify genes associated with NASH. After that, through integrative analyses of co-expression gene network, functional annotation, TF-target regulatory network and validation analysis, we detected several promising candidate biomarkers for NASH. Our integrative study provides a comprehensive view on the molecular processes of NASH and may discover potential therapeutic target for NASH treatment. ## Data collection We obtained the expressing profiles of mRNA of NASH and normal control from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/) [26]. We searched the microarray and next-generation sequencing (NGS) studies with the keywords: “Fatty liver”, “Non-alcoholic”, “Gene expression”, “Homo sapiens”, “Microarray” and “RNA sequencing”. Datasets were selected based on the following criterial [1]: Containing at least 10 total samples [2]; Samples must Contain at least five patients in both NASH group and healthy control group [3]; *Raw data* or gene expression profiles were available in GEO [4]. Pathways related to lipid metabolism, inflammation and fibrosis were significantly (normalized enrichment score (NES) more than 1.0 and a false discovery rate (FDR) below 0.25) enriched between the two groups in the gene set enrichment analysis (GSEA) (Supplementary Tables S2, S3), which was carried out with the Java GSEA (version 3.0) [27] platform with the ‘Signal2Noise’ metric to create a ranked list and a ‘gene set’ permutation type. The flowchart was shown in Figure 1. **Figure 1:** *Flowchart.* ## Data processing For each dataset, we download raw expression data and pre-processed using standard approaches. Specially, gene chip datasets were normalized by the robust multi-average (RMA) method with oligo/Bioconductor [28]. For RNA-seq datasets, reads count information were generated by StringTie using a Python script (prepDE.py) and raw counts were normalized across samples following TMM method in edgeR package. After filtering low abundance expression genes and outlier samples, we applied the ComBat (version 3.20.0) method in the sva R package to remove the batch effects [29] from five datasets (GSE48452, GSE37031, GSE61260, GSE63067 and GSE130970) and combined these five datasets into a single cohort (MergeCohort), which contains 67 normal and 97 NASH tissue samples. Subsequently, the expression matrix of MergeCohort was used for differentially expressed genes (DEGs) identification between NASH and healthy control samples. It is worth noticing that we applied Wilcoxon’s rank-sum test to assess the differential expression, the corrected threshold was p less than 0.05, and the absolute difference of means more than 0.3. Gene ontology (GO) and Reactome enrichment analyses were performed for DEGs using hypergeometric test, which is conducted by the python package gseapy (version 0.9.16; https://github.com/zqfang/gseapy), all gene sets of GO term and Reactome pathway were obtained from database source of Enrichr [30]. Only GO terms or Reactome pathways were considered as significantly enriched by using the criterion with a corresponding p value less than 0.05. ## Weight gene co-expression network construction, module detection and preservation analysis of theco-expression modules 5,000 transcripts with maximal variability across all patients ($$n = 164$$) based on the median absolute deviation in the MergeCohort were kept for WGCNA and tested by the WGCNA R package [22]. In our work, the power threshold of 5 was selected to calculate biweight midcorrelations and weighted adjacency matrix, the soft thresholding parameter was defined using the scale-free topology fit model. We identified the gene modules based on the ‘hybrid’ method and parameters deepSplit = 4, mergeCutHeight = 0.15 and minModuleSize = 50. Modules are identified as branches in the dendrogram with Dynamic Tree Cut algorithm [22]. Subsequently, we assessed the relevance of a module eigengene (ME) to the disease status using the Pearson correlation. An intramodular connectivity (Kin) was defined to measure for each gene on the base of its correlation with the remaining genes in a given module. Genes with highest Kin are identified as hub genes. Cytoscape version 3.8.2 was used for visualization. In order to understand the extent of module preservation in MergeCohort, a publicly available expression profiling of high throughput RNA sequencing dataset GSE135251 including 10 controls, 51 NAFL and 155 NASH was used, processed as described above. Module preservation analysis was carried out by using Module preservation function in WGCNA package introduced by Langfelder et al. [ 31] and described in detail in Oldham et al. [ 32]. Moreover, to investigate the module similarity among different cohorts, we applied hypergeometric test to evaluate whether the genes from each MergeCohort module significantly overlapped with the genes from each of GSE135251 module. The overlap was regarded as significant when p value below 0.05. ## Functional annotation of the modules In order to determine the functional significance of the identified modules, we firstly performed GO and KEGG pathway enrichment analysis for the gene lists of each module of co-expression network on the basis of Enrichr [30] as described above. Moreover, we carried out disease enrichment analysis for the gene lists of each module by using DisGeNet [33]. The statistical significance threshold level for all disease terms was p value less than 0.05 (Benjamini-Hochberg corrected for multiple comparisons) and we presented top 20 for each disease-associated module. Additionally, to obtain regulatory information of transcription factors (TFs) and target genes, Transcriptional Regulatory Relationships Unraveled by Sentence based Text mining (TRRUST) v2 database (https://www.grnpedia.org/trrust/) [34] were supplied for Enrichr [30], conducted by the python package gseapy (version 0.9.16; https://github.com/zqfang/gseapy). In addition, ChIP-X Enrichment Analysis 3 (ChEA3) database (https://maayanlab.cloud/chea3/) [35] was adopted to further validate the significantly enriched transcription factors over module genes. After obtaining TF–target regulatory relationships, a TF-target network, which contained TFs regulating Turquoise modules’ genes, was reconstructed. ## Single cell RNA-sequencing analyses We investigated the expression patterns of top 25 hub genes in Turquoise module using scRNA-seq analyses of human liver tissues from public scRNA-seq data (GSE136103) [36]. In our study, only four samples including two healthy liver tissue samples (GSM4041156 and GSM4041159) and two NAFLD liver tissue samples (GSM4041162 and GSM4041163) were analyzed with Seurat package (version 3.1.5) [37]. First, 2000 highly variable genes ($$n = 2$$,000) were identified using the R package SCTransfom (version 0.2.1). Subsequently, principal component analysis was performed, and the appropriate principal components (PCs) for dimensionality reduction were decided using the JackStraw function. Clusters were identified with the Seurat function FindClusters with the resolution set at 0.4. This method resulted in 18 clusters, which were visualized by Uniform Manifold Approximation and Projection (UMAP) analysis. Clusters were then annotated by using the expression of known genes. We annotated cell types based on cell markers and the R package SingleR [36, 38]. ## Information of included GEO datasets According to the previously established inclusion criteria, GSE48452, GSE37031, GSE61260, GSE63067 and GSE130970 were included in this study. There are 104 NASH patients and 70 controls in these five datasets. After outlier removal, 97 NASH patients and 67 controls were retained in the following analysis. The detail information of the five datasets was shown in Supplementary Table S1. In order to eliminate the bath effect from different platforms and batches, we used the combat function to eliminate the batch effect from five datasets. A total of 12579 genes were detected by merging different platforms. Before removing the batch effect, samples were clusters in batch according to the top two principal components (PCs) of the expression values before normalization (Figure S1A). In contrast, when the samples from five platforms were merged, the overall expression in the samples was uniformly distributed based on principal component analysis, suggesting that the batch effect caused by different platforms that had effect on the estimation of molecular biological differences was successfully corrected (Figure S1B). In addition, we used dataset GSE135251 as the validation dataset in this study. ## Identification of DEGs in the NASH patients Principle component analysis plot of the gene expression matrix of five combined dataset (MergeCohort) distinguished between NASH and control group is shown in Figure 2A. Total of 831 DEGs (Benjamin-Hochberg adjusted p value < 0.05, absolute difference of mean > 0.3) among control and NASH in MergeCohort were identified, consisting of 600 upregulated and 231 downregulated DEGs (Figure 2B; Supplementary Table S4). **Figure 2:** *Overview of combining gene expression profiles in healthy controls and nonalcoholic steatohepatitis (NASH) patients. (A) Principle component plot of samples based on top 500 most variable gene expression from combining gene expression profiles (MergeCohort). NASH patients are marked in red; healthy controls are marked in green. (B) Volcano plot of differentially expressed genes (DEGs) between NASH patients and healthy controls. DEGs are listed in Supplemental Table S4 . 600 genes upregulated and 200 genes downregulated are shown in red and blue, respectively. (C) Top 10 enriched biological functions of DEGs determined by Gene Ontology (GO) enrichment analysis. (D) Top 10 enriched Reactome pathways of DEGs determined by Reactome pathway enrichment analysis.* ## Function and pathway enrichment analysis of DEGs In the present study, we performed GO and Reactome pathway enrichment analysis to determine the potential functions of 831 DEGs in the pathogenesis of NASH. The biological process analysis (Figure 2C; Supplementary Table S5) revealed that in the NASH, these genes were associated with multiple immunity-related pathways, such as the cytokine-mediated signaling pathway, cellular response to cytokine stimulus and neutrophil activation involved in immune response. Several ECM-related pathways were also enriched such as extracellular matrix organization and extracellular structure organization. Moreover, metabolic process, such as cholesterol metabolic process, fatty acid metabolic process, cholesterol biosynthetic process and other biological process (Supplementary Table S5) were also identified. Reactome pathway analysis was performed to investigate the pathway based on the DEGs (Supplementary Table S6). The top 10 pathways are shown in Figure 2D. Among them, metabolism, metabolism of lipids and lipoproteins, extracellular matrix organization, immune system, chemokine receptors bind chemokines were significantly enriched. Therefore, the outcomes above suggested that metabolism, ECM-related pathways and immunity-related pathways play an important role in development and procession of NASH. ## WGCNA and identification of module associated with NASH disease status To capture discrete groups of co-expression genes correlated with NASH status and to integrate the identified expression divergences into a higher system level context, a co-expression network analysis (WGCNA) was conducted based on the top 5000 median absolute deviation (MAD) genes from the MergeCohort. Keep to the scale-free topology criterion, β=5 was considered in this study (Figure 3A). According to dynamic tree cut, the hierarchical clustering dendrogram resulted in 17 different gene modules, as displayed in Figure 3B. 909 genes failed to fit within a distinct group and were assigned to the Grey module which was neglected in the present study. The size of modules ranged from 86 (Grey60 module) to 734 (Turquoise module) (Figure 3C). DEGs enrichment in each module was shown in Figure 3D, in which upregulated genes was mostly significantly enriched in Turquoise ($$n = 233$$, $$p \leq 1.93$$ × 10-44), and followed by Cyan ($$n = 54$$, $$p \leq 1.24$$ × 10-15), Grey60 ($$n = 40$$, $$p \leq 2.05$$ × 10-13), Tan ($$n = 48$$, $$p \leq 1.59$$× 10-9) and Magenta ($$n = 47$$, $$p \leq 2.77$$ × 10-4), downregulated genes was significantly enriched in Black ($$n = 107$$, $$p \leq 9.25$$ × 10-86) and Brown module ($$n = 68$$, $$p \leq 1.07$$ × 10-24). To investigate which co-expression modules are associated with NASH status, we then correlated the expression of eigengenes (genes representing the expression profile of each module) with NASH status. The relationship between all the modules and the NASH status are displayed in a correlation heatmap, in which Y-axis corresponds to groups of genes (modules) and the X-axis represents the NASH status (Figure 3E). Of the 17 co-expression modules, 11 WGCNA modules to be correlated with NASH status at a Pearson correlation ($p \leq 1.47$ × 10-3), which is determined based on Bonferroni correction. Among them, nine modules (Cyan, Grey60, Turquoise, Magenta, Purple, Lightcyan, Tan, Midnightblue and Blue) were positively correlated with NASH disease status, two modules (Black and Brown) were negatively associated with NASH disease status (Figure 3E). **Figure 3:** *WGCNA network and module identification. (A) Soft-thresholding calculation of MergeCohort. The left panel displays the scale-free fit index versus soft-thresholding power. The right panel shows the mean connectivity versus soft-thresholding power. Power 5 was selected, for which the fit index curve flattens out upon reaching a high value (> 0.9). (B) The Cluster dendrogram of co-expression network modules from WGCNA depending on a dissimilarity measure (1-TOM). The leaves in the tree represent genes and the colors in the horizontal bar indicate co-expression module determined by the dynamic tree cut algorithm. (C) Number of genes in each module. (D) Enrichment of upregulated and downregulated DEGs in each module. (E) Heatmap showing the association between module eigengenes (rows) and NASH disease status (column). Associated p values were computed using the cor.test R function. The color scale in the heat map represents the magnitude of the Pearson correlation coefficients. Number in each cell contained corresponding correlation coefficient and p value (in brackets). WGCNA, weighted gene correlation network analysis; TOM, topological overlap matrix.* ## Functional characterization of co-expression modules of interest Because we were more concerned about the modules whose expression was different between NASH and control group, we compared the eigengenes from NASH samples to the expression of control in every module, and these results were used to further assess whether the modules were associated with NASH status. Modules Cyan, Grey60 and Turquoise exhibited an upregulation of the eigengenes in NASH, whereas module black showed lower expression in NASH (Figure 4A). In order to investigate whether the co-expression modules cover the information associated with validated networks, the existing data on protein-protein interactions from the STRING database was used to test the biological characteristics of the detected modules in this study. All the modules showed significant enrichment in interactions ($p \leq 0.01$), therefore indicating that the modules detected in the present work are biologically relevant (Supplementary Table S7). In addition, the NASH status positively correlated modules showed much higher average node degree (AND), particularly module Turquoise (AND = 22.4). **Figure 4:** *Functional characterization of co-expression modules of interest identified by WGCNA. (A) Box and Whisker plots representing the expression of module eigengenes Turquoise, Grey60, Cyan, Black between NASH (n = 97) and healthy control (n = 67) samples. Data are presented as median with first and third quartiles as the box edges. Differences between group were estimated by Student’s t test. (B–E) The network of hub genes (module genes within the top 25 genes with the highest intromodular connectivity values (kWithin)) (left panel) and top GO terms (right panel) of the modules Turquoise (B), Grey60 (C), Cyan (D) and Black (E) are shown. In the network diagrams, node sizes correspond to kWithin in the module. For the bars plot, the bars in the GO enrichment results represent the -log10(pvalue). (F) Scatterplots of module eigengenes show positive correlation between Turquoise and Cyan, and negative correlation between Grey60, Cyan, Turquoise and Black, respectively.* We then conducted GO and KEGG pathway enrichment of the NASH-associated modules to further investigate the gene functions by Enrichr. Top biological process and KEGG pathway in each module are shown in Table 1. Turquoise module was upregulated in NASH patients, contained hub genes related to immune response (CD53, LAPTM5, LCP1, NCKAP1L, C3AR1 and FGL2) (Figure 4B), and enriched for GO categories to cytokine-mediated signaling pathway, neutrophil activation involved in immune response and neutrophil degranulation (Figure 4B). Grey60 module with hub genes such as FDFT1, NSDHL, IDI1, SQLE, ACSS2, SREBF2, HMGCR, FASN, LSS, ACAT2, FADS1, FADS2 and ELOVL6 was upregulated in NASH (Figure 4C), which were mainly participating in cholesterol and lipid metabolic process (Figure 4C). The majority of the GO terms enriched in module Cyan were primarily related to extracellular matrix organization and extracellular structure organization (Figure 4D), including hub genes related to fibrosis (PDGFA, LOXL4, MSN, LAMA3 and AKR1B10) (Figure 4D). However, the majority of the GO terms enrich in Black module were related to cellular amino acid catabolic and primary alcohol metabolic process (ACADSB, AASS and ALDH6A1) (Figure 4E). The complete annotation for each module can be found in Supplementary Tables S8, S9. **Table 1** | Module | Category | Term | P-value | FDR | | --- | --- | --- | --- | --- | | Black | GOTERM_BP | Cellular amino acid catabolic process | 2.37 × 10-12 | 3.95 × 10-09 | | Blue | GOTERM_BP | Extracellular matrix organization | 6.18 × 10-37 | 1.57 × 10-33 | | Brown | GOTERM_BP | Cellular amino acid catabolic process | 5.27 × 10-09 | 1.06 × 10-05 | | Cyan | GOTERM_BP | Extracellular matrix organization | 4.82 × 10-07 | 5.88 × 10-04 | | Grey60 | GOTERM_BP | Secondary alcohol biosynthetic process | 2.39 × 10-32 | 1.54 × 10-29 | | Lightcyan | GOTERM_BP | T cell activation | 4.17 × 10-13 | 3.44 × 10-10 | | Magenta | GOTERM_BP | DNA metabolic process | 2.69 × 10-45 | 3.48 × 10-42 | | Midnightblue | GOTERM_BP | IRE1-mediated unfolded protein response | 7.75 × 10-16 | 6.39 × 10-13 | | Purple | GOTERM_BP | Regulation of glycogen metabolic process | 2.31 × 10-06 | 3.06 × 10-03 | | Tan | GOTERM_BP | Neutrophil degranulation | 8.86 × 10-16 | 7.05 × 10-13 | | Turquoise | GOTERM_BP | Cytokine-mediated signaling pathway | 3.47 × 10-39 | 8.55 × 10-36 | | Black | KEGG_PATHWAY | Metabolism of xenobiotics by cytochrome P450 | 2.94 × 10-05 | 3.85 × 10-03 | | Blue | KEGG_PATHWAY | ECM-receptor interaction | 3.54 × 10-19 | 8.42 × 10-17 | | Brown | KEGG_PATHWAY | Glycine, serine and threonine metabolism | 2.24 × 10-08 | 5.78 × 10-06 | | Cyan | KEGG_PATHWAY | Mitophagy | 9.22 × 10-04 | 0.11 | | Grey60 | KEGG_PATHWAY | Steroid biosynthesis | 1.01 × 10-14 | 8.99 × 10-13 | | Lightcyan | KEGG_PATHWAY | Primary immunodeficiency | 1.14 × 10-17 | 1.39 × 10-15 | | Magenta | KEGG_PATHWAY | DNA replication | 5.62 × 10-27 | 7.20 × 10-25 | | Midnightblue | KEGG_PATHWAY | Protein processing in endoplasmic reticulum | 3.05 × 10-21 | 2.75 × 10-19 | | Purple | KEGG_PATHWAY | Axon guidance | 1.62 × 10-04 | 3.11 × 10-02 | | Tan | KEGG_PATHWAY | Cytokine-cytokine receptor interaction | 4.47 × 10-12 | 8.81 × 10-10 | | Turquoise | KEGG_PATHWAY | Osteoclast differentiation | 2.48 × 10-18 | 6.45 × 10-16 | We next explored the relationship of eigengenes among the annotated modules. Upregulated immune Turquoise module was positively correlated with Cyan module related to fibrosis ($r = 0.32$, $$p \leq 3.0$$ × 10-5) (Figure 4F), suggesting that Turquoise module related to immune response that drives fibrosis in NASH, which confirmed the results of previous studies [20]. Interestingly, Cyan, Grey60 and Turquoise modules was negatively correlated with Black module that is enriched in amino acid metabolic processes (Figure 4F). The high negatively correlation (r = -0.77, $$p \leq 2.0$$ × 10-33) between the upregulated fibrosis module Cyan and downregulated Black module that is enriched in metabolic processes (Figure 4F), which indicated that perturbations in amino acid metabolism are likely involved in NASH pathogenesis [39, 40]. ## Module preservation analysis indicates the presence of NASH-associated co-expression module function in immune response To find out whether the identified modules were common in another dataset, we examined the module preservation statistics between the MergeCohort and one recently published large NASH datatset GSE135251 [13]. In particular, we assumed co-expression modules of MergeCohort as reference dataset and the co-expression modules of GSE135251 as test dataset. We utilized the principle described in [22]. The score of Zsummary more than 10 represents strongly preserved module, less than 2 denotes non-preserved module while the value between 2 and 10 implies moderately preserved module. We plotted the scatterplot of Zsummary scores against the sizes of MergeCohort modules (Figure 5A). All modules have a Zsummary statics greater than 2, suggesting that all modules were preserved in GSE135251. The lowest preservation is the Red module (Zsummary = 6.37). Particularly, MergeCohort module Turquoise (MergeCohort_Turquoise) exhibited Zsummary preservation score (Zsummary = 42.68) higher than 40. To provide a more intuitive picture of the preservation of each co-expression module identified, we evaluated module overlaps of MergeCohort and GSE135251 (Figure 5B), we found that MergeCohort_Turquoise show the most significantly overlapping with GSE135251 module Turquoise (GSE135251_Turquoise). Moreover, we discovered a highly positively correlation between the intromodular connectivity of 289 genes overlapped in MergeCohort_Turquoise and GSE135251_Turquoise (Spearman’s correlation = 0.62, $$p \leq 1.3$$ × 10-9) (Figures 6A, B), which indicated those two modules have similar co-expression pattern. **Figure 5:** *Module preservation of MergeCohort in GSE135251 dataset. (A) Preservation Zsummary statistics of MergeCohort in GSE135251 dataset. Each point represents a module. Point color reflects the module color as used in Figures 3B–E of MergeCohort. Points are also labeled by the name of the module. The dashed blue and red lines indicate the rough thresholds for week (Z = 2) and strong (Z = 10) evidence of module preservation. (B) Overlaps of MergeCohort and GSE135251 modules. Each axis is labelled by the corresponding module name. The size of each dot represents the number of overlapping genes in the intersection of corresponding MergeCohort and GSE135251 modules while the color implies -log10 of the hypergeometric enrichment p value.* **Figure 6:** *Functional enrichment of MergeCohort_Turquoise and GSE135251_Turquoise module. (A) Venn diagram displays number of genes overlapped between MergeCohort_Turquoise and GSE135251_Turquoise module. (B) Spearman’s correlation between the kWithin of common genes (n = 289) overlapped between each module. Top 25 hub genes with the highest kWithin from MergeCohort_Turquoise module are shown. (C) Dot-plot heatmap shows top 20 significantly enriched disease by genes in each module. The size of each dot represent the gene counts enriched in each disease term. (D) Dot-plot heatmap shows top 20 significantly enriched KEGG pathways by genes in each module. The size of each dot represents the -log10 of p value for each KEGG pathway term.* To comprehensively evaluate the biological functions related to MergeCohort_Turquoise and GSE135251_Turquoise, we next calculated the statistical significance of enrichment of genes with the association in disease-related gene sets from the DisGeNET database [33] and KEGG pathway gene sets. We observed that genes in MergeCohort_Turquoise and GSE135251_Turquoise were significantly enriched by liver disease-related gene sets (liver cirrhosis) and multiple immune disease-related gene sets (autoimmune disease, immunosuppression and inflammatory bowel disease) (Figure 6C; Supplementary Tables S10, S11). Interestingly, these two modules were also significantly enriched in atherosclerosis and arteriosclerosis. Notably, we observed that genes in MergeCohort_Turquoise, which shows the highest module similarity with GSE135251_Turquoise (289 out of 734; hypergeometric test p value = 5.33 × 10-168) (Figure 6A) are both significant enriched in phagosome, osteoclast differentiation, cell adhesion molecules, antigen processing and presentation, B cell receptor signaling pathway (Figure 6D). In addition, the MergeCohort_Turquoise was upregulated in NASH and is also the third most significant module, and showed the greater number of statistically differential expressed genes, with 233 of the 734 genes being upregulated (fold change > 1.2; $p \leq 0.05$) and none significantly downregulated (Figure 3D). Considering all these results, we will choose the co-expression Turquoise module from MergeCohort for further analysis. ## Validation of hub genes in Turquoise module *Hub* genes were upregulated in the liver from NASH patients. Focusing on the MergeCohort_Turquoise module, we firstly explored the top 25 hub genes including CD53, LCP1, LAPTM5, NCKAP1L, C3AR1, PLEK, FCER1G, HLA-DRA and SRGN that had a high intramodular connectivity (K.in). The expression level of those core genes were all upregulated in four cohorts (GSE130970, GSE48452, GSE61260 and GSE63067) involved in this study Figure 7A, suggesting that these hub genes may play fundamental role in NASH development. The PPI network of these 25 hub genes was showed in Figure 7B. **Figure 7:** *Validation of hub genes in MergeCohort_Turquoise module. (A) Heatmap shows the expression patterns of top 25 hub genes in human liver tissues according to four datasets (GSE130970, GSE48452, GSE61260 and GSE63067). The numbers in heatmap represent log2 value of fold change between NASH patients and healthy controls. (B) The protein-protein interactions among top 25 hub genes were retrieved by the STRING database. (C) Heatmap shows the Person correlation coefficients of top 25 hub genes and clinical parameters of NAFLD according to GSE130970 dataset. p values are overlaid on the heatmap (** p < 0.01 and *** p < 0.001). (D) Heatmap shows the expression patterns of top 25 hub genes in mouse liver tissue according to GSE120977 dataset. The numbers in heatmap represent log2 value of fold change between the CDAHFD and chow diet control group. CDAHFD, choline deficient L-amino acid defined high fat diet.* *Hub* genes were positively correlated with clinical characteristics. We further investigated the relationship between the changes in expression of these 25 hub genes and the histological phenotype in GSE130970 (Figure 7C). Our results demonstrated that each of the 25 key genes were positively correlated with the NAFLD activity score, and FPR3 has the highest correlation ($r = 0.53$, $$p \leq 1.49$$ × 10-4). LCP1 gene was the most associated gene with steatosis grade ($r = 0.46$, $$p \leq 1.16$$ × 10-3) and the lobular inflammation grade ($r = 0.32$, $$p \leq 3.06$$ × 10-2). Moreover, FPR3 associated most with the cytological ballooning grade ($r = 0.53$, $$p \leq 1.82$$ × 10-4). SRGN was the most relevant gene with the fibrosis stage ($r = 0.35$, $$p \leq 1.84$$ × 10-2). Additionally, C3AR1 showed significant correlation with all the clinical parameters, especially higher correlation with the cytological ballooning grade ($r = 0.51$, $$p \leq 2.94$$ × 10-4). *Hub* genes were upregulated in the liver from the choline deficient L-amino acid defined high fat diet (CDAHFD) model of NASH in mouse. Furthermore, to explore the significance of the hub genes in mouse, we mined public available microarray data (GSE120977) [41] to validate the mRNA levels of the abovementioned genes, except Hla-dra, Clic2 and *Fpr3* gene which was lacking in the dataset. Intriguingly, several of the hub genes displayed either a significant or a trending higher expression in mouse individuals fed with CDAHFD diets at 12 weeks compared with the controls. For instance, 14 genes, namely Cd53, Laptm5, Nckap1l, C3ar1, Hck, Mpeg1, Cybb, Iqgap1, Dock2, Plek, Fcer1g, Igsf6, Ptprc and Havcr2, which were strongly upregulated in mouse fed with CDAHFD chow (Figure 7D), supporting the notion that these hub genes were also activated during progression of mouse NASH model. ## Identification of cell clusters contributions to the NASH-associated Turquoise module integrating single-cell RNA-seq analysis To investigate how potential hub genes identified in MergeCohort_Turquoise module change within specific cell populations during NASH progression, we carried out an integrated scRNA-seq analysis using publicly available scRNA-seq data from healthy and cirrhotic liver samples. Clustering revealed 17 populations of cells comprising 10 distinct cell types (Figures 8A, B; Supplementary Figure S2). We identified Endothelial cells, macrophages, cholangiocytes, NK cells, T cells, mesenchyme, dendritic cells, B cells, fibroblasts, and hepatocytes within the scRNA-seq data based on the expression of lineage specific markers as annotated with integration of discoveries from human liver cell atlas and the annotation analysis with SingleR. The expression patterns of the top 25 genes in the MergeCohort_Turquoise module were analyzed by scRNA-seq analyses of liver tissues. Those key genes in MergeCohort_Turquoise module including CD53, LCP1, LAPTM5, PTPRC and SRGN expressed by distinct immune cells such as microphages, NK cells, T cells, dendritic cells and B cells, and most of them, namely FGL2, HCK, MPEG1, CYBB, CSF1R, IGSF6, CPVL and HLA-DRA were mainly expressed by macrophages, dendritic cells (Figure 8C; Supplementary Figure S3), which indicated that the macrophages and dendritic cells play an important role in the pathogenesis of NASH. **Figure 8:** *Assessment of the expression patterns of hub genes in MergeCohort_Turquoise module in different types of cells using publicly available healthy and cirrhotic scRNA-seq from dataset GSE136103. (A) UMAP visualization of different cell clusters from healthy (n = 2) and cirrhotic (n = 2) human livers. (B) UMAP visualization of cell types from healthy (n = 2) and cirrhotic (n = 2) human livers. Cells were annotated as endothelial cells, macrophages, cholangiocytes, NK cells, T cells, mesenchyme, dendritic cells, B cells, fibroblasts, and hepatocytes based on the expression of lineage markers. (C) Dot plot shows the expression patterns of top 25 hub genes in different types of liver cells. Size of the dot indicates proportion of the cell population that expresses each gene. Color represents level of expression. UMAP, uniform manifold approximation and projection.* ## Identification of TFs that regulate the Turquoise modules The results of the analysis above showed that hub genes in MergeCohort_Turquoise module were enriched in immunity. Because co-expressed genes tend to be co-regulated by the common transcription factors (TFs), we further conducted TFs enrichment analysis (hypergeometric test) using the genes from the MergeCohort_Turquoise and GSE135251_Turquoise modules to obtain key regulatory genes, based on TRRUST database [34]. Our results indicated that NFKB1, SPI1, RELA, CIITA, HIVEP2, SP1, RFXANK, RFXAP, RFX5, IRF1 are the top 10 most significantly enriched TFs in MergeCohort_Turquoise module (Figure 9A). Moreover, we adopted ChEA3 database [35] to validate the significantly enriched transcription factors over MergeCohort_Turquoise module genes. As a result, ChEA3 analysis identified 27 of the 33 significant TFs for MergeCohort_Turquoise module genes with TRRUST database, the other six TFs were part of their targets (Table S12). We also found that NFKB1, SPI1, RELA, CIITA, SP1, RFXANK, RFXAP, RFX5, TRERF1, ELF1, STAT3, ERG, ETS1, ILF3, CEBPA, HDAC1 and IRF8 are significantly enriched TFs in both MergeCohort_Turquoise and GSE135251_Turquoise module (Figure 9A). Furthermore, we observed significantly increased of hepatic expression of RFX5, ILF3, NFKB1, STAT3, ELF1, SPI1, ETS1 and CEBPA in NAFL and NASH compared to the control group ($p \leq 0.05$) (Figure 9B). **Figure 9:** *Regulatory relationship between enriched transcription factors and their target genes in NASH-associated module. (A) Dot-plot heatmap shows enriched transcription factors in MergeCohort_Turquoise and GSE135251_Turquoise module. The size of each dot represents the -log10 of adjusted p value for each transcription factor. (B) Boxplots shows mRNA hepatic expression of the enriched transcription factors including RFX5, ILF3, NFKB1, STAT3, ELF1, SPI1, ETS1 and CEBPA according to GSE135251 dataset. The p value was calculated by Student’s t test. (C, D) The regulatory networks between enriched transcription factors and associated target genes in MergeCohort_Turquoise (C) and GSE135251_Turquoise module (D), respectively. Red color represents transcription factors, blue color represents target hub genes, grey color represents other target genes. (E) Pearson correlations for mRNA hepatic expression of transcription factors (RFX5 and ILF3) and associated target genes (HLA-DQB2, HLA-DOA, HLA-DMA, HLA-DQA1, HLA-DMB, HLA-DPB1, HLA-DPA1 and HLA-DRA) in GSE135251 dataset. * p < 0.05, ** p < 0.01, *** p < 0.001 and **** p < 0.0001.* Next, the regulatory networks were constructed for the enriched TFs and associated target genes in each of the modules (Figures 9C, D). We observed that RFX5 and ILF3, an important transcriptional factor mainly expressed in the liver, upregulated from mild to advanced NASH, regulates the expression of genes involved in antigen processing and presentation of exogenous peptide antigen via MHC class II, including HLA-DQB2, HLA-DOA, HLA-DMA, HLA-DQA1, HLA-DMB, HLA-DPB1, HLA-DPA1 and HLA-DRA. Notably, the gene expression of RFX5 and ILF3 positively correlated with MHCII gene expression (Figure 9E). We found 41 genes are regulated by the NFKB1 transcription factor. As known, NFKB1 regulates the expression of genes associated with cytokine-mediated signaling pathway (e.g., TNF, CXCL10, MMP9 and TGFB1) and immune response (e.g., CD74, CD58, CD80 and CD86) (Figure 9C). Moreover, STAT3 regulates the expression of gene in Wound healing involved in inflammatory response, including HMOX1, TIMP1, TGFB1 and F2R. Interestingly, SPI1 regulated gene involved in immune effector process (e.g., CTSG, CD68, IFIT3 and IL18) including hub genes (CYBB and HCK) in MergeCohort_Turquoise module. SP1 regulated gene involved in cell activation (e.g., TIMP1, LTF, FGL2 and LYZ). For further analysis the expression of the hub genes and key TFs in vitro models of NASH, we retrieved public available RNA-seq data (the RNA-seq data of L02 hepatocytes (PRJNA726826) and murine primary hepatocytes (PRJNA726846) treated with palmitic acid and oleic acid (PAOA) for 0h, 12h and 24h, respectively [42]), we found hub genes (CD53 and SRGN) and key TFs (NFKB1, ELF1 and EST1) displayed higher expression in L02 hepatocytes treated with PAOA (Figure S4A). Moreover, we observed that hub genes (Lcp1 and Fcer1g) and key TFs (Ilf3, stat3 and Est1) showed increased expression in murine primary hepatocytes with PAOA treatment (Figure S4B). Together, these TFs and target genes identified in our study provide a promising list for investigators or companies interested in conducting preclinical study into the mechanisms of and treatments for NASH both in vitro and in vivo. ## Discussion The global epidemic of NASH is a serious public health problem, the pathogenesis of NASH still remains unclear. Moreover, although liver biopsy currently remains the reference standard for diagnosis of NASH, it is an intrusive operation with risks and many shortcomings. Thus, identifying novel non-invasive biomarkers in NASH is of paramount importance in the prevention and therapy of this disease. Thanks to the rapid development of high-throughput sequencing technology and gene chip technology, more and more researchers are actively pursuing molecular markers using data mining and analysis of sequencing data or gene chips to the diagnosis and treatment of disease [19, 43, 44]. In our study, we analyzed gene expression profiles of NASH patients and normal controls from five independent GEO data sets. The batch of various platforms or batches is removed. DEGs were identified between normal liver tissues and NASH tissues, based on 831 DEGs between Normal-NASH group, we performed GO and Reactome pathway analysis to explore underlying mechanism of NASH. The results showed that enriched pathways were involved in metabolism pathways, inflammatory response and immune response, extracellular matrix organization (Figures 2C, D), conforming their association with NASH development and progression. Subsequently, we constructed a co-expression network and identified 17 different modules by WGCNA, among which 11 modules were significantly associated with the status of NASH. DEG numbers showed a significant enrichment in seven important modules (Figure 3D). The results of this study indicated that the identified modules are biologically rational, majority of which are enriched for specific GO terms and KEGG pathways, sharing some commonality with the existing literature. For example, module Black and Brown, are markedly negative correlated with NASH status. Both the Black and Brown were most significantly enriched in cellular amino acid catabolic process. Recent studies showed that deregulation in amino acid metabolism seem to be involved in the appearance of NASH [39, 45]. In addition, previous research has demonstrated that lipid metabolism significantly altered during NASH progression [46]. Our data found Grey60 module that was significantly upregulated in NASH, enriched in the lipid metabolism pathways, encompassing hub genes related to cholesterol metabolism (FDFT1, NSDHL, IDI1, SQLE, MVD, HMGCS1, HMGCR and LSS) as well as fatty acid metabolism (FASN, ELOVL6, FADS1, FADS2, ACACA, ELOVL6, PKLR and THRSP) (Figure 4C). Similarly, previous biological network analysis identified cholesterol synthesis genes in human NAFLD (e.g., FDFT1, NSDHL, IDI1, SQLE, MVD, HMGCS1 and HMGCR) and fatty acid metabolism genes (e.g., Fasn, Thrsp and Pklr) in NAFLD mouse model that were also reported to be deregulated by [47] and [18], respectively. Thus, despite the differences in study design, the three studies coverage on a number of key biological findings. Inflammation is an important factor driving NASH progression. Our current systematic transcriptomic analysis also highlighted the importance of the Turquoise module in modulating NASH occurrence and development. This study found that the immune-related pathways were mostly enriched in the Turquoise module, which contained the highest number of differentially deregulated genes (Figure 3D). Moreover, we demonstrated the highest preservation of the Turquoise module between the MergeCohort and validation dataset GSE135251 (Figure 5A). The top hub genes overexpression in NASH samples and linking immune-related pathways belonged to CD53, LCP1, LAPTM5, NCKAP1L, C3AR1, FGL2, PLEK, HLA-DRA, FPR3 and SRGN, which also showed positive correlation with histological grade (Figure 7C). Further validation by mouse NASH model, the expression of CD53, LCP1, LAPTM5, NCKAP1L, C3AR1, FGL2, PLEK and SRGN were significantly upregulated (Figure 7D). The role of CD53, C3AR1, NCKAP1L and FGL2 genes in regulation of immune responses has recently been proposed in previous studies. CD53 is a member of the tetraspanin membrane protein family that may be involved in transmembrane signal transduction [48]. CD53 has been reported to associate with liver inflammation and insulin sensitivity [49]. LAPTM5 is a transmembrane protein which is preferentially expressed in immune cells, and it acts as a positive regulator of proinflammatory signaling pathways in macrophages [50]. Previous study revealed that LAPTM5 could interact with CDC42, and promote its degradation, then suppressed the activation of MAPK signaling pathway, hence ameliorated NASH in mouse [51]. Besides, LAPTM5 has been shown to be significantly upregulated in HCC tissues compared to normal liver tissues, and Pan et al. reported that LAPTM5 could remarkably accelerate autophagic flux by promoting fusion of lysosomes with autophagosomes to drive lenvatinib resistance in HCC [52]. Moreover, C3AR1 is a G protein-coupled receptor (GPCR) protein, which participates in the complement system and can stimulate the production of IL-1β and TGFβ [53]. Interestingly, Han et al. found that C3ar1 knockout mice showed drastically less severe fibrosing steatohepatitis, concomitantly with reduced hepatic stellate cells (HSCs) activation when compared with the wildtype littermates [54]. In addition, the mRNA level of LCP1 in liver tissue of NAFLD patients was strongly increased ($300\%$) compare to the control group in a previous GWAS study [55], and Miller et al. used proteomic method to describe the proteome of NAFLD and observed that LCP1 performed well in distinguishing the disease state from control group, NAFL from NASH and fibrosis grading [56]. Notably, our study also found that the Turquoise module including hub gene HLA-DRA, displayed higher expression in NASH, which associated with NAFLD loci found by GWAS, and genetic variants of HLA−DRA has been recently reported to affect hepatitis development in a Korean population [57]. Additionally, it has been shown that SRGN, CD53, NCKAP1L, LCP1, EVI2B, MPEG1 and TYROBP may be potential pathological target gene for NAFLD and NASH, which is highly similar to our Turquoise module [58]. It should be noted that NASH is regarded as an inflammatory subtype of NAFLD with steatosis and evidence of hepatocyte injury and interactions between multiple immune cells. Increasing evidence has demonstrated the high heterogeneity and plasticity of macrophage populations in human liver [59]. For example, Ramachandran et al. adopted scRNA-seq approach to discover a disease-associated TREM2+/CD9+ macrophage population that was remarkably expanded in human cirrhotic livers. Therapeutic inhibition of CCR2+ bone marrow-derived macrophages has been reported to alleviate inflammation and fibrosis in mouse NASH and fibrosis in human disease [36, 60]. Similarly, our integrated scRNA-seq analysis revealed that the hub genes in the Turquoise module were mainly enriched in macrophage and dendritic cells, conforming the importance of which during NASH progression. For instance, our study found that expression of FGL2 was elevated in macrophages and dendritic cells (Figure 8C). A recent study demonstrated that Fgl2 expression in the livers of both humans and mice with NASH was significantly increased along with the accumulation of hepatic macrophages [61]. Moreover, we found that the expression of CSF1R gene, a marker for pan-macrophages reported to be involved in hepatic fibrosis, was also considered as a potential marker for hepatocarcinogenesis [62]. By analyzing the association between LCP1 and immune cells, Zhang et al. found LCP1 was significantly positively related to memory B cells as well as M1 macrophages [58]. Our study also observed that hub gene HLA-DRA was higher expressed in both macrophages and dendritic cells (Figure 8C). Intriguingly, previous reports examining human NASH livers using single-cell RNA sequencing reported that M-Mac-1 included three genes, HLA-DRA, HLA-DQA2 and HLA-DQB2 [63], which was related to NAFLD loci [57, 64, 65]. Further, recent study reported that cDC-related gene expression signatures in human livers were associated with NASH pathology [66]. These findings emphasized the importance of further studies of the subpopulations of inflammatory macrophages and dendritic cells in NASH progression. However, more single-cell transcriptome data focusing on NASH progression among NASH patients are needed in future studies. Several studies involving transcription factors have indicated therapeutic effects in NASH [67, 68], for example, transcription factors including PPARs, LXR and FXR are mainly known for their roles in altering lipid metabolism in NAFLD/NASH development. Agonists of PPARs and FXR have been investigated extensively in mouse models [69, 70], clinical trials presently are ongoing to test the effects of these drugs for potential NASH treatments. In addition, PPARs, LXR and FXR not only regulate lipid metabolism but also exert anti-inflammatory functions via direct and indirect mechanisms as shown by the suppression of several proinflammatory genes (71–74). Therefore, the detection of an immune-related transcription factor seems to be essential for the identification of novel therapeutic targets in NAFLD/NASH. In present study, we observed that the immune-related module enriched TFs including NFKB1, STAT3, RFX5, ILF3, ELF1, SPI1, ETS1 and CEBPA, the expression of which enhanced with NASH progression (Figure 9B). Among the TFs, NFKB1, STAT3, SPI1, ETS1, CEBPA and ELF1 have been reported to be linked to NAFLD/NASH by literature searching. NF-κB is a protein complex that plays a central role in regulating the expression of cytokines and chemokines, and recent studies suggest that NF-κB is highly activated both in mice and patients with NASH [75, 76]. NFKB1 (p105/p50), a member of NF-κB family, emerging evidence suggests that NF-κB1-gene-coded proteins p105 and p50 have critical regulatory activities of inflammatory responses [77, 78]. Previous study have showed that Nfkb1-deficient mice enhanced NASH progression to fibrosis by favouring NKT cell recruitment [79]. In addition, Jurk et al. reported that loss of Nfkb1 in mouse promoted ageing-related chronic liver disease, featured by steatosis, hepatitis, fibrosis and HCC [80], which point to the possible relevance of polymorphisms in human NFKB1 gene as a risk factor for the progression of inflammatory disease [81]. STAT family members with inflammatory biological functions notably STAT1 and STAT3 have been linked to NAFLD and NASH. Grohmann and colleagues demonstrated that the oxidative hepatic environment in obesity restrained the STAT1 and STAT3 phosphatase TCPTP, which led to potentiate STAT1 and STAT3 signaling, and further increase the risk of developing NASH and HCC in the setting of nutritional excess [82]. On the other hand, the suppression of TCPTP, coupled with heightened STAT1 and STAT3 signaling, were easily detectable events in the livers of patients with NASH [82]. Moreover, a recently study revealed that dampening IL6/STAT3 activity alleviated the I148M-mediated susceptibility to NAFLD, while boosting it in wild-type liver cultures enhanced the development of NAFLD [83]. Additionally, downregulation of STAT3 expression can activate autophagy and inhibit the inflammatory response of NASH [84, 85]. Interestingly, other transcription factor such as SPI1, ETS1 and CEBPA have been described to be a promising target for NASH prevention and treatment. Liu et al. applied proteomics strategy to identify SPI1 as critical TF, SPI1 expression was positively related to resistance indicator HOMA-IR and the inflammatory marker TNFA in human liver biopsies, and inhibition of SPI1 ameliorated metabolic dysfunction and NASH [86]. It has been proven that Ets1 acted as a positive regulator of TGF-β1 signaling, which accelerated the development of NASH in mice [87]. Notably, Vujkovic et al. recently presented a GWAS study and identified 77 genome-wide loci significantly associated with NAFLD (diagnosed using elevated ALT as a proxy for NAFLD), of interest is that for nine SNPs, the cATL risk allele was associated with lower BMI including CEBPA [65]. There are few studies of RFX5, ELF1 and ILF3 that have been reported at present in the field of NAFLD and NASH. RFX5, a classical transcription regulator of MHCII gene expression in the immune system. It has been previously shown that RFX5 displayed higher transcriptional activity in both human NASH and mouse model of NASH [68]. Interestingly, RFX5 mRNA has previously been shown overexpressed in HCC compared with non-tumor tissue, which promoted HCC progression via transcriptionally activating KDM4A, TPP1 and YWHAQ (88–90). Moreover, our results also showed that RFX5 are the prominent regulators of expression of HLA class II genes in the immune-related module. Interestingly, RFX5 was recently reported to enhance surface expression of HLA-DR molecules, which promoted tissue macrophages-dependent expansion of antigen-specific T cells in rheumatoid arthritis [91]. In addition, ELF1 regulated hub gene CYBB in MergeCohort_Turquoise module, the mechanism of TAZ-induced Cybb leading to liver tumor formation in NASH has been well defined [92]. ILF3, also known as NF90/NF110, encodes a double-stranded RNA (dsRNA)-binding protein which can regulate gene expression and stabilize mRNA [93, 94]. Recent studies have reported insights into the possible physiological roles of ILF3 in dyslipidemia, the cardiovascular system, neurodegenerative disorder as well as in tumorigenesis and progression of different cancers. Zhang et al. demonstrated that ILF3 together with another eight transcription regulators control late-onset Alzheimer’s disease (LOAD) risk genes HLA-DRB1 and HLA-DQA1 expression in human microglial cells [95]. Moreover, there is evidence that ILF3 could have an important role in inflammatory pathophysiology in vivo, Nazitto et al. identified ILF3 as negative regulator of innate immune response and dendritic cell (DC) maturation, and found that knockdown of ILF3 led to significantly elevated expression of genes (CD86, CD80 and HLA-DR) associated with DC maturation in the primary human monocyte-derived DCs during stimulation with viral mimetics or classic innate agonists [96]. In addition, previous studies have revealed the essential roles of deregulated lncRNA ILF3 divergent transcript (ILF3-AS1) in HCC, Bo et al. found that ILF3-AS1 expression was significantly increased in HCC tissues and also associated with prognosis of HCC patients, and knockdown of ILF3-AS1 expression suppressed HCC cell proliferation, migration and invasion [97]. Yan et al. also observed that ILF3-AS1 silencing inhibited the hepatocellular carcinoma tumor growth [98]. However, the regulation roles of RFX5 and ILF3 on HLA-DR molecules in the progression of NASH have also not been well defined. Therefore, our results provide a very meaningful direction for future research. In summary, unlike previous studies with limitation of a few human NASH transcriptome data or focusing on individual genes influencing NASH progression, our network-driven strategy generated a comprehensive and unbiased view of the modules, hub genes and critical transcriptional factors associated with NASH. In particular, the Turquoise module and regulators involving immune-related pathways especially transcription factor RFX5 coordinating antigen processing and presenting function in NASH progression deserve further attention. The main limitation of present study is that all conclusions are based on transcriptomic data from human and lack verification from relevant experiments in vitro/in vivo disease models. Nevertheless, it provides useful and novel molecular candidates in dysregulated pathways for NASH prognosis and therapeutic targets. ## Data availability statement The original contributions presented in the study are included in the article/ Supplementary Material. Further inquiries can be directed to the corresponding authors. ## Author contributions Conception and design: J-JZ and FX. Acquisition and analysis of data: J-JZ, YS and X-YC. Investigation: J-JZ, YS, X-YC, M-LJ, F-HY and JZ. Software: J-JZ. Validation: YS, X-YC, M-LJ, S-LX and JZ. Visualization: J-JZ. Writing–original draft: J-JZ. Writing–review & editing: J-JZ, X-YC, F-HY and FX. Funding: J-JZ. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2023.1115890/full#supplementary-material ## References 1. Younossi ZM, Koenig AB, Abdelatif D, Fazel Y, Henry L, Wymer M. **Global epidemiology of nonalcoholic fatty liver disease-Meta-Analytic assessment of prevalence, incidence, and outcomes**. *Hepatology* (2016) **64** 73-84. DOI: 10.1002/hep.28431 2. Brunt EM. **Pathology of nonalcoholic fatty liver disease**. *Nat Rev Gastroenterol Hepatol* (2010) **7** 195-203. DOI: 10.1038/nrgastro.2010.21 3. Williams CD, Stengel J, Asike MI, Torres DM, Shaw J, Contreras M. **Prevalence of nonalcoholic fatty liver disease and nonalcoholic steatohepatitis among a largely middle-aged population utilizing ultrasound and liver biopsy: A prospective study**. *Gastroenterology* (2011) **140**. DOI: 10.1053/j.gastro.2010.09.038 4. Anstee QM, Reeves HL, Kotsiliti E, Govaere O, Heikenwalder M. **From NASH to HCC: Current concepts and future challenges**. *Nat Rev Gastroenterol Hepatol* (2019) **16**. DOI: 10.1038/s41575-019-0145-7 5. Chalasani N, Younossi Z, Lavine JE, Charlton M, Cusi K, Rinella M. **The diagnosis and management of nonalcoholic fatty liver disease: Practice guidance from the American association for the study of liver diseases**. *Hepatology* (2018) **67**. DOI: 10.1002/hep.29367 6. Sanyal AJ. **Past, present and future perspectives in nonalcoholic fatty liver disease**. *Nat Rev Gastroenterol Hepatol* (2019) **16**. DOI: 10.1038/s41575-019-0144-8 7. Romeo S, Kozlitina J, Xing C, Pertsemlidis A, Cox D, Pennacchio LA. **Genetic variation in PNPLA3 confers susceptibility to nonalcoholic fatty liver disease**. *Nat Genet* (2008) **40**. DOI: 10.1038/ng.257 8. Speliotes EK, Yerges-Armstrong LM, Wu J, Hernaez R, Kim LJ, Palmer CD. **Genome-wide association analysis identifies variants associated with nonalcoholic fatty liver disease that have distinct effects on metabolic traits**. *PloS Genet* (2011) **7**. DOI: 10.1371/journal.pgen.1001324 9. Kozlitina J, Smagris E, Stender S, Nordestgaard BG, Zhou HH, Tybjærg-Hansen A. **Exome-wide association study identifies a TM6SF2 variant that confers susceptibility to nonalcoholic fatty liver disease**. *Nat Genet* (2014) **46**. DOI: 10.1038/ng.2901 10. Abul-Husn NS, Cheng X, Li AH, Xin Y, Schurmann C, Stevis P. **A protein-truncating HSD17B13 variant and protection from chronic liver disease**. *N Engl J Med* (2018) **378**. DOI: 10.1056/NEJMoa1712191 11. Emdin CA, Haas ME, Khera AV, Aragam K, Chaffin M, Klarin D. **A missense variant in mitochondrial amidoxime reducing component 1 gene and protection against liver disease**. *PloS Genet* (2020) **16**. DOI: 10.1371/journal.pgen.1008629 12. Anstee QM, Darlay R, Cockell S, Meroni M, Govaere O, Tiniakos D. **Genome-wide association study of non-alcoholic fatty liver and steatohepatitis in a histologically characterised cohort**. *J Hepatol* (2020) **73**. DOI: 10.1016/j.jhep.2020.04.003 13. Govaere O, Cockell S, Tiniakos D, Queen R, Younes R, Vacca M. **Transcriptomic profiling across the nonalcoholic fatty liver disease spectrum reveals gene signatures for steatohepatitis and fibrosis**. *Sci Transl Med* (2020) **12**. DOI: 10.1126/scitranslmed.aba4448 14. Sveinbjornsson G, Ulfarsson MO, Thorolfsdottir RB, Jonsson BA, Einarsson E, Gunnlaugsson G. **Multiomics study of nonalcoholic fatty liver disease**. *Nat Genet* (2022) **54**. DOI: 10.1038/s41588-022-01199-5 15. Zhang X-J, She Z-G, Wang J, Sun D, Shen L-J, Xiang H. **Multiple omics study identifies an interspecies conserved driver for nonalcoholic steatohepatitis**. *Sci Transl Med* (2021) **13**. DOI: 10.1126/scitranslmed.abg8117 16. Jia X, Zhai T. **Integrated analysis of multiple microarray studies to identify novel gene signatures in non-alcoholic fatty liver disease**. *Front Endocrinol* (2019) **10**. DOI: 10.3389/fendo.2019.00599 17. Wu C, Zhou Y, Wang M, Dai G, Liu X, Lai L. **Bioinformatics analysis explores potential hub genes in nonalcoholic fatty liver disease**. *Front Genet* (2021) **12**. DOI: 10.3389/fgene.2021.772487 18. Yang H, Arif M, Yuan M, Li X, Shong K, Türkez H. **A network-based approach reveals the dysregulated transcriptional regulation in non-alcoholic fatty liver disease**. *iScience* (2021) **24**. DOI: 10.1016/j.isci.2021.103222 19. Gao R, Wang J, He X, Wang T, Zhou L, Ren Z. **Comprehensive analysis of endoplasmic reticulum-related and secretome gene expression profiles in the progression of non-alcoholic fatty liver disease**. *Front Endocrinol* (2022) **13**. DOI: 10.3389/fendo.2022.967016 20. Esmaili S, Langfelder P, Belgard TG, Vitale D, Azardaryany MK, Alipour Talesh G. **Core liver homeostatic Co-expression networks are preserved but respond to perturbations in an organism- and disease-specific manner**. *Cell Syst* (2021) **12** 432-45.e7. DOI: 10.1016/j.cels.2021.04.004 21. Misselbeck K, Parolo S, Lorenzini F, Savoca V, Leonardelli L, Bora P. **A network-based approach to identify deregulated pathways and drug effects in metabolic syndrome**. *Nat Commun* (2019) **10** 5215. DOI: 10.1038/s41467-019-13208-z 22. Langfelder P, Horvath S. **WGCNA: An r package for weighted correlation network analysis**. *BMC Bioinform* (2008) **9**. DOI: 10.1186/1471-2105-9-559 23. Zhang B, Horvath S. **A general framework for weighted gene Co-expression network analysis**. *Stat Appl Genet Mol Biol* (2005) **4**. DOI: 10.2202/1544-6115.1128 24. Saris CG, Horvath S, van Vught PW, van Es MA, Blauw HM, Fuller TF. **Weighted gene Co-expression network analysis of the peripheral blood from amyotrophic lateral sclerosis patients**. *BMC Genom* (2009) **10**. DOI: 10.1186/1471-2164-10-405 25. Yang Y, Han L, Yuan Y, Li J, Hei N, Liang H. **Gene Co-expression network analysis reveals common system-level properties of prognostic genes across cancer types**. *Nat Commun* (2014) **5** 3231. DOI: 10.1038/ncomms4231 26. Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M. **NCBI GEO: Archive for functional genomics data sets–update**. *Nucleic Acids Res* (2013) **41**. DOI: 10.1093/nar/gks1193 27. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA. **Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles**. *Proc Natl Acad Sci USA* (2005) **102**. DOI: 10.1073/pnas.0506580102 28. Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U. **Exploration, normalization, and summaries of high density oligonucleotide array probe level data**. *Biostatistics* (2003) **4**. DOI: 10.1093/biostatistics/4.2.249 29. Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. **The sva package for removing batch effects and other unwanted variation in high-throughput experiments**. *Bioinformatics* (2012) **28**. DOI: 10.1093/bioinformatics/bts034 30. Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z. **Enrichr: A comprehensive gene set enrichment analysis web server 2016 update**. *Nucleic Acids Res* (2016) **44**. DOI: 10.1093/nar/gkw377 31. Langfelder P, Luo R, Oldham MC, Horvath S. **Is my network module preserved and reproducible**. *PloS Comput Biol* (2011) **7**. DOI: 10.1371/journal.pcbi.1001057 32. Oldham MC, Horvath S, Geschwind DH. **Conservation and evolution of gene coexpression networks in human and chimpanzee brains**. *Proc Natl Acad Sci USA* (2006) **103**. DOI: 10.1073/pnas.0605938103 33. Piñero J, Bravo À, Queralt-Rosinach N, Gutiérrez-Sacristán A, Deu-Pons J, Centeno E. **Disgenet: A comprehensive platform integrating information on human disease-associated genes and variants**. *Nucleic Acids Res* (2016) **45**. DOI: 10.1093/nar/gkw943 34. Han H, Cho JW, Lee S, Yun A, Kim H, Bae D. **TRRUST v2: An expanded reference database of human and mouse transcriptional regulatory interactions**. *Nucleic Acids Res* (2018) **46**. DOI: 10.1093/nar/gkx1013 35. Keenan AB, Torre D, Lachmann A, Leong AK, Wojciechowicz ML, Utti V. **ChEA3: Transcription factor enrichment analysis by orthogonal omics integration**. *Nucleic Acids Res* (2019) **47**. DOI: 10.1093/nar/gkz446 36. Ramachandran P, Dobie R, Wilson-Kanamori JR, Dora EF, Henderson BEP, Luu NT. **Resolving the fibrotic niche of human liver cirrhosis at single-cell level**. *Nature* (2019) **575**. DOI: 10.1038/s41586-019-1631-3 37. Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck WM. **Comprehensive integration of single-cell data**. *Cell* (2019) **177** 1888-902.e21. DOI: 10.1016/j.cell.2019.05.031 38. Aran D, Looney AP, Liu L, Wu E, Fong V, Hsu A. **Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage**. *Nat Immunol* (2019) **20**. DOI: 10.1038/s41590-018-0276-y 39. Rom O, Liu Y, Liu Z, Zhao Y, Wu J, Ghrayeb A. **Glycine-based treatment ameliorates nafld by modulating fatty acid oxidation, glutathione synthesis, and the gut microbiome**. *Sci Transl Med* (2020) **12**. DOI: 10.1126/scitranslmed.aaz2841 40. Leung H, Long X, Ni Y, Qian L, Nychas E, Siliceo SL. **Risk assessment with gut microbiome and metabolite markers in nafld development**. *Sci Transl Med* (2022) **14**. DOI: 10.1126/scitranslmed.abk0855 41. Min-DeBartolo J, Schlerman F, Akare S, Wang J, McMahon J, Zhan Y. **Thrombospondin-I is a critical modulator in non-alcoholic steatohepatitis (NASH)**. *PloS One* (2019) **14**. DOI: 10.1371/journal.pone.0226854 42. Wang L, Zhang X, Lin ZB, Yang PJ, Xu H, Duan JL. **Tripartite motif 16 ameliorates nonalcoholic steatohepatitis by promoting the degradation of phospho-TAK1**. *Cell Metab* (2021) **33** 1372-88.e7. DOI: 10.1016/j.cmet.2021.05.019 43. Xie X, Zhang Y, Yu J, Jiang F, Wu C. **Significance of m**. *Front Physiol* (2022) **13**. DOI: 10.3389/fphys.2022.918270 44. Yu J, Xie X, Zhang Y, Jiang F, Wu C. **Construction and analysis of a joint diagnosis model of random forest and artificial neural network for obesity**. *Front Med* (2022) **9**. DOI: 10.3389/fmed.2022.906001 45. Hoyles L, Fernández-Real J-M, Federici M, Serino M, Abbott J, Charpentier J. **Molecular phenomics and metagenomics of hepatic steatosis in non-diabetic obese women**. *Nat Med* (2018) **24**. DOI: 10.1038/s41591-018-0061-3 46. Loomba R, Quehenberger O, Armando A, Dennis EA. **Polyunsaturated fatty acid metabolites as novel lipidomic biomarkers for noninvasive diagnosis of nonalcoholic steatohepatitis**. *J Lipid Res* (2015) **56**. DOI: 10.1194/jlr.P055640 47. Chella Krishnan K, Kurt Z, Barrere-Cain R, Sabir S, Das A, Floyd R. **Integration of multi-omics data from mouse diversity panel highlights mitochondrial dysfunction in non-alcoholic fatty liver disease**. *Cell Syst* (2018) **6** 103-15.e7. DOI: 10.1016/j.cels.2017.12.006 48. Yeung L, Anderson JML, Wee JL, Demaria MC, Finsterbusch M, Liu YS. **Leukocyte tetraspanin CD53 restrains α**. *J Immunol* (2020) **205**. DOI: 10.4049/jimmunol.1901054 49. Ehses JA, Lacraz G, Giroix MH, Schmidlin F, Coulaud J, Kassis N. **IL-1 antagonism reduces hyperglycemia and tissue inflammation in the type 2 diabetic GK rat**. *Proc Natl Acad Sci USA* (2009) **106**. DOI: 10.1073/pnas.0810087106 50. Glowacka WK, Alberts P, Ouchida R, Wang JY, Rotin D. **LAPTM5 protein is a positive regulator of proinflammatory signaling pathways in macrophages**. *J Biol Chem* (2012) **287**. DOI: 10.1074/jbc.M112.355917 51. Jiang L, Zhao J, Yang Q, Li M, Liu H, Xiao X. **Lysosomal-associated protein transmembrane 5 ameliorates non-alcoholic steatohepatitis through degradating CDC42**. *Res Square* (2022). DOI: 10.21203/rs.3.rs-2065929/v1 52. Pan J, Zhang M, Dong L, Ji S, Zhang J, Zhang S. **Genome-scale CRISPR screen identifies LAPTM5 driving lenvatinib resistance in hepatocellular carcinoma**. *Autophagy* (2022) **7** 1-15. DOI: 10.1080/15548627.2022.2117893 53. Li L, Yin Q, Tang X, Bai L, Zhang J, Gou S. **C3a receptor antagonist ameliorates inflammatory and fibrotic signals in type 2 diabetic nephropathy by suppressing the activation of TGF-β/smad3 and IKBα pathway**. *PloS One* (2014) **9**. DOI: 10.1371/journal.pone.0113639 54. Han J, Zhang X, Lau JK-C, Fu K, Lau HC, Xu W. **Bone marrow-derived macrophage contributes to fibrosing steatohepatitis through activating hepatic stellate cells**. *J Pathol* (2019) **248** 488-500. DOI: 10.1002/path.5275 55. Adams LA, White SW, Marsh JA, Lye SJ, Connor KL, Maganga R. **Association between liver-specific gene polymorphisms and their expression levels with nonalcoholic fatty liver disease**. *Hepatology* (2013) **57** 590-600. DOI: 10.1002/hep.26184 56. Miller MH, Walsh SV, Atrih A, Huang JT, Ferguson MA, Dillon JF. **Serum proteome of nonalcoholic fatty liver disease: A multimodal approach to discovery of biomarkers of nonalcoholic steatohepatitis**. *J Gastroenterol Hepatol* (2014) **29**. DOI: 10.1111/jgh.12614 57. Hong M, Jung J, Jin H-S, Hwang D. **Genetic polymorphism of HLA-DRA and alcohol consumption affect hepatitis development in the Korean population**. *Genes Genomics* (2022) **44**. DOI: 10.1007/s13258-022-01286-1 58. Zhang X, Li J, Liu T, Zhao M, Liang B, Chen H. **Identification of key biomarkers and immune infiltration in liver tissue after bariatric surgery**. *Dis Markers* (2022) **2022**. DOI: 10.1155/2022/4369329 59. MacParland SA, Liu JC, Ma X-Z, Innes BT, Bartczak AM, Gage BK. **Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations**. *Nat Commun* (2018) **9** 4383. DOI: 10.1038/s41467-018-06318-7 60. Xiong X, Kuang H, Ansari S, Liu T, Gong J, Wang S. **Landscape of intercellular crosstalk in healthy and Nash liver revealed by single-cell secretome gene analysis**. *Mol Cell* (2019) **75** 644-60.e5. DOI: 10.1016/j.molcel.2019.07.028 61. Hu J, Wang H, Li X, Liu Y, Mi Y, Kong H. **Fibrinogen-like protein 2 aggravates nonalcoholic steatohepatitis**. *Theranostics* (2020) **10**. DOI: 10.7150/thno.44297 62. Iio E, Ocho M, Togayachi A, Nojima M, Kuno A, Ikehara Y. **A novel glycobiomarker, wisteria floribunda agglutinin macrophage colony-stimulating factor receptor, for predicting carcinogenesis of liver cirrhosis**. *Int J Cancer* (2016) **138**. DOI: 10.1002/ijc.29880 63. Fred RG, Steen Pedersen J, Thompson JJ, Lee J, Timshel PN, Stender S. **Single-cell transcriptome and cell type-specific molecular pathways of human non-alcoholic steatohepatitis**. *Sci Rep* (2022) **12** 13484. DOI: 10.1038/s41598-022-16754-7 64. Doganay L, Katrinli S, Colak Y, Senates E, Zemheri E, Ozturk O. **HLA DQB1 alleles are related with nonalcoholic fatty liver disease**. *Mol Biol Rep* (2014) **41**. DOI: 10.1007/s11033-014-3688-2 65. Vujkovic M, Ramdas S, Lorenz KM, Guo X, Darlay R, Cordell HJ. **A multiancestry genome-wide association study of unexplained chronic ALT elevation as a proxy for nonalcoholic fatty liver disease with histological and radiological validation**. *Nat Genet* (2022) **54**. DOI: 10.1038/s41588-022-01078-z 66. Deczkowska A, David E, Ramadori P, Pfister D, Safran M, Li B. **XCR1**. *Nat Med* (2021) **27**. DOI: 10.1038/s41591-021-01344-3 67. Steensels S, Qiao J, Ersoy BA. **Transcriptional regulation in non-alcoholic fatty liver disease**. *Metabolites* (2020) **10**. DOI: 10.3390/metabo10070283 68. Loft A, Alfaro AJ, Schmidt SF, Pedersen FB, Terkelsen MK, Puglia M. **Liver-Fibrosis-Activated transcriptional networks govern hepatocyte reprogramming and intra-hepatic communication**. *Cell Metab* (2021) **33** 1685-700.e9. DOI: 10.1016/j.cmet.2021.06.005 69. Lefere S, Puengel T, Hundertmark J, Penners C, Frank AK, Guillot A. **Differential effects of selective- and pan-PPAR agonists on experimental steatohepatitis and hepatic macrophages**. *J Hepatol* (2020) **73**. DOI: 10.1016/j.jhep.2020.04.025 70. Radun R, Trauner M. **Role of FXR in bile acid and metabolic homeostasis in NASH: Pathogenetic concepts and therapeutic opportunities**. *Semin Liver Dis* (2021) **41**. DOI: 10.1055/s-0041-1731707 71. Cariello M, Piccinin E, Moschetta A. **Transcriptional regulation of metabolic pathways**. *Cell Mol Gastroenterol Hepatol* (2021) **11**. DOI: 10.1016/j.jcmgh.2021.01.012 72. Gordon S. **Alternative activation of macrophages**. *Nat Rev Immunol* (2003) **3** 23-35. DOI: 10.1038/nri978 73. Joseph SB, Castrillo A, Laffitte BA, Mangelsdorf DJ, Tontonoz P. **Reciprocal regulation of inflammation and lipid metabolism by liver X receptors**. *Nat Med* (2003) **9**. DOI: 10.1038/nm820 74. Wang YD, Chen WD, Wang M, Yu D, Forman BM, Huang W. **Farnesoid X receptor antagonizes nuclear factor kappaB in hepatic inflammatory response**. *Hepatology* (2008) **48**. DOI: 10.1002/hep.22519 75. Mussbacher M, Salzmann M, Brostjan C, Hoesel B, Schoergenhofer C, Datler H. **Cell type-specific roles of NF-κB linking inflammation and thrombosis**. *Front Immunol* (2019) **10**. DOI: 10.3389/fimmu.2019.00085 76. Severa M, Islam SA, Waggoner SN, Jiang Z, Kim ND, Ryan G. **The transcriptional repressor BLIMP1 curbs host defenses by suppressing expression of the chemokine CCL8**. *J Immunol* (2014) **192**. DOI: 10.4049/jimmunol.1301799 77. Beinke S, Ley SC. **Functions of NF-kappaB1 and NF-KappaB2 in immune cell biology**. *Biochem J* (2004) **382** 393-409. DOI: 10.1042/bj20040544 78. Panzer U, Steinmetz OM, Turner JE, Meyer-Schwesinger C, von Ruffer C, Meyer TN. **Resolution of renal inflammation: A new role for NF-kappaB1 (p50) in inflammatory kidney diseases**. *Am J Physiol Renal Physiol* (2009) **297**. DOI: 10.1152/ajprenal.90435.2008 79. Locatelli I, Sutti S, Vacchiano M, Bozzola C, Albano E. **NF-κB1 deficiency stimulates the progression of non-alcoholic steatohepatitis (NASH) in mice by promoting NKT-Cell-Mediated responses**. *Clin Sci* (2013) **124**. DOI: 10.1042/cs20120289 80. Jurk D, Wilson C, Passos JF, Oakley F, Correia-Melo C, Greaves L. **Chronic inflammation induces telomere dysfunction and accelerates ageing in mice**. *Nat Commun* (2014) **2** 4172. DOI: 10.1038/ncomms5172 81. Cheng CW, Su JL, Lin CW, Su CW, Shih CH, Yang SF. **Effects of NFKB1 and NFKBIA gene polymorphisms on hepatocellular carcinoma susceptibility and clinicopathological features**. *PloS One* (2013) **8**. DOI: 10.1371/journal.pone.0056130 82. Grohmann M, Wiede F, Dodd GT, Gurzov EN, Ooi GJ, Butt T. **Obesity drives STAT-1-Dependent NASH and STAT-3-Dependent HCC**. *Cell* (2018) **175** 1289-306.e20. DOI: 10.1016/j.cell.2018.09.053 83. Park J, Zhao Y, Zhang F, Zhang S, Kwong AC, Zhang Y. **IL-6/STAT3 axis dictates the PNPLA3-mediated susceptibility to non-alcoholic fatty liver disease**. *J Hepatol* (2022). DOI: 10.1016/j.jhep.2022.08.022 84. Li YL, Li XQ, Wang YD, Shen C, Zhao CY. **Metformin alleviates inflammatory response in non-alcoholic steatohepatitis by restraining signal transducer and activator of transcription 3-mediated autophagy inhibition**. *Biochem Biophys Res Commun* (2019) **513** 64-72. DOI: 10.1016/j.bbrc.2019.03.077 85. Mohammed S, Nicklas EH, Thadathil N, Selvarani R, Royce GH, Kinter M. **Role of necroptosis in chronic hepatic inflammation and fibrosis in a mouse model of increased oxidative stress**. *Free Radic Biol Med* (2021) **164**. DOI: 10.1016/j.freeradbiomed.2020.12.449 86. Liu Q, Yu J, Wang L, Tang Y, Zhou Q, Ji S. **Inhibition of PU.1 ameliorates metabolic dysfunction and non-alcoholic steatohepatitis**. *J Hepatol* (2020) **73**. DOI: 10.1016/j.jhep.2020.02.025 87. Liu D, Wang K, Li K, Xu R, Chang X, Zhu Y. **Ets-1 deficiency alleviates nonalcoholic steatohepatitis**. *Cell Death Dis* (2019) **10** 458. DOI: 10.1038/s41419-019-1672-4 88. Zhao Y, Xie X, Liao W, Zhang H, Cao H, Fei R. **The transcription factor RFX5 is a transcriptional activator of the TPP1 gene in hepatocellular carcinoma**. *Oncol Rep* (2017) **37**. DOI: 10.3892/or.2016.5240 89. Chen DB, Zhao YJ, Wang XY, Liao WJ, Chen P, Deng KJ. **Regulatory factor X5 promotes hepatocellular carcinoma progression by transactivating tyrosine 3-Monooxygenase/Tryptophan 5-monooxygenase activation protein theta and suppressing apoptosis**. *Chin Med J* (2019) **132**. DOI: 10.1097/cm9.0000000000000296 90. Chen DB, Xie XW, Zhao YJ, Wang XY, Liao WJ, Chen P. **RFX5 promotes the progression of hepatocellular carcinoma through transcriptional activation of Kdm4a**. *Sci Rep* (2020) **10** 14538. DOI: 10.1038/s41598-020-71403-1 91. Hu Z, Zhao TV, Huang T, Ohtsuki S, Jin K, Goronzy IN. **The transcription factor RFX5 coordinates antigen-presenting function and resistance to nutrient stress in synovial macrophages**. *Nat Metab* (2022) **4**. DOI: 10.1038/s42255-022-00585-x 92. Wang X, Zeldin S, Shi H, Zhu C, Saito Y, Corey KE. **TAZ-induced cybb contributes to liver tumor formation in non-alcoholic steatohepatitis**. *J Hepatol* (2022) **76**. DOI: 10.1016/j.jhep.2021.11.031 93. Shi L, Godfrey WR, Lin J, Zhao G, Kao PN. **NF90 regulates inducible IL-2 gene expression in T cells**. *J Exp Med* (2007) **204**. DOI: 10.1084/jem.20052078 94. Jayachandran U, Grey H, Cook AG. **Nuclear factor 90 uses an ADAR2-like binding mode to recognize specific bases in dsRNA**. *Nucleic Acids Res* (2016) **44**. DOI: 10.1093/nar/gkv1508 95. Zhang X, Zou M, Wu Y, Jiang D, Wu T, Zhao Y. **Regulation of the late onset alzheimer’s disease associated**. *Am J Alzheimers Dis Other Demen* (2022) **37**. DOI: 10.1177/15333175221085066 96. Nazitto R, Amon LM, Mast FD, Aitchison JD, Aderem A, Johnson JS. **ILF3 is a negative transcriptional regulator of innate immune responses and myeloid dendritic cell maturation**. *J Immunol* (2021) **206**. DOI: 10.4049/jimmunol.2001235 97. Bo C, Li N, He L, Zhang S, An Y. **Long non-coding RNA ILF3-AS1 facilitates hepatocellular carcinoma progression by stabilizing ILF3 mRNA in an m**. *Hum Cell* (2021) **34**. DOI: 10.1007/s13577-021-00608-x 98. Yan G, Chang Z, Wang C, Gong Z, Xin H, Liu Z. **LncRNA ILF3-AS1 promotes cell migration, invasion and emt process in hepatocellular carcinoma**. *Dig Liver Dis* (2022) **54**. DOI: 10.1016/j.dld.2021.04.036
--- title: Low carbohydrate intake correlates with trends of insulin resistance and metabolic acidosis in healthy lean individuals authors: - Fatema Al-Reshed - Sardar Sindhu - Ashraf Al Madhoun - Fatemah Bahman - Halemah AlSaeed - Nadeem Akhter - Md. Zubbair Malik - Fawaz Alzaid - Fahd Al-Mulla - Rasheed Ahmad journal: Frontiers in Public Health year: 2023 pmcid: PMC10061153 doi: 10.3389/fpubh.2023.1115333 license: CC BY 4.0 --- # Low carbohydrate intake correlates with trends of insulin resistance and metabolic acidosis in healthy lean individuals ## Abstract ### Introduction Both obesity and a poor diet are considered major risk factors for triggering insulin resistance syndrome (IRS) and the development of type 2 diabetes mellitus (T2DM). Owing to the impact of low-carbohydrate diets, such as the keto diet and the Atkins diet, on weight loss in individuals with obesity, these diets have become an effective strategy for a healthy lifestyle. However, the impact of the ketogenic diet on IRS in healthy individuals of a normal weight has been less well researched. This study presents a cross-sectional observational study that aimed to investigate the effect of low carbohydrate intake in healthy individuals of a normal weight with regard to glucose homeostasis, inflammatory, and metabolic parameters. ### Methods The study included 120 participants who were healthy, had a normal weight (BMI 25 kg/m2), and had no history of a major medical condition. Self-reported dietary intake and objective physical activity measured by accelerometry were tracked for 7 days. The participants were divided into three groups according to their dietary intake of carbohydrates: the low-carbohydrate (LC) group (those consuming <$45\%$ of their daily energy intake from carbohydrates), the recommended range of carbohydrate (RC) group (those consuming 45–$65\%$ of their daily energy intake from carbohydrates), and the high-carbohydrate (HC) group (those consuming more than $65\%$ of their daily energy intake from carbohydrates). Blood samples were collected for the analysis of metabolic markers. HOMA of insulin resistance (HOMA-IR) and HOMA of β-cell function (HOMA-β), as well as C-peptide levels, were used for the evaluation of glucose homeostasis. ### Results Low carbohydrate intake (<$45\%$ of total energy) was found to significantly correlate with dysregulated glucose homeostasis as measured by elevations in HOMA-IR, HOMA-β% assessment, and C-peptide levels. Low carbohydrate intake was also found to be coupled with lower serum bicarbonate and serum albumin levels, with an increased anion gap indicating metabolic acidosis. The elevation in C-peptide under low carbohydrate intake was found to be positively correlated with the secretion of IRS-related inflammatory markers, including FGF2, IP-10, IL-6, IL-17A, and MDC, but negatively correlated with IL-3. ### Discussion Overall, the findings of the study showed that, for the first time, low-carbohydrate intake in healthy individuals of a normal weight might lead to dysfunctional glucose homeostasis, increased metabolic acidosis, and the possibility of triggering inflammation by C-peptide elevation in plasma. ## 1. Introduction Insulin resistance syndrome (IRS) is a modern-day epidemic. With the increase in research endeavors and on the focus on IRS, it has become evident that IRS can drive the disease pathogenesis of several clinical syndromes, such as T2DM and cardiovascular diseases [1, 2]. Interestingly, while IRS is often regarded as the primary underlying mechanism for T2DM, several reports from sub-Saharan Africa and South Asian populations indicate that pancreatic beta-cell secretory dysfunction is the driving factor of the lean T2DM phenotype [3, 4]. The current recommended dietary guidelines for treating obesity and obesity-related complications revolve around reducing daily energy intake, improving portion control, and improving the quality of the diet to achieve a calorie deficit status [5]. However, over the past decade, further research has unraveled the benefits of redirecting the weight loss strategy toward readjusting levels of macronutrients, such as consuming fewer carbohydrates and a larger quantity of proteins in daily meals (5–7). The three macronutrients found in food include carbohydrates (4 kcal/g), proteins (4 kcal/g), and fat (9 kcal/g). A daily intake of <$10\%$ or 20–50 g of carbohydrates is considered a very low carbohydrate intake, <$26\%$ or <130 g is considered a low carbohydrate intake, 26–$44\%$ is considered a moderate carbohydrate intake, and ≥$45\%$ is regarded as a high carbohydrate intake [8]. There are more than a dozen types of low-carbohydrate diets, of which the ketogenic or keto, Atkins, and paleo diets are relatively more widely known. Keto diets are characterized by reduced carbohydrate content (<50 g per day) and relatively increased fat and protein content. Keto diets are further categorized as follows: (i) standard keto diet (SKD), which contains very low carbohydrate ($10\%$), moderate protein ($20\%$), and high fat ($70\%$) content; (ii) cyclical keto diet (CKD), which involves periods of high-carbohydrate diet in between keto diets, e.g., 5 keto days followed by two high-carbohydrate days as a dietary cycle; (iii) targeted keto diet (TKD), which allows for adding additional carbohydrates around periods of intensive physical workout; and (iv) high-protein keto diet (HPKD), which has a relatively high-protein content ($35\%$) with a low carbohydrate content ($5\%$) but still a high fat ($60\%$) content [9]. With carbohydrates being the macronutrient with the greatest impact on postprandial blood glucose response, low-carbohydrate diets, such as the Atkins and keto diets, have become an effective strategy for weight loss [10, 11]. We assumed that, during low carbohydrate intake, the body undergoes ketogenesis, a process that switches the utilization of glucose from the carbohydrate as an energy source to the use of ketone bodies in the mitochondria for ATP synthesis, shifting the metabolic process to the ketosis-favoring pathways [12]. This metabolic shift following low-carbohydrate dietary interventions is the cornerstone of weight loss mechanisms. Nevertheless, the use of a low-carbohydrate diet in healthy individuals of a normal weight and in children has been associated with unwanted diet-induced ketoacidosis (13–15). The influence of a low-carbohydrate diet and the activation of the ketogenesis pathway in individuals with obesity, especially those with metabolic complications, such as T2DM, have been proven to be quite effective in reducing body weight [11]; however, the significance and adaptation of this lifestyle in individuals of a normal weight in the absence of a family history of diabetes or in those with no history of a major ailment remain unclear. Therefore, the present study aimed to investigate the effect of low carbohydrate intake in healthy, normal weight individuals with regard to glucose homeostasis and inflammatory and metabolic parameters. Herein, we identified that low carbohydrate intake in healthy individuals of a normal weight correlates with dysfunctional glucose homeostasis, increased metabolic acidosis, and the risk of inflammation suggested to be triggered by the elevation in plasma C-peptide levels. ## 2.1. Anthropometric, clinical, and dietary characteristics of the study participants This is a cross-sectional observational study that involved healthy men and women of a normal weight aged 21–65 years. Data were collected between January 2016 and December 2019 and were processed at the Dasman Diabetes Institute, Kuwait. The sample size was determined using the ClinCalc tool software (www.clincalc.com). The incident rate of T2DM onset in individuals of a normal weight is estimated to be 7.7–$21\%$ worldwide. Considering $21\%$ as the guiding reference, we achieved a statistical power (1-β) of $95\%$ and a level of significance (α) of 0.05, which yielded the minimum sample size required for this study as 52 individuals. Taking into account a fair margin for possible dropouts, we thus aimed to recruit 100 participants. A total of 138 adult (>18 years) Kuwaiti individuals were reached out to randomly by word of mouth, through flyers, or through social media contacts and were invited to participate in the study. Out of these 138 participants, 120 of them (57 men and 63 women) with a mean age of 31.9 ± 5.7 years and BMI of ≤25 kg/m2, were found to be eligible for the final analysis. The study was conducted in accordance with the Helsinki Declaration and the institutional review board ethics of the Kuwait Ministry of Health (MOH) Ethics Board ($\frac{2017}{542}$) [16]. Each participant was required to complete a full-length health screening questionnaire that tracked their past and current health status and history. The health screening questionnaire also asked participants about the health of their immediate family members and inquired about any family-related disease problems. The exclusion criteria were as follows: patients who were physically diagnosed with diabetes, hypertension (>$\frac{160}{90}$ mmHg), and anti-hypertensive therapy, those with a previous history of established coronary heart disease, e.g., myocardial infarction, coronary artery bypass graft surgery, coronary angioplasty, or a family history of diabetes or early cardiac death (<40 years), those with a history of cancer within the past 2 years, those diagnosed with depression, and those under medications that could influence body weight due to effects on the lipid or carbohydrate metabolism, as well as those who were pregnant or lactating women. A flow chart of participant recruitment is summarized in Supplementary Figure 1A. None of the participants had physical disabilities that would prevent or severely limit physical mobility or physical activity. The characteristics of male and female participants are summarized below in Supplementary Table 1. The presented study follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) recommendations [17]. ## 2.2. Physical evaluations In the physical activity laboratory, a standard protocol was used to carry out all anthropometric assessments for all participants wearing tight-fitting clothes and using the same equipment throughout the study. Measurements were made to the nearest 0.1 unit. Height (cm) was taken by instructing the volunteer to stand with their feet together and back and heels against the upright bar of the height scale. The volunteer was asked to position their head upright against the backboard. The volunteer was requested to take a deep breath as the investigator applied gentle, upward pressure under the angle of the mandible. Other investigators slid down the horizontal bar attached to the scale so that it rested snugly on the examinee's head, and measurements were taken. Body weight (kg) was measured using a beam balance, and BMI was calculated as follows: BMI = weight (kg)/height (m2). Waist and hip circumferences (cm) were measured in duplicate using non-elastic tape. Waist circumference was measured at the minimum circumference horizontally between the iliac crest and the rib cage, while hip circumference was measured at the maximum protuberance of the buttocks, and the waist-to-hip ratio was calculated. The same investigator performed these measurements for all volunteers on every occasion. Whole body composition, including body fat percentage, soft lean mass, and total body water, were measured using an IOI 353 Body Composition Analyzer (Jawon Medical, South Korea). ## 2.3. Physical activity measurements All participants in this study were given an electronic accelerometer (ActiGraph GT3X; ActiGraph LLC, Pensacola, FL, USA) to measure daily physical activity (PA) levels. Subjects were advised to maintain their normal daily habitual PA levels during the study period. The accelerometers were attached to an elasticized belt and worn on the right hip for 7 consecutive days (except when bathing and during water activity). The accelerometer provided PA measurements that included activity counts, vector magnitude, energy expenditure, step counts, PA intensity levels, and metabolic equivalents of tasks (METs). A 1-min epoch was used in this study with activity counts assessed at 1-min intervals to ensure that the data quality for the participants included at least 4 days in which the accelerometer was worn for at least $60\%$ of the time of the day. A non-wear time was taken as any block of time ≥60 min wherein the activity count was equal to zero [18]. Individuals that did not meet those criteria were excluded from the study, and their collected data were removed from the data pool. Freedson's cutoffs [14] were used to differentiate between PA intensity levels, including light-intensity activity (100–1,951 counts/min), moderate-intensity activity (1,952–5,724 counts/min), and high-intensity activity (>5,725 counts/min). All counts ≤99 counts/min were considered sedentary. The data were also expressed as the mean intensity for each activity during the monitoring time (total accelerometer counts per total monitoring time). ## 2.4. Measurement of metabolic and inflammatory markers Volunteers were asked for a second visit after an overnight fast of at least 10 h. Blood pressure and heart rate were measured for each participant using a semiautomatic Omron portable monitor. In brief, the cuff was placed on the upper arm to ensure uniform compression of the brachial artery, and three consecutive readings were collected [19]. Blood samples were collected in 10 mL Ethylenediaminetetraacetic acid (EDTA) tubes (BD Vacutainer system, Plymouth, UK). Plasma was separated and frozen immediately at−80°C for further analysis. Total blood glucose, fasting plasma insulin, cholesterol, HDL-cholesterol, and triglycerides were determined by biochemical analysis using a single assay upon the completion of the sampling (refer to Supplementary Table 2 for information regarding normal ranges). Quality control sera were used to monitor the accuracy and precision of the assays. Quantitative insulin sensitivity indices, HOMA-IR and HOMA-β, were calculated as follows: The anion gap was calculated according to the following equation: ## 2.5. Dietary monitoring and analysis All participants were given food diaries and were instructed to weigh and record their daily intake of food and drinks on electronic scales for the length of the study (7 days) (Salter Housewares, Kent, United Kingdom). A visual demonstration of how to use scales and diaries was given to each individual prior to the start of the study. All individuals were advised to maintain their normal dietary intake. Diaries were completed prior to the second visit. Food diary data were analyzed using CompEat pro (Nutrition systems, Banbury, United Kingdom), and an average of the daily nutrient intake was calculated. According to the international health guidelines established by the Food and Nutrition Board of the National Academies of Sciences, Engineering, and Medicine for the Recommended Dietary Allowance (RDA) for carbohydrates, the recommended daily energy intake from carbohydrates is set between 45 and $65\%$ of daily calorie intake since this amount has been linked to a lower risk of chronic illnesses [15, 20]. Based on these criteria, study participants were divided into three groups as follows: the low-carbohydrate (LC) group (those consuming <$45\%$ of daily energy intake from carbohydrates), the recommended range of carbohydrate (RC) group (those consuming 45–$65\%$ of daily energy intake from carbohydrates), and the high-carbohydrate (HC) group (those consuming higher than $65\%$ of daily energy intake from carbohydrates). ## 2.6. Enzyme-linked immunosorbent assay Commercially available ELISA kits were used for the detection of plasma levels of fasting insulin and C-peptide (Mercodia, Uppsala, Sweden), following instructions from the manufacturers. ## 2.7. Determination of plasma cytokines/chemokines A total of 41 cytokines and chemokines were measured using the MILLIPLEX MAP Human Cytokine/Chemokine panel with Magnetic Bead Panel-Premixed 41 Plex-Immunology Multiplex Assay (Milliplex map kit, HCYTMAG-60 K-PX41; Millipore, USA), following the manufacturer's instructions. Data from the reactions were acquired by Luminex using a MILLIPLEX analyzer, while a digital processor managed the data output. MILLIPLEX Analyst software was used to determine the mean fluorescence intensity (MFI) and analyte concentration (pmol/mL). ## 2.8. Statistical analysis Data were analyzed using SPSS version 25 (SPSS, Inc., Chicago, IL) and GraphPad Prism 7.01 (version 6.05; San Diego, CA, USA) and expressed as the mean ± standard deviation (SD). The data were tested for normality using the Shapiro–Wilk normality test. For comparing the means between two groups, two-tailed t-tests and Wilcoxon–Mann–Whitney U tests were used to assess the differences between means of parametric and non-parametric data, respectively. For comparing the means between the three groups, a one-way ANOVA and exact Kruskal–Wallis tests were used when comparing the differences between the means of parametric and non-parametric data, respectively. Multiple linear regression analysis was conducted to examine the correlation between the calculated anion gap and blood electrolyte levels that were found to be associated with LC intake. Exact chi-squared tests of independence were performed to evaluate differences in immune–metabolic parameters. The correlation between energy intake from carbohydrates and immune–metabolic parameters was evaluated with Spearman's correlation coefficients. All p-values of ≤0.05 were considered statistically significant. ## 3.1. Participants' characteristics A total of 138 people were invited to participate in the ActiGraph track assessments. Following a comprehensive health screening, only 134 individuals were found to meet the inclusion health criteria. Of those 138 individuals, only 120 of them (57 men and 63 women) had sufficient data from the ActiGraph that included at least 4 days in which the accelerometer was worn for at least $60\%$ of the time. *The* general characteristics of the study participants and dietary and energy intake data are summarized in Supplementary Table 3. Based on the WHO chart for age and sex, all participants were within the normal range of BMI, with an average BMI of 22.7 ± 2.4 kg/m2. In our study, $52.5\%$ of participants were women, and the mean age of all participants was 32.2 ± 5.7. The mean systolic and diastolic blood pressure measurements were normal (109.9 ± 11.3 and 67.15 ± 9.8, respectively), and the average heart rate per min (HR) was 71 ± 10. The study participants had normal levels of serum triglycerides (TG) (0.87 ± 0.38 mmol/L), total cholesterol (4.6 ± 0.8 mmol/L), and HDL-C (1.49 ± 0.34 mmol/l). All participants also showed normal glucose homeostasis, with an average fasting glucose of 4.9 ± 0.64 mmol/L and a serum insulin level of 3.7 ± 2.11 U/ml. All individuals were within the normal range of HOMA-β (%) (74.8 ± 58.9) and HOMA-IR (<2.5) indices. The participants also had normal fasting blood C-peptide levels (1.4 ± 0.37 ng/mL). The average total calorie intake per day for all participants was 2143.8 ± 571.9 kcal, with most energy consumed from carbohydrates at 49.5 ± $12.5\%$ of daily energy. To explore the contribution of the level of carbohydrate energy intake on the general health of the participants, we decided to pool both the male and female data. Based on the international health guidelines of daily RDA, study participants were divided into three groups as follows: the low-carbohydrate (LC) group (those consuming <$45\%$ of daily energy intake from carbohydrates), the recommended range of the carbohydrate (RC) group (those consuming 45–$65\%$ of daily energy intake from carbohydrate); and the high-carbohydrate (HC) group (those consuming higher than $65\%$ of daily energy intake from carbohydrate). Group characteristics are summarized in Table 1, which shows significant cross-group differences with regard to lean weight (LC vs. RC and HC), HOMA-beta-cell function (HOMA-β%) (LC vs. RC), fasting glucose, insulin, C-peptide, and the homeostasis model assessment of insulin resistance (HOMA-IR) index (LC vs. RC and RC vs. HC). Concurrently, no significant differences were found regarding anthropometric characteristics, lipid profile, and total calorie intake per day. **Table 1** | Physical characteristics of subjects | <45% (LC) | Unnamed: 2 | 45–65% (RC) | Unnamed: 4 | >65% (HC) | Unnamed: 6 | p-value | | --- | --- | --- | --- | --- | --- | --- | --- | | | (n = 38) 20 M/18 F | Median-IQR | (n = 62) 30 M/32 F | Median-IQR | (n = 20) 7 M/ 13 F | Median-IQR | | | Age (years) | 32 ± 4 | | 31 ± 4 | | 33 ± 6 | | 0.551 | | Weight (kg) | 64.1 ± 10.1 | | 66.1 ± 12.8 | | 63.0 ± 13.7 | | 0.538 | | Height (cm) | 167.8 ± 11.4 | | 170.1 ± 11.6 | | 163.4 ± 12.5 | | 0.09 | | BMI (kg/m2) | 22.7 ± 2.9 | 22.9–43 | 22.9 ± 2.4 | 22.6–2.9 | 23.2 ± 2.6 | 22.4–3.2 | 0.796 | | Waist circumference (cm) | 75.9 ± 9.1 | | 80.5 ± 7.8 | | 79.5 ± 13.7 | | 0.079 | | Hip circumference (cm) | 105.1 ± 30.7 | 39.2–4.6 | 100.5 ± 10.1 | 39.7–5.2 | 100.5 ± 9.6 | 40–3.2 | 0.483 | | Fat weight (kg) | 20.3 ± 11.2 | | 23.4 ± 12.1 | | 27.8 ± 11.6 | | 0.074 | | Lean weight (kg) | 49.5 ± 11.6 | 51–23.1 | 43.4 ± 11.4 | 45.2–10.5 | 42.2 ± 9.3 | 43.1–13 | 0.0075 | | Fat % | 21.3 ± 10.3 | | 25.9± 10.0 | | 26.8 ± 7.3 | | 0.092 | | Total calorie intake (Kcal) | 2104.7 ± 581 | | 2124 ± 588 | | 2277 ± 504 | | 0.514 | | BP/systolic (mmHg) | 110.6 ± 11.7 | 110–14 | 109.3 ± 11.1 | 108.5–14 | 110.9 ± 11.6 | 110–10.5 | 0.8 | | BP/diastolic (mmHg) | 67.7 ± 10.4 | 70–18.2 | 66.5 ± 10.2 | 68–16.2 | 67.8 ± 7.1 | 67–4 | 0.81 | | Heart rate | 70 ± 11 | 71–20.5 | 70 ± 10 | 73–15 | 74 ± 9 | 76–12.1 | 0.381 | | Fasting glucose (mmol/l) | 5.2 ± 0.7 | 5.1–0.86 | 4.7 ± 0.5 | 4.7–0.60 | 5.5 ± 0.5 | 5.2–0.85 | 0.0008 | | Triglycerides (mmol/l) | 0.8 ± 0.3 | 0.9–0.43 | 0.8 ± 0.4 | 0.7–0.48 | 0.8 ± 0.3 | 0.9–0.47 | 0.602 | | Total cholesterol (mmol/l) | 4.5 ± 0.7 | 4.5–0.86 | 4.6 ± 0.7 | 4.5–0.77 | 4.9 ± 1.0 | 4.83–0.96 | 0.469 | | HDL cholesterol (mmol/l) | 1.5 ± 0.32 | 1.3–0.53 | 1.45 ± 0.37 | 1.3–0.63 | 1.6 ± 0.3 | 1.5–0.54 | 0.064 | | Insulin Con. (mU/l) | 4.5 ± 1.4 | 4.4–2.0 | 3.2 ± 2.3 | 2.7–4.1 | 4.6 ± 2.2 | 5.0–3.0 | 0.001 | | HOMA-IR | 1.0 ± 0.37 | 1.0–0.42 | 0.70 ± 0.47 | 0.6–0.7 | 1.0± 0.55 | 1.0–0.57 | 0.013 | | HOMA-β | 56.3 ± 30.3 | 52.2–42.9 | 80.5 ± 59.4 | 45.7–68.8 | 58.9 ± 37.0 | 51.8–56.8 | 0.016 | | C-Peptide (pg/ml) | 1.5 ± 0.36 | 1.4–0.64 | 1.3 ± 0.34 | 1.3–0.37 | 1.56 ± 0.36 | 1.6–0.62 | 0.019 | We also found no significant differences in the level of objectively measured physical activity, as indicated in Table 2. However, individuals consuming RC were found to have significantly lower HOMA-IR than those consuming LC (p ≤ 0.05) and HC (p ≤ 0.05) and significantly higher HOMA-β (%) than those consuming LC (p ≤ 0.05) (Figures 1A, B). It was also observed that participants consuming RC had significantly lower C-peptides in their serum than in the LC and HC groups. However, only LC was found to be significantly higher than RC ($p \leq 0.05$) (Figure 1C). To further investigate these findings, a Spearman correlation test was performed to determine the correlation between carbohydrate energy % and surrogate markers of insulin resistance, β-cell function, and insulin secretion [HOMA-IR, HOMA-β (%), and c-peptide levels, respectively]. No significant correlation was found between HOMA-IR and carbohydrate energy % across all groups (Figure 2A). However, unlike the RC and HC groups, a clear trend of negative HOMA-IR associated with carbohydrate energy % was observed in the LC group. Interestingly, fasting serum C-peptide levels in the LC group had a significant negative correlation (p ≤ 0.05) with carbohydrate energy %, while C-peptide levels in the RC and HC groups tended to have a direct correlation with carbohydrate energy % (Figure 2B). In terms of β-cell function assessment, the HOMA-β% index was associated positively with levels of carbohydrate energy % in both LC and RC groups, whereas HOMA-β% tended to have a negative correlation with carbohydrate energy % in the HC group (Figure 2C). On the whole, these data clearly indicate that low carbohydrate intake might be correlated with insulin resistance and that the consumption of 45–$65\%$ of energy intake from carbohydrates is important to maintain normal glucose hemostasis in individuals of a normal weight. **Figure 2:** *Association between carbohydrate intake levels and glucose homeostasis. Spearman correlation test was conducted to investigate the association between (A) HOMA-IR, (B) C-peptide secretion and (C) HOMA-β% and the level of carbohydrate intake % of energy in all three groups. All data are expressed as mean ± SD. P ≤ 0.05 was considered statistically significant. Low Carbohydrate intake (<45% energy from carb; green), Recommended range of carbohydrate intake (45–65% energy from carb; red), High carbohydrate intake (>65% energy from carb; blue).* ## 3.2. Association of the percentage of energy intake from dietary carbohydrate with the serum anion gap marker for metabolic acidosis Because of the role of ketone bodies in causing acid-base disruption, measuring plasma electrolytes and calculating anion gap became standard clinical practice for the evaluation of metabolic acidosis. Similar to other metabolic blood markers, the mean values of serum bicarbonate (24 ± 1.7 mmol/l), serum albumin (64.3 ± 2.8), serum sodium (Na) (136.5 ± 3.9 mmol/l), serum chloride (Cl) (98.3 ± 2.9), and calculated anion gap (10.5 ± 3.2) were all within ranges considered normal. However, the LC group displayed significantly lower serum bicarbonate and serum albumin levels (Figures 3A, B) than the RC group. The HC group was also found to have significantly upregulated serum sodium levels compared to the RC group only (Figure 3C), while no significance was found in the level of serum chloride across all groups (Figure 3D). Both disturbances of serum bicarbonate and albumin are considered signs of metabolic acidosis. Indeed, the calculated anion gap further reflected a significant upregulation in metabolic acidosis in the LC group compared to the RC group, with trends of upregulation in the HC group being found to not be significant (Figure 3E). Through the use of the Spearmen r coefficient, it was observed that the serum anion gap was inversely associated with the percentage of energy intake consumed from carbohydrates (Figure 3F); we also found a negative correlation between the anion gap and C-peptide (Figure 3G). As indicated by multilinear regression analyses, both serum albumin levels as well bicarbonates were found to be associated independently with the calculated anion gap (Table 3). Together these observations indicate an increase in high anion gap metabolic acidosis triggered by imbalanced serum bicarbonate and albumin under the consumption of a low-carbohydrate diet. **Figure 3:** *Effects of the levels of carbohydrate intake on metabolic acidosis. Serum levels of (A) Bicarbonate; (B) Albumin; (C) Chloride; and (D) Sodium are shown in individuals with low-carbohydrate (LC group: <$45\%$ of daily energy from carbohydrate), recommended range of carbohydrate (RC group: 45–$65\%$ of daily energy from carbohydrate) and high-carbohydrate (HC group: >$65\%$ of daily energy from carbohydrate) intake. (E) Calculated anion gap is shown for individuals in LC, RC, and HC dietary groups. (F) Association (Spearman r correlation test) between anion gap and carbohydrate intake. (G) Association (Spearman r correlation test) between anion gap and C-peptide. Data are expressed as mean ± SD. ns, non-significant; *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.005.* TABLE_PLACEHOLDER:Table 3 ## 3.3. Association of the C-peptide levels with circulatory inflammatory markers C-peptide is a biologically active short polypeptide (31 amino acids) that serves as a diagnostic biomarker to distinguish between type 1 and type 2 diabetes and is a strong indicator of insulin biosynthesis and insulin resistance syndrome (IRS) (21–23). Over the past decade, several studies demonstrated a biological effect of plasma circulating C-peptide on activating inflammatory signaling pathways [24, 25]. Thus, we questioned the possible association of elevated levels of C-peptide under low carbohydrate intake with insulin resistance-related inflammatory cytokines. A multiplex cytokine assay was conducted to investigate the secretion of these cytokines, known to be involved in several metabolic disorders, such as diabetes and insulin resistance syndrome. Out of the 41 inflammatory mediators investigated, only seven of them (IP-10; $$p \leq 0.045$$, VEGF; $$p \leq 0.049$$, IL-6; 0.049, IL-17A; p = < 0.0001, FGF-2; $$p \leq 0.025$$, MDC; $$p \leq 0.019$$, and GRO; $$p \leq 0.035$$) were found to be significantly elevated in the LC group when compared with the RC group, and only one cytokine (IL-3; $$p \leq 0.036$$) was found to be significantly reduced in the LC group when compared with HC group, as depicted in Table 4. The Spearmen correlation analysis further showed that, out of those eight cytokines/bioactive factors, five were found to be positively correlated with C-peptide expression (FGF-2; $r = 0.52$, $$p \leq 0.001$$, IP-10; $r = 0.33$, $$p \leq 0.04$$, IL-6; $r = 0.31$, $$p \leq 0.05$$, IL-17A; $r = 0.39$, $$p \leq 0.015$$, and MDC; $r = 0.36$, $$p \leq 0.025$$) (Figures 4A–E), one was negatively correlated with C-peptide expression (IL-3; $r = 0.45$, $$p \leq 0.005$$) (Figure 4F), and two (VEGF and GRO) had no correlation with C-peptide levels (Figures 4G, H). Together, the presented data suggest that, under the condition of low dietary carbohydrate intake, a correlation is found between the plasma C-peptide levels and IRS-related cytokine/mediator expression, supporting the active role of C-peptide as a bioactive molecule and its significance as an IRS biomarker. ## 4. Discussion The combination of many mechanisms, including homoeostatic, environmental, and behavioral, regulates body weight. The hypothalamus is central to homoeostatic control because it integrates information about food intake, energy balance, and body weight. However, an “obesogenic” environment and behavioral patterns influence the amount and kind of food consumed and physical activity. Unfortunately, physiological weight loss adaptations have been found to favor weight recovery [26]. These modifications include changes in the circulatory levels of hunger-related hormones, energy homeostasis, nutrition metabolism, and subjective appetite [27]. Notably, individuals need to adhere to behaviors that resist physiological adaptations and other variables that favor weight recovery to successfully sustain weight reduction. With the global rise of obesity and T2DM in humans, various dietary strategies that target the restriction of calorie intake have been used not only to promote weight loss but also to prevent and reduce the onset of T2DM [28, 29]. Over the past decade, a low-carbohydrate diet has been centered on weight loss in individuals with obesity and those who are overweight, as well as in patients with or at risk of T2DM [30, 31]. Even though the impact of a low-carbohydrate diet, especially the ketogenic diet, has been found to be very effective in the rapid induction of weight loss in both individuals with obesity and those who are overweight, the impact of such a diet remains to be well characterized in normal weight or lean counterparts. In this study, to the best of our knowledge, we investigated, for the first time, the effect of different dietary carbohydrate intake levels on glucose hemostasis, blood electrolyte balance, and T2DM-related inflammatory markers in 120 individuals of a normal weight (BMI ≤ 25 kg/m2). The data presented herein show that individuals with low carbohydrate intake, i.e., those consuming ≤$45\%$ of their daily calorie intake from carbohydrates, presented with the trends of IRS. We found that, under the condition of low carbohydrate intake, plasma insulin levels and, consequently, the HOMA-IR were both significantly elevated compared to weight-matched counterparts that consumed sufficient levels of carbohydrate for energy (45–$56\%$ of daily calorie intake), while the HOMA index representing beta-cell function (HOMA-β%) was found to be decreased under low carbohydrate intake diet. In this regard, we observed increased plasma insulin levels and HOMA-IR values in individuals of a normal weight who had low dietary carbohydrate intake, which may explain why proteolytic and lipolytic responses are enhanced under low dietary carbohydrate intake as part of alternate compensatory mechanisms to generate glucose from amino acids and glycerol [32]. Such gluconeogenic responses following a carbohydrate-restricted diet could be helpful for maintaining glycemia in healthy individuals; however, exacerbated glucose production and ketogenesis remain the major concerns involved [33]. Carbohydrate restriction to very low levels may also have deleterious effects on intestinal homeostasis and fiber-derived antioxidant phenolic acids compared with a moderate or high carbohydrate intake [34]. Furthermore, a relative increase of ketone concentrations under low dietary carbohydrate intake may at first stimulate the pancreas to increase insulin release, which may accumulate metabolic stress over time [35]. In addition, carbohydrate restriction induces lipolysis, releases free fatty acids, and increases citric acid cycle flux, all of which are known reasons to promote reactive oxygen species (ROS) production [36] and suppress the function of beta cells [37], which may be explained by the lower HOMA-β% values that we observed in individuals of a normal weight on low dietary carbohydrate intake. Interestingly, plasma C-peptide levels were also found to be significantly elevated under low carbohydrate intake. A significant correlation was found between glucose homeostasis markers and low dietary carbohydrate intake, further supporting the effect of low-carbohydrate diet intake on glucose homeostasis. Carbohydrate metabolism is a fundamental biochemical process that ensures a constant supply of energy to living cells. With the prolonged consumption of low carbohydrate intake, the liver starts to produce ketone bodies as an alternative source of energy. Ketone bodies travel from the liver to extrahepatic tissues to provide energy to different organs by breaking down fatty acids and ketogenic amino acids [38, 39]. In the studied cohort, an elevation in the anion gap was observed in the LC group. Through further multi-regression analysis, it was discovered that this elevation was caused by the reduction in both serum bicarbonate and serum albumin levels. An association was also observed between the level of anion gap and the level of energy from carbohydrate intake. It is irrefutable that any diet based on restrictions and exclusions of certain foods will induce a possible increase in the risk of mineral deficiencies and electrolyte imbalance. In fact, studies have shown that consuming a low-carbohydrate diet while maintaining a high intake of protein can lead to a disturbance in fluid and electrolytes, which can further cause kidney damage [40, 41]. Interestingly, in our cohort, we also highlight a correlation between the anion gap and C-peptide levels. C-peptide is the part of proinsulin that is cleaved from pancreatic beta cells prior to co-secretion with insulin. A 20-year follow-up study by Fung et al. [ 42] revealed that increased dietary intake of protein and high-fat dairy products is positively associated with higher plasma C-peptide levels and directly associated with the risk of colorectal cancer. While another study presented by Seidelmann et al. [ 43] concluded the presence of a U-shaped association between the percentage of energy consumed from carbohydrates and mortality, as they reported that both low-carbohydrate consumption (<$40\%$) and high-carbohydrate consumption (>$70\%$) presented a greater risk of mortality than moderate carbohydrate intake. Even though the role of C-peptide in the regulation of inflammation remains controversial, in our study, we observed a significant correlation between lower carbohydrate intake and higher plasma C-peptide. Multiplex analysis of several inflammatory cytokines further revealed that plasma C-peptide upregulation correlated positively with plasma FGF2, IP-10, IL-6, MDC, and IL-17A levels. Notably, these cytokines have been previously identified to induce the development of insulin resistance and cause the pathogenesis of T2DM (44–48). However, a negative correlation was observed between the C-peptide levels and the expression of the anti-inflammatory cytokine IL-3, a pleiotropic regulator of inflammation [49]. Nevertheless, the present study is limited by certain caveats. In this study, sample collection was achieved randomly and not systemically. Even though such an analysis provides a better approximation of the entire population, several limitations should be considered. For instance, the dietary intake in this study was assessed through self-reported diary logs and not by intervention. Even though adequate training was given to each participant along with a food scale, we could not possibly rule out false reporting. We also have no record of how long each individual would have maintained this dietary lifestyle beyond the 7-day follow-up period. Therefore, the effects of long-term vs. short-term dietary interventions involving carbohydrate intake may not be evaluated. Nevertheless, the most substantial limitation found in this study was the sample size in the HC group. Throughout the investigation, it became clear that a U-shaped effect was observed, indicating that both LC and HC intake reflect unwanted outcomes. This observation falls in line with observations made by other groups [43]. However, owing to the small number of participants at the higher end of carbohydrate intake (<$70\%$), it was difficult to reach statistical significance in this group. It is also crucial to note that, although there is a significant correlation between low carbohydrate consumption and IRS risk indicators, this should not be interpreted as a direct impact but rather as a contributory behavioral factor that could enhance the probability of such an outcome if continued uninterrupted over time. All in all, the presented data highlight, for the first time, the effect of low carbohydrate intake on factors related to IRS in the normal-weight population. We have shown that individuals of a normal weight consuming <$45\%$ of their daily energy intake from carbohydrates had trends of dysregulated glucose homeostasis with elevated plasma C-peptide levels and higher anion gap metabolic acidosis. Under the consumption of low carbohydrate intake, we also observed the upregulation of T2DM-related inflammatory markers that were found to significantly correlate with C-peptide levels. ## Data availability statement The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors. ## Ethics statement The studies involving human participants were reviewed and approved by Kuwait Ministry of Health (MOH) Ethics Board ($\frac{2017}{542}$). The patients/participants provided their written informed consent to participate in this study. ## Author contributions FA-R conceived the idea, guided the research study, provided material support, procured funds, collected and analyzed data, and wrote the manuscript. SS participated in performing some statistical analysis, writing, and reviewing the manuscript. AA participated in performing experiments and analyzing data. FB, HA, and NA participated in performing experiments and data collection. MM participated in performing statistical analysis and statistical methodology and contributed to writing and reviewing. FA participated in performing some statistical analysis and in writing and reviewing the manuscript. FA-M reviewed and critically commented on the manuscript. RA guided the research study, provided material support, procured funds, wrote, edited, and approved the manuscript for submission. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2023.1115333/full#supplementary-material ## References 1. Parekh S, Anania FA. **Abnormal lipid and glucose metabolism in obesity: implications for nonalcoholic fatty liver disease**. *Gastroenterology.* (2007) **132** 2191-207. DOI: 10.1053/j.gastro.2007.03.055 2. Singer K, Lumeng CN. **The initiation of metabolic inflammation in childhood obesity**. *J Clin Invest.* (2017) **127** 65-73. DOI: 10.1172/JCI88882 3. Kibirige D, Sekitoleko I, Lumu W, Jones AG, Hattersley AT, Smeeth L. **Understanding the pathogenesis of lean non-autoimmune diabetes in an African population with newly diagnosed diabetes**. *Diabetologia.* (2022) **65** 675-83. DOI: 10.1007/s00125-021-05644-8 4. Gujral UP, Pradeepa R, Weber MB, Narayan KM, Mohan V. **Type 2 diabetes in South Asians: similarities and differences with white Caucasian and other populations**. *Ann N Y Acad Sci.* (2013) **1281** 51-63. DOI: 10.1111/j.1749-6632.2012.06838.x 5. Mongioì LM, Cimino L, Greco E, Cannarella R, Condorelli RA, La Vignera S. **Very-low-calorie ketogenic diet: an alternative to a pharmacological approach to improve glycometabolic and gonadal profile in men with obesity**. *Curr Opin Pharmacol.* (2021) **60** 72-82. DOI: 10.1016/j.coph.2021.06.013 6. Ren Y, Cheng W, He Q, Shao L, Xiang B, Yang M. *Wei Sheng Yan Jiu.* (2022) **51** 767-79. DOI: 10.19813/j.cnki.weishengyanjiu.2022.05.015 7. Chawla S, Tessarolo Silva F, Amaral Medeiros S, Mekary RA, Radenkovic D. **The effect of low-fat and low-carbohydrate diets on weight loss and lipid levels: a systematic review and meta-analysis**. *Nutrients* (2020) **12** 3774. DOI: 10.3390/nu12123774 8. Trumbo P, Schlicker S, Yates AA, Poos M. **Food, Nutrition Board of the Institute of Medicine TNA. Dietary reference intakes for energy, carbohydrate, fiber, fat, fatty acids, cholesterol, protein and amino acids**. *J Am Diet Assoc.* (2002) **102** 1621-30. DOI: 10.1016/S0002-8223(02)90346-9 9. Shilpa J, Mohan V. **Ketogenic diets: boon or bane?**. *Indian J Med Res.* (2018) **148** 251-3. DOI: 10.4103/ijmr.IJMR_1666_18 10. d'Avignon DA, Puchalska P, Ercal B, Chang Y, Martin SE, Graham MJ. **Hepatic ketogenic insufficiency reprograms hepatic glycogen metabolism and the lipidome**. *JCI Insight.* (2018) **3** e99762. DOI: 10.1172/jci.insight.99762 11. Youngson NA, Morris MJ, Ballard JWO. **The mechanisms mediating the antiepileptic effects of the ketogenic diet, and potential opportunities for improvement with metabolism-altering drugs**. *Seizure.* (2017) **52** 15-9. DOI: 10.1016/j.seizure.2017.09.005 12. Cardoso L, Vicente N, Rodrigues D, Gomes L, Carrilho F. **Controversies in the management of hyperglycaemic emergencies in adults with diabetes**. *Metabolism.* (2017) **68** 43-54. DOI: 10.1016/j.metabol.2016.11.010 13. Slade S, Ashurst J. **Diet-induced ketoacidosis in a non-diabetic: a case report**. *Clin Pract Cases Emerg Med.* (2020) **4** 259-62. DOI: 10.5811/cpcem.2020.2.44736 14. Freedson PS, Melanson E, Sirard J. **Calibration of the computer science and applications, Inc. accelerometer**. *Med Sci Sports Exerc.* (1998) **30** 777-81. DOI: 10.1097/00005768-199805000-00021 15. Buyken AE, Mela DJ, Dussort P, Johnson IT, Macdonald IA, Stowell JD. **Dietary carbohydrates: a review of international recommendations and the methods used to derive them**. *Eur J Clin Nutr.* (2018) **72** 1625-43. DOI: 10.1038/s41430-017-0035-4 16. Association WM. **World medical association declaration of helsinki: ethical principles for medical research involving human subjects**. *JAMA.* (2013) **310** 2191-4. DOI: 10.1001/jama.2013.281053 17. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. *Rev Esp Salud Publica.* (2008) **82** 251-9. DOI: 10.1016/j.jclinepi.2007.11.008 18. Bakrania K, Yates T, Edwardson CL, Bodicoat DH, Esliger DW, Gill JM. **Associations of moderate-to-vigorous-intensity physical activity and body mass index with glycated haemoglobin within the general population: a cross-sectional analysis of the 2008 Health Survey for England**. *BMJ Open.* (2017) **7** e014456. DOI: 10.1136/bmjopen-2016-014456 19. Pickering TG. **Principles and techniques of blood pressure measurement**. *Cardiol Clin.* (2002) **20** 207-23. DOI: 10.1016/S0733-8651(01)00009-1 20. Zhang X, Gong Y, Della Corte K, Yu D, Xue H, Shan S. **Relevance of dietary glycemic index, glycemic load and fiber intake before and during pregnancy for the risk of gestational diabetes mellitus and maternal glucose homeostasis**. *Clin Nutr.* (2021) **40** 2791-9. DOI: 10.1016/j.clnu.2021.03.041 21. Cardellini M, Farcomeni A, Ballanti M, Morelli M, Davato F, Cardolini I. **C-peptide: a predictor of cardiovascular mortality in subjects with established atherosclerotic disease**. *Diab Vasc Dis Res.* (2017) **14** 395-9. DOI: 10.1177/1479164117710446 22. de Cleva R, Kawamoto F, Borges G, Caproni P, Cassenote AJF, Santo MA. **C-peptide level as predictor of type 2 diabetes remission and body composition changes in non-diabetic and diabetic patients after Roux-en-Y gastric bypass**. *Clinics.* (2021) **76** e2906. DOI: 10.6061/clinics/2021/e2906 23. Jamiołkowska-Sztabkowska M, Głowińska-Olszewska B, Bossowski A. **C-peptide and residual β-cell function in pediatric diabetes - state of the art**. *Pediatr Endocrinol Diabetes Metab.* (2021) **27** 123-33. DOI: 10.5114/pedm.2021.107165 24. Kitamura T, Kimura K, Jung BD, Makondo K, Sakane N, Yoshida T. **Proinsulin C-peptide activates cAMP response element-binding proteins through the p38 mitogen-activated protein kinase pathway in mouse lung capillary endothelial cells**. *Biochem J.* (2002) **366** 737-44. DOI: 10.1042/bj20020344 25. Luo J, Jiang J, Huang H, Jiang F, Xu Z, Zhou Z. **C-peptide ameliorates high glucose-induced podocyte dysfunction through the regulation of the Notch and TGF-β signaling pathways**. *Peptides.* (2021) **142** 170557. DOI: 10.1016/j.peptides.2021.170557 26. Maclean PS, Bergouignan A, Cornier MA, Jackman MR. **Biology's response to dieting: the impetus for weight regain**. *Am J Physiol Regul Integr Comp Physiol.* (2011) **301** R581-600. DOI: 10.1152/ajpregu.00755.2010 27. Astrup A, Rössner S, Van Gaal L, Rissanen A, Niskanen L, Al Hakim M. **Effects of liraglutide in the treatment of obesity: a randomised, double-blind, placebo-controlled study**. *Lancet.* (2009) **374** 1606-16. DOI: 10.1016/S0140-6736(09)61375-1 28. Hall KD, Ayuketah A, Brychta R, Cai H, Cassimatis T, Chen KY. **Ultra-processed diets cause excess calorie intake and weight gain: an inpatient randomized controlled trial of ad libitum food intake**. *Cell Metab.* (2020) **32** 690. DOI: 10.1016/j.cmet.2020.08.014 29. Abbasi J. **Interest in the ketogenic diet grows for weight loss and type 2 diabetes**. *JAMA.* (2018) **319** 215-7. DOI: 10.1001/jama.2017.20639 30. Li S, Lin G, Chen J, Chen Z, Xu F, Zhu F. **The effect of periodic ketogenic diet on newly diagnosed overweight or obese patients with type 2 diabetes**. *BMC Endocr Disord.* (2022) **22** 34. DOI: 10.1186/s12902-022-00947-2 31. Cui M, Li X, Yang C, Wang L, Lu L, Zhao S. **Effect of carbohydrate-restricted dietary pattern on insulin treatment rate, lipid metabolism and nutritional status in pregnant women with gestational diabetes in Beijing, China**. *Nutrients* (2022) **14** 359. DOI: 10.3390/nu14020359 32. Bisschop PH, Pereira Arias AM, Ackermans MT, Endert E, Pijl H, Kuipers F. **The effects of carbohydrate variation in isocaloric diets on glycogenolysis and gluconeogenesis in healthy men**. *J Clin Endocrinol Metab.* (2000) **85** 1963-7. DOI: 10.1210/jcem.85.5.6573 33. Boden G. **Gluconeogenesis and glycogenolysis in health and diabetes**. *J Investig Med.* (2004) **52** 375-8. DOI: 10.2310/6650.2004.00608 34. Russell WR, Gratz SW, Duncan SH, Holtrop G, Ince J, Scobbie L. **High-protein, reduced-carbohydrate weight-loss diets promote metabolite profiles likely to be detrimental to colonic health**. *Am J Clin Nutr.* (2011) **93** 1062-72. DOI: 10.3945/ajcn.110.002188 35. Madison LL, Mebane D, Unger RH, Lochner A. **The hypoglycemic action of ketones. II evidence for a stimulatory feedback of ketones on the pancreatic beta cells**. *J Clin Invest.* (1964) **43** 408-15. DOI: 10.1172/JCI104925 36. Vigouroux C, Caron-Debarle M, Le Dour C, Magre J, Capeau J. **Molecular mechanisms of human lipodystrophies: from adipocyte lipid droplet to oxidative stress and lipotoxicity**. *Int J Biochem Cell Biol.* (2011) **43** 862-76. DOI: 10.1016/j.biocel.2011.03.002 37. Sakai K, Matsumoto K, Nishikawa T, Suefuji M, Nakamaru K, Hirashima Y. **Mitochondrial reactive oxygen species reduce insulin secretion by pancreatic beta-cells**. *Biochem Biophys Res Commun.* (2003) **300** 216-22. DOI: 10.1016/S0006-291X(02)02832-2 38. Kolb H, Kempf K, Röhling M, Lenzen-Schulte M, Schloot NC, Martin S. **Ketone bodies: from enemy to friend and guardian angel**. *BMC Med.* (2021) **19** 313. DOI: 10.1186/s12916-021-02185-0 39. Puchalska P, Crawford PA. **Metabolic and signaling roles of ketone bodies in health and disease**. *Annu Rev Nutr.* (2021) **41** 49-77. DOI: 10.1146/annurev-nutr-111120-111518 40. Kostogrys RB, Franczyk-Zarów M, Maślak E, Topolska K. **Effect of low carbohydrate high protein (LCHP) diet on lipid metabolism, liver and kidney function in rats**. *Environ Toxicol Pharmacol.* (2015) **39** 713-9. DOI: 10.1016/j.etap.2015.01.008 41. Chen SH, Liang YJ. **The role of lifestyle intervention, in addition to drugs, for diabetic kidney disease with sarcopenic obesity**. *Life* (2022) **12** 380. DOI: 10.3390/life12030380 42. Fung TT, Hu FB, Schulze M, Pollak M, Wu T, Fuchs CS. **A dietary pattern that is associated with C-peptide and risk of colorectal cancer in women**. *Cancer Causes Control.* (2012) **23** 959-65. DOI: 10.1007/s10552-012-9969-y 43. Seidelmann SB, Claggett B, Cheng S, Henglin M, Shah A, Steffen LM. **Dietary carbohydrate intake and mortality: a prospective cohort study and meta-analysis**. *Lancet Public Health.* (2018) **3** e419-e28. DOI: 10.1016/S2468-2667(18)30135-X 44. Lin X, Zhao L, Tang S, Zhou Q, Lin Q, Li X. **Metabolic effects of basic fibroblast growth factor in streptozotocin-induced diabetic rats: A**. *Sci Rep.* (2016) **6** 36474. DOI: 10.1038/srep36474 45. Rehman K, Akash MSH, Liaqat A, Kamal S, Qadir MI, Rasul A. **Role of interleukin-6 in development of insulin resistance and type 2 diabetes mellitus**. *Crit Rev Eukaryot Gene Expr.* (2017) **27** 229-36. DOI: 10.1615/CritRevEukaryotGeneExpr.2017019712 46. Chang CC, Wu CL, Su WW, Shih KL, Tarng DC, Chou CT. **Interferon gamma-induced protein 10 is associated with insulin resistance and incident diabetes in patients with nonalcoholic fatty liver disease**. *Sci Rep.* (2015) **5** 10096. DOI: 10.1038/srep10096 47. Wu C, Borné Y, Gao R, López Rodriguez M, Roell WC, Wilson JM. **Elevated circulating follistatin associates with an increased risk of type 2 diabetes**. *Nat Commun.* (2021) **12** 6486. DOI: 10.1038/s41467-021-26536-w 48. Qiu AW, Cao X, Zhang WW, Liu QH. **IL-17A is involved in diabetic inflammatory pathogenesis by its receptor IL-17RA**. *Exp Biol Med.* (2021) **246** 57-65. DOI: 10.1177/1535370220956943 49. Dougan M, Dranoff G, Dougan SK. **IL-3, and IL-5 family of cytokines: regulators of inflammation**. *Immunity.* (2019) **50** 796-811. DOI: 10.1016/j.immuni.2019.03.022
--- title: Circulating neutrophil extracellular traps in cats with hypertrophic cardiomyopathy and cardiogenic arterial thromboembolism authors: - Ronald H. L. Li - Arianne Fabella - Nghi Nguyen - Joanna L. Kaplan - Eric Ontiveros - Joshua A. Stern journal: Journal of Veterinary Internal Medicine year: 2023 pmcid: PMC10061180 doi: 10.1111/jvim.16676 license: CC BY 4.0 --- # Circulating neutrophil extracellular traps in cats with hypertrophic cardiomyopathy and cardiogenic arterial thromboembolism ## Abstract ### Background Cats with hypertrophic cardiomyopathy (HCM) are at risk of cardiogenic arterial thromboembolism (CATE). Neutrophil extracellular traps (NETs) may be a potential biomarker and therapeutic target for cardiomyopathy in cats. ### Hypothesis/Objectives Characterize NETs in cats with HCM or CATE. We hypothesized that circulating NETs assessed in the form of cell‐free DNA (cfDNA) and citrullinated histone H3 (citH3) are increased in cats with HCM and CATE and associated with reported predisposing factors for thrombus formation. ### Animals Eighty‐five cats including client‐owned cats with HCM and CATE and staff‐ and student‐owned clinically healthy cats without HCM. ### Methods After echocardiographic evaluations, NETs were measured as cfDNA and citH3. ### Results Cats with CATE had significant increases in cfDNA (11.2 ng/μL; interquartile range [IQR], 8.1 to 29.6) compared to those without HCM (8.2 ng/μL; IQR, 5.7 to 11.7 μL; $$P \leq .01$$) and were responsible for $75\%$ to $83\%$ of cases with cfDNA fragments sized 100 to 2000 base pairs. Citrullinated histone 3, detected in $52\%$ of cats with HCM (31.1 ng/mL; IQR, 16.9 to 29.8), was significantly lower than in those with CATE (48.2 ng/mL; IQR, 34.2 to 60.2; $$P \leq .007$$). The citH3 concentrations correlated significantly with reported risk factors of CATE, such as left atrial auricular velocity. ### Conclusions and Clinical Importance Neutrophil extracellualr traps, especially citH3, are increased in cats with HCM and CATE. They may serve as a novel therapeutic target and biomarker of thrombosis in cats with HCM. ## INTRODUCTION Hypertrophic cardiomyopathy (HCM) is the most common cardiac disease in cats affecting approximately $15\%$ of the cat population, and characterized by highly variable disease outcomes from subclinical to severe morbidity and mortality. Cats with HCM that develop clinical signs usually succumb to congestive heart failure (CHF), fatal arrhythmias, or cardiogenic arterial thromboembolism (CATE). Recent studies identified CATE as a major contributor to morbidity and mortality in cats with HCM with an incidence of $11.3\%$ in 1008 cats with HCM. 1 This finding suggests that the prevalence of CATE was previously underreported. 2, 3 Because cats with CATE often present acutely with extreme pain and no prior warning, CATE remains a distressing emergency for both cat owners and veterinarians, with a mortality rate of up to $67\%$. 3, 4, 5, 6 Despite the devastating outcome, clinicians have limited tools to recognize cats at risk of CATE. Echocardiographic assessment of risk for CATE is most commonly employed including the presence of spontaneous echo‐contrast (SEC), left atrial (LA) enlargement and left atrial appendage (LAA) dysfunction. 7 Identification of these risk factors is complicated by the fact that many cats at risk of CATE appear healthy and do not have auscultatory abnormalities, and hence, may be unlikely to undergo echocardiographic screening. 5, 8 Reliable and accessible techniques to recognize cats with HCM at risk of CATE have yet to be identified. The pathophysiology of CATE is poorly understood and likely results from dysregulation of each component of Virchow's triad, which includes endothelial dysfunction, hypercoagulability and blood stasis. Formation of neutrophil extracellular traps (NETs), which are web‐like fragments of cell‐free DNA (cfDNA) decorated with histones and neutrophil granular proteins, is an important component of innate immunity. 9 The prothrombotic properties of NETs, which facilitate microvascular thrombosis, are an important first‐line of defense because they prevent systemic dissemination of pathogens. However, excess circulating cfDNA and NETs proteins can have proinflammatory and prothrombotic consequences. In dogs, neutrophil‐derived cfDNA decreases clot lysis, whereas histones on NETs accelerate clot formation. 10 Circulating cfDNA fragments also facilitate clot formation by carrying tissue factor and binding to factors XI, XII and high molecular weight kininogen, all critical for promoting thrombin generation. 11, 12, 13, 14, 15 The web‐like scaffold of NETs also fortifies clots by binding to circulating erythrocytes, platelets and fibrin. 16 Increased cfDNA concentrations and fragment sizes are known to affect fibrin formation and have diagnostic and prognostic value in humans with metastatic neoplasia and ischemic stroke. 17, 18, 19 Recently, we identified the presence of NETs as structural components in arterial thrombi from cats with CATE. 20 Not only were NETs found in all layers of arterial thrombi, but their distribution also varied greatly in relation to their proximity to the initial site of vascular occlusion. 21 This finding suggests that NETs also may play an important role in the pathogenesis of intracardiac thrombosis and thrombus growth in cats with HCM and CATE. In humans, NETs within coronary thrombi are associated with poor outcome in myocardial infarction and contribute to resistance to thrombolytic therapy. 22, 23 Despite these findings, no studies have yet characterized circulating NETs in cats with HCM or CATE. A better understanding of the role of NETs in thrombosis in cats with HCM is needed. Direct measurement of NETs quantity in blood is not possible and as such independent assessment of NETs components is required. Demonstrating the presence of NETs markers such as cfDNA and citrullinated histones in HCM cats before development of thrombosis may offer diagnostic value in predicting clot formation and could help guide veterinarians toward implementing life‐saving antithrombotic treatments. We hypothesized that circulating NETs assessed as cfDNA and citrullinated histone H3 (citH3) would be increased in cats with HCM and CATE. We further hypothesized that circulating NETs markers would be increased in cats with HCM before development of thrombosis. In addition, the presence of NETs is expected to be associated with known predisposing factors for CATE in cats. To test our hypotheses, we measured, compared, and characterized concentrations and fragment sizes of circulating cfDNA and citH3 in cats with CATE, in cats with overt HCM (without CATE) and in clinically healthy cats without HCM. We also determined if concentrations of circulating cfDNA and citH3 were associated with probable predisposing factors for thrombus formation in cats with HCM and CATE. ## Animals and study groups The study protocol was approved by the Institutional Animal Care and Use Committee at the University of California, Davis [21303] with written informed consent provided for each enrolled patient. Cats in the HCM and CATE groups consisted of client‐owned cats presented to the Veterinary Medical Teaching Hospital, University of California Davis. Cats in the non‐HCM group consisted of staff‐ and student‐owned clinically healthy cats without HCM. All cats underwent complete physical examination, echocardiographic examination, blood pressure measurement by Doppler sphygmomanometer, serum biochemical profile (VC2, Abaxis, Zoetis), CBC (HM5, Abaxis, Zoetis, Parsippany, New Jersey) and measurement of total thyroxine (T4) concentration (VC2, Abaxis, Zoetis, Parsippany, New Jersey). Diagnosis of CATE was made based on physical examination findings and additional diagnostic findings according to the algorithm presented in Table 1. 4, 5, 18 *Clinical diagnosis* of CHF was made based on physical examination findings combined with radiographic evidence of pulmonary edema or pleural effusion on thoracic radiographs and echocardiographic findings of LA or biatrial enlargement. **TABLE 1** | • At least one of the following echocardiographic findings is required: | | --- | | Left atrial enlargement (LA:Ao ≥ 1.6) | | Spontaneous echo‐contrast | | Intracardiac thrombus | | Diastolic LV wall thickness (≥6 mm) | | • At least four of the following physical examination findings is required: | | Sudden onset of vocalizing | | Paralysis or paresis of one or more limbs | | Lower motor neuron signs in one or more limbs (absent motor function with absent skin sensation) | | Absent femoral and/or dorsal pedal pulses | | Pale or cyanotic foot pads/nailbeds of one or more limbs | | Firmness of the cranial tibial or gastrocnemius muscles | | Low rectal temperature (<37.6°C or <99.7°F) | | • Diagnostic findings considered suggestive but not required for diagnosis: | | Confirmed aortic or arterial thrombus by abdominal ultrasound | | Absence of audible Doppler signal over the artery in question | Cats in the HCM and non‐HCM groups were excluded if they had any of the following: (a) apparent unrelated systemic diseases based on abnormal physical examination findings that otherwise could not be explained by cardiac disease, CHF, or CATE, (b) abnormalities of any serum biochemistry variable or CBC showing evidence of anemia (hematocrit ≤$24\%$), leukocytosis or leukopenia (≤5.5 × 109/L or ≥19.5 × 109/L), or thrombocytopenia (platelet count ≤ 150 × 109/L) confirmed by peripheral blood smear evaluation, (c) systemic hypertension with systolic blood pressure ≥ 160 mmHg, (d) hyperthyroidism (total T4 ≥ 4.8 μg/dL), (e) uncooperative temperament for echocardiography and blood collection, and (f) treatments with an antithrombotic or antiplatelet drug in the past 30 days. Cats in the CATE group were excluded if echocardiographic evidence of cardiomyopathy was absent, and if they were deemed too unstable for echocardiography or blood collection. At home administration of gabapentin (50 to 100 mg PO) before enrollment was permitted. Butorphanol (0.1 to 0.2 mg/kg IV or IM) also was permitted to facilitate safe handling for cats in respiratory distress. ## Echocardiography Transthoracic echocardiographic examinations were performed by a board‐certified veterinary cardiologist or cardiology resident in training under direct supervision of a board‐certified veterinary cardiologist (J.A.S). Cats were gently restrained in right and left lateral recumbency to obtain standard imaging planes as previously described. 24 For the purpose of the study, the following findings were considered probable risk factors for thrombus formation from previous published disease associations or increased hazard ratios. Evaluated risk factors of CATE included LA enlargement, decreased LAA peak flow velocity, presence of spontaneous echocardiographic contrast (SEC) or intracardiac thrombus or both. 7, 25 Left atrial appendage function was measured by transthoracic pulsed Doppler‐derived maximum left atrial auricular (LAu) velocity as previously described. 7 Left atrial size was evaluated as a ratio to the aortic root and measured as previously described. 26 Left atrial enlargement was defined as an absolute 2‐dimensional (2D) long axis value ≥1.6 cm or 2D LA:Ao in short axis ≥ 1.6. All other echocardiographic assessments were performed as previously described. 26 Diagnosis of HCM required identification of idiopathic regional or global left ventricular wall thickness ≥ 6 mm, as determined by 2D or m‐mode echocardiography avoiding inclusion of moderator band insertion sites and in the absence of systemic disease, systemic hypertension and hyperthyroidism. The presence or absence of any observed intracardiac thrombi along with their location also was recorded. Spontaneous echocardiographic contrast at standard gain setting for the remainder of the examination was recorded as present or absent. Spontaneous echocardiographic contrast was defined as a dynamic and organized swirling pattern visualized within any of the cardiac chambers. All echocardiographic measurements were analyzed by a single investigator (J.A.S) blinded to the study group using off‐line image analysis software (Syngo Dynamics, Siemens). ## Blood collection Blood was drawn from the medial saphenous vein or jugular vein using a 23G butterfly needle within 6 hours of presentation. Blood was drawn from the cephalic or jugular vein in cats with CATE. Whole blood was immediately aliquoted to tubes containing $3.2\%$ trisodium citrate (BD Vacutainer, Franklin Lakes, New Jersey) and lithium heparin (BD Microtainer, Franklin Lakes, New Jersey), placed on ice, and processed within 1 hour of collection. After gentle inversion and inspection for blood clots, a CBC and blood smear evaluation were performed on heparinized whole blood and the remainder of the sample was used for biochemical analysis and total T4 measurement (VS2, Zoetis, Parsippany, New Jersey). A portion of the remnant sample was used for analyzing NETs markers. Citrated and heparinized whole blood then underwent centrifugation (2000 × G, 10 min, 4°C). After extraction of plasma, protease inhibitor cocktail (1× HALT, Thermo Scientific, Waltham, Massachusetts) was added to heparinized plasma to prevent histone degradation, flash‐frozen in liquid nitrogen, and stored at −80°C before analysis. ## Isolation and purification of plasma cell free DNA Citrated plasma first was thawed at room temperature before cfDNA purification using a magnetic bead‐based cleanup kit (QIAamp Minelute ccfDNA mini kit, Qiagen, Germantown, Maryland) as previously described. 27 The component mixture consisting of proteinase K, magnetic bead suspension and bead binding buffer was adjusted according to the available volume of plasma from each cat. The maximum and minimum volumes of plasma processed for cfDNA purification were 1000 and 500 μL, respectively. To optimize isolated cfDNA concentration, 20 μL of 1× tris‐EDTA buffer was applied directly on the center of column, which underwent 3 additional rounds of elution by reapplying eluate to the column. Eluted cfDNA was stored at 4°C for further analysis. ## Quantification of plasma cell free DNA Concentrations of cfDNA were quantified by spectrophotometry (Nanodrop 2000c, Thermo Fisher Scientific, Waltham, Massachusetts) as previously described. 28 Tris‐EDTA buffer was used as a blank before sample analysis. Double‐stranded nucleic acid concentration was measured based on absorbance at 280 nm. Purity of cfDNA was determined by a $\frac{260}{280}$ ratio of approximately 1.8. Each sample was measured in duplicate and was reanalyzed if the percent difference between the 2 measurements was >$5\%$. Measured cfDNA concentrations then were corrected to plasma volume using the follow calculation: Corrected cfDNAngμL=1000Plasma volumeμL×cfDNAmeasured. ## Chip‐based capillary electrophoresis of cell‐free DNA Purified cfDNA was standardized to a final concentration of 5 ng/μL before chip‐based capillary electrophoresis and high sensitivity DNA assay (Agilent 2100 Expert Bioanalyzer, Santa Clara, California). Samples were loaded onto an 11‐well nanochip (Agilent, Santa Clara, California) and analyzed according to the manufacturer's instructions. For samples with cfDNA concentrations ≤5 ng/μL, samples were loaded as is. Briefly, nanochips were primed with 9 μL of gel‐dye mixture followed by loading of standard ladder markers into each well. One microliter of the standard ladder then was loaded into the appropriate labeled well for control, followed by 1 μL of purified samples to each of the remaining wells. The loaded nanochips were placed into the receptacle and the pre‐programed assay was selected to run for 45 min. The standard ladder appeared as 15 distinct peaks on the electropherogram after the first 50 s of the assay. The cfDNA concentrations for DNA fragment length sizes between 35 and 10 380 bp were calculated using commercial software (Agilent 2100 Bioanalyzer software) based on area under the peaks of the electropherogram (Figure 1B). The operator was blinded to treatment groups. Patients with samples that did not have any distinct peaks on electropherogram were reported as undetectable. Fragments of DNA >2000 bp were considered genomic DNA contamination and were not analyzed further. 29 **FIGURE 1:** *Representative electrograms of plasma cell‐free DNA fragment size and concentrations in plasma from a cat with cardiogenic arterial thromboembolism (CATE) (A) and a cat without hypertrophic cardiomyopathy (non‐HCM) (B). The X‐axis and Y‐axis represent DNA fragment sizes (base pairs; bp) and concentration, based on relative fluorescence units (FU), respectively. (A) A cat with CATE had a high concentration of short fragment size peaking at 165 bp (black solid arrow). (B) Electrogram in a cat without HCM showing a minimal detectable level of cfDNA fragments (black arrow) (lower and upper marker ‐ open/white arrow).* ## Semi‐quantitative analysis of plasma citrullinated histone H3 by sodium dodecyl sulfate‐polyacrylamide gel electrophoresis (SDS‐PAGE) and Western blot analysis To optimize detection of free histones, chromatin was fragmented by first sonicating heparinized plasma using a digital ultrasonic bath (Fisher Scientific, Waltham, Massachusetts) for 30 min, followed by treatment with 80 U/mL of DNase I (New England Biolabs Inc, Ipswich, Massachusetts; overnight incubation at 37°C). Recombinant proteins were subjected to the same conditions to ensure that sonication and DNA digestion did not degrade histone proteins. Plasma protein concentration, measured by UV‐visible spectrophotometry, was standardized to 2.5 mg/mL with buffered saline before boiling in 1× Laemmli buffer with 2‐mercaptoethanol (Biorad, Hercules, California) and placing on ice for 5 min. After treatment with protease inhibitor (Halt, Thermo Fisher Scientific, Waltham, Massachusetts), samples were stored at −80°C and analyzed within 6 months of collection. The optimization and standardization of protein loading were established by sodium dodecyl sulfate‐polyacrylamide gel electrophoresis (SDS‐PAGE) and staining of SDS gel by Coomassie blue (Figure S1). A constant quantity of plasma proteins (43.75 μg or 1.25 mg/mL) from each subject then was separated by SDS‐PAGE before transfer to polyvinylidene fluoride membranes (Biorad, Hercules, California). Membranes were stained with $0.1\%$ Ponceau S (Sigma‐Aldrich, St. Louis, Missouri) to confirm adequate transfer of proteins and then blocked in $10\%$ bovine serum albumin (Fisher Scientific, Pittsburgh, Pennsylvania; overnight, 4°C) before incubation with a rabbit polyclonal anti‐human citH3 antibody (1:2000, ab5103, Abcam, Cambridge, Massachusetts; 2 hours, room temperature), previously shown to cross react in cats. 20 After washing 5 times in 1× tris‐buffered saline with $2\%$ Tween (TBST), membranes were incubated in goat anti‐rabbit secondary antibody conjugated to horseradish peroxidase (1:20000, Abcam, Cambridge, Massachusetts) for 1 hour at room temperature. Chemiluminescent substrate (WesternBright Quantum, Advansta, San Jose, California) was added directly on the blots and imaged (Fluorchem E, Protein Simple, San Jose, California). To confirm loading and cross‐reactivity of anti‐citH3 antibody in cats, a serial dilution of human recombinant citH3 (0, 1.95, 3.9, 7.8, 15.6, 31.25, 62.5, 125, and 250 ng/mL) was prepared in commercially available heparinized pooled feline plasma, DNA‐digested (BioChemed Services, Winchester, Virginia). Pooled heparinized feline plasma (BioChemed, Winchester, Virginia) also was subjected to sonication and DNA digestion as described above and denatured, reduced and underwent immunoprobing along with patient plasma samples. Densitometry was performed using available software (ImageJ, NIH), which then was used to generate a standard curve for each blot, extrapolated based on nominal log concentrations of human recombinant citH3. The suitable linear interval (densitometry: 4245 to 30 665) was used to interpolate the standard curve by utilizing the best‐fit linear equation, y=mx+b. Incubation times, and dilution of antibodies were optimized in preliminary experiments. A dilution factor of 3 was used to measure the final concentration of citH3 in all samples. ## Statistical analysis Sample size calculation was performed based on preliminary data and an anticipated difference of $25\%$, an $80\%$ power, and a priori alpha of 0.05. Because cats in the CATE group were expected to have substantially higher cfDNA concentrations, an unequal sample size comparison was selected with at least 15 cats with an anticipated difference of $35\%$ and $90\%$ power when compared to non‐HCM or HCM groups. Normality was tested using the Shapiro‐Wilk normality test. Normally distributed continuous data, presented as mean ± SD, were analyzed using t test or 1‐way ANOVA, followed by post‐hoc analysis using Tukey's multiple comparisons test. Nonparametric data, presented as median and interquartile range (IQR), were analyzed using the Mann‐Whitney or Kruskal‐Wallis test followed by post‐hoc analysis by Dunn's multiple comparisons test. Categorical data between 2 and 3 groups were compared using Fisher's exact test and Chi‐squared test, respectively. Pearson correlation coefficients were calculated to describe the relationship between selected echocardiographic results, total cfDNA concentration and citH3 densiometry results, as well as the relationship between cfDNA, and citH3 and neutrophil count from the CBC. Statistical analysis was performed using commercially available software (Graphpad Prism 8). A P value <.05 was considered significant. ## Animals Eighty‐five cats were evaluated for enrollment at the Veterinary Medical Teaching Hospital, University of California, Davis from July 2019 to January 2021. Baseline demographics, hematological and biochemical findings are summarized in Table 2. Of the 17 cats with CATE, 7 were presented with rectal temperature < 37.6°C, and $\frac{12}{17}$ ($70.6\%$) cats had more than 1 limb affected. Six cats ($42.9\%$) had documented hyperkalemia (>5.8 mmol/L) on presentation. Eleven cats ($64.71\%$) were euthanized shortly after the diagnosis of CATE and study enrollment. Of the 8 cats that were hospitalized, 4 cats ($50\%$) survived to discharge. The remaining 4 cats were euthanized because of the development of refractory hyperkalemia and acute kidney injury. Complete echocardiography was not performed in $\frac{2}{17}$ cats with CATE because of patient instability. Table 3 summarizes the echocardiographic findings in the 3 groups. Of the 33 cats in the HCM group, 6 ($18.18\%$) were in CHF at the time of enrollment and 3 ($9.09\%$) had evidence of SEC or intracardiac thrombosis or both with the remainder of the cats in this group being subclinical for their HCM. ## Cats with CATE have increased cell‐free DNA Plasma from 79 cats was available for cfDNA analysis by spectrophotometry (CATE = 17, Non‐HCM = 28, HCM = 34). Samples from 4 cats in the non‐HCM group were not included because of technical errors that occurred during the isolation and purification process. One cat in the HCM group was excluded because of sample loss. Overall, cats with CATE had increased cfDNA (11.2 ng/μL; IQR, 8.1 to 29.6) compared to those in the HCM (6.6 ng/μL; IQR, 5.1 to 8.3; $$P \leq .0003$$) and non‐HCM groups (8.2 ng/μL; IQR, 5.7 to 11.7; $$P \leq .01$$; Figure 2A). Subgroup analysis showed that cats in the HCM group with concurrent CHF had higher plasma cfDNA concentrations compared to those without CHF (11.0 ng/μL; IQR, 6.4 to 16.8 vs 6.3 ng/μL; IQR, 4.8 to 7.3; $$P \leq .01$$; Figure 2B). **FIGURE 2:** *Plasma cell‐free DNA (cfDNA) concentrations measured by spectrophotometry in 34 cats of the non‐hypertrophic cardiomyopathy (HCM) group, 28 cats in the HCM group and 17 cats with HCM and cardiogenic arterial thromboembolism (CATE). (A) Cats with CATE had significantly higher concentrations of plasma cfDNA compared to cats with or without HCM. (B) Cats in congestive heart failure (CHF) secondary to HCM were found to have higher plasma cfDNA concentrations than those without CHF. Red dots indicate cats with intracardiac thrombus and/or spontaneous echo‐contrast. Bar represents median and error bars represent interquartile ranges. *P < .05.* ## Cell‐free DNA size distribution profiles differ among cats with CATE To further characterize cfDNA fragment sizes, chip‐based microfluidic electrophoresis was utilized. Representative electropherograms are shown in Figure 1. Of the $\frac{78}{79}$ samples available for further analysis, cfDNA fragment size >2000 bp was detected in 35 cats ($44.9\%$) whereas fragment size <2000 bp was identified in 71 cats ($91.03\%$). Overall, the proportion of cats with cfDNA <2000 bp was significantly higher in cats with CATE ($\frac{16}{17}$, $89\%$) compared to those in the HCM ($\frac{12}{23}$, $52\%$; $$P \leq .02$$) and non‐HCM ($\frac{12}{31}$, $38.71\%$; $$P \leq .0008$$) groups. Figure 3A presents the proportion of cats grouped based on their cfDNA size profile. No cats in the non‐HCM group had detectable cfDNA fragments sized between 101 and 2000 bp. Cats in the CATE group were responsible for $75\%$ to $83\%$ of cases with detectable cfDNA fragments between 100 and 2000 bp whereas cats in the HCM group were responsible for the remaining $17\%$‐$25\%$. When cfDNA concentrations in the 3 groups were compared by fragment size distribution, cats with CATE had the highest concentration of cfDNA between 36 and 100 bp (82 pg/μL; IQR: 41 to 3132; $$P \leq .02$$) compared to non‐HCM controls (19 pg/μL; IQR, 5 to 30; $$P \leq .02$$) and cats with HCM (24 pg/μL; IQR, 4 to 49; $$P \leq .03$$). Additionally, cats with CATE had a significantly higher concentration of cfDNA between 101 and 300 bp compared to cats with HCM (332 pg/μL vs 65 pg/μL; $P \leq .001$; Figure 3B). **FIGURE 3:** *Proportion of cats grouped according to detectable cell‐free DNA (cfDNA) fragment sizes, 36‐100 base pairs (bp), 101‐300 bp, 301‐500 bp, or 501‐2000 bp using high‐sensitivity electrophoresis. (A) Only cats with CATE or hypertrophic cardiomyopathy (HCM) had detectable cfDNA fragments between 100 and 2000 bp. (B) Distribution of mean cfDNA concentrations grouped based on cfDNA fragment size and groups. Cats with CATE had significantly higher cfDNA at 36‐100 bp and 101‐300 bp compared to cats in the HCM group. *P < .05.* ## Cats with HCM and CATE have increased concentrations of plasma citrullinated histone H3 We demonstrated cross‐reactivity and specificity of the anti‐human citH3 antibody in cats by first comparing immunoblots of human recombinant citH3 and feline plasma samples. Both human recombinant citH3 and feline citH3 migrated to the same molecular weight marker of approximately 15 kDa (Figure 4A). Representative immunoblots of plasma citH3 in cats from the 3 groups are shown in Figure 4A. To semi‐quantitatively measure the concentrations of patient citH3, a standard curve for each immunoblot was generated by serial dilution of human recombinant citH3 prepared in pooled feline plasma as shown in Figure 4B,C. Inadequate amount of plasma in 12 HCM cats, 12 non‐HCM, and 1 cat CATE prevented analysis of plasma citH3 in these cats. Overall, 60 cats, including 23 non‐HCM cats, 21 HCM cats and 16 cats with CATE were further analyzed for plasma citH3 concentrations using Western blot analysis. Plasma citH3 concentrations were significantly different among the 3 groups ($P \leq .0001$). Cats with HCM had higher concentrations of plasma citH3 (4101 ± 2350 ng/ml) compared to cats in the non‐HCM group (2380 ± 1254 ng/ml; $$P \leq .005$$) whereas cats with CATE had the highest plasma citH3 concentration (8618 ± 3978 ng/ml) compared to cats with ($P \leq .0001$) or without HCM ($P \leq .0001$; Figure 4D). After factoring in the lower limit of detection (23.2 ng/mL) based on linear standard curves generated from recombinant human citH3 (Figure 4C), we found that $\frac{23}{23}$ cats ($100\%$) in the non‐HCM group, $\frac{11}{21}$ cats ($52\%$) in the HCM group and $\frac{3}{16}$ ($18.8\%$) cats in the CATE group had citH3 concentrations that were below the lower limit of detection. The number of cats with citH3 concentrations below the lower limit of detection was significantly different among the 3 groups ($P \leq .0001$). Plasma citH3 concentrations in cats with CATE remained significantly higher than those in cats with HCM (48.2 ng/mL; IQR, 34.2 to 60.2 vs 31.1 ng/mL; IQR, 25.3 to 38.8; $$P \leq .007$$; Figure 4E). **FIGURE 4:** *Plasma citrullinated histone H3 (citH3) analyzed by Western blot in 23 cats without (hypertrophic cardiomyopathy [HCM]), 21 cats with HCM and 16 cats with cardiogenic arterial thromboembolism (CATE). (A) A representative immunoblot of human recombinant citH3 (r‐citH3) (31.25 ng/mL), plasma from a cat without HCM, a cat with CATE, and a cat with HCM demonstrating identical molecular weight of 15 kDa and cross‐reactivity with anti‐human citH3 antibody. (B) A representative immunoblot of human recombinant citH3 (r‐citH3) proteins diluted to various concentrations in 50% pooled feline plasma. (C) Densitometry (arbitrary unit) was used to generate a representative standard curve for quantifying plasma citH3 concentration in feline patients. (D) Cats with HCM and CATE had significantly higher plasma levels of citH3 based on densitometry compared to cats without HCM. Those with CATE had higher citH3 than those with HCM. (E) After converting the densitometry signals to concentrations, none of the cats without HCM and 11 cats with HCM had plasma citH3 levels above the lower limit of detection (23.2 ng/mL). Plasma citH3 concentration in cats with CATE was significantly higher than cats with HCM. Bar presents mean and error bars represent standard deviations. *P < .05.* ## Plasma citrullinated histone H3 and cfDNA correlate with selected echocardiographic variables No significant correlations were found between circulating cfDNA and left auricular flow (LAu) velocity (r = −0.17; $95\%$ confidence interval [CI] −0.39 to 0.064; $$P \leq .15$$), LA:Ao ($r = 0.20$; $95\%$ CI, −0.078 to 0.37; $$P \leq .10$$), LV fractional shortening (FS%; r = −0.12; $95\%$ CI, −0.35 to 0.12; $$P \leq .31$$) or neutrophil count (r = −0.044; $95\%$ CI, −0.26 to 0.18; $$P \leq .70$$; Figure 5A). **FIGURE 5:** *Plasma citrullinated histones H3 (citH3) correlate with predisposing factors to thrombosis in cats. (A) Cell‐free DNA (cfDNA, ng/μL) did not correlate with neutrophil count or other echocardiographic variables. (B) In contrast, negative and significant correlations were found among plasma citH3 (densiometry), LA function, measured as LAu velocity and left ventricular fractional shortening (FS%). Plasma citH3 was positively correlated with LA to aortic root ratio (LA:Ao) and neutrophil count.* We found significant negative correlations between plasma citH3 and LAu velocity (r = −0.50; $95\%$ CI, −0.69 to −0.26; $$P \leq .0002$$), and fractional shortening (r = −0.47; $95\%$ CI, −0.66 to −0.22; $$P \leq .0006$$). Plasma citH3 was most strongly correlated with LA:Ao ($r = 0.56$; $95\%$ CI, 0.34 to 0.72; $P \leq .0001$) and weakly correlated with neutrophil count ($r = 0.31$; $95\%$ CI, 0.058 to 0.53; $$P \leq .018$$; Figure 5B). Interestingly, plasma cfDNA did not correlate significantly with citH3 (r = −0.09; r 2 = 0.008; $$P \leq .7$$). However, when cfDNA concentrations were grouped according to bp, a moderate and significant correlation was found between plasma citH3 concentration and cfDNA <2000 bp ($r = 0.44$; $95\%$ CI, 0.17 to 0.66; $$P \leq .002$$). This determination was made only in patients with both measurements of citH3 and fragment size reported for cfDNA. Finally, citH3 concentration did not correlate with mean cfDNA fragments ≥2000 bp (r = −0.024; $95\%$ CI, −0.31 to 0.27; $$P \leq .87$$). ## DISCUSSION We found that circulating NETs markers were significantly increased in cats with HCM and CATE. We also found that citH3 correlated significantly with predisposing risk factors of intracardiac thrombosis and thromboembolism in cats with HCM. NETosis, a term that describes the active cellular processes underlying the formation of NETs, is dependent on complex and coordinated signaling facilitated by extracellular stimuli such as pathogens, danger‐associated molecular patterns, cytokines, reactive oxygen species and, most importantly, platelet‐neutrophil interactions. 30, 31, 32, 33 Although the underlying mechanisms of NETosis in cardiomyopathies remain poorly understood, evidence in animal models of carotid artery thrombosis suggest that NETs formation is dependent on platelet activation, histone citrullination and upregulation of the neutrophil integrin, α9β1. 34, 35, 36 These findings suggest that neutrophils in cats with HCM and CATE may be activated at the site of endothelial injury or via platelet‐neutrophil interactions resulting in NETosis. Given that NETs previously were found to be a component of arterial thrombi in cats, the presence of circulating NET markers in plasma further supports the notion that the bidirectional feedback of inflammation and thrombosis may contribute to a prothrombotic state that further promotes thrombosis. 21 We found that circulating cfDNA was significantly higher in cats with CATE but did not correlate with selected risk factors of thrombosis on echocardiogram. There are several explanations for these findings. First, quantification of total cfDNA could not discriminate between genomic DNA contamination originating from necrotic muscle cells associated with CATE and secondary ischemic injury or active release of cfDNA from living cells. In addition, in vitro processing also could lead to cell lysis and subsequent genomic DNA contamination. Although every effort was made to minimize genomic DNA contamination by standardizing pre‐analytical variables such as venipuncture sites, blood collection techniques, sample processing and duration of storage, DNA fragmentation or degradation also may negatively affect the quality of the samples. Second, the variable volume of plasma from some cats may decrease the quality or amount of cfDNA isolated. To further determine the origin of cfDNA, DNA size profiling by high‐sensitivity electrophoresis was performed, which indicated that 50 to $60\%$ of cats with or without HCM did not have detectable cfDNA fragments <2000 bp. Based on this observation, high‐sensitivity electrophoresis alone appears to be insufficient to characterize the size profile of cfDNA in most HCM and healthy cats. This finding suggests that high‐sensitivity electrophoresis should be coupled with amplification methods such as massive parallel sequencing and quantitative real‐time PCR. 37 Still, the size profile of 100 to 500 bp, with a maximum peak at 167 bp, found in cats with CATE and HCM indicates that cfDNA in these cats likely originated from living cells because this pattern closely mirrors that found in nucleosomes. 38 This finding also indicates that the increased cfDNA found in cats with CATE is secondary to an active process, most likely from NETosis, as opposed to necrotic tissues, which release long fragments of cfDNA >10 000 bp. Such findings have important clinical implications because cfDNA can serve as a novel therapeutic target for the prevention and treatment of cardiogenic thrombosis. For instance, cfDNA not only strengthens clot density by binding to erythrocytes and platelets, its poly‐anionic surface also binds to von Willebrand factor (vWF) molecules. Together, this DNA‐vWF interaction may play a crucial role in entrapping leukocytes and further propagating clot formation at the site of vascular injury. 39 In addition, ischemic stroke models in mice found that lysing of cfDNA with DNase I not only facilitated recanalization of occluded arteries but also decreased tissue plasminogen activator‐associated hemorrhage. 40, 41 Considering that thrombolytic treatment largely has failed to improve outcomes in cats with CATE, more research is needed in targeting cfDNA. 42 In contrast to plasma cfDNA, citH3 was significantly increased in cats in the HCM as well as the HCM and CATE groups. In addition, all non‐HCM cats had citH3 concentrations below the limit of detection whereas approximately $50\%$ of cats with subclinical HCM had increased plasma citH3. This finding is not surprising considering that citH3 is a more specific NETs marker. 43 Citrullination of histones, which is a form of histone modification, is facilitated by the enzyme peptidyl arginine deiminase 4 (PAD4) across multiple species, but has not been characterized in cats 44 The increase in plasma citH3 in our study suggests that PAD4, which is highly expressed in neutrophils, may be activated in cats with cardiomyopathy. In other species, PAD4 converts arginine residues to citrulline, thereby altering the electrostatic interactions between histones and DNA, resulting in chromatin decondensation and the subsequent release of DNA and NETs. 45 In dogs, NETs formation induced by reactive oxygen species‐dependent pathways via phorbal myristate acetate or lipopolysaccharide is dependent on PAD4. 28 Although increased PAD4 activity in neutrophils is essential for NETosis in other species, the underlying mechanisms mediating NETs formation and histone citrullination in cats remain unclear. Additional studies are needed to investigate if histones or upregulation of PAD4 could serve as potential therapeutic targets for preventing CATE in cats with HCM. Plasma citH3 also correlated moderately and significantly with probable predisposing risk factors of cardiogenic thrombosis in cats with HCM. This observation suggests that plasma citH3 may be a biomarker in predicting thrombosis and CATE in cats. Considering that no healthy cat had increased citH3 concentrations, we believe increased citH3 is a marker of clinical concern. Currently, veterinarians have limited tools to recognize cats at risk of CATE because existing methods of identifying risk factors rely solely on echocardiographic findings such as presence of SEC, LA enlargement and LAA dysfunction. This identification is complicated by the fact that many cats at risk for CATE are apparently healthy and do not have auscultatory abnormalities, hence they are unlikely to be selected for echocardiographic screening. 5, 8 In human beings, a number of studies indicate that increased citH3 is associated with thrombotic risk in cardiovascular disorders such as ischemic stroke, atrial fibrillation and myocardial infarction. 46, 47, 48 Thus, measurement of free circulating citH3 maybe an accessible way to determine cats at risk of CATE. Unfortunately, our small sample size and an inadequate amount of residual plasma for citH3 measurements in some cats prevented us from performing subgroup analyses to further explore the associations between increasedcitH3 and prothrombotic risk factors. Additonal studies are needed to assess the diagnostic utility of plasma citH3 in cats with HCM so that thromboprophylaxis can be administered sufficiently early to prevent CATE. Although histone H3 is highly conserved among species and the feline protein is found to be $100\%$ homologous to the human amino acid sequence, we noticed that feline plasma proteins consistently interfere with the detection of human protein standards in ELISA kits. This interference may be caused by nonspecific binding to the detection or capture antibodies or formation of histone‐plasma protein complexes. Given the limitations of Western blot analysis such as long processing times and its semi‐quantitative nature, custom development of a feline‐specific citH3 ELISA is a possible future method to accurately measure citH3 concentrations as a clinical test. Our study had several limitations. First, although chip‐based capillary electrophoresis provides a reasonable estimation of cfDNA concentration, the small amount of cfDNA might have represented false negatives and this data should be interpreted with caution. More precise cfDNA fragment concentrations will need to be obtained by digital droplet PCR in future studies. 49 Second, although correlations among NETs, especially in free citH3, were found in cats with HCM and CATE, NETs could be a marker of disease severity. The latter would require a longitudinal study in HCM cats characterizing both citH3 and cfDNA fragment size using high‐sensitivity detection and represents a future direction for this work. Third, we did not assess coagulation status of our study population and hence could not confirm the associations between circulating NETs and thrombosis in cats. Finally, inadequate volume of plasma samples and the semiquantitative nature of citH3 measurements prevented advanced subgroup analyses to further assess the diagnostic and prognostic utility of circulating citH3. Hence development of a feline‐specific assay to measure plasma citH3 reliably and accurately in cats is critically needed. ## CONCLUSION We found that cats with HCM and CATE had increased concentrations of circulating NETs, in the form of cfDNA at 100 to 300 bp and citH3. Approximately $40\%$ and $80\%$ of cats with HCM and CATE, respectively, had detectable plasma citH3, which correlated with thrombotic risk factors. ## CONFLICT OF INTEREST DECLARATION Johaua A Stern serves as Associate Editor for the Journal of Veterinary Internal Medicine. He was not involved in review of this manuscript. No other authors declare a conflict of interest. ## OFF‐LABEL ANTIMICROBIAL DECLARATION Authors declare no off‐label use of antimicrobials. ## INSTITUTIONAL ANIMAL CARE AND USE COMMITTEE (IACUC) OR OTHER APPROVAL DECLARATION Approved by the University of California, Davis, IACUC, approval number 21303. ## HUMAN ETHICS APPROVAL DECLARATION Authors declare human ethics approval was not needed for this study. ## References 1. Fox PR, Keene BW, Lamb K. **International collaborative study to assess cardiovascular risk and evaluate long‐term health in cats with preclinical hypertrophic cardiomyopathy and apparently healthy cats: the REVEAL study**. *J Vet Intern Med* (2018) **32** 930-943. PMID: 29660848 2. Payne JR, Brodbelt DC, Luis Fuentes V. **Cardiomyopathy prevalence in 780 apparently healthy cats in rehoming centres (the CatScan study)**. *J Vet Cardiol* (2015) **17** S244-S257. PMID: 26776583 3. Smith SA, Tobias AH, Jacob KA, Fine DM, Grumbles PL. **Arterial thromboembolism in cats: acute crisis in 127 cases (1992‐2001) and long‐term management with low‐dose aspirin in 24 cases**. *J Vet Intern Med* (2003) **17** 73-83. PMID: 12564730 4. Borgeat K, Wright J, Garrod O, Payne JR, Fuentes VL. **Arterial thromboembolism in 250 cats in general practice: 2004‐2012**. *J Vet Intern Med* (2014) **28** 102-108. PMID: 24237457 5. Schoeman JP. **Feline distal aortic thromboembolism: a review of 44 cases (1990‐1998)**. *J Feline Med Surg* (1999) **1** 221-231. PMID: 11714239 6. Atkins CE, Gallo AM, Kurzman ID, Cowen P. **Risk factors, clinical signs, and survival in cats with a clinical diagnosis of idiopathic hypertrophic cardiomyopathy: 74 cases (1985‐1989)**. *J Am Vet Med Assoc* (1992) **201** 613-618. PMID: 1517140 7. Schober KE, Maerz I. **Assessment of left atrial appendage flow velocity and its relation to spontaneous echocardiographic contrast in 89 cats with myocardial disease**. *J Vet Intern Med* (2006) **20** 120-130. PMID: 16496931 8. Smith SE, Sande AA. **Measurement of intra‐abdominal pressure in dogs and cats**. *J Vet Emerg Crit Care (San Antonio)* (2012) **22** 530-544. PMID: 23110567 9. Brinkmann V, Zychlinsky A. **Beneficial suicide: why neutrophils die to make NETs**. *Nat Rev Microbiol* (2007) **5** 577-582. PMID: 17632569 10. Jeffery U, LeVine DN. **Canine neutrophil extracellular traps enhance clot formation and delay lysis**. *Vet Pathol* (2018) **55** 116. PMID: 28346125 11. Gould TJ, Vu TT, Swystun LL. **Neutrophil extracellular traps promote thrombin generation through platelet‐dependent and platelet‐independent mechanisms**. *Arterioscler Thromb Vasc Biol* (2014) **34** 1977-1984. PMID: 25012129 12. Noubouossie DF, Whelihan MF, Yu YB. **In vitro activation of coagulation by human neutrophil DNA and histone proteins but not neutrophil extracellular traps**. *Blood* (2017) **129** 1021-1029. PMID: 27919911 13. Skendros P, Mitsios A, Chrysanthopoulou A. **Complement and tissue factor‐enriched neutrophil extracellular traps are key drivers in COVID‐19 immunothrombosis**. *J Clin Invest* (2020) **130** 6151-6157. PMID: 32759504 14. Song DY, Gu JY, Yoo HJ. **Activation of factor XII and kallikrein‐kinin system combined with neutrophil extracellular trap formation in diabetic retinopathy**. *Exp Clin Endocrinol Diabetes* (2021) **129** 560-565. PMID: 31426112 15. Oehmcke S, Morgelin M, Herwald H. **Activation of the human contact system on neutrophil extracellular traps**. *J Innate Immun* (2009) **1** 225-230. PMID: 20375580 16. Martinod K, Wagner DD. **Thrombosis: tangled up in NETs**. *Blood* (2014) **123** 2768-2776. PMID: 24366358 17. Longstaff C, Varju I, Sotonyi P. **Mechanical stability and fibrinolytic resistance of clots containing fibrin, DNA, and histones**. *J Biol Chem* (2013) **288** 6946-6956. PMID: 23293023 18. Chen E, Cario CL, Leong L. **Cell‐free DNA concentration and fragment size as a biomarker for prostate cancer**. *Sci Rep* (2021) **11** 5040. PMID: 33658587 19. Grosse GM, Blume N, Abu‐Fares O. **Endogenous deoxyribonuclease activity and cell‐free deoxyribonucleic acid in acute ischemic stroke: a cohort study**. *Stroke* (2022) **53** 1235-1244. PMID: 34991335 20. Duler L, Nguyen N, Ontiveros E, Li RHL. **Identification of neutrophil extracellular traps in paraffin‐embedded feline arterial thrombi using immunofluorescence microscopy**. *J Vis Exp* (2020). DOI: 10.3791/60834 21. Li RH, Nguyen N, Stern JA, Duler LM. **Neutrophil extracellular traps in feline cardiogenic arterial thrombi: a pilot study**. *J Feline Med Surg* (2022) **24** 580. PMID: 34542355 22. Ducroux C, Di Meglio L, Loyau S. **Thrombus neutrophil extracellular traps content impair tPA‐induced thrombolysis in acute ischemic stroke**. *Stroke* (2018) **49** 754-757. PMID: 29438080 23. Mangold A, Alias S, Scherz T. **Coronary neutrophil extracellular trap burden and deoxyribonuclease activity in ST‐elevation acute coronary syndrome are predictors of ST‐segment resolution and infarct size**. *Circ Res* (2015) **116** 1182-1192. PMID: 25547404 24. Oldach MS, Ueda Y, Ontiveros ES, Fousse SL, Visser LC, Stern JA. **Acute pharmacodynamic effects of pimobendan in client‐owned cats with subclinical hypertrophic cardiomyopathy**. *BMC Vet Res* (2021) **17** 89. PMID: 33622315 25. Payne JR, Borgeat K, Brodbelt DC, Connolly DJ, Luis Fuentes V. **Risk factors associated with sudden death vs. congestive heart failure or arterial thromboembolism in cats with hypertrophic cardiomyopathy**. *J Vet Cardiol* (2015) **17** S318-S328. PMID: 26776589 26. Oldach MS, Ueda Y, Ontiveros ES, Fousse SL, Harris SP, Stern JA. **Cardiac effects of a single dose of pimobendan in cats with hypertrophic cardiomyopathy; a randomized, placebo‐controlled, crossover study**. *Front Vet Sci* (2019) **6** 15. PMID: 30778391 27. Diefenbach RJ, Lee JH, Kefford RF, Rizos H. **Evaluation of commercial kits for purification of circulating free DNA**. *Cancer Genet* (2018) **228‐229** 21-27 28. Li RHL, Ng G, Tablin F. **Lipopolysaccharide‐induced neutrophil extracellular trap formation in canine neutrophils is dependent on histone H3 citrullination by peptidylarginine deiminase**. *Vet Immunol Immunopathol* (2017) **193‐194** 29-37 29. Fernando MR, Jiang C, Krzyzanowski GD, Ryan WL. **Analysis of human blood plasma cell‐free DNA fragment size distribution using EvaGreen chemistry based droplet digital PCR assays**. *Clin Chim Acta* (2018) **483** 39-47. PMID: 29655637 30. Pilsczek FH, Salina D, Poon KK. **A novel mechanism of rapid nuclear neutrophil extracellular trap formation in response to Staphylococcus aureus**. *J Immunol* (2010) **185** 7413-7425. PMID: 21098229 31. Yipp BG, Kubes P. **NETosis: how vital is it?**. *Blood* (2013) **122** 2784-2794. PMID: 24009232 32. Huang H, Tohme S, Al‐Khafaji AB. **Damage‐associated molecular pattern‐activated neutrophil extracellular trap exacerbates sterile inflammatory liver injury**. *Hepatology* (2015) **62** 600-614. PMID: 25855125 33. Etulain J, Martinod K, Wong SL, Cifuni SM, Schattner M, Wagner DD. **P‐selectin promotes neutrophil extracellular trap formation in mice**. *Blood* (2015) **126** 242-246. PMID: 25979951 34. Dhanesha N, Nayak MK, Doddapattar P. **Targeting myeloid‐cell specific integrin alpha9beta1 inhibits arterial thrombosis in mice**. *Blood* (2020) **135** 857-861. PMID: 31951649 35. Semeraro F, Ammollo CT, Morrissey JH. **Extracellular histones promote thrombin generation through platelet‐dependent mechanisms: involvement of platelet TLR2 and TLR4**. *Blood* (2011) **118** 1952-1961. PMID: 21673343 36. Ammollo CT, Semeraro F, Xu J. **Extracellular histones increase plasma thrombin generation by impairing thrombomodulin‐dependent protein C activation**. *J Thromb Haemost* (2011) **9** 1795-1803. PMID: 21711444 37. Bruning‐Fann CS, Robbe‐Austerman S, Kaneene JB. **Use of whole‐genome sequencing and evaluation of the apparent sensitivity and specificity of antemortem tuberculosis tests in the investigation of an unusual outbreak of Mycobacterium bovis infection in a Michigan dairy herd**. *J Am Vet Med Assoc* (2017) **251** 206-216. PMID: 28671497 38. Stroun M, Lyautey J, Lederrey C, Olson‐Sand A, Anker P. **About the possible origin and mechanism of circulating DNA apoptosis and active DNA release**. *Clin Chim Acta* (2001) **313** 139-142. PMID: 11694251 39. Grassle S, Huck V, Pappelbaum KI. **von Willebrand factor directly interacts with DNA from neutrophil extracellular traps**. *Arterioscler Thromb Vasc Biol* (2014) **34** 1382-1389. PMID: 24790143 40. Pena‐Martinez C, Duran‐Laforet V, Garcia‐Culebras A. **Pharmacological modulation of neutrophil extracellular traps reverses thrombotic stroke tPA (tissue‐type plasminogen activator) resistance**. *Stroke* (2019) **50** 3228-3237. PMID: 31526124 41. Wang R, Zhu Y, Liu Z. **Neutrophil extracellular traps promote tPA‐induced brain hemorrhage via cGAS in mice with stroke**. *Blood* (2021) **138** 91-103. PMID: 33881503 42. Guillaumin J, Gibson RM, Goy‐Thollot I. **Thrombolysis with tissue plasminogen activator (TPA) in feline acute aortic thromboembolism: a retrospective study of 16 cases**. *J Feline Med Surg* (2019) **21** 340. PMID: 29807505 43. Wang Y, Li M, Stadler S. **Histone hypercitrullination mediates chromatin decondensation and neutrophil extracellular trap formation**. *J Cell Biol* (2009) **184** 205-213. PMID: 19153223 44. Li RHL, Tablin F. **A comparative review of neutrophil extracellular traps in sepsis**. *Front Vet Sci* (2018) **5** 291. PMID: 30547040 45. Li P, Li M, Lindberg MR, Kennett MJ, Xiong N, Wang Y. **PAD4 is essential for antibacterial innate immunity mediated by neutrophil extracellular traps**. *J Exp Med* (2010) **207** 1853-1862. PMID: 20733033 46. Ferre‐Vallverdu M, Latorre AM, Fuset MP. **Neutrophil extracellular traps (NETs) in patients with STEMI. Association with percutaneous coronary intervention and antithrombotic treatments**. *Thromb Res* (2022) **213** 78-83. PMID: 35306431 47. Molek P, Zabczyk M, Malinowski KP. **Markers of NET formation and stroke risk in patients with atrial fibrillation: association with a prothrombotic state**. *Thromb Res* (2022) **213** 1-7. PMID: 35276507 48. Mangold A, Hofbauer TM, Ondracek AS. **Neutrophil extracellular traps and monocyte subsets at the culprit lesion site of myocardial infarction patients**. *Sci Rep* (2019) **9** 16304. PMID: 31704966 49. Alcaide M, Cheung M, Hillman J. **Evaluating the quantity, quality and size distribution of cell‐free DNA by multiplex droplet digital PCR**. *Sci Rep* (2020) **10** 12564. PMID: 32724107
--- title: 'Prevalence of signs of lower urinary tract disease and positive urine culture in dogs with diabetes mellitus: A retrospective study' authors: - Valerie Nelson - Amy Downey - Stacie Summers - Sarah Shropshire journal: Journal of Veterinary Internal Medicine year: 2023 pmcid: PMC10061181 doi: 10.1111/jvim.16634 license: CC BY 4.0 --- # Prevalence of signs of lower urinary tract disease and positive urine culture in dogs with diabetes mellitus: A retrospective study ## Abstract ### Background No recent studies have evaluated the association between clinical signs of lower urinary tract disease (LUTD) and positive urine culture in dogs with diabetes mellitus. ### Objective Determine the prevalence of subclinical bacteriuria (ie, positive urine culture without signs of LUTD) in dogs with diabetes mellitus. ### Animals One hundred seven dogs with diabetes mellitus were evaluated at a university veterinary hospital. ### Methods Retrospective study evaluating diabetic dogs with a single sample paired urinalysis and urine culture. Relationship between the presence of signs of LUTD, pyuria, and bacteriuria and urine culture results were compared using Fisher exact testing. ### Results Fifteen dogs ($14\%$) had a positive urine culture via cystocentesis or free catch, of which 8 ($53\%$) had pyuria, and 4 ($27\%$) had signs of LUTD. Of the 88 dogs ($82\%$) without signs of LUTD, 11 ($13\%$) had a positive culture. A significant association was found between a positive urine culture and pyuria (OR infinity; $95\%$ CI 20.34‐infinity, $P \leq .00001$) and bacteriuria (OR infinity; $95\%$ CI 164.4‐infinity, $P \leq .00001$). No association was found between urine culture results and signs of LUTD (OR 1.87; $95\%$ CI 0.59‐6.85, $$P \leq .46$$). ### Conclusion and Clinical Importance Subclinical bacteriuria occurred in this cohort of dogs, and our findings reinforce the recommendation that urine cultures should not be routinely performed in diabetic dogs particularly if pyuria and bacteriuria are absent. ## INTRODUCTION Recommendations for when to submit urine cultures in diabetic dogs have dramatically changed over the last 25 years. Previously, routine submission of urine cultures was recommended and commonly performed in diabetic dogs regardless of clinical signs or urinalysis results. 1, 2 Reasons for this recommendation included concerns for infection promoting insulin resistance and diabetic ketoacidosis, increased risk for urinary tract infections because of altered immune function of immune cells such as granulocytes, and these potential lower urinary tract infections serving as a source of septicemia if left untreated. 1, 2, 3 In human medicine, a urinary tract infection (UTI) in a diabetic patient was categorized as a complicated UTI and this verbiage was initially adopted in veterinary medicine. 4 Studies in dogs show that a positive urine culture can occur without signs of lower urinary tract disease (LUTD) or expected urine sediment changes such as pyuria or cytologic presence of bacteria. 1, 2, 5 Therefore, screening urinalyses and urine cultures in diabetic dogs were commonly performed in general and specialty practice. Over time these recommendations have changed to mirror current human guidelines and to support better antimicrobial stewardship in veterinary medicine. Consensus guidelines for antimicrobial use in animals discouraged the submission of urine cultures in dogs that are not showing signs of lower urinary tract disease. 6 Expected signs of LUTD associated with a UTI could include stranguria, pollakiuria, dysuria, malodorous urine, hematuria, inappropriate urination, or a combination of these signs. 7, 8 *The consensus* guidelines also recommended to not treat subclinical bacteriuria (SB). Subclinical bacteriuria is defined as a positive bacterial urine culture in an animal that is not showing any clinical signs of urinary tract disease. 8 In humans, asymptomatic bacteriuria (AB) is a common finding, particularly in healthy women or individuals with concurrent urologic abnormalities. 9 Screening for AB is only recommended in certain clinical scenarios such as in pregnant women or in patients undergoing invasive endourologic procedures. Screening or treating for AB is not recommended in people with diabetes mellitus. 9 *Subclinical bacteriuria* under the current definition also occurs in healthy dogs 5, 10, 11 and is documented in diabetic dogs. 1, 2 Despite the aforementioned guidelines, in diabetic dogs lacking clinical evidence of active lower urinary disease, the finding of inflammatory changes or cytologic evidence of bacteria in the urine, a concurrent positive bacterial culture, or a combination of these findings can make veterinarians uneasy if left untreated. However, treating dogs that have subclinical bacteriuria can contribute to antimicrobial resistance, increase cost to the owner, affect the human and animal bond during medication administration, and potentially result in adverse effects because of the prescribed antimicrobial. With these newer changes in guidelines, there have been no recent studies in diabetic dogs evaluating the presence of signs of LUTD and subclinical bacteriuria under the current definitions. Therefore, the purpose of this study was to determine the prevalence of subclinical bacteriuria in diabetic dogs. Our hypothesis was that subclinical bacteriuria is common in diabetic dogs reinforcing the current guidelines that urine cultures should only be submitted when clinically indicated. ## Medical record review This was a retrospective study that evaluated medical records of dogs with diabetes mellitus that were presented to Colorado State University Veterinary Teaching Hospital between January 1, 2008, and January 1, 2019. All owners signed the standard intake form upon admission which approves the use of data reporting for research purposes. Dogs were diagnosed with diabetes mellitus based on consistent clinical signs (ie, polyuria (PU), polydipsia (PD), ± weight loss with polyphagia), and hyperglycemia (>300 mg/dL) with glucosuria. Signs of LUTD were considered present if stranguria, pollakiuria, dysuria, hematuria, licking at the vulva in the absence of discharge or urinary incontinence, or inappropriate urination were reported by the owners. Initial screening criteria of medical records included an initial paired urinalysis and urine culture in a dog with diabetes mellitus. Urine cultures that were submitted greater than 3 days after the urinalysis submission were excluded. Exclusion criteria at initial evaluation included suspected or confirmed hyperadrenocorticism, hypothyroidism, azotemic chronic kidney disease, systemic hypertension, diabetic ketoacidosis, urolithiasis, urinary polyps, lower urinary system neoplasia, intervertebral disc disease (IVDD), a previous diagnosis of or receiving medications for urethral sphincter mechanism incompetence (USMI), abnormal vulvar conformation, vaginal discharge, diagnosis of vaginitis, ectopic ureters, nonambulatory state, concurrent daily steroid administration at any dose or route, and immunosuppressive treatment. Dogs were also excluded if they had been treated with an antimicrobial within 60 days of the urine collection. Antimicrobials to treat diarrhea (ie, tylosin, metronidazole) within 60 days of urine collection were not considered a reason for exclusion. Data gathered from the medical record included age, breed, sex, body weight, presence of signs of LUTD, urinalysis, urine culture, current, and recent medications. Information specific to the urinalysis included the date of submission, route of collection, urine specific gravity, pH, protein, and glucose on urine dipstick, cytology findings (white blood cells [WBCs], red blood cells [RBCs], bacteria, casts). Sulfosalicylic acid test (SSA) results were recorded, if performed. Classification and quantification were recorded for applicable parameters (crystals, casts, WBCs, RBCs, etc.). Pyuria was defined as >6 WBC/high powered field (hpf) in a non‐hematuric (defined as <250 RBC/hpf) urine sample. Similarly, urine culture results recorded were the date submitted, aerobic culture results, anaerobic culture results, colony forming units (cfu) per mL of bacteria present, and if and which antimicrobial was prescribed. Additionally, any available follow‐up information after the initial visit was recorded. If the route of urine collection was not listed within the medical record and could not be determined, the sample was assigned to the voided group. The definitions used to describe WBC and RBC numbers are the following: rare and trace were considered equivalent terminology, rare was defined as 1 to 10 cells on the entire slide for both WBCs and RBCs, occasional was defined as 0 to 5 cells per hpf for RBCs and 0 to 3 cells per hpf for WBCs, and packed field was defined as many cells were present where the determination of an actual number was not possible. During the study timeframe, the laboratory changed how casts were reported. For the purposes of the study, both classifications (descriptive and numerical) were recorded. For the descriptive classifications, the terms were defined as the following: few casts were defined as $0\%$ to $10\%$ per hpf, difficult to find on the slide, and up to one or more in almost every field; moderate casts were defined as $10\%$ to $50\%$ per hpf, easy to find, and the number present in the field of view (FOV) varies; and many casts were defined as >$50\%$ per hpf with a large number present on all FOVs. For the numerical classifications, 0 to 1, 1 to 3, 3 to 6, 6 to 10, and >10 casts present per low power field (lpf) were recorded. Routine urinalysis with sediment examination was performed by the clinical pathology service with at least 5 mL of freshly submitted refrigerated urine within a few hours as described. 12 At least a 1 mL aliquot of urine was submitted for urine culture to the Colorado State University Veterinary Diagnostic Laboratory. ## Statistical analysis Descriptive statistics were reported for population data and various urinalysis and urine culture data. A Fisher exact test was performed to evaluate if there was a difference between proportions of dogs based on urine culture results (positive or negative) and the presence or absence of signs of lower urinary tract disease. The same analysis was performed to evaluate if there was a difference between proportions of dogs based on urine culture results (positive or negative) and whether there was cytologic presence of bacteria or pyuria detected on urine sediment microscopy. Statistical analysis was performed using GraphPad Prism version 9 with a Fisher's exact test and was considered significant if $P \leq 0.05.$ ## Animals This study initially contained 657 potential paired urinalysis and culture samples for evaluation but after screening for exclusion criteria, 107 dogs were included for evaluation. Median age was 9 years old (range 14 weeks‐15 years). Breeds with multiple dogs represented included mixed breed ($\frac{30}{107}$, $28\%$), Labrador Retriever ($\frac{12}{107}$, $11.2\%$), Miniature Schnauzer ($\frac{6}{107}$, $5.6\%$), Bichon Frise ($\frac{4}{107}$, $3.7\%$), West Highland White Terrier ($\frac{4}{107}$, $3.7\%$), Rottweiler ($\frac{4}{107}$, $3.7\%$), Cairn Terrier ($\frac{3}{107}$, $2.8\%$), Dachshund ($\frac{3}{107}$, $2.8\%$), and Miniature Poodle ($\frac{3}{107}$, $2.8\%$). Reproductive status was $47.7\%$ male castrated ($\frac{51}{107}$), $44.9\%$ female spayed ($\frac{48}{107}$), $4.7\%$ female intact ($\frac{5}{107}$), and $2.8\%$ male intact ($\frac{3}{107}$). Weight was assigned to one of 3 categories 0 to 10 kg ($\frac{44}{107}$, $41.1\%$), 10.1 to 40 kg ($\frac{52}{107}$, $48.6\%$), or >40.1 kg ($\frac{11}{107}$, $10.3\%$). ## Urinalysis The route of urine collection was predominantly via cystocentesis ($\frac{96}{107}$, $89.7\%$) and the remaining samples were collected from voided urine. All samples were refrigerated and processed by the diagnostic laboratory within a few hours, and all were cultured on‐site as per our standard protocol. Urine specific gravity (USG) ranged from 1.006 to 1.073, with a median value of 1.033. The urine pH minimum was 5.0 and the maximum was 9.0, with a median value 6.4. Crystalluria was rare and included amorphous $5.6\%$ ($\frac{6}{107}$), calcium oxalate dihydrate $4.7\%$ ($\frac{5}{107}$), and struvite $1.9\%$ ($\frac{2}{107}$) crystals. In the 2 dogs with struvite crystalluria, neither dog demonstrated signs of LUTD and the urine cultures were both negative. Granular ($11.2\%$, $\frac{12}{107}$) casts were found in a subset of the study population (rare, $41.6\%$, $\frac{5}{12}$; occasional, $58.3\%$, $\frac{7}{12}$). Proteinuria was present in $57.9\%$ of samples ($\frac{62}{107}$) as defined by dipstick analysis. Samples were considered proteinuric if urine dipstick reading was >1+. Most samples with proteinuria had trace protein ($26.2\%$, $\frac{28}{107}$). Samples with proteinuria of 1+ was $15.9\%$ ($\frac{17}{107}$), of 2+ was $10.3\%$ ($\frac{11}{107}$), and of 3+ was $5.6\%$ ($\frac{6}{107}$). SSA was performed in most of the samples ($81.3\%$, $\frac{87}{107}$). Of samples that had SSA performed, $44\%$ ($\frac{38}{87}$) of the time protein was not documented. SSA detected protein in trace quantities $17\%$ ($\frac{15}{87}$), 1+ $22\%$ ($\frac{19}{87}$), 2+ $10\%$ ($\frac{9}{87}$), 3+ $6\%$ ($\frac{5}{87}$), and 4+ $1\%$ of the time ($\frac{1}{87}$). Glucosuria was present in $71.0\%$ ($\frac{76}{107}$) of samples. The prevalence of trace glucosuria was $4.7\%$ ($\frac{5}{107}$), $1.9\%$ had 1+ glucosuria ($\frac{2}{107}$), $2.8\%$ had 2+ glucosuria ($\frac{3}{107}$), $8.4\%$ had 3+ glucosuria ($\frac{9}{107}$), and $53.3\%$ had 4+ glucosuria ($\frac{57}{107}$). Cytologic presence of bacteria was noted in $15\%$ of samples ($\frac{16}{107}$). When the number of bacteria was quantified into groups, 2 samples were recorded as “few,” 6 samples were recorded as “moderate,” and 7 were recorded as “many.” In one sample, the number of bacteria was not quantified. Most samples showed bacteria with rod morphology ($\frac{15}{16}$) and one sample showed cocci on cytology ($\frac{1}{16}$). Pyuria was present in $7.5\%$ ($\frac{8}{107}$) of the urine samples. Two samples ($1.9\%$, $\frac{2}{107}$) were considered hematuric (>250 RBC/hpf). ## Urine culture Most urine samples ($94.4\%$) were submitted for aerobic culture ($\frac{101}{107}$) with the remaining $5.6\%$ submitted for aerobic and anaerobic culture ($\frac{6}{107}$). Urine cultures were submitted on the same day as the urinalysis for $62.6\%$ ($\frac{67}{107}$) of the dogs with the remaining samples being submitted 1 day ($28\%$, $\frac{30}{107}$), 2 days ($6.5\%$, $\frac{7}{107}$), or 3 days later ($2.8\%$, $\frac{3}{107}$). Urine cultures were positive in 15 dogs ($14\%$, $\frac{15}{107}$). The organisms that were isolated included *Escherichia coli* ($80\%$, $\frac{12}{15}$), Enterobacter aerogenes ($7\%$, $\frac{1}{15}$), Enterococcus spp., ( $7\%$, $\frac{1}{15}$), and *Pseudomonas aeruginosa* ($7\%$, $\frac{1}{15}$). One culture grew Aspergillus spp., which was considered contaminated by the clinical pathology lab and not a true positive. As such, this urine sample was reported in the “No Growth” category. Of the samples that cultured E. coli, 5 samples isolated E. coli hemolytic and one isolated an equal mixture of both colonies (ie, E. coli and E. coli hemolytic). In the samples that cultured bacteria, the majority had >100 000 cfu/mL ($46.7\%$, $\frac{7}{15}$). Of those 7 urine cultures, 6 grew a single isolate at 100000 cfu/mL. The remaining samples grew 2 isolates with each measuring 100 000 cfu/mL. The remaining bacteria counts were between 10 000 to 100 000 cfu/mL in $33.3\%$ ($\frac{5}{15}$) and less than or equal to 5000 cfu/mL in $20\%$ ($\frac{3}{15}$) of the samples. Of the 15 samples which yielded a positive urine culture, 15 ($100\%$) displayed the cytologic presence of bacteria. Only one sample which yielded a negative urine culture ($1\%$), displayed the cytologic presence of bacteria. Note: this sample was obtained via cystocentesis and was submitted for culture within 24 hours from collection. There was a significant association found between urine culture and bacteriuria (OR infinity; $95\%$ CI 164.4‐infinity, $P \leq .00001$). Of the 15 samples with a positive urine culture, 8 ($53\%$) were found to be pyuric. No samples yielding a negative urine culture were found to be pyuric. There was a significant association found between urine culture and pyuria (OR infinity; $95\%$ CI 20.34‐infinity, $P \leq .00001$). Of the 107 samples, 96 ($90\%$) were obtained via cystocentesis, and 11 ($10\%$) were collected via voided sample. Of those collected via cystocentesis, 11 ($11\%$) yielded a positive urine culture. Of those obtained via voided sample, 4 ($36\%$) yielded a positive urine culture (these 4 samples were submitted for culture within 24 hours of collection). ## Signs of lower urinary tract disease and urine culture results Signs of lower urinary tract disease were present in 19 dogs ($\frac{19}{107}$, $17.8\%$). When described in the medical record, the most commonly reported signs of LUTD included pollakiuria and inappropriate urination. Of the 15 diabetic dogs with a positive urine culture, 11 dogs ($73.3\%$, $\frac{11}{15}$) did not have signs of LUTD present as reported by the owner, therefore $13\%$ ($\frac{11}{88}$) of dogs with diabetes mellitus fit the definition of subclinical bacteriuria. Of these 11 dogs, 8 dogs had urine collected by cystocentesis and 3 dogs had urine collected from a voided sample but were submitted for culture within 24 hours of collection. Of the 15 dogs with a positive urine culture, 4 dogs had signs of LUTD, therefore only $4\%$ of diabetic dogs ($\frac{4}{107}$) had evidence of a possible urinary tract infection. Of the 92 dogs with a negative urine culture, 15 dogs exhibited signs of LUTD ($16.3\%$). No association was found between urine culture results and the presence of signs of lower urinary tract disease (OR 1.87; $95\%$ CI 0.59‐6.85, $$P \leq .46$$). ## Changes in insulin treatment Out of the 11 dogs that had subclinical bacteriuria, $\frac{3}{11}$ ($27\%$) had a change in insulin dosage. The insulin dose was decreased in these 3 dogs because of concern for overregulation of diabetes mellitus because of the findings of suspected seizure‐like activity and intermittent concurrent hypoglycemia events, episodes of collapse shortly after receiving insulin, and intermittent periods of lack of glucose in the urine. The remaining 8 dogs with subclinical bacteriuria ($\frac{8}{11}$, $73\%$) had no change in insulin treatment. ## Antimicrobial treatment An antimicrobial was prescribed in 20 dogs ($18.7\%$, $\frac{20}{107}$), of which 5 ($25\%$) dogs had signs of LUTD, 12 ($60\%$) dogs had a positive urine culture, and 4 ($20\%$) dogs had both a positive urine culture and signs of LUTD. The most common antimicrobial prescribed was amoxicillin/clavulanic acid ($85\%$, $\frac{17}{20}$) followed by enrofloxacin ($10\%$, $\frac{2}{20}$) and amoxicillin ($5\%$, $\frac{1}{20}$). Three of the 15 dogs ($20\%$, $\frac{3}{15}$) with positive urine cultures were not prescribed antimicrobials; however, none of them were found to display signs of LUTD. ## Follow‐up information Of the 11 dogs with a positive urine culture but no signs of LUTD, follow‐up information was available for 10 dogs. All 11 dogs received antimicrobial treatment. Two dogs were given 3 additional courses of antimicrobials of varying duration (10 days‐5 weeks) because of the presence of bacteria in the urine and a positive urine culture. Nocturnal enuresis was later noted in both dogs thought to be consistent with urethral sphincter mechanism incompetence, the previously diagnosed infections were no longer deemed clinically relevant, and no additional courses of antimicrobials were pursued. Three dogs were subsequently diagnosed with subclinical bacteriuria and routine urinalyses were no longer performed and the dogs were reportedly doing well for a follow‐up average of 3 to 7 years. Three dogs were documented to have a negative urine culture after antimicrobial treatment and had no further reports of concern for urinary tract infections. One dog was diagnosed with overcontrol of diabetes mellitus and improved on a lower dose of insulin and had no additional documentation of urinary tract infections. One dog developed seizures thought to be related to poorly controlled diabetes mellitus 17 days after the initial visit and was euthanized. Necropsy did not show histologic evidence of cystitis or pyelonephritis, but a culture of the bladder wall or urine was not obtained. Of the 15 dogs with signs of LUTD but a negative culture, follow‐up information was available in all 15 dogs. Only one dog received antimicrobial treatment because of the presence of bacteria in the urine and before receiving urine culture results. All of the dogs were re‐evaluated, and no dogs were documented to have a urinary tract infection and were reportedly doing well with an average follow‐up of 3 months to 4 years. Reported reasons for signs of LUTD in these dogs included the following: overflow incontinence because of poorly controlled diabetes mellitus in 6 dogs which resolved with changes to insulin treatment, newly diagnosed diabetes mellitus with overflow incontinence because of severe PU/PD in 5 dogs, newly diagnosed diabetes mellitus with overflow incontinence because of severe PU/PD and concurrent degenerative myelopathy in 1 dog, urethral sphincter mechanism incompetence that was diagnosed several months later in 1 dog, behavioral causes in 1 dog, and suspected cystitis because of previous cystotomy performed 1 month before because of calcium oxalate urolithiasis. ## DISCUSSION This study demonstrated that although subclinical bacteriuria does occur in diabetic dogs, in contrast to our hypothesis, this was relatively uncommon. Additionally, this study found no association between signs of LUTD and a positive urine culture. Our study did show an association between pyuria and bacteriuria and a positive urine culture, however, it should be noted that a small number of dogs were noted to have pyuria or bacteriuria so future studies with a larger cohort of dogs should be pursued to further support this association. Like previous studies 1, 3, 13 these findings suggest pyuria and bacteriuria to be significant predictors of a positive urine culture. This is in contrast however to previous works which have suggested poor or equivocal agreement between urine cytology findings and urine culture results. 1, 5 Our findings suggest that routine urine cultures are not a clinically useful diagnostic tool in diabetic patients, particularly in the absence of pyuria and bacteriuria but further studies are needed to investigate this clinical question. The findings of pyuria or the cytological presence of bacteria even in the absence of clinical signs can be unsettling to many clinicians. There can be poor agreement between the presence of white blood cells in the urine or the cytologic presence of bacteria and a positive urine culture, 1, 2, 3 leading to the recommendation that a urine culture should be submitted regardless of the urine sediment results in diabetic dogs. However, considering the recent definition of SB, the question of whether these dogs had a true UTI and required antimicrobial treatment is unknown. In our study, there was a statistical association between a positive urine culture and pyuria and cytological evidence of bacteria, thus these 2 parameters were the best predictors of a positive urine culture in our diabetic patients. From a proportion standpoint, this is true as dogs with a positive urine culture may or may not have pyuria but in our study, no dogs with a negative urine culture had documented pyuria. Additionally, only one dog with cytologic evidence of bacteria on urine sediment had a negative urine culture, and all dogs with a positive urine culture had bacteria present on urine sediment. However, it should be noted again that this was only observed in a small number of dogs in our study. Additionally, it is important to remember that pyuria and bacteriuria are not an indication to treat with an antimicrobial and findings still need to be taken into consideration of the clinical context. Some dogs with a negative urine culture were exhibiting signs of LUTD as described by the owner. Only one of these dogs received an antimicrobial but none of the remaining dogs were documented to develop a urinary tract infection or pyelonephritis over a follow‐up period of months to years. There are confounding factors that make the reliance on signs of LUTD difficult to determine in dogs, especially those with diabetes mellitus. Examples include the ability to identify signs of LUTD in dogs with free access to the yard or owners misinterpreting clinical features of disease such as pollakiuria (more suggestive of UTI) and polyuria (common in diabetes mellitus). For example, in our study, the only signs of LUTD that was reported in these dogs was inappropriate urination and was reported because of poorly regulated or newly diagnosed diabetes mellitus in most of the dogs and resolved with initiation of insulin treatment or adjustments in insulin treatment. As a result, a detailed history is particularly important in these patients and performing a routine urine culture in every diabetic patient can potentially result in undue costs to the pet owner, unnecessary use of hospital resources, and potential inappropriate antimicrobial use. More information is currently needed about the outcomes of treating both diabetic and healthy dogs with subclinical bacteriuria (SB). In our study, 20 dogs were prescribed an antimicrobial, of which 15 dogs had a positive urine culture and 19 dogs had evidence of signs of LUTD. Although our study was retrospective in nature, follow‐up information was available in almost all of the dogs. Although this was observed in a small number of dogs, in the dogs that were treated with an antimicrobial and had a positive urine culture but lacked signs of LUTD, several of these dogs were prescribed additional multiple prolonged courses of antimicrobials that did not appear to affect diabetic control or outcome. Some literature suggests treating SB in animals thought to be at risk for ascending infection such as patients with chronic kidney disease or that are immunocompromised 7 however, the recent guidelines 8 suggest that there is a lack of evidence to support treatment of SB in any animal, regardless of comorbidities. This is based on the lack of evidence found in human literature to support treatment of AB in either healthy individuals or those with comorbidities including endocrinopathies 9 however more prospective studies with outcome measures are also needed in human medicine. Treatment of bacteriuria in humans without clinical signs increases the risk of adverse drug reactions and promotes antimicrobial resistance and often will result in recolonization of bacteria and lack of appropriate bacterial clearance. 9 This recommendation is for both healthy, and diabetic patients, 9 and there is no current strong evidence to suggest that dogs should be treated differently from human patients in this regard. There are limitations to our study. An inherent limitation is because of the retrospective nature of the study design and lack of standardized follow‐up and diagnostics. However, the medical records utilized for this study were obtained from an academic hospital with extensive patient histories. To ensure signs of LUTD were not confused with signs of urinary incontinence at the initial evaluation, any dog with a diagnosis of urinary incontinence, USMI, or was currently on medications for control (diethylstilbestrol, phenylpropanolamine, etc.) of urinary incontinence was excluded. Although polyuria could be considered a sign of LUTD, it was not included in our definition for diabetic dogs as polyuria could be a common manifestation of a poorly regulated diabetic. Another limitation already mentioned is that the presence of signs of LUTD was based on retrospective evaluation of medical records and reliance on the owner to report such observations. Signs of LUTD may have been present but not communicated to the veterinarian writing the medical record. Less likely, signs of LUTD could have been mentioned by the client but not recorded within the medical record or misinterpreted as uncontrolled diabetes mellitus or urinary incontinence. Another limitation of this study because of the retrospective study design is that we cannot make conclusions regarding adverse consequences of not treating a diabetic dog with a positive urine culture. Only 3 dogs with a positive urine culture were not treated with an antimicrobial but they also lacked signs of signs of LUTD. Although we evaluated the follow‐up visits in these 3 dogs and did not find any documentation of clinical complications of not treating over multiple months, the number of dogs not treated was too small to make any meaningful conclusions. The findings of this study discourage the routine submission of urine cultures in diabetic dogs, particularly when pyuria and bacteriuria are absent but future larger scale studies are needed. Urine cultures are expensive and likely impose an unnecessary financial burden on clients so the decision to submit a urine culture should be taken into consideration of the clinical context of the patient and urine cytologic findings. ## CONFLICT OF INTEREST DECLARATION Authors declare no conflict of interest. ## OFF‐LABEL ANTIMICROBIAL DECLARATION Authors declare no off‐label use of antimicrobials. ## INSTITUTIONAL ANIMAL CARE AND USE COMMITTEE (IACUC) OR OTHER APPROVAL DECLARATION Authors declare no IACUC or other approval was needed. ## HUMAN ETHICS APPROVAL DECLARATION Authors declare human ethics approval was not needed for this study. ## References 1. Forrester SD, Troy GC, Dalton MN, Huffman JW, Holtzman G. **Retrospective evaluation of urinary tract infection in 42 dogs with hyperadrenocorticism or diabetes mellitus or both**. *J Vet Intern Med* (1999) **13** 557-560. PMID: 10587255 2. McGuire NC, Schulman R, Ridgway MD. **Detection of occult urinary tract infections in dogs with diabetes mellitus**. *J Am Anim Hosp Assoc* (2002) **38** 541-544. PMID: 12428885 3. Diehl KJ. **Long‐term complications of diabetes mellitus, part II: gastrointestinal and infectious**. *Vet Clin North Am Small Anim Pract* (1995) **25** 731-751. PMID: 7660544 4. Weese JS, Blondeau JM, Boothe D. **Antimicrobial use guidelines for treatment of urinary tract disease in dogs and cats: antimicrobial guidelines working group of the international society for companion animal infectious diseases**. *Vet Med Int* (2011) **4** 1-9 5. McGhie JA, Stayt J, Hosgood GL. **Prevalence of bacteriuria in dogs without clinical signs of urinary tract infection presenting for elective surgical procedures**. *Aust Vet J* (2014) **92** 33-37. PMID: 24471880 6. Weese JS, Giguere S, Guardabassi L. **ACVIM consensus statement on therapeutic antimicrobial use in animals and antimicrobial resistance**. *J Vet Intern Med* (2015) **29** 487-498. PMID: 25783842 7. Olin SJ, Bartges JW. **Urinary tract infections: treatment/comparative therapeutics**. *Vet Clin North Am Small Anim Pract* (2015) **45** 721-746. PMID: 25824394 8. Weese JS, Blondeau J, Boothe D. **International Society for Companion Animal Infectious Diseases (ISCAID) guidelines for the diagnosis and management of bacterial urinary tract infections in dogs and cats**. *Vet J* (2019) **247** 8-25. PMID: 30971357 9. Nicolle LE, Gupta K, Bradley SF. **Clinical practice guideline for the Management of Asymptomatic Bacteriuria: 2019 update by the Infectious Diseases Society of America**. *Clin Infect Dis* (2019) **68** e83-e110. PMID: 30895288 10. O'Neil E, Horney B, Burton S. **Comparison of wet‐mount, Wright‐Giemsa and gram‐stained urine sediment for predicting bacteriuria in dogs and cats**. *Can Vet J* (2013) **54** 1061-1066. PMID: 24179241 11. Wan SY, Hartmann FA, Jooss MK, Viviano KR. **Prevalence and clinical outcome of subclinical bacteriuria in female dogs**. *J Am Vet Med Assoc* (2014) **245** 106-112. PMID: 24941394 12. Vap LM, Shropshire SB. **Urine cytology: collection, film preparation, and evaluation**. *Vet Clin North Am Small Anim Pract* (2017) **47** 135-149. PMID: 27562934 13. Torre M, Furrow E, Foster JD. **Effect of urine‐specific gravity on performance of bacteriuria in predicting urine culture results**. *J Small Anim Pract* (2022) **63** 286-292. PMID: 34897695
--- title: Utility of 1,2‐o‐dilauryl‐rac‐glycero glutaric acid‐(6′‐methylresorufin)‐ester‐lipase for monitoring dogs with chronic pancreatitis authors: - Sharon Kuzi - Dana Adlersberg - Itamar Aroch - Gilad Segev journal: Journal of Veterinary Internal Medicine year: 2023 pmcid: PMC10061187 doi: 10.1111/jvim.16638 license: CC BY 4.0 --- # Utility of 1,2‐o‐dilauryl‐rac‐glycero glutaric acid‐(6′‐methylresorufin)‐ester‐lipase for monitoring dogs with chronic pancreatitis ## Abstract ### Background The utility of 1,2‐o‐dilauryl‐rac‐glycero glutaric acid‐(6′‐methylresorufin)‐ester‐(DGGR)‐lipase activity (DLA) in monitoring clinical progression of chronic pancreatitis (CP) in dogs is unknown. ### Objective To examine the association of DLA with clinical signs of CP, as assessed by a CP clinical severity score (CPCSS). Animals: Twenty‐four dogs. ### Methods This is a retrospective study. Chronic pancreatitis was diagnosed based on clinical signs and DLA > 250 U/L and monitored using CPCSS and DLA. ### Results The study included 134 visits (median, 10 visits/dog; range, 2‐11). Mild‐moderate (CPCSS, 0‐3) and severe (CPCSS, ≥4) disease were documented in 94 ($70\%$) and 40 ($30\%$) visits, respectively. In emergency visits ($$n = 44$$; $33\%$) CPCSS (median, 5; range, 0‐15) and DLA (median, 534 U/L; range, 63‐7133) were higher ($P \leq .001$ and $$P \leq .003$$, respectively) than in scheduled ones ($$n = 90$$; $67\%$; median, 1; range, 0‐6 and median, 384 U/L; range, 49‐3747, respectively). DGGR‐lipase activity was associated ($$P \leq .009$$) with the CPCSS, with a lower activity documented in mild‐moderate CPCSS (median 391 U/L; range, 49‐3747), compared to severe score (median, 558 U/L; range, 63‐7133). DGGR‐lipase activity was significantly, but weakly, correlated with CPSS ($r = 0.233$, $$P \leq .007$$). DGGR‐lipase activity inefficiently discriminated mild‐moderate vs severe CP (area under the receiver operator characteristics curve, 0.64; $95\%$ confidence interval, 0.53‐0.75; $$P \leq .012$$), with DLA cutoff of 428 U/L corresponding to sensitivity of $65\%$ and specificity of $63\%$. ### Conclusions and Clinical Importance Increased DLA is associated with emergency revisits in dogs with CP, possibly reflecting acute flare‐ups. DGGR‐lipase activity was associated with the CPCSS over the follow‐ups but could not differentiate disease severity. ## INTRODUCTION Chronic pancreatitis (CP) is continuous pancreatic inflammation characterized by irreversible histopathological lesions and possibly leading to pancreatic exocrine and endocrine function loss. 1, 2 *Chronic pancreatitis* presents a diagnostic challenge in dogs, because of its variable nonspecific clinical presentation (including vomiting, inappetence, and abdominal pain, ranging from subclinical to debilitating disease, and nonspecific laboratory findings), which could be attributed to comorbidities. 3, 4, 5 Necropsy‐based, histological studies suggest that CP is common in dogs, with a prevalence of $34\%$‐$64\%$ in all necropsied dogs. 1, 6 However, some necropsy‐based studies do not distinguish between different histopathologic forms of CP, and do not correlate histologic abnormalities with clinical findings. 1, 7, 8, 9, 10 Consequently, the clinical importance of postmortem findings of pancreatic inflammation and fibrosis are mostly unknown. 1, 7, 8, 9, 10 Obtaining pancreatic biopsies antemortem is invasive. Moreover, biopsies might be diagnostically insensitive to detect mild or early CP, because of patchy lesion distribution and limited tissue sample size. 1, 6 Considering the associated morbidity and the limited impact on clinical decision‐making, pancreatic biopsies are mostly not acquired in the clinical setting. 2 *There is* no validated system for diagnosing CP in dogs. Similar to human medicine, noninvasive diagnosis of CP relies on presence of appropriate clinical signs, diagnostic imaging and pancreatic‐specific laboratory test findings, and vigorously ruling out concurrent diseases. 2 Common clinical signs of dogs with histologically confirmed CP included lethargy ($80\%$), decreased appetite ($70\%$), vomiting ($63\%$), diarrhea ($36\%$), and abdominal pain ($27\%$). 4, 5 Aside from abdominal pain, these clinical characteristics comprise the canine inflammatory bowel disease activity index (CIBDAI), 11 which is useful for monitoring and prognosticating chronic inflammatory enteropathies (CIEs). 12, 13 Recently, a modified CIBDAI‐based canine activity index showed excellent prognostic accuracy in dogs with acute pancreatitis (AP). 14 Therefore, similar clinical severity index scoring systems might serve in monitoring and prognosticating CP in dogs. Nevertheless, such an index has not been evaluated. Pancreatic lipases, such as 1,2‐o‐dilauryl‐rac‐glycero glutaric acid‐(6′‐methylresorufin) ester‐(DGGR)‐lipase, or serum canine pancreatic lipase immunoreactivity (cPLI), are useful in the diagnosis of AP. 15, 16, 17, 18 These were evaluated in only small cohorts of dogs with CP, with unsatisfactory sensitivity of $42\%$‐$67\%$. 4, 15 Combining increased serum cPLI concentration and presence of pancreatic sonographic abnormalities has diagnostic sensitivity <$60\%$ for CP. 5 Possibly, pancreatic tissue loss, alongside minimal tissue edema and inflammation, contribute to the low diagnostic sensitivity of pancreatic lipase assays and sonography. 2 Nevertheless, both cPLI and DGGR‐lipase were $100\%$ diagnostically specific in 8 dogs with histologically confirmed CP. 15 Additionally, higher serum pancreatic lipase activities occur more frequently in nonsurviving dogs with AP and cPLI concentrations correlated with an AP clinical severity score, suggesting that both are disease severity markers. 14, 19, 20 *Similar data* of the utility of pancreatic lipases in monitoring and prognosticating dogs with CP are unavailable. In addition to pancreatic lipase essays, C‐reactive protein (CRP) was significantly and positively correlated with the clinical severity of AP in dogs, as well as with serum cPLI concentration. 14, 21 C‐reactive protein is a readily available and a commonly used acute phase protein for assessing disease severity and monitoring response to treatment in various chronic inflammatory diseases in dogs (eg, polyarthritis and CIEs), 11, 22, 23 but the clinical utility of CRP in CP in dogs, or its correlations with DGGR‐lipase activity and clinical signs were not investigated. The main aim of this retrospective study was to examine the association of a clinical severity scoring index, comprised of the CIBDAI criteria, alongside abdominal pain, with DGGR‐lipase activity, and to assess their utility in monitoring dogs diagnosed with CP as the sole or major disease. An additional aim was to assess the correlations between CRP and either the clinical severity scoring index score, or DGGR‐lipase activity, to assess its utility as an additional marker of disease severity, alongside evidence of systemic inflammation, as manifested by CRP, in dogs with CP. The study hypothesis was that serum DGGR‐lipase activity will be positively associated with the CPCSS, and will thus be a useful marker in monitoring dogs with CP, similarly to its diagnostic utility in dogs with AP. 14, 18, 19, 20 ## Selection of dogs, definitions, and data collection This was a retrospective study, including dogs diagnosed with CP in a referral veterinary teaching hospital (VTH) between 2017 and 2021. Chronic pancreatitis was diagnosed based on the combined presence of compatible chronic or intermittent (≥3 weeks duration) gastro‐intestinal clinical signs, including ≥2 of the following: hyporexia, vomiting, diarrhea, abdominal pain, 14 and ≥1 documentation of DGGR‐lipase activity >250 U/L (reference interval [RI], 5‐107 U/L) at any point during the follow‐up period. 1,2‐o‐dilauryl‐rac‐glycero glutaric acid‐(6′‐methylresorufin) ester‐lipase activity was measured by a colorimetric lipase assay (LIPC, Roche, Mannheim, Germany) using an autoanalyzer (Cobas 6000; at 37°C) as previously reported. 17 A DGGR‐lipase activity cutoff of >250 U/L was chosen based on a previous study, where this same assay was used, that reported an equivocal range of 109‐216 U/L. 17 Thus, the chosen cutoff in the present study is conservative and considered more specific for pancreatitis. A similar serum DGGR‐lipase activity cutoff (>245 U/L), albeit using a different DGGR‐lipase assay, was considered to have $100\%$ specificity for AP or CP in another previous study. 15 Abdominal ultrasonography was performed in all dogs, although presence of sonographic changes indicative of CP (ie, pancreatic mixed‐echogenicity or hyper‐echogenicity) 24, 25 was not a prerequisite inclusion criterion. Only dogs followed for ≥2 revisits scheduled for monitoring and management of CP, with available DGGR‐lipase activities were considered. The study included dogs with and without concurrent diseases, fed various diets, and treated by different treatment regimens, as these represent the complex clinical actuality, the therapeutic and monitoring challenges in canine CP. Dogs were included with concomitant diseases only if these were deemed secondary processes, with minimal or no contribution to the clinical signs. For example, dogs with chronic kidney disease (CKD) with serum creatinine concentration ≤2.0 mg/dL during all revisits were included, as CKD was deemed subclinical, with minimal or no contribution to the relevant clinical signs. Dogs with diabetes mellitus (DM) and hyperadrenocorticism were included only if deemed stable, with no diabetic ketoacidosis or iatrogenic hypocortisolemia during follow‐up. Dogs treated for lymphoma and multiple myeloma and in complete remission were also included, if CP was considered the main disease process responsible for clinical signs. Dogs were excluded if comorbidities potentially had a major contribution to the clinical signs, or if such comorbidities could not be ruled out based on physical examination, CBC, serum chemistry, and abdominal ultrasound (performed in all dogs included upon diagnosis of CP). Efforts were made to exclude dogs with concomitant CIEs and hepatobiliary diseases. The classical presentation of CP is described as waxing and waning, mostly low‐grade, anorexia, and gastrointestinal signs. 2 Therefore, dogs were excluded if pre‐existing CIEs were suspected to cause persistent, rather than intermittent, gastrointestinal disease, or with presence of sonographic findings of increased intestinal mucosal echogenicity and enlarged mesenteric lymph nodes, 26 abnormal intestinal histological findings, or abnormally low intestinal absorptive function. Unless attributed to DM and hyperadrenocorticism, or to corticosteroid therapy, dogs with abnormally high alanine aminotransferase, alkaline phosphatase, and gamma‐glutamyl transferase activity above 1.5‐fold their upper RIs were excluded, unless available liver histology ruled out primary hepatobiliary diseases. Visits in which DGGR‐lipase or clinical signs were not fully recorded, were excluded. Historical AP (before inclusion) or acute flare‐up of chronic pancreatitis (A/CP), or A/CP episodes during the study period, were recorded when dogs were presented to the emergency service with acute historical clinical signs or acute clinical deterioration during the follow‐up period, with compatible clinical signs of anorexia, frequent vomiting and marked lethargy, 14 and sonographic evidence of pancreatomegaly, pancreatic hypoechogenicity, peri‐pancreatic hyperechoic mesentery, and peri‐pancreatic abdominal free fluid. 24, 25 After the diagnosis of CP and evaluation of comorbidities, data collected on each visit included the clinical signs and DGGR‐lipase activity. Serum CRP concentration (Canine CRP, Randox Laboratories LTD, Crumlin, UK) 27, 28 was recorded when available. Additionally, data pertaining to the severity of CP at each visit were recorded, including the type of revisit (ie, a scheduled elective follow‐up visits or unscheduled emergency revisits suggestive of A/CP), and administration of supportive therapy (eg, antiemetics, analgesics). Revisits were scheduled at the attending internal‐medicine clinicians' discretion, to assess the clinical response to treatment changes, and to modify therapeutic interventions when warranted, as well as to monitor DGGR‐lipase activity. Surviving dogs were defined as those surviving during the 3‐month period after the last recorded visit. ## Chronic pancreatitis clinical severity score A CP clinical severity score (CPCSS) system was utilized herein, based on presence and severity of the clinical signs comprising the CIBDAI (ie, attitude, appetite, vomiting, feces consistency, feces frequency, and weight loss), 11 with addition of abdominal pain (Table 1), based on the history provided by dogs' owners, and physical examination findings in each visit, as recorded in the medical records. Each clinical sign was assigned a score as follows: 0 (normal), 1 (mild), 2 (moderate), and 3 (severe). 11 Abdominal pain was scored as present (score = 1) or absent (score = 0). Vomiting and fecal frequency and consistency were scored based on the history of the 7 days before each visit, whereas the attitude, appetite, and presence of abdominal pain were scored based on data recorded in the physical examination on each visit. Changes in body weight were compared to the body weight recorded in the previous visit. The scores of each clinical sign were assigned by a single primary investigator. The final CPCSS was comprised of the sum of individual scores, and could range between 0 and 19 points. The CPCSS was recorded on each visit, and was categorized as follows: 0‐3, mild‐moderate; ≥4, severe; based on an association ($P \leq .001$) between occurrence of an emergency visit and CPCSS ≥4. **TABLE 1** | Criterion | No clinical signs | Mild clinical signs | Moderate clinical signs | Marked clinical signs | | --- | --- | --- | --- | --- | | Criterion | Score 0 | Score 1 | Score 2 | Score 3 | | Attitude/activity | Normal | Slightly decreased | Moderately decreased | Markedly decreased | | Appetite | Normal | Slightly decreased | Moderately decreased | Markedly decreased | | Vomiting | | Mild (1/week) | Moderate (2‐3/week) | Severe (>3/week) | | Feces consistency | Normal | Slightly soft feces | Very soft feces | Watery diarrhea | | Feces frequency | Normal | Slightly increased (2‐3 times a day) or fecal blood, mucus, or both | Moderately increased (4‐5 times a day) | Markedly increased (>5 times a day) | | Weight loss | | Mild (<5%) | Moderate (5%‐10%) | Marked (>10%) | | Abdominal pain | | Abdominal wall resistance, a or the dog resists abdominal palpation, or presence of other signs of pain elicited upon abdominal palpation; the dog ambulates slowly, or is reluctant to ambulate or lie down; occasional praying position noted at home; occasional or persistent unusual vocalization | Abdominal wall resistance, a or the dog resists abdominal palpation, or presence of other signs of pain elicited upon abdominal palpation; the dog ambulates slowly, or is reluctant to ambulate or lie down; occasional praying position noted at home; occasional or persistent unusual vocalization | Abdominal wall resistance, a or the dog resists abdominal palpation, or presence of other signs of pain elicited upon abdominal palpation; the dog ambulates slowly, or is reluctant to ambulate or lie down; occasional praying position noted at home; occasional or persistent unusual vocalization | ## Statistical analysis Normality was assessed using the Shapiro‐Wilk's test. The Mann‐Whitney's test was used to compare continuous variables (eg, DGGR‐lipase activities) between groups. Associations between categorical parameters (eg, type of visit) were evaluated using the Chi‐square or Fisher's exact tests, as appropriate. The CPCSS was then divided into a severe and a mild‐moderate clinical disease severity categories, based on significant differences in frequency of emergency visits in each CPCSS level category. Associations between DGGR‐lipase activity and severity of CP (ie, CPCSS) during the follow‐up period were examined using generalized linear equations. Receiver operator characteristics analysis of clustered data, with its area under the curve (ROC AUC) and its $95\%$ confidence interval ($95\%$ CI), was performed to assess the diagnostic accuracy of serum DGGR‐lipase activity in classifying the clinical severity, as reflected by the CPCSS. The clinical score was categorized to mild‐moderate (CPCSS, 0‐3) vs severe (CPCSS ≥4) CP. The optimal serum DGGR‐lipase activity cutoff values, with their corresponding sensitivity and specificity, were those associated with the least number of misclassifications, chosen using the Youden index. Associations between continuous variables were examined using the Spearman's correlation test. All tests were 2‐tailed, and $P \leq .05$ was considered significant in all. Statistical analyses were performed using statistical software packages (SPSS 28.0.1.0, IBM, Armonk, New York; STATA 15, Stata Corp., College Station, Texas). ## Signalment, history, concurrent diseases, and treatments The study included 24 dogs with CP (neutered females, 13; $54\%$; males, 11; $46\%$; neutered, 9; $82\%$), of the following breeds: mixed breed (16 dogs; $67\%$), toy poodle (2; $8\%$), and Belgian Malinois, Maltese, Yorkshire terrier, Shih tzu, miniature schnauzer, and Weimaraner (1 each; $4\%$). The median age was 12.5 years (range, 7.5‐17.0), and the median body weight was 9.4 kg (range, 2.4‐45.7). In 20 dogs ($83\%$), at least 1 episode of AP or A/CP was documented before inclusion in this study (1 episode, 7 dogs [$25\%$]; 2 episodes, 6 dogs [$25\%$]; 3 episodes, 1 dog [$4\%$]; 4 episodes, 2 dogs [$8\%$]). The median duration of historical CP‐associated clinical signs, before the first recorded visit, was 55 days (range, 21‐270 days). Sonographic pancreatic abnormalities suggestive of CP were noted in 9 dogs ($37\%$) upon their enrollment. The results of the CBC and serum chemistry upon diagnosis of CP are presented in Tables S1 and S2. One concurrent chronic disease was diagnosed in 16 dogs ($67\%$), including 6 dogs ($25\%$) with stable (ie, no change during the follow‐up period) CKD, of which 1 dog was classified as International Renal Interest Society (IRIS) Stage‐1, and 5 dogs as IRIS Stage‐2, 29 4 dogs ($17\%$) with DM, 2 dogs ($8\%$) with neoplasia in remission (lymphoma and multiple myeloma), and 1 dog ($4\%$) each of hyperadrenocorticism, hypothyroidism, mild portal lymphocytic hepatitis, and familial hypertriglyceridemia of Miniature Schnauzer. Endocrinopathies were treated with trilostane and levothyroxine, respectively, with no treatment changes made during the follow‐up period. In 3 additional dogs ($12\%$), 2 concurrent chronic diseases were diagnosed, including DM with IRIS stage‐2 CKD (2 dogs; $8\%$) and DM with hyperadrenocorticism (1 dog; $4\%$). The study included 134 follow‐up visits overall (median number of visits per dog, 10; range, 2‐11), of which 90 ($67\%$) were scheduled elective visits, and 44 ($33\%$) were emergency visits (of which in 30 [$68\%$], dogs were hospitalized). The median overall follow‐up period (from first to last visit) was 163 days (range, 14‐1247). The median frequency of visits was every 17 days (range, 3‐58), while the median between‐visit interval was 31 days (range, 3‐63), and the median interval between visits when DGGR‐lipase activity was >250 U/L was 30.5 days (range, 5‐619). All dogs survived during the follow‐up period. Low‐fat commercial prescription dry diets were exclusively administered in 116 visits ($87\%$), including Hill's Prescription Diet i/d low fat (Etten‐Leur, the Netherlands), Purina Pro Plan Veterinary Diets Overweight Management Canine Formula (Portogruaro, Italy), and Royal Canin Gastrointestinal low‐fat (Aimargues, France), whereas in 18 ($13\%$), such diets were combined with low‐fat homemade diet (fat <$25\%$ metabolizable energy). Supportive symptomatic therapy was recorded in 115 visits ($86\%$), including antiemetics (84 visits [$63\%$]; maropitant‐citrate [cerenia, Pfizer PGM, France], 74 visits [$55\%$]; metoclopramide [pramin, RAFA Laboratories LTD, Jerusalem, Israel], 12 visits [$9\%$]), omeprazole (omepradex, [Dexel Pharma, Or Akiva, Israel], 40 visits; $30\%$), appetite stimulant (mirtazapine [mirtazapine, Teva Pharmaceuticals Industries, Tel Aviv, Israel] in 74 visits; $55\%$), and analgesia (maropitant citrate, 74 visits; $55\%$). Five dogs ($21\%$; 53 visits, $40\%$) were administered immunosuppressives (prednisone, generic [Rekah Pharmaceutical Industries, Holon, Israel], 2 dogs, $8\%$; leflunomide [arava, Sanofi Winthrop Industrie, Paris, France], 3 dogs; $12\%$). ## Chronic pancreatitis clinical severity score indices and serum DGGR‐lipase activity The scores of each individual clinical sign that were assigned during the follow‐up period, which collectively formed the CPCSS, are summarized in Table 2. Clinical signs recorded were hyporexia (59 revisits; $44\%$), weight loss (58 revisits, $43\%$), attitude changes (44 revisits, $29\%$), vomiting (32 revisits, $24\%$), abdominal pain (29 revisits, $22\%$), abnormal fecal consistency (28 revisits, $21\%$), and increased defecation frequency (6 revisits, $4\%$). The associations between serum DGGR‐lipase activity and each clinical sign are presented in Table S3. The CPCSS was higher ($P \leq .001$) in unscheduled emergency visits (median, 5; range, 0‐15) compared to elective visits (median, 1; range, 0‐6; Figure 1A). Serum DGGR‐lipase activity was also higher ($$P \leq .003$$) on emergency visits (median, 534 U/L; range, 63‐7133) compared to elective visits (median, 384 U/L; range, 49‐3747; Figure 1B). Based on the significant association between frequency of emergency unscheduled revisits and the CPCSS level, the CPCSS scores were categorized as mild‐moderate ($\frac{94}{134}$ visits; $70\%$, including $\frac{81}{90}$ planned visits; $90\%$) and severe ($\frac{40}{134}$ visits; $30\%$, including $\frac{31}{44}$ emergency visits; $70\%$, $P \leq .001$) CP. Serum DGGR‐lipase activity differed ($$P \leq .009$$) between the CPCSS categories, with lower activity measured in mild‐moderate CP (CPCSS 0‐3; median, 391 U/L; range, 49‐3747), compared with severe CP (CPCSS score ≥4; median, 558 U/L; range 63‐7133; Figure 1C). Neither the CPCSS nor DGGR‐lipase activity changed significantly over the follow‐up period (Figures 2 and 3). Overall, there was an association between DGGR‐lipase activity and the CPCSS ($$P \leq .01$$). The CPCSS ($$P \leq .96$$), or DGGR‐lipase activity ($$P \leq .55$$) over time were both not associated with administration of immunosuppressive therapy. For every 1000 U/L increase in DGGR‐lipase activity, the CPSS increases by 0.6 ($95\%$ CI, 1.015‐1.123). 1,2‐o‐dilauryl‐rac‐glycero glutaric acid‐(6′‐methylresorufin) ester‐lipase activity was significantly, albeit weakly correlated with the CPSS ($r = 0.233$, $$P \leq .007$$). Serum DGGR‐lipase activity poorly discriminated mild‐moderate from severe clinical disease (ROC AUC, 0.64; $95\%$ CI, 0.53‐0.75; $$P \leq .01$$). The optimal cutoff point, 428 U/L, corresponded to sensitivity of $65\%$ and specificity of $63\%$. **FIGURE 2:** *Chronic pancreatitis clinical severity (CPCS) score through 11 follow‐up visits from diagnosis of 24 dogs with chronic pancreatitis presented to the hospital. In each visit, the dot represents the mean CPCS score, and the whisker represents the SD. “n” is the number of dogs evaluated in each re‐visit.* **FIGURE 3:** *Mean and SD of 1,2‐o‐dilauryl‐rac‐glycero glutaric acid‐(6′‐methylresorufin) ester (DGGR)‐lipase activity through 11 follow‐up visits of 24 dogs with chronic pancreatitis presented to the hospital. In each visit, the dot represents the mean DGGR‐lipase activity, and the whisker represents the SD. “n” is the number of dogs evaluated in each re‐visit.* Serum CRP concentration (median, 0.0; range, 0.0‐319.1) was measured in 35 visits ($26\%$) and was above RI in 8 visits ($23\%$). Serum CRP was neither correlated with serum DGGR‐lipase activity ($$P \leq .85$$) nor with the CPCSS ($$P \leq .32$$). ## DISCUSSION The CPCSS and serum DGGR‐lipase activity were both significantly associated with the type of visit (emergency or elective) to the hospital, which is suggestive of the severity of the disease, thus adding potentially useful information for monitoring CP in dogs. Although serum DGGR‐lipase activity and the CPCSS were significantly associated, the former was poorly predictive of the clinical severity of CP, suggesting that DGGR‐lipase activity has limited utility in monitoring the clinical progression of CP in dogs. Importantly, increases in DGGR‐lipase activity overtime were associated with higher CPCSS, but the impact on the CPCSS was minor, suggesting that only major increases in DGGR‐lipase activity overtime, might be of clinical importance. The partial clinical utility of DGGR‐lipase activity in monitoring dogs with CP was possibly negatively affected by presence of limited ongoing pancreatic active damage and pancreatic lipase activation and leakage in CP. 15 While not evaluated in dogs with CP, DGGR‐lipase activity is a sensitive diagnostic marker of AP in dogs. Its specificity is moderate, hampered, among others, by presence of extra‐pancreatic diseases. 9, 15, 30, 31 Other pancreatic lipase assays have similar diagnostic limitations. For instance, abnormally high Spec cPLI concentration was recorded in various nonpancreatic diseases (eg, intervertebral disc disease, 32 mitral valve disease, 33 hyperadrenocorticism, 34 and CIEs 35), and in dogs with AP, the Spec cPL diagnostic sensitivity ($21\%$‐$91\%$) and specificity ($74\%$‐$100\%$) vary. 16, 36, 37 *There is* higher serum DGGR‐lipase activity in dogs with various diseases treated in an intensive care unit, where AP was not clinically diagnosed; yet, presence of pancreatopathy could not be excluded. 30 Nevertheless, according to that study, serum DGGR‐lipase activity is significantly higher in dogs with sonographic evidence of AP (median, 245 U/L; IQR, 74‐1542), compared to dogs where these are absent (median, 83 U/L; IQR, 41‐186 U/L). 30 Most importantly, serum DGGR‐lipase activity shows poor to moderate sensitivity, but excellent specificity for histologically confirmed CP. 15 In the current study, severe extra‐pancreatic comorbidities were primarily ruled out, and to increase specificity, a relatively high inclusion cut‐off value of 250 U/L was selected. 15, 17 Similarly to the present cohort, dogs diagnosed with CP are typically middle‐aged to older at diagnosis. 4, 5 Most dogs herein, males and females, were neutered, in agreement with a previous report, where dogs with CP are more likely to be neutered, regardless of sex. 2 Although toy, terrier, and nonsporting breeds are reportedly more affected by CP, 2, 5 most dogs herein ($67\%$) were of mixed breed, likely reflecting geographical breed prevalence differences. The chronic, low‐grade gastrointestinal‐related clinical signs characteristically reported in CP 2 are consistent with the overall low CPCSSs, even upon emergency visits (median, 5; range, 0‐15 out of a possible maximal CPSS of 19), when dogs were assessed to require further diagnostics and in‐hospital emergency care. Additionally, the characteristic waxing and waning clinical presentation of CP 2 was also evident herein by a lack of change in CPCSS trajectory over time. Clinical deterioration of CP, and A/CP episodes, are presumably explained by the “necrosis‐fibrosis” theory, suggesting that pancreatic fibrosis reduces its distensibility, resulting in duct obstruction, impairing secretion, culminating into acute flare‐ups, and disease progression. 2 All the dogs in this cohort survived the follow‐up period, during which they showed little and mild clinical signs; however, chronic supportive treatment was commonly required, and revisits were quite frequent, of which $33\%$ were unscheduled emergency visits. Therefore, it seems that CP, as a sole or major disease, was not life‐threatening during the duration of this study. Nevertheless, dogs sustaining CP should be expected to need frequent revisits, warranting appropriate client education regarding the prognosis and treatment, including its cost, and implications on life quality. Comorbidities, mainly CKD and endocrinopathies, were frequent in our cohort, and such common disorders are probably inevitable in studies of naturally occurring CP. 2 Dogs with CP are more likely to sustain concurrent DM, and less commonly hyperadrenocorticism and hypothyroidism. 2, 3, 38 Although poorly defined, the association between AP or CP and endocrinopathies was described, with undetermined cause and effect. 39 For example, hyperlipidemia and hypercholesterolemia occur commonly secondary to DM, and are implicated in the pathogenesis of CP, and vice versa, CP might lead to pancreatic islet loss, with consequent DM. 40 Other comorbidities documented (ie, neoplasia and CKD) likely occurred independently of CP and were age‐related. 41, 42 Yet, CKD and lymphocytic portal hepatitis might have been sequels of systemic or local inflammation (respectively), or were possibly secondary complications of A/CP (eg, dehydration affecting kidney perfusion). 4, 10, 41, 43 Immune‐mediated CP is reported in cocker spaniel and Cavalier King Charles spaniel breeds in the United Kingdom 5, 44 and administrating glucocorticoids or cyclosporine is reported in dogs and cats with CP and AP to suppress inflammation. 45, 46 Immunosuppressive therapy was not associated with the clinical score or with serum DGGR‐lipase activity changes (either improved or worsened) herein; nevertheless, these results should be interpreted cautiously. Only 1 spaniel dog was included in the present study, suggesting that immune‐mediated CP was possibly uncommon in this cohort. 5, 44 C‐reactive protein is a useful marker for assessing severity and prognosis of AP in dogs, although not an early disease marker, since its maximal concentrations are documented days from disease onset. 14, 21 In the current study, serum CRP concentration was not associated with neither the CPCSS nor with serum DGGR‐lipase activity, suggesting that CRP is less useful for monitoring clinically milder, or localized, chronic pancreatic disease. Nevertheless, CRP was measured in only $26\%$ of the revisits, warranting future larger‐scale studies. This study has several limitations, mainly attributed to its retrospective nature, including variable monitoring and treatment protocols, implemented by different clinicians, and the variable number of revisits per dog. While collection of clinical score indices was performed by a single investigator, some potential inaccuracies might have occurred when retrospectively and subjectively assessing the attitude and appetite of dogs upon visits. Furthermore, while some acute changes are reflected by the CPCSS, some of the clinical indices reflect changes that have occurred over a longer time period. The latter might have contributed to the weak correlation with serum DGGR‐lipase activity, which is a single‐point measure of a leakage enzyme, reflecting active pancreatic tissue damage. A shorter duration of clinical signs before presentation is associated with higher DGGR‐lipase activities in AP, affecting its diagnostic performance. 30 In CP, it is plausible that variable intervals between DGGR‐lipase activity measurements, as well as acute flare‐ups, or presence of active pancreatic inflammation, affect the diagnostic accuracy of DGGR‐lipase activity in reflecting clinical severity. Nonetheless, the visits herein were frequent (median between‐visit time interval, 17 days), and even small changes (eg, <$5\%$) in body weight affected the clinical score; thus, the CPCSS did reflect recent changes. Small changes in the score of individual clinical signs, or concurrent small score changes in several clinical signs, might lead to an overall change in the severity of clinical CP. Thus, cautious interpretation is recommended in actual clinical settings, as such changes do not necessarily justify therapeutic or monitoring changes. Additionally, while efforts were made to rule out comorbidities that potentially affected the CPCSS score, possibly, concurrent diseases were undiagnosed in some dogs. On the other hand, the rather strict inclusion criteria potentially led to loss of dogs with CP, which if included, would have increased the size of this limited cohort, possibly strengthening the statistical analyses. Treading this fine line between 2 conflicting trends was deemed necessary for obtaining reliable results. Still, some comorbidities and treatments might have affected DGGR‐lipase activity. Specifically, several dogs in the current study were exposed to either exogenous or increased endogenous glucocorticoids. 34, 47 Yet, in a study evaluating prednisolone effect on DGGR‐lipase activity, the latter was only mildly increased (from activity range between 14.45‐31.48 U/L and 15.91‐48.48 U/L), and all results were within RI. 48 Additionally, the higher DGGR‐lipase activity in dogs with hyperadrenocorticism is mostly mild (median, 180 U/L), 49 and lower than the currently set cutoff of >250 U/L. Thus, higher DGGR‐lipase activity in hypercortisolemic dogs most likely reflects ongoing CP rather than merely the mild sole glucocorticoid effect on pancreatic lipase level. 50 Decreased glomerular filtration rate, resulting in decreased DGGR‐lipase clearance might have also contributed to increase in its activity. 51 Nevertheless, experimental and clinical studies of dogs with kidney injury support the presence of ongoing pancreatic damage as a more probable source of higher DGGR‐lipase activities. 30, 50, 51 *In this* cohort, a minority of dogs had low stage and stable CKD, and it is therefore likely that CKD had marginal effect on DGGR‐lipase activity. Another common limitation to many studies of pancreatitis in dogs is the lack of pancreatic histopathology. 2, 16 Thus, CP was not definitely diagnosed, and pancreatic inflammation and structural changes were not examined. Therefore, possibly dogs with recurrent AP, mimicking ongoing CP, were included. 2 Nevertheless, this distinction between recurrent AP and “true” CP is less important in terms of clinical management, as their treatment and complications (eg, DM) 2 are virtually the same. 2, 52 Finally, the study was conducted in a tertiary‐care hospital, which likely introduced selection bias toward complicated and refractory CP cases, and highly compliant dog owners, which might have affected results. Nevertheless, the monitoring measures assessed in this study are widely accessible in primary care clinic setup, providing useful data regarding interpretation and limitations of serum DGGR‐lipase activity in dogs with CP. In conclusion, serum DGGR‐lipase activity is only weakly correlated with the CPCS in dogs with CP. ## CONFLICT OF INTEREST DECLARATION Authors declare no conflict of interest. ## OFF‐LABEL ANTIMICROBIAL DECLARATION Authors declare no off‐label use of antimicrobials. ## INSTITUTIONAL ANIMAL CARE AND USE COMMITTEE (IACUC) OR OTHER APPROVAL DECLARATION This is a retrospective study, based on data collected from medical files. An ethical approval was not required. ## HUMAN ETHICS APPROVAL DECLARATION Authors declare human ethics approval was not needed for this study. ## References 1. Watson PJ, Roulois AJ, Scase T. **Prevalence and breed distribution of chronic pancreatitis at post‐mortem examination in first‐opinion dogs**. *J Small Anim Pract* (2007) **48** 609-618. PMID: 17696987 2. Watson P. **Chronic pancreatitis in dogs**. *Top Companion Anim Med* (2012) **27** 133-139. PMID: 23148854 3. Xenoulis PG, Suchodolski JS, Steiner JM. **Chronic pancreatitis in dogs and cats**. *Compend Contin Educ Vet* (2008) **30** 166-180. PMID: 18409143 4. Bostrom BM, Xenoulis PG, Newman SJ, Pool RR, Fosgate GT, Steiner JM. **Chronic pancreatitis in dogs: a retrospective study of clinical, clinicopathological, and histopathological findings in 61 cases**. *Vet J* (2013) **195** 73-79. PMID: 22835863 5. Watson PJ, Archer J, Roulois AJ, Scase TJ, Herrtage ME. **Observational study of 14 cases of chronic pancreatitis in dogs**. *Vet Rec* (2010) **167** 968-976. PMID: 21262713 6. Newman S, Steiner J, Woosley K, Barton L, Ruaux C, Williams D. **Localization of pancreatic inflammation and necrosis in dogs**. *J Vet Intern Med* (2004) **18** 488-493. PMID: 15320585 7. Coddou MF, Constantino‐Casas F, Scase T, Day MJ, Blacklaws B, Watson PJ. **Chronic inflammatory disease in the pancreas, kidney and salivary glands of English Cocker Spaniels and dogs of other breeds shows similar histological features to human IgG4‐related disease**. *J Comp Pathol* (2020) **177** 18-33. PMID: 32505237 8. Kent AC, Constantino‐Casas F, Rusbridge C. **Prevalence of pancreatic, hepatic and renal microscopic lesions in post‐mortem samples from Cavalier King Charles spaniels**. *J Small Anim Pract* (2016) **57** 188-193. PMID: 26918814 9. Newman SJ, Steiner JM, Woosley K, Williams DA, Barton L. **Histologic assessment and grading of the exocrine pancreas in the dog**. *J Vet Diagn Invest* (2006) **18** 115-118. PMID: 16566269 10. Watson PJ, Roulois AJ, Scase TJ. **Prevalence of hepatic lesions at post‐mortem examination in dogs and association with pancreatitis**. *J Small Anim Pract* (2010) **51** 566-572. PMID: 20973784 11. Jergens AE, Schreiner CA, Frank DE. **A scoring index for disease activity in canine inflammatory bowel disease**. *J Vet Intern Med* (2003) **17** 291-297. PMID: 12774968 12. Rychlik A, Nieradka R, Kander M, Nowicki M, Wdowiak M, Kołodziejska‐Sawerska A. **A correlation between the canine inflammatory bowel disease activity index score and the histopathological evaluation of the small intestinal mucosa in canine inflammatory bowel disease**. *Pol J Vet Sci* (2012) **15** 315-321. PMID: 22844710 13. Munster M, Horauf A, Bilzer T. **Assessment of disease severity and outcome of dietary, antibiotic, and immunosuppressive interventions by use of the canine IBD activity index in 21 dogs with chronic inflammatory bowel disease**. *Berl Munch Tierarztl Wochenschr* (2006) **119** 493-505. PMID: 17172138 14. Keany KM, Fosgate GT, Perry SM, Stroup ST, Steiner JM. **Serum concentrations of canine pancreatic lipase immunoreactivity and C‐reactive protein for monitoring disease progression in dogs with acute pancreatitis**. *J Vet Intern Med* (2021) **35** 2187-2195. PMID: 34250650 15. Goodband EL, Serrano G, Constantino‐Casas F. **Validation of a commercial 1,2‐o‐dilauryl‐rac‐glycero glutaric acid‐(6′‐methylresorufin) ester lipase assay for diagnosis of canine pancreatitis**. *Vet Rec Open* (2018) **5**. PMID: 29868172 16. Cridge H, MacLeod AG, Pachtinger GE. **Evaluation of SNAP cPL, Spec cPL, VetScan cPL rapid test, and precision PSL assays for the diagnosis of clinical pancreatitis in dogs**. *J Vet Intern Med* (2018) **32** 658-664. PMID: 29424454 17. Kook PH, Kohler N, Hartnack S, Riond B, Reusch CE. **Agreement of serum Spec cPL with the 1,2‐o‐dilauryl‐rac‐glycero glutaric acid‐(6′‐methylresorufin) ester (DGGR) lipase assay and with pancreatic ultrasonography in dogs with suspected pancreatitis**. *J Vet Intern Med* (2014) **28** 863-870. PMID: 24597630 18. Abrams‐Ogg ARK, Kocmarek H, Reggeti F. **Correlation of serum catalytic lipase activity and pancreatic lipase immunoreactivity in clinically abnormal dogs with and without ultrasonographic evidence of pancreatitis**. *J Vet Intern Med* (2014) **28** 1045-1046 19. Sato T, Ohno K, Tamamoto T. **Assessment of severity and changes in C‐reactive protein concentration and various biomarkers in dogs with pancreatitis**. *J Vet Med Sci* (2017) **79** 35-40. PMID: 27666150 20. Mansfield CS, Jones BR, Spillman T. **Assessing the severity of canine pancreatitis**. *Res Vet Sci* (2003) **74** 137-144. PMID: 12589738 21. Kuzi S, Mazaki‐Tovi M, Suchodolski JS. **Protease inhibitors, inflammatory markers, and their association with outcome in dogs with naturally occurring acute pancreatitis**. *J Vet Intern Med* (2020) **34** 1801-1812. PMID: 32893923 22. Otoni CC, Heilmann RM, Garcia‐Sancho M. **Serologic and fecal markers to predict response to induction therapy in dogs with idiopathic inflammatory bowel disease**. *J Vet Intern Med* (2018) **32** 999-1008. PMID: 29624721 23. Ohno K, Yokoyama Y, Nakashima K. **C‐reactive protein concentration in canine idiopathic polyarthritis**. *J Vet Med Sci* (2006) **68** 1275-1279. PMID: 17213695 24. Hecht S, Henry G. **Sonographic evaluation of the normal and abnormal pancreas**. *Clin Tech Small Anim Pract* (2007) **22** 115-121. PMID: 17844817 25. Gori E, Pierini A, Lippi I, Citi S, Mannucci T, Marchetti V. **Evaluation of diagnostic and prognostic usefulness of abdominal ultrasonography in dogs with clinical signs of acute pancreatitis**. *J Am Vet Med Assoc* (2021) **259** 631-636. PMID: 34448616 26. Gaschen L, Kircher P, Stussi A. **Comparison of ultrasonographic findings with clinical activity index (CIBDAI) and diagnosis in dogs with chronic enteropathies**. *Vet Radiol Ultrasound* (2008) **49** 56-64. PMID: 18251296 27. Kjelgaard‐Hansen M. **Comments on measurement of C‐reactive protein in dogs**. *Vet Clin Pathol* (2010) **39** 402-403. PMID: 21198730 28. Kjelgaard‐Hansen M, Jacobsen S. **Assay validation and diagnostic applications of major acute‐phase protein testing in companion animals**. *Clin Lab Med* (2011) **31** 51-70. PMID: 21295722 29. **IRIS Staging of CKD (modified 2019). International Renal Interest Society (IRIS)**. (2019) 30. Prummer JK, Howard J, Grandt LM. **Hyperlipasemia in critically ill dogs with and without acute pancreatitis: prevalence, underlying diseases, predictors, and outcome**. *J Vet Intern Med* (2020) **34** 2319-2329. PMID: 32945588 31. Hammes K, Kook PH. **Effects of medical history and clinical factors on serum lipase activity and ultrasonographic evidence of pancreatitis: analysis of 234 dogs**. *J Vet Intern Med* (2022) **36** 935-946. PMID: 35438226 32. Schueler RO, White G, Schueler RL, Steiner JM, Wassef A. **Canine pancreatic lipase immunoreactivity concentrations associated with intervertebral disc disease in 84 dogs**. *J Small Anim Pract* (2018) **59** 305-310. PMID: 29355958 33. Han D, Choi R, Hyun C. **Canine pancreatic‐specific lipase concentrations in dogs with heart failure and chronic mitral valvular insufficiency**. *J Vet Intern Med* (2015) **29** 180-183. PMID: 25586363 34. Mawby DI, Whittemore JC, Fecteau KA. **Canine pancreatic‐specific lipase concentrations in clinically healthy dogs and dogs with naturally occurring hyperadrenocorticism**. *J Vet Intern Med* (2014) **28** 1244-1250. PMID: 24903625 35. Kathrani A, Steiner JM, Suchodolski J. **Elevated canine pancreatic lipase immunoreactivity concentration in dogs with inflammatory bowel disease is associated with a negative outcome**. *J Small Anim Pract* (2009) **50** 126-132. PMID: 19261082 36. Trivedi S, Marks SL, Kass PH. **Sensitivity and specificity of canine pancreas‐specific lipase (cPL) and other markers for pancreatitis in 70 dogs with and without histopathologic evidence of pancreatitis**. *J Vet Intern Med* (2011) **25** 1241-1247. PMID: 22092611 37. Xenoulis PG, Steiner JM. **Canine and feline pancreatic lipase immunoreactivity**. *Vet Clin Pathol* (2012) **41** 312-324. PMID: 22861648 38. Hess RS, Saunders HM, Van Winkle TJ. **Clinical, clinicopathologic, radiographic, and ultrasonographic abnormalities in dogs with fatal acute pancreatitis: 70 cases (1986‐1995)**. *J Am Vet Med Assoc* (1998) **213** 665-670. PMID: 9731261 39. Hess RS, Saunders HM, Van Winkle TJ. **Concurrent disorders in dogs with diabetes mellitus: 221 cases (1993‐1998)**. *J Am Vet Med Assoc* (2000) **217** 1166-1173. PMID: 11043687 40. Xenoulis PG, Steiner JM. **Lipid metabolism and hyperlipidemia in dogs**. *Vet J* (2010) **183** 12-21. PMID: 19167915 41. Dunaevich A, Chen H, Musseri D. **Acute on chronic kidney disease in dogs: etiology, clinical and clinicopathologic findings, prognostic markers, and survival**. *J Vet Intern Med* (2020) **34** 2507-2515. PMID: 33044036 42. Pittaway C, Schofield I, Dobson J, O'Neill DG, Brodbelt DC. **Incidence and risk factors for the diagnosis of lymphoma in dogs in UK primary‐care practice**. *J Small Anim Pract* (2019) **60** 581-588. PMID: 31328276 43. Kuzi S, Mazor R, Segev G. **Prognostic markers and assessment of a previously published clinical severity index in 109 hospitalised dogs with acute presentation of pancreatitis**. *Vet Rec* (2020) **187**. PMID: 31662578 44. Watson PJ, Roulois A, Scase T, Holloway A, Herrtage ME. **Characterization of chronic pancreatitis in English Cocker Spaniels**. *J Vet Intern Med* (2011) **25** 797-804. PMID: 21689157 45. Bjornkjaer‐Nielsen KA, Bjornvad CR. **Corticosteroid treatment for acute/acute‐on‐chronic experimental and naturally occurring pancreatitis in several species: a scoping review to inform possible use in dogs**. *Acta Vet Scand* (2021) **63** 28. PMID: 34256804 46. Hoeyrup N, Spillmann T, Toresson L. **Cyclosporine treatment in cats with presumed chronic pancreatitis—a retrospective study**. *Animals (Basel)* (2021) **11** 2993. PMID: 34680012 47. Bennaim M, Shiel RE, Forde C, Mooney CT. **Evaluation of individual low‐dose dexamethasone suppression test patterns in naturally occurring hyperadrenocorticism in dogs**. *J Vet Intern Med* (2018) **32** 967-977. PMID: 29498108 48. Mendoza B, Dias MJ, Nunes T, Basso MA, Hernandez J, Leal RO. **Effect of prednisolone therapy on serum levels of 1,2‐O‐dilauryl‐rac‐glycero glutaric acid‐(6′‐methylresorufin) ester lipase in dogs**. *J Vet Intern Med* (2020) **34** 2330-2336. PMID: 33146921 49. Linari G, Dondi F, Segatore S. **Evaluation of 1,2‐O‐dilauryl‐rac‐glycero glutaric acid‐(6′‐methylresorufin) ester (DGGR) and 1,2‐diglyceride lipase assays in dogs with naturally occurring hypercortisolism**. *J Vet Diagn Invest* (2021) **33** 817-824. PMID: 34078197 50. Cridge H, Lim SY, Algul H. **New insights into the etiology, risk factors, and pathogenesis of pancreatitis in dogs: potential impacts on clinical practice**. *J Vet Intern Med* (2022) **36** 847-864. PMID: 35546513 51. Hulsebosch SE, Palm CA, Segev G, Cowgill LD, Kass PH, Marks SL. **Evaluation of canine pancreas‐specific lipase activity, lipase activity, and trypsin‐like immunoreactivity in an experimental model of acute kidney injury in dogs**. *J Vet Intern Med* (2016) **30** 192-199. PMID: 26678019 52. Watson P. **Pancreatitis in dogs and cats: definitions and pathophysiology**. *J Small Anim Pract* (2015) **56** 3-12. PMID: 25586802
--- title: Prognostic value of serum cystatin C concentration in dogs with myxomatous mitral valve disease authors: - Naoki Iwasa - Rie Kumazawa - Saki Nomura - Mamu Shimizu - Munetaka Iwata - Mayuka Hara - Mifumi Kawabe - Yui Kobatake - Satoshi Takashima - Naohito Nishii journal: Journal of Veterinary Internal Medicine year: 2023 pmcid: PMC10061204 doi: 10.1111/jvim.16669 license: CC BY 4.0 --- # Prognostic value of serum cystatin C concentration in dogs with myxomatous mitral valve disease ## Abstract ### Background Impaired renal function is 1 of the poor prognostic factors in dogs with myxomatous mitral valve disease (MMVD). However, the value of cystatin C (Cys‐C), a marker of renal function, as a prognostic marker for MMVD in dogs has not yet been explored. ### Objective This study aims to investigate the prognostic value of Cys‐C in dogs with MMVD. ### Animals Fifty client‐owned small‐breed dogs with MMVD were included in this study. ### Methods This is a retrospective, cross‐sectional study. The prognostic value of serum Cys‐C concentration was assessed using univariable and multivariable Cox hazard regression analyses. Kaplan‐Meier survival curves for MMVD‐specific survival in dogs stratified into high and low Cys‐C groups were generated and analyzed using the log‐rank test. ### Results Serum Cys‐C concentrations were significantly associated with MMVD‐related death ($P \leq .01$) in both univariable (hazard ratio [HR], 5.086; $95\%$ confidence interval [CI], 1.950‐13.270) and multivariable Cox hazard regression analysis (HR, 4.657; $95\%$ CI, 1.767‐12.270). The high Cys‐C group ($$n = 14$$) had a significantly shorter MMVD‐specific survival time than the low Cys‐C group ($$n = 36$$; $P \leq .01$). In dogs with normal blood creatinine concentrations, the high Cys‐C group ($$n = 10$$) had a significantly shorter MMVD‐specific survival time than the low Cys‐C group ($$n = 36$$; $P \leq .01$). ### Conclusions and Clinical Importance High serum Cys‐C concentrations were associated with a worse prognosis of MMVD. Furthermore, serum Cys‐C could be a predictor of MMVD prognosis even in dogs with normal blood creatinine concentration. ## INTRODUCTION Myxomatous mitral valve disease (MMVD) is the most common heart disease in small‐breed dogs and can result in death because of pulmonary edema, syncope, and dyspnea caused by left ventricular volume overload. 1 The severity of MMVD is classified by the American College of Veterinary Internal Medicine (ACVIM) stages. The ACVIM stage was related to the prognosis of dogs with MMVD, and the higher the stage, the worse the prognosis. 1 Renal function is 1 of the important prognostic factors for dogs with MMVD. 1, 2 Renal dysfunction could be a negative prognostic factor in dogs with MMVD. 2 The most common marker of renal function is serum creatinine (Cr), and the survival time of dogs with MMVD and high serum Cr concentrations is shorter than that of dogs with MMVD and low serum Cr concentrations. 2 However, serum Cr concentrations do not increase until the glomerular filtration rate (GFR) decreases by $75\%$. 3 Furthermore, their serum Cr concentrations tend to remain normal because small‐breed dogs have less skeletal muscle mass. 4 In addition, dogs with severe MMVD often develop cardiac cachexia, which can lead to lower serum Cr. Therefore, more sensitive markers that are independent of nonrenal factors and can detect renal dysfunction and prognosis earlier than serum Cr in dogs with MMVD are needed. Serum cystatin C (Cys‐C) is used in dogs as a GFR biomarker. 5, 6 In humans, serum Cys‐C is a better GFR marker for renal disease than serum Cr. 7, 8, 9, 10 Serum Cys‐C concentrations are significantly higher in dogs with renal failure than in clinically healthy individuals. 11, 12 Despite that it is only useful in small‐breed dogs, serum Cys‐C is a better GFR marker than serum Cr. 13, 14 Thus, serum Cys‐C could be a promising sensitive renal marker in small‐breed dogs. In humans, serum Cys‐C concentrations are linked with prognosis not only in patients with renal insufficiency but also in patients with heart failure. Higher serum Cys‐C concentrations have been detected in human patients with heart disease and have been linked to heart failure and a poor prognosis, particularly in those with coronary artery disease. 15, 16, 17, 18, 19, 20 Furthermore, serum Cys‐C concentrations in human patients detect a poor prognosis of heart disease earlier than serum Cr concentrations. 17 However, the prognostic value of Cys‐C in dogs with heart disease has not yet been reported. The present study aims to evaluate the prognostic value of serum Cys‐C concentrations in dogs with MMVD. ## Case selection This is a retrospective cross‐sectional study. The data were acquired from the medical records of dogs diagnosed with MMVD at a primary care veterinary hospital between February 2015 and April 2021. Dogs weighting ≥15 kg were excluded from this study because serum Cys‐C concentration is an inferior renal marker in larger breed dogs. 13 Of the dogs included, those with missing data required for this study were excluded from the study. Dogs who had received oral administration of prednisolone within the previous month were excluded because oral administration of prednisone increases serum Cys‐C concentrations. 21 In addition, dogs with atrial flutter, fibrillation, other concomitant cardiac (eg, cardiomyopathy or infective endocarditis) and systemic diseases, as well as dogs that had mitral valve repair surgery, were excluded. Data on the cause of death, survival period, medication history, clinical manifestations, body weight, age, sex, breed, serum Cys‐C concentration, blood urea nitrogen (BUN) concentration, blood Cr concentration, urinary specific gravity (USG), urea protein/Cr ratio (UPC), plasma atrial natriuretic peptide (ANP) concentration, vertebral heart score (VHS), vertebral left atrial size (VLAS), left atrial‐to‐aortic ratio (LA/AO), and left ventricular end‐diastolic internal diameter (LVIDDN) were extracted from the medical record. Information about feeding status was not available. According to the American College of Veterinary Internal Medicine (ACVIM) consensus guidelines, the MMVD stage was classified as B1, B2, C, or D. 1 Survival was followed up, and dates of MMVD‐related deaths and deaths from other causes were recorded. MMVD‐related deaths were defined as deaths occurring as a result of progression of MMVD evidenced by 1 or more of pulmonary edema, syncope, or dyspnea. This study was approved by the local ethics committee for animal clinical research (approval no. E22002). Informed consent by dog owners was waived because of the study's retrospective nature. All dog owners were given the option to opt out of the present study, which was conveyed via the animal hospital's bulletin board. ## Chest radiography The VHS 1, 22, 23 and VLAS 1, 24 were measured using lateral thoracic radiography, as previously described. ## Echocardiography The LA/AO ratio and LVIDDN were measured using echocardiographic images. M‐mode, Doppler, and 2‐dimensional (2D) echocardiography was performed by a veterinarian using an ultrasound unit (Noblus, FUJIFILM Healthcare, Tokyo, Japan) with a 2.0‐ to 9.0‐MHz sector probe (S‐31 Probe, FUJIFILM Healthcare). During the examination, dogs were restrained on the right lateral recumbency. The LA/AO ratio was calculated from the right parasternal short‐axis 2D view on the first frame after the aortic valve was closed. 1, 25, 26 The LVIDDN was calculated as follows: left ventricular dimensionenddiastolic diametercm/weightkg0.294. 1, 27 ## Blood biochemistry Data on blood biochemistry was obtained during the initial visit. Blood was collected from the cephalic vein and delivered into plain, heparin, and aprotinin tubes. After allowing the samples in plain tubes to clot, the serum was separated by centrifugation at 5000 rpm for 4 minutes at room temperature. Plasma was separated by centrifuging heparin tubes at 5000 rpm for 4 minutes at room temperature. Aprotinin tubes were centrifuged at 3000 rpm for 10 minutes at 4°C to separate aprotinin plasma. Serum Cys‐C concentrations were measured using a latex immunoturbidimetric assay designed for human use (Iatro Cys‐C, LSI Medience, Tokyo, Japan) on an automatic analyzer (JCA‐BM 6070, JEOL, Tokyo, Japan) that had previously been validated in dogs. 14 Serum was used to measure BUN and Cr concentrations, while plasma was used in 2 dogs. The BUN and Cr concentrations were measured using enzyme assays (vBUN‐P and vCre‐P, Fujifilm, Tokyo, Japan, respectively) on an automatic analyzer (DRI‐CHEM NX500V, FUJIFILM Healthcare Co, Ltd). Plasma ANP concentrations in aprotinin plasma were measured using a chemiluminescent enzyme immunoassay (Determiner CL ANP, Hitachi Chemical Diagnostics Systems, Tokyo, Japan) on a fully automated chemiluminescence system (CL‐JACK RK, Hitachi Chemical Diagnostics Systems) that had previously been validated in dogs. 28 ## Urinalysis Urinary specific gravity was measured using a clinical refractometer (MASTER‐URC, ATAGO, Tokyo, Japan). Urinary protein (Micro TP‐AR, Fujifilm WAKO Pure Chemical, Osaka, Japan) and Cr concentrations (L‐type Wako Cre‐M, Fujifilm WAKO Pure Chemical) were measured using a colorimetric assay on an automatic analyzer (JCA‐BM 6070, JEOL), and the UPC ratio was then calculated. ## Statistical analysis Statistical analyses were performed using the R software (The R Foundation for Statistical Computing, version 3.0.2) in combination with the EZR software. 29 Serum Cys‐C concentrations were compared among various ACVIM stages using the Kruskal‐Wallis test. The cutoff values for Cox proportional hazard regression analysis were determined using the manufacturer's reference value of BUN, blood Cr, and Cys‐C, as well as the median for other variables. The prognostic value of age, body weight, serum Cys‐C concentration, BUN concentration, blood Cr concentration, USG, UPC, ACVIM stage, ANP concentration, VHS, VLAS, LA/AO ratio, and LVIDDN was assessed using a univariable Cox proportional hazard regression model. Furthermore, multivariable Cox proportional hazard regression analysis was performed with serum Cys‐C concentration and age as possible confounders, as serum Cys‐C concentrations were higher in older dogs. 30, 31 Proportional hazard assumption was checked graphically by complementary log‐log plot. The Mann‐Whitney U‐test was used to compare the values of variables between groups with Cys‐C higher and equal or lower than the upper reference limit (0.4 mg/L). Kaplan‐Meier survival curves for MMVD or all‐cause survival rates in dogs were generated and analyzed using the log‐rank test. A P value of <.05 was considered statistically significant. ## Dogs During the study period, 88 dogs were diagnosed with MMVD, with 64 having complete data for use in this study. Furthermore, 10 dogs with cardiac disease other than MMVD, 2 dogs who had received oral administration of prednisolone within the previous month, and 2 dogs who had mitral valve repair surgery were excluded from this study. Finally, 50 dogs with MMVD were included in the study: 9 males, 19 neutered males, 4 females, and 18 spayed females. Dog breeds included 20 Chihuahua, 9 Cavalier King Charles Spaniel, 7 Miniature Schnauzer, 5 Pomeranian, 4 mixed breeds, 1 Shetland Sheepdog, 1 Shih Tzu, 1 Yorkshire Terrier, 1 Papillon, and 1 Beagle. Other dogs' characteristics in this study are shown in Table 1. According to the ACVIM consensus guidelines for MMVD, the included dogs were classified into 4 stages, with 15, 11, 16, and 8 dogs in stages B1, B2, C, and D, respectively. Serum Cys‐C concentrations did not differ significantly between ACVIM stages (Figure 1). Dogs in stages B1 and B2 were asymptomatic and were not treated for heart disease. All dogs diagnosed with ACVIM stage B2 were subsequently administered pimobendan. However, 16 dogs in stage C had clinical signs (eg, cough ($\frac{10}{16}$), exercise intolerance ($\frac{10}{16}$), dyspnea ($\frac{4}{16}$), or tachypnea/labored respiration with pulmonary edema ($\frac{2}{16}$)) or cyanosis ($\frac{1}{16}$) and were treated with furosemide ($\frac{8}{16}$), angiotensin‐converting enzyme (ACE) inhibitors ($\frac{2}{16}$), spironolactone ($\frac{1}{16}$), or pimobendan ($\frac{10}{16}$). Furthermore, 8 dogs in stage D had clinical signs, for example, cough ($\frac{6}{8}$), exercise intolerance ($\frac{4}{8}$), dyspnea ($\frac{2}{8}$), or tachypnea/labored respiration with pulmonary edema ($\frac{2}{8}$), and were treated with furosemide ($\frac{8}{8}$), pimobendan ($\frac{8}{8}$), spironolactone ($\frac{7}{8}$), Sildenafil ($\frac{3}{8}$), or ACE inhibitors ($\frac{1}{8}$). ## Serum Cys‐C concentration and prognosis of MMVD The median follow‐up period was 358 days (range, 6‐1901). During the follow‐up period, 21 of the 50 dogs expired from MMVD‐related causes ($$n = 21$$), with 5 of 15 dogs at stage B1, 2 of 11 dogs at stage B2, 7 of 16 dogs at stage C, and 7 of 8 dogs at stage D dying from MMVD‐related causes. However, 8 of the 50 dogs died from other causes, including renal disease ($$n = 2$$), malignant tumors ($$n = 4$$), neurologic disease ($$n = 1$$), and hepatic failure ($$n = 1$$). Univariable Cox hazard regression analysis revealed that serum Cys‐C, BUN, ANP, LVIDDN ($P \leq .01$), blood Cr, and ACVIM stage D ($P \leq .05$) were all significantly associated with MMVD‐related death (Table 2). Furthermore, multivariable Cox hazard regression analysis with age as a confounding factor revealed that high serum Cys‐C concentration ($P \leq .01$) remained significantly associated with MMVD‐related death (Table 3). ## Serum Cys‐C concentration and MMVD‐related death MMVD‐specific survival time was analyzed in high (>0.4 mg/L) and low (≤0.4 mg/L) Cys‐C groups. Table 4 shows the variables in the high ($$n = 14$$) and low ($$n = 36$$) Cys‐C groups. The high Cys‐C group had significantly higher serum Cys‐C concentrations, BUN concentrations, blood Cr concentrations (all $P \leq .01$), age, UPC, and LVIDDN ($P \leq .05$) than the low Cys‐C group. MMVD‐related death occurred in 9 of the 14 dogs in the high Cys‐C group and 12 of the 36 dogs in the low Cys‐C group. The high Cys‐C group had significantly shorter MMVD‐specific and all‐cause survival times than the low Cys‐C group ($P \leq .01$; Figure 2). ## Survival analysis in dogs with normal blood Cr concentrations A subgroup analysis was performed on 46 dogs with normal blood Cr concentrations (≤1.4 mg/dL), with 10 dogs assigned to the high Cys‐C (>0.4 mg/L) group and 36 dogs assigned to the low Cys‐C (≤0.4 mg/L) group. Six of the 10 dogs in the high Cys‐C group and 12 of the 36 dogs in the low Cys‐C group expired from MMVD‐related causes. Even in dogs with normal blood Cr concentrations, the high Cys‐C group had significantly shorter MMVD‐specific and all‐cause survival times than the low Cys‐C group ($P \leq .01$; Figure 3). **FIGURE 3:** *Kaplan‐Meier survival curves in dogs with normal blood creatinine concentrations. (A) MMVD‐related survival time and (B) all‐cause survival time. Even in dogs with normal blood creatinine concentrations, the high Cys‐C group (>0.4 mg/L, n = 10) had significantly shorter MMVD‐specific and all‐cause survival times than the low Cys‐C group (≤0.4 mg/L, n = 36; P < .01). Vertical lines censored dogs, solid line high Cys‐C group, and dashed line low Cys‐C group.* ## DISCUSSION In this study, the high Cys‐C group had a shorter MMVD‐specific survival time than the low Cys‐C group. Thus, high serum Cys‐C concentrations in dogs with MMVD are suggested to be associated with MMVD‐related deaths and poor prognosis for MMVD. This study establishes that a high serum Cys‐C concentration is a negative prognostic factor for MMVD in dogs. Serum Cys‐C is a marker of renal function that reflects GFR. 13 Therefore, the current findings imply that impaired renal function might be associated with MMVD progression. Previous reports have also indicated that dogs with MMVD and chronic kidney disease (CKD) have a worse prognosis than those without CKD. 2 Cardiovascular‐renal disease (CvRD), a condition in which both cardiac and renal dysfunction negatively affect each other, 2, 32 is caused by sympathetic activation, renin‐angiotensin‐aldosterone system activation, hypertension, and GFR reduction. 32 CvRD might have rapidly worsened MMVD in dogs with high Cys‐C concentrations, resulting in a poor prognosis for MMVD. However, the present study was unable to determine whether elevated Cys‐C concentrations are linked to CvRD. It is unknown whether Cys‐C is associated with causes of death other than MMVD and kidney disease. However, only 6 dogs died from other causes and in these 6 dogs, serum Cys‐C concentrations were low (data not shown). Therefore, it is unlikely that serum Cys‐C concentrations are associated with causes of death other than MMVD and kidney disease. In this study, high serum Cys‐C concentrations tended to worsen MMVD prognosis even in dogs with normal blood Cr concentrations. These findings are consistent with those reported in human cardiac patients, where high serum Cys‐C concentrations were associated with a poor prognosis of cardiac disease even with normal serum Cr concentrations. 17, 33 In small‐breed dogs, serum Cys‐C is more sensitive than serum Cr in identifying reduced GFR. 13 In dogs with normal Cr but high Cys‐C, renal function was impaired, which might have contributed to a worse prognosis of MMVD. Thus, even in dogs with normal Cr, it could be useful to measure Cys‐C to evaluate the prognosis of MMVD. There is a correlation between MMVD severity and the International Renal Interest Society stage of CKD in dogs. 2 In addition, the higher the MMVD stage, the higher the serum Cr and Cys‐C concentrations in dogs. 34 However, in this study, no significant difference in the serum Cys‐C concentrations was noted between the ACVIM stages of MMVD, and dogs in the high Cys‐C group did not necessarily have advanced MMVD stage. The LVIDDN was significantly higher in the high Cys‐C group than in the low Cys‐C group, without significant differences in other MMVD‐related factors (eg, VHS, VLAS, LA/AO ratio, and plasma ANP concentration). These findings indicate that dogs with high serum Cys‐C concentrations did not necessarily have an advanced MMVD at the time of study enrolment. In humans, serum Cys‐C concentration does not correlate with cardiac disease‐related factors and was an independent prognostic factor for cardiac disease. 16, 17 Serum Cys‐C, like in humans, has been suggested to be an independent predictor of MMVD prognosis in dogs. In this study, the high Cys‐C group was significantly older than the low Cys‐C group. Previous studies have discovered that serum Cys‐C concentrations correlate with age, 30, 31, 35, 36, 37 and that aging reduces renal function while increasing Cys‐C concentrations in humans. 35, 36, 37 Moreover, Cys‐C concentrations are higher in elderly dogs than in younger ones. 30, 31 Therefore, in this study, considering the possibility that older age might have contributed to the relationship between high Cys‐C concentrations and a poorer prognosis of MMVD was necessary. Multivariable analysis using age as a confounding factor revealed that serum Cys‐C concentration remained significantly associated with MMVD‐related death. These findings imply that the poor MMVD prognosis in dogs with elevated serum Cys‐C concentrations was independent of age. This study has some limitations. First, a limited number of samples exist. However, the prognosis between the high and low Cys‐C groups differed markedly. Therefore, the present study's sample size was sufficient to demonstrate the difference. Second, data on GFR measurements were missing because of the retrospective nature of the study. If GFR had been assessed in this study, it might have demonstrated that the poor prognosis caused by elevated serum Cys‐C concentrations was attributed to impaired renal function. Third, data on body condition scores and feeding status were missing. Obesity might affect serum Cys‐C concentrations. 38 Although Cys‐C is unaffected by diet in cats 39 and humans, 40, 41 it can be affected by diet in humans. 42, 43 Therefore, the possibility that the degree of obesity or the interval between feedings altered serum Cys‐C concentrations in the present study cannot be ruled out. In conclusion, the present study demonstrated that serum Cys‐C was a prognostic factor for poor outcome for MMVD in small‐breed dogs. High serum Cys‐C concentrations were associated with a worse prognosis regardless of MMVD severity. Furthermore, serum Cys‐C could be a predictor of MMVD prognosis even in dogs with normal blood Cr. ## CONFLICT OF INTEREST DECLARATION Validation data on measurement of canine serum cystatin C level was provided by FUJIFILM VET Systems Co, Ltd (Tokyo, Japan). ## OFF‐LABEL ANTIMICROBIAL DECLARATION Authors declare no off‐label use of antimicrobials. ## INSTITUTIONAL ANIMAL CARE AND USE COMMITTEE (IACUC) OR OTHER APPROVAL DECLARATION This study was approved by the ethics committee for animal clinical research of Gifu University (approval no. E22002). Informed consent by dog owners was waived because of the study's retrospective nature. All dog owners were given the option to opt out of the present study, which was conveyed via the bulletin board in Hashima Animal Hospital. ## HUMAN ETHICS APPROVAL DECLARATION Authors declare human ethics approval was not needed for this study. ## References 1. Keene BW, Atkins CE, Bonagura JD. **ACVIM consensus guidelines for the diagnosis and treatment of myxomatous mitral valve disease in dogs**. *J Vet Intern Med* (2019) **33** 1127-1140. PMID: 30974015 2. Martinelli E, Locatelli C, Bassis S. **Preliminary investigation of cardiovascular‐renal disorders in dogs with chronic mitral valve disease**. *J Vet Intern Med* (2016) **30** 1612-1618. PMID: 27717188 3. Finco DR, Brown SA, Vaden SL. **Relationship between plasma creatinine and glomerular filtration rate in dogs**. *J Vet Pharmacol Ther* (1995) **18** 418-421. PMID: 8789693 4. Feeman WE, Couto CG, Gray TL. **Serum creatinine concentrations in reitred racing greyhounds**. *Vet Clin Pathol* (2003) **32** 40-42. PMID: 12655489 5. Almy FS, Christopher MM, King DP, Brown SA. **Evaluation of cystatin C as an endogenous marker of glomerular filtration rate in dogs**. *J Vet Intern Med* (2002) **16** 45-51. PMID: 11822803 6. Ghys L, Paepe D, Smets P, Lefebvre H, Delanghe J, Daminet S. **Cystatin C: a new renal marker and its potential use in small animal medicine**. *J Vet Intern Med* (2014) **28** 1152-1164. PMID: 24814357 7. Laterza OF, Price CP, Scott MG. **Cystatin C: an improved estimator of glomerular filtration rate?**. *Clin Chem* (2002) **48** 699-707. PMID: 11978596 8. Peralta CA, Shlipak MG, Judd S. **Detection of chronic kidney disease with creatinine, cystatin C, and urine albumin‐to‐creatinine ratio and association with progression to end‐stage renal disease and mortality**. *JAMA* (2011) **305** 1545-1552. PMID: 21482744 9. Hari P, Ramakrishinan L, Gupta R. **Cystatin C‐based glomerular filtration rate estimating equations in early chronic kidney disease**. *Indian Pediatr* (2014) **51** 273-277. PMID: 24825263 10. Wu C, Lin J, Caffrey JL. **Cystatin C and long‐term mortality among subjects with normal creatinine‐based estimated glomerular filtration rates**. *J Am Coll Cardiol* (2010) **56** 1930-1936. PMID: 21109116 11. Jensen AL, Bomholt M, Moe L. **Preliminary evaluation of a particle‐enhanced turbidimetric immunoassay (PETIA) for the determination of serum cystatin C‐like immunoreactivity in dogs**. *Vet Clin Pathol* (2001) **30** 86-90. PMID: 12024321 12. Wehner A, Hartmann K, Hirschberger J. **Utility of serum cystatin C as a clinical measure of renal function in dogs**. *J Am Anim Hosp Assoc* (2008) **44** 131-138. PMID: 18451071 13. Miyagawa Y, Akabane R, Ogawa M, Nagakawa M, Miyakawa H, Takemura N. **Serum cystatin C concentration can be used to evaluate glomerular filtration rate in small dogs**. *J Vet Med Sci* (2021) **82** 1828-1834. PMID: 33177264 14. Iwasa N, Takashima S, Iwasa T. **Serum cystatin C concentration measured routinely is a prognostic marker for renal disease in dogs**. *Res Vet Sci* (2018) **119** 122-126. PMID: 29913326 15. Abid L, Charfeddine S, Kammoun S, Turki M, Ayedi F. **Cystatin C: a prognostic marker after myocardial infarction in patients without chronic kidney disease**. *J Saudi Heart Assoc* (2016) **28** 144-151. PMID: 27358531 16. Carrasco‐Sánchez FJ, Galisteo‐Almeda L, Páez‐Rubio I. **Prognostic value of cystatin C on admission in heart failure with preserved ejection fraction**. *J Card Fail* (2011) **17** 31-38. PMID: 21187262 17. Arimoto T, Takeishi Y, Niizeki T. **Cystatin C, a novel measure of renal function, is an independent predictor of cardiac events in patients with heart failure**. *J Card Fail* (2005) **11** 595-601. PMID: 16230262 18. Shlipak MG, Katz R, Sarnak MJ. **Cystatin C and prognosis for cardiovascular and kidney outcomes in elderly persons without chronic kidney disease**. *Ann Intern Med* (2006) **145** 237-246. PMID: 16908914 19. Xie L, Terrand J, Xu B, Tsaprailis G, Boyer J, Chen QM. **Cystatin C increases in cardiac injury: a role in extracellular matrix protein modulation**. *Cardiovasc Res* (2010) **87** 628-635. PMID: 20489058 20. Negrusz‐Kawecka M, Poreba R, Hulok A. **Evaluation of the significance of cystatin C levels in patients suffering from coronary artery disease**. *Adv Clin Exp Med* (2014) **23** 551-558. PMID: 25166439 21. Munoz J, Soblechero P, Duque FJ. **Effects of oral prednisone administration on serum cystatin C in dogs**. *J Vet Intern Med* (2017) **31** 1765-1770. PMID: 28921665 22. Buchanan JW, Bucheler J. **Vertebral scale to measure canine heart size in radiographs**. *J Am Vet Med Assoc* (1995) **206** 194-199. PMID: 7751220 23. Jepsen‐grant K, Pollard RE, Johnson LR. **Vertebral heart scores in eight dog breeds**. *Vet Radiol Ultrasound* (2013) **54** 3-8. PMID: 22994206 24. Malcolm EL, Visser LC, Phillips KL, Johnson LR. **Diagnostic value of vertebral left atrial size as determined from thoracic radiographs for assessment of left atrial size in dogs with myxomatous mitral valve disease**. *J Am Vet Med Assoc* (2018) **253** 1038-1045. PMID: 30272515 25. Rishniw M, Erb HN. **Evaluation of four 2‐dimensional echocardiographic methods of assessing left atrial size in dogs**. *J Vet Intern Med* (2000) **14** 429-435. PMID: 10935894 26. Hansson K, Haggstrom J, Kvart C, Lord P. **Left atrial to aortic indices using two‐dimensional and M‐mode echocardiography in cavalier king charles spaniels with and without left atrial enlargement**. *Vet Radiol Ultrasound* (2002) **43** 568-575. PMID: 12502113 27. Cornell CC, Kittleson MD, Torre PD. **Allometric scaling of M‐mode C cardiac measurements in normal adults dogs**. *J Vet Intern Med* (2004) **18** 3113-3121 28. Hori Y, Iguchi M, Hirakawa A. **Evaluation of atrial natriuretic peptide and cardiac troponin I concentrations for assessment of disease severity in dogs with naturally occurring mitral valve disease**. *J Am Vet Med Assoc* (2020) **256** 340-348. PMID: 31961274 29. Kanda Y. **Investigation of the freely available easy‐to‐use software “EZR” for medical statistics**. *Bone Marrow Transplant* (2013) **48** 452-458. PMID: 23208313 30. Braun JP, Perxachs A, Perchereaus D. **Plasma cystatin C in the dog: reference values and variations with renal failure**. *Clin Pathol* (2002) **11** 44-49 31. Pelander L, Haggstrom J, Larsson A. **Comparison of the diagnostic value of symmetric dimethylarginine, cystatin C, and creatinine for detection of decreased glomerular filtration rate in dogs**. *J Vet Intern Med* (2019) **33** 630-639. PMID: 30791142 32. Pouchelon JL, Atkins CE, Bussadori C. **Cardiovascular‐renal axis disorders in the domestic dog and cat: a veterinary consensus statement**. *J Small Anim Pract* (2015) **56** 537-552. PMID: 26331869 33. Shlipak MG, Sarnak MJ, Katz R. **Cystatin C and the risk of death and cardiovascular events among elderly persons**. *N Engl J Med* (2005) **20** 2049-2060 34. Choi BS, Moon HS, Seo SH. **Evaluation of serum cystatin‐C and symmetric dimethylarginine concentrations in dogs with heart failure from chronic mitral valvular insufficiency**. *J Vet Med Sci* (2017) **79** 41-46. PMID: 27725349 35. Denic A, Glassock RK, Rule AD. **Structural and functional changes with aging kidney**. *Adv Chronic Kidney Dis* (2016) **23** 19-28. PMID: 26709059 36. Finney H, Newman DJ, Thakkar H, Fell JM, Price CP. **Reference ranges for plasma cystatin C and creatinine measurements in premature infants, neonates, and older children**. *Arch Dis Child* (2000) **82** 71-75. PMID: 10630919 37. Finney H, Newman DJ, Price CP. **Adult reference ranges for serum cystatin C, creatinine and predicted creatinine clearance**. *Ann Clin Biochem* (2000) **37** 49-59. PMID: 10672373 38. Tvarijonaviciute A, Ceron JJ, Holden SL, Biourge V, Morris PJ, German AJ. **Effect of weight loss in obese dogs on indicators of renal function or disease**. *J Vet Intern Med* (2013) **27** 31-38. PMID: 23278113 39. Ghy LF, Paepe D, Lefebvre HP. **The effect of feeding, storage and anticoagulant on feline serum cystatin C**. *Vet J* (2015) **206** 91-96. PMID: 26324637 40. Shah KF, Stevens PE, Lamb EJ. **The influence of a cooked‐fish meal on estimated glomerular rate**. *Ann Clin Biochem* (2020) **57** 182-185. PMID: 31856580 41. Preiss DJ, Godber IM, Lamb EJ, Dalton RN, Gunn IR. **The influence of a cooked‐meat meal on estimated glomerular filtration rate**. *Ann Clin Biochem* (2007) **44** 35-42. PMID: 17270090 42. Toffaletti JG, Hammett‐stabler C, Handel EA. **Effect of beef ingestion by humans on plasma concentrations of creatinine, urea, and cystatin C**. *Clin Biochem* (2018) **58** 26-31. PMID: 29842868 43. Fuhrman DY, Maier PS, Schwartz GJ. **Rapid assessment of renal reserve in young adults by cystatin C**. *Scand J Clin Lab Invest* (2013) **73** 265-268. PMID: 23461550
--- title: Maternal vaginal fluids play a major role in the colonization of the neonatal intestinal microbiota authors: - Jingxian Xie - Chen Tang - Shouqiang Hong - Yuntian Xin - Jie Zhang - Yi Lin - Lindong Mao - Yunshan Xiao - Quanfeng Wu - Xueqin Zhang - Heqing Shen journal: Frontiers in Cellular and Infection Microbiology year: 2023 pmcid: PMC10061231 doi: 10.3389/fcimb.2023.1065884 license: CC BY 4.0 --- # Maternal vaginal fluids play a major role in the colonization of the neonatal intestinal microbiota ## Abstract ### Background Caesarean section (CS) is associated with newborns’ health risks due to the blocking of microbiome transfer. The gut microbiota of CS-born babies was different from those born vaginally, which may be attributed to reduced exposure to maternal vaginal microbes during labour. To understand the microbial transfer and reduce CS disadvantages, the effect of vaginal microbiota exposure on infant gut microbiota composition was evaluated using 16s rDNA sequencing-based techniques. ### Results Pregnant women were recruited in the Women and Children’s Hospital, School of Medicine, Xiamen University from June 1st to August 15th, 2017. Maternal faeces ($$n = 26$$), maternal vaginal fluids ($$n = 26$$), and neonatal transitional stools ($$n = 26$$) were collected, while the participants underwent natural delivery (ND) ($$n = 6$$), CS ($$n = 4$$) and CS with the intervention of vaginal seedings (I) ($$n = 16$$). 26 mothers with the median age 26.50 (25.00-27.25) years showed no substantial clinical differences. The newborns’ gut microbiota altered among ND, CS and I, and clustered into two groups (PERMANOVA $$P \leq 0.001$$). Microbial composition of ND babies shared more features with maternal vaginal samples (PERMANOVA $$P \leq 0.065$$), while the microbiota structure of ND babies was obviously different from that of sample of maternal faeces. The genus Bacteroides in CS-born babies with intervention approached to vaginal-born neonates, compared with CS-born neonates without intervention. ### Conclusions Neonatal gut microbiota was dependent on the delivery mode. And the gut microbiota CS newborns with vaginal seeding shared more features with those of ND babies, which hinted the aberrant gut microbiota composition initiated by CS might be partly mitigated by maternal vaginal microbiota exposure. ## Background Caesarean section (CS) is a common obstetric surgical procedure entailing the incision of a woman’s abdomen/uterus to deliver their offspring(s) (Seidu et al., 2020) with the intent to increase the chances of successful childbirth and to protect the life security of both the mother and the newborn (Betran et al., 2016; Cegolon et al., 2020). Over the past two to three decades, global CS surgery rates have been growing steadily but rapidly for women of all ages, races, and medical conditions. According to the World Health Organization (WHO), an estimated $21.1\%$ of births occurred by CS in 2015, which was almost twice than that in 2000 ($12.1\%$), and the rates were even higher in certain developed countries and regions (Ekstrom et al., 2020; Betran et al., 2018; Boerma et al., 2018; Wells et al., 2019). Similar patterns could also be observed in China, in which $28.8\%$ and $34.9\%$ of babies were delivered by CS in 2008 and 2014, respectively (Li et al., 2017). However, prior publications have demonstrated that CS was associated with adverse short- or long-term effects on newborns, including the dysplasia of the immune system, infections, allergies, and inflammatory disorders (Mueller et al., 2015a; Mueller et al., 2017a; Rusconi et al., 2017). The conventional view concerning this correlation was that CS newborns were subjected to different hormonal, physical (mechanical forces), bacterial, and therapeutic conditions (Sandall et al., 2018). Among these conditions, differences in microbial colonization induced by delivery mode were thought to be one of the determining factors (Martin et al., 2016; Mueller et al., 2017a). The gut microbiota has been increasingly recognized as an important contributor to human health, especially for infants, whose maturity of immune system and overall physiology are influenced the gut microbiota (Korpela et al., 2018). Altered colonization of the gut microbiota in CS-born babies may partially account for the increased risk of adverse health conditions (Butler É et al., 2020). The sterility of the womb was widely accepted for many years (Perez-Muñoz et al., 2017). Although it is controversial about whether the presence of bacterial DNA contradicts the “sterile womb paradigm”, it does not demonstrate the presence of a living microbiota (Ferretti et al., 2018; He et al., 2020). The prevailing view held that human fetal environment is sterile and the neonate’s microbiome is acquired during and after birth (Perez-Muñoz et al., 2017; Walter and Hornef, 2021). Fetal development is an important period for human beings, and the extent to which modern practices, like CS, alter the microbial composition are still not completely understood (Dominguez-Bello et al., 2019). The exposure of newborns to the maternal vaginal microbiota might be interrupted by CS. Unlikely to vaginally delivered newborns, who are first exposed to a wide array of microbes during labour via direct contact with the birth canal, CS newborns are initially contacted with microbes from the delivery room and the mothers’ skin (Chu et al., 2017). In addition, prophylactic antibiotics administered before or during caesarean surgery may also result in the failure of newborns to acquire the normal microbial inoculum (Azad et al., 2016; Stearns et al., 2017). Thus, vaginal fluids exposure (also known as “vaginal seedings”) on CS-born babies shortly after birth is a potential way for reducing differences in the gut microbiota between CS-born and vaginal-born neonates (Butler É et al., 2020; The American College of Obstetricians and Gynecologists, 2017). A better understanding about such practice on infant gut microbiota composition are required to break this cycle. Moreover, exploring an effective intervention measure which is simple, convenient, and cost effective to compensate for the differences in gut microbiota composition of CS births could be a prospective application. Here, we used transitional stool as the biological sample in our present work, which was defined as the faeces excreted by newborns between 36 h and 72 h after birth (Mueller et al., 2017b). It represents the transitional state of meconium and may vary quickly via early-life microbial colonization in the surrounding environment. Only a few studies have aimed to explore the microbial community in transitional faeces. Via the swabbing of CS newborns with their mothers’ vaginal fluids, we aimed to explore the effect of vaginal microbiota exposure on infant gut microbiota composition in this study. By analyzing the pros and cons of the treatment through a population-based intervention study in Xiamen, PR China, we assess its effectiveness and performance in altering the microbes in newborns’ transitional stools. ## The recruitment of subjects This intervention study invited pregnant women who received antenatal care at the Women and Children’s Hospital, School of Medicine, Xiamen University, from June 1st, 2017 to August 15th, 2017. The inclusion criteria were briefly listed as follows: 1) gestational age between 37 and 42 weeks; 2) pregnant women who had regular prenatal visits and for whom all clinical data could be obtained; and 3) newborns who were full-term deliveries. Pregnant women with the following complications were excluded: 1) infectious diseases caused by bacteria, viruses, or parasites; 2) inflammatory diseases (e.g., ankylosing spondylitis); 3) metabolic diseases such as diabetes mellitus; 4) pregnancy-associated illnesses including preeclampsia and gestational hypertension; 5) reproductive system disorders (e.g., an ovarian cyst); 6) abnormal pregnancy state (e.g., preterm birth); 7) genetic diseases such as thalassemia; and 8) tumors, including pituitary adenomas and uterine fibroids. The specific analytical protocol of this study is shown in Figure S1. The basic information of the pregnant were collected through the last prenatal health checkup and lifestyle questionnaires. The protocol of our present research was approved by the Medical Ethics Committee of the Women and Children’s Hospital, School of Medicine, Xiamen University (KY-2018-020). All procedures were conducted in accordance with the Declaration of Helsinki. All patients were required to provide written informed consent prior to participation. No adverse events were reported for any of the newborns in this study. ## Treatment and sample collection Pregnant women scheduled to have a CS surgery were administrated with prophylactic antibiotics 15 to 60 minutes before skin incision and divided into the two groups according to their willingness to have their newborns swabbed with the vaginal fluids. Before delivery (CS or natural delivery), a 7 × 5 cm four-layered piece of gauze was double-folded and soaked in sterile saline and then inserted into the lower vagina for at least 30 mins before the administration of prophylactic antibiotics, and then removed and kept at room temperature in a sterile collector. The gauze was then divided vertically into two equal parts. One part of the gauze was temporally stored in a 50 mL microcentrifuge tube for subsequent analyses of microbiome, while the other part was used to treat CS newborns ($$n = 16$$) as soon as they were born. The principle of ‘center-to-periphery’ was strictly followed during the swabbing procedure, which began on the lips, followed by the face, the thorax, the arms, the legs, and finally the back; the complete process took approximately 30 s for each newborn. Six vaginal-born and four CS-born babies were swabbed with gauze containing sterile saline solution and were used as positive or negative references. All treatments were performed in the delivery room. Transitional stool was first collected by sterile diapers between 36 h and 72 h after birth, and then 1-2 g of the sample was transferred into a 50 mL microcentrifuge tube. Approximately 1-2 g of fresh maternal faeces was collected into a 50 mL microcentrifuge tube at the last excretion before delivery. All samples were placed into containers of ice and transported to the laboratory within 1 h after collection and stored at -80°C for long-term storage until further processing. ## Microbial DNA extraction and 16S rDNA amplification Microbial DNA in all samples was extracted utilizing the MoBio Powersoil DNA Isolation Kit (QIAGEN, German) according to the manufacturer’s instructions. A NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, USA) was used to determine the DNA concentration. The extracted DNA was stored at -20°C until further analysis. The hypervariable V3-V4 regions of the 16S rDNA gene were amplified with the specific primers 341F (5’-CCTACGGGNGGCWGCAG-3’) and 806R (5’-GGACTACHVGGGTATCTAAT-3’). PCRs were performed in triplicate in a 50 μL mixture containing 5 μL of KOD buffer (10 ×), 5 μL of dNTPs (2 mM), 3 μL of MgSO4 (25 mM), 1.5 μL of 341F/806R primer (10 μM), 1 μL of KOD Polymerase, and 100 ng of template DNA. The PCR reagents were purchased from TOYOBO, Japan, and the PCR protocol was carried out for 30 cycles using the following parameters: 94°C pre-denaturation for 2 min, 98°C denaturation for 10 s, 50°C annealing for 30 s, 68°C annealing for 30 s, and hold at 4°C. After amplification, all of the PCR products were pooled, purified, and quantified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, USA) and the ABI Step-One-Plus Real-Time PCR System (Life Technologies, USA) according to the standard protocols. ## 16S rDNA sequencing and data processing The microbiome profiles were analyzed by 16S rRNA gene amplicon sequencing with the Illumina MiSeq 250PE platform (Illumina, San Diego, CA, USA). USEARCH software (version 8.1.1861) was applied to turn paired-end sequencing reads into merged, denoised, chimera-free, inferred sample sequences, and the sequence processing steps are described below in detail. Raw sequencing reads were filtered using FASTP software (version 0.18.0) to remove adapters and low-quality reads that would affect the subsequent assembly and analysis to obtain clean high-quality paired-end reads. Paired-end reads were merged as raw tags using FLASH (version 1.2.11), with a minimum overlap of 10 bp and mismatch error rates of $20\%$ (Magoč and Salzberg, 2011), and further merged as raw amplicon sequence variants (ASVs) with a minimum overlap of 10 bp. Noisy sequences of raw tags were filtered under specific filtering conditions to obtain clean tags. The high-quality clean tags were clustered into OTUs of ≥ $97\%$ similarity using the UPARSE (version 9.2.64) pipeline. All chimeric tags were removed using the UCHIME algorithm, and effective tags were finally obtained for the next analysis step. The tag sequence with the highest abundance was selected as the representative sequence within each cluster. The representative OTU sequences were classified into organisms by a naive Bayesian model using the RDP classifier (version 2.2) (Wang et al., 2007) based on the SILVA database (version 132) (Pruesse et al., 2007). ## Statistical and bioinformatic analyses Because the number of subjects was less than 50, the Shapiro-Wilk test and Levene’s test were performed to assess the normality of distributions and the homogeneity of variance, respectively. One-way analysis of variance (ANOVA) was performed to compare normally distributed continuous variables, while the Kruskal-Wallis H test was conducted to compare unevenly distributed variables. Comparisons of Alpha and Beta diversity between any two groups were performed by utilizing Welch’s t test, whereas Kruskal-Wallis H test was used for the comparison among groups. Statistical analysis of the clinical data was achieved by Statistic Package for Social Science software (SPSS) (SPSS Inc., USA) (version 26.0). $P \leq 0.05$ was considered to be statistically significant. Alpha-diversity was assessed by the observed species (Sobs) index, inverse Simpson index, Shannon index, and Pielou evenness index, while beta-diversity was calculated by the Bray-Curtis distance and illustrated by NMDS analysis. Alpha- and beta-diversity, as well as the NMDS were generated in the R project Vegan package (version 2.5.3) (Dixon, 2003) and plotted in the R project ggplot2 package (version 2.2.1) (Wickham et al., 2016). Circular layout representations of species abundance were graphed using circos (version 0.69-3) (Krzywinski et al., 2009). Between groups, *Venn analysis* was performed in the R project VennDiagram package (version 1.6.16) (Chen and Boutros, 2011). A ternary plot of taxa abundance was plotted using the R ggtern package (version 3.1.0) (Hamilton and Ferry, 2018). ## Baseline clinical information of pregnant women A total of 111 mother-newborn dyads were initially recruited. As shown in Figure S2, we excluded 85 pairs due to the lack of integrated bio-samples ($$n = 69$$), health check-up data ($$n = 9$$), and self-reported diseases information ($$n = 7$$). Finally, 26 mother-newborn dyads were ultimately included according to our inclusion and exclusion criteria, and they provided 78 samples for the subsequent analyses. Missing values of age ($$n = 2$$), enrolment age ($$n = 1$$), haemoglobin ($$n = 1$$), alanine aminotransferase (ALT) ($$n = 1$$), aspartate aminotransferase (AST) ($$n = 1$$), serum total bilirubin (STB) ($$n = 1$$), and serum conjugated bilirubin (SCB) ($$n = 1$$) were interpolated using the multiple imputation method. The basic information of the pregnant women is shown in Table 1. The median maternal age was 26.50 (25.00-27.25) years, and the average body mass index (BMI) was 20.82 ± 2.45. No participants in the present research subjected active or passive smoking. Other demographic and clinical indicators among groups were roughly similar except for spouse age (SA) and AST (ANOVA, P SA=0.033; Kruskal-Wallis H test, P AST=0.022). Despite a significant difference in AST, other maternal biological factors were well homogenized among the different groups. In combination with the clinical indices, participants in this study were overall of a relatively good health status, and the maternal clinical statuses were comparable. **Table 1** | Variables | Overall | VD | CS | I | P value | | --- | --- | --- | --- | --- | --- | | Variables | N=26 | n=6 (23.08%) | n=4 (15.38%) | n=16 (61.54%) | P value | | Marriage Age (years) | 26.50 (25.00-27.25) | 26.00 (25.75-31.15) | 25.25 ± 1.11 | 26.16 ± 0.70 | 0.707 | | Enrollment Age (years) | 30.00 (27.00-32.00) | 26.67 ± 1.33 | 33.25 ± 1.97 | 30.00 (28.55-32.00) | 0.055 | | Spouse Age (years) | 32.08 ± 5.52 | 26.98 ± 2.51 | 34.00 (34.00-40.00) | 33.00 ± 1.11 | 0.033 | | Age of Menarche (years) | 14.52 ± 1.68 | 14.00 ± 0.68 | 15.75 ± 0.75 | 14.41 ± 0.42 | 0.255 | | SBP (mmHg) | 103.04 ± 9.91 | 105.17 ± 3.44 | 109.25 ± 5.31 | 100.69 ± 2.49 | 0.262 | | DBP (mmHg) | 64.40 ± 7.62 | 65.33 ± 3.03 | 68.00 ± 4.97 | 63.15 ± 1.82 | 0.512 | | Height (cm) | 158.82 ± 5.14 | 161.08 ± 2.27 | 160.50 ± 2.99 | 157.55 ± 1.18 | 0.288 | | Weight (Kg) | 53.82 ± 7.91 | 53.35 ± 1.46 | 53.50 ± 4.94 | 54.08 ± 2.24 | 0.979 | | BMI (kg/m2) | 20.82 ± 2.45 | 20.03 ± 0.31 | 20.18 ± 1.28 | 21.27 ± 0.70 | 0.507 | | Hemoglobin (g/L) | 122.60 ± 8.80 | 125.83 ± 3.04 | 114.00 (113.00-121.75) | 122.98 ± 2.37 | 0.208 | | Leukocyte (×109/L) | 7.08 (6.50-8.01) | 7.86 ± 0.75 | 5.95 (5.18-7.10) | 7.29 ± 0.27 | 0.155 | | Platelet (×109/L) | 230.52 ± 41.64 | 232.50 ± 11.68 | 214.50 ± 4.13 | 233.78 ± 12.55 | 0.644 | | Blood Glucose (mmol/L) | 4.82 ± 0.31 | 4.74 ± 1.60 | 4.79 ± 0.18 | 4.86 ± 0.07 | 0.720 | | ALT (U/L) | 13.30 (10.75-18.02) | 11.00 ± 1.02 | 39.52 ± 16.21 | 15.34 ± 1.33 | 0.120 | | AST (U/L) | 16.90 (14.00-19.00) | 14.18 ± 0.71 | 22.00 ± 2.86 | 17.34 ± 1.01 | 0.022 | | Albumin (g/L) | 42.63 ± 5.78 | 44.03 ± 1.74 | 40.60 ± 1.64 | 41.55 (38.83-44.05) | 0.350 | | STB (μmol/L) | 10.82 ± 2.73 | 10.44 ± 0.97 | 7.10 (6.63-10.50) | 11.64 ± 0.64 | 0.091 | | SCB (μmol/L) | 3.59 ± 1.54 | 3.52 ± 0.52 | 2.91 ± 0.95 | 3.78 ± 0.40 | 0.613 | | SCr (μmol/L) | 56.50 ± 11.18 | 58.05 ± 5.00 | 50.48 ± 3.80 | 57.43 ± 2.91 | 0.518 | | BUN (mmol/L) | 3.07 ± 0.74 | 3.11 ± 0.24 | 2.87 ± 0.45 | 3.10 ± 0.20 | 0.856 | | Smoking (n, %) | 0 (0) | 0 (0) | 0 (0) | 0 (0) | – | | Use of Antibiotic before Delivery (n, %) | 0 (0) | 0 (0) | 0 (0) | 0 (0) | – | | Use of Prebiotics/Probiotics (n, %) | 0 (0) | 0 (0) | 0 (0) | 0 (0) | – | ## Microbiome overview of the maternal and neonatal samples The diversity of the microbiota in a given habitat reflects the composition and relative abundance of the community. Approximately 460 bp of PCR products were generated by amplifying the V3-V4 region of the 16S rRNA gene to compare the bacterial diversity among samples. DNA sequencing after quality filtering yielded 8.29 million paired-end reads, which further merged into 7.21 million tags, with a minimum of 64253 tags per sample (average of 92448 ± 8822), and ultimately formed 27801 operational taxonomic units (OTUs). Rarefaction curves evaluated the OTU richness and represented whether a reasonable sampling size (sequencing depth) was used. As shown in Figure S3, the almost horizontal asymptotic curves indicated that the sequencing depth was sufficient for our research. The values of observed species (Sobs), Pielou evenness, Shannon, and inverse Simpson indices were calculated to thoroughly assess alpha-diversity. The Sobs index indicated the actual detected OTUs, while the Pielou evenness index referred to the species equitability within each group. The Simpson and Shannon indices measured the degree of species asynchrony and stability, and higher values represented higher richness, evenness, or both (Luo et al., 2017). As shown in Figure 1A, the highest Sobs value could be observed in the transitional stool samples of naturally delivered newborns (TSN), and the OTUs differed substantially between the transitional stool samples of caesarean-section newborns (TSC) and the TSN, which demonstrated that CS surgery reduced the actual number of species of newborn gut microbes compared with vaginal delivery. In contrast, the TSC group presented the lowest evenness according to the value of the Pielou evenness index (Figure 1B) and there was a significant difference between the TSC and TSN groups (Welch’s t test, P Pielou =0.001). The above analyses revealed that CS surgery led to an obvious alteration in bacterial richness and evenness. Good agreement was exhibited in the Simpson index and Shannon index, which suggested that the gut microbial composition of CS newborns was less diverse (Welch’s t test, P Simpson =0.017, P Shannon <0.001), while a lesser degree of dominant bacteria and distribution homogeneity were observed in CS newborns with swabbing vaginal fluid intervention (I) (Figures 1C, D). The aggregate analyses of alpha-diversity indicated that the neonatal gut microbiota was affected by both the mode of delivery and the swabbing treatment. Beyond that, pregnant women who underwent different labour modes showed no significant differences in intestinal (Welch’s t test, P Sobs =0.169, P Pielou =0.207, P Simpson =0.081, P Shannon =0.186) and vaginal fluid microbiota (Welch’s t test, P Sobs =0.152, P Pielou =0.188, P Simpson =0.246, P Shannon =0.178). **Figure 1:** *Microbial alpha diversity indices. The ecological diversity of microbiota in transitional stools of newborns (36-72h after birth), virginal fluids (before delivery) and stool of mothers (the last excretion before delivery) was measured by Sobs (A), Pielou evenness index (B), the Simpson index (C), and the Shannon index (D). The P values were conducted by Welch’s t test. Statistical significance is displayed as *P<0.05 and **P<0.01. Sobs, Observed species; TSC, Transitional stools of caesarean delivered neonates; I, Transitional stools of caesarean delivered neonates with the treatment of swabbing maternal vaginal fluid; TSN, Transitional stools of natural delivered neonates; V, Vaginal fluids of the pregnant women who underwent caesarean delivery; VN, Vaginal fluids of the pregnant women who underwent natural delivery; F, Feces of the pregnant women who underwent caesarean delivery; FN, Feces of the women who underwent natural delivery. Symbol "*" was presented above each plot.* We further investigated beta-diversity according to the Bray-Curtis distance to compare the microbial community structures among groups, as this method provided a model to describe the overall pattern of community composition based on OTUs. Interestingly, some results seem to be inconsistent with those obtained from alpha-diversity analyses (Figure 2A). Briefly, significant differences could be observed between the neonates of CS and I group (Adonis test, $$P \leq 0.001$$), as well as the CS and vaginally delivered newborns (Adonis test, $$P \leq 0.011$$). The gut microbiota of CS with vaginal seedings and vaginally delivered newborns seemed to show no substantial difference in terms of beta-diversity. The above-mentioned results hinted the microbial composition of neonates was highly dependent on the mode of delivery. Besides, the microbiome of vaginal fluids and stools samples were different between pregnant women who underwent different labour modes (Adonis test, P V-VN =0.034, P F-FN =0.047). Further, the visualization of principal coordinate analysis (PcoA) and non-metric multidimensional scaling (NMDS) analysis based on the Bray-Curtis distance all displayed clear ordinations that indicate the gut microbes of the CS newborns with swabbing treatment were more similar with those of the vaginally delivered newborns, rather than CS babies (Figures 2B, C). The above-mentioned results concluded that the infant gut microbiota shared more features with maternal vaginal fluids, and the microbial composition of transitional stool samples in CS-born babies with vaginal seedings tended to be more similar to that of TSN samples. The detailed information of statistical analyses was shown in Table 2. **Figure 2:** *Visualization of beta-diversity index (A), PcoA (B), and NMDS (C). These plots were conducted basing on Bray-Curtis Distance. Each dot represents one sample. In (A), mean beta-diversity (distance from centroid) ± standard error.Statistical significance is displayed as *$P \leq 0.05$ and **$P \leq 0.01.$ TSC, Transitional stools of caesarean delivered neonates; I, Transitional stools of caesarean delivered neonates with the treatment of swabbing maternal vaginal fluid intervention; TSN, Transitional stools of natural delivered neonates; VN, Vaginal fluids of the pregnant women who underwent natural delivery; V, Vaginal fluids of the pregnant women who underwent caesarean delivery; FN, Feces of the women who underwent natural delivery; F, Feces of the pregnant women who underwent caesarean delivery.* TABLE_PLACEHOLDER:Table 2 ## The variation in microbiota caused by delivery modes and treatment The analysis at the genus level indicated the microbial community of CS newborns consisted primarily of Bacteroides ($12.10\%$), Lactobacillus ($6.38\%$), and Escherichia-Shigella ($6.03\%$) after the vaginal seedings intervention (Table 3; Figures 3, 4), which was more closed to vaginally delivered babies. Among them, the genus of Bacteroides tended to be similar to those of vaginally delivered newborns ($14.95\%$), which was significantly higher than CS-born neonates ($0.15\%$). Statistical analysis further indicated that no obvious restoration pattern existed at the genus level, although the genus of Faecalibacterium, Enterobacter, and Akkermansia presented such trends (Table 3). **Figure 3:** *Taxa distribution plots at phylum and genus level. The relative abundances of microbial communities at genus level. TSC, Transitional stools of caesarean delivered neonates; I, Transitional stools of caesarean delivered neonates with the treatment of swabbing maternal vaginal fluid intervention; TSN, Transitional stools of natural delivered neonates; VN, Vaginal fluids of the pregnant women who underwent natural delivery; V, Vaginal fluids of the pregnant women who underwent caesarean delivery; FN, Feces of the women who underwent natural delivery; F, Feces of the pregnant women who underwent caesarean delivery.* **Figure 4:** *The Effect Analyses of Swabbing Exposure. The characteristic taxa were measured by indicator analysis (A) and ternary plot (B). The P values were conducted by Tukey HSD test. Statistical significance is displayed as *$P \leq 0.05$ and **$P \leq 0.01.$ TSC, Transitional stools of caesarean delivered neonates; I, Transitional stools of caesarean delivered neonates with the treatment of swabbing maternal vaginal fluid intervention; TSN, Transitional stools of natural delivered neonates; VN, Vaginal fluids of the pregnant women who underwent natural delivery; V, Vaginal fluids of the pregnant women who underwent caesarean delivery; FN, Feces of the women who underwent natural delivery; F, Feces of the pregnant women who underwent caesarean delivery.* TABLE_PLACEHOLDER:Table 3 The VENN plots confirmed the above results from another aspect, which more shared taxa were detected between vaginally delivered and CS newborns in the intervention group, than those with CS newborns (Figure 5). **Figure 5:** *VENN diagrams at genus level. VENN diagram was used to represented the common and unique genera among different groups. TSC, Transitional stools of caesarean delivered neonates; I, Transitional stools of caesarean delivered neonates with the treatment of swabbing maternal vaginal fluid; V, Vaginal fluids of the pregnant women who underwent caesarean delivery.* ## Discussion In this population-based intervention study, the notion that neonatal microbial composition was dependent on the mode of delivery was confirmed by our results, which were in good agreement with previous researches (Fouhy et al., 2019). In addition, we observed the presence of restoration pattern of microbial composition at the genus level. It is a potential method of neonatal exposure to maternal vaginal microbiota shortly after CS birth to promote the development of the gut microbiome, which has gained traction in recent years (Obstetricians and Gynecologists, 2017). However, very few studies have focused on the microbiota in transitional stool to date. A pilot study conducted by Mueller et al. showed that significantly lower relative abundances of the genera Bacteroidetes, Parabacteroides, and *Clostridium were* observed in CS-born babies (Mueller et al., 2017b). In our present work, Bacteroides exhibited restoration pattern after vaginal seedings, while no other observed differences between the both CS groups. According to previous studies, the gut microbiome in CS-born infants might not be altered via vaginal seedings. Wayne S. Cutfield et al. assessed the effect of oral administration of maternal vaginal microbes to restore gut microbial composition among CS-born infants. However, there were no observed differences in gut microbiome composition between CS-born infants with or without the intervention at 1 month or 3 months after birth. And CS-born infants displayed the characteristic signature of low Bacteroides abundance compared with vaginal-born ones (Wilson et al., 2021). Our results may not align completely with others, which might be attributed to the dissimilarity in the collection, storage processing or analytic platforms used in the different studies (Perez-Muñoz et al., 2017). Maternal fecal microbiota transplantation (FMT) appears to be a more promising strategy. Willem M. de Vos et al. designed a proof-of-concept study that verified the gut microbial development could be restored rapidly via FMT (Korpela et al., 2020). Meanwhile, this study also demonstrated the neonatal gut microbiota is highly dependent on the delivery mode, which were in general agreement with our conclusions. Interestingly, the microbiome appeared to show significant discrepancies in the vaginal fluid samples of pregnant women who were subjected to different labour modes. This conclusion was consistent with Marta Selma-Royo et al. ( Selma-Royo et al., 2020) and Romero R et al. ( Romero et al., 2014), which described the variations occurred during gestation, mainly in the intestinal and vaginal microbiomes. These studies also indicated that delivery mode significantly affected the maternal microbiota composition at delivery. One of the probable reasons was that medical decisions for different delivery modes depended on maternal health states and might be directly reflected in the vaginal microbiota. Moreover, previous investigations have proposed that the use of antibiotics during the gestation or the periparturient period would also cause those differences (Mueller et al., 2015b). Early studies observed that the abundance of the phylum Bacteroidetes participated in modulating the weight development of newborns (Ley et al., 2006; Turnbaugh et al., 2006). Additionally, it has been well documented that an increase in the abundance of the phylum Proteobacteria was associated with onset risks of diabetes and obesity (Su et al., 2018). In our present work, the increased richness of the phyla Bacteroidetes and the decreased richness of Proteobacteria might exert helpful impacts on the proper development of babies. At the genus level, the reduced abundance of pernicious bacteria Escherichia-Shigella was considered to be beneficial to CS neonates. Taken together, these findings indicate that the finer targeting of the vaginal fluids before exposure would determine the extent to which this intervention method could be popularized. The microbiome of transitional faeces reflects the colonization of microorganisms in utero as well as under the influence of external environmental factors shortly after birth (Mueller et al., 2017b). In this research, the gut bacterial composition of vaginally born babies was similar to that of the maternal vaginal fluid, but similar results could not be observed in CS newborns without treatment, while vertical transmission might be achieved by swabbing treatment among CS newborns. This phenomenon has aroused our concern. It is widely accepted that the effects of the natural delivery process are comprehensive, including physical, chemical, and biological effects, which generally last for one to two hours, covering the whole period of the second stage of labour (Sandall et al., 2018). During childbirth, newborns may inhale (swallow) the vaginal contents in their mother’s birth canal (Butler É et al., 2020). The stomachs of newborns are pH neutral for several hours post birth as a result of swallowing the amniotic fluid in utero, thereby enabling the survival of ingested bacteria (Avery et al., 1966). However, the exposure application in our work deliberately avoided contact with the CS newborns’ oral cavity, which might eliminate microbial colonization via the oral transmission route. Hence, the restoration by swabbing vaginal fluids may be inefficient due to incomplete biological effects and the absence of chemical and physical effects. On the other hand, antibiotic therapy is a common preventive measure to avoid intra- or post-surgical infections. Nevertheless, residual concentrations of antibiotics in maternal blood might act on the newborn through circulation and last for a period of time post birth, which also means that the microorganisms in the vaginal secretions may not fully colonize the newborn because of the influence of the residual antibiotics. ## Strengths The main strength is that our present study is the first research to our best knowledge focusing on the microbial transfer and the intervention programs for CS-born neonates using the samples of transitional stools among Chinese population. The other strength is that the refined processes of sampling and experimental manipulations made the results more reliable and accurate. ## Limitations There were several main limitations in our research. First, due to the difficulty in sampling, the number of subjects in CS group was relatively small, potentially leading to misinterpretation. Second, this study was based on cross-sectional data, the dynamic changes of microbiome could not be observed, which limited the generalizability of this research. ## Conclusion Through the intervention project conducted in the Chinese population, we first verified the microbial alterations induced by different delivery modes, including noticeable changes in alpha- and beta-diversity, the structure of gastrointestinal bacterial communities. Second, the issue of the source of gut microbiota for newborns born via different delivery modes was partly resolved according to NMDS analysis. The structure of neonatal gut microbes shared more features with maternal vaginal samples among vaginally delivered babies. But such phenomenon could not be found among CS newborns without vaginal seedings. Last through ternary plots and Venn diagrams, we concluded that swabbing exposure could partly restored the dysbiosis of gut microbiota caused by CS. ## Data availability statement The data presented in the study are deposited in the NCBI repository (https://www.ncbi.nlm.nih.gov/sra), accession number PRJNA890171. ## Ethics statement The studies involving human participants were reviewed and approved by Medical Ethics Committee of the Women and Children’s Hospital, School of Medicine, Xiamen University. The patients/participants provided their written informed consent to participate in this study. ## Author contributions CT and XZ contributed to design the study, research data and wrote the manuscript. JX and SH contributed to data interpretation and the discussion of the results. YTX contributed to methodology. JZ and YL contributed to design the study and critically reviewed the manuscript. YSX, QW, and LM contributed to subject recruitment, sample collection, and perform clinical examination. HS conceptualized and designed the protocol and critically reviewed the manuscript. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcimb.2023.1065884/full#supplementary-material ## References 1. Avery G. B., Randolph J. G., Weaver T.. **Gastric acidity in the first day of life**. *Pediatrics* (1966) **37** 1005-1007. DOI: 10.1542/peds.37.6.1005 2. Azad M. B., Konya T., Persaud R. R., Guttman D. S., Chari R. S., Field C. J.. **Impact of maternal intrapartum antibiotics, method of birth and breastfeeding on gut microbiota during the first year of life: A prospective cohort study**. *Bjog* (2016) **123** 983-993. DOI: 10.1111/1471-0528.13601 3. Betran A. P., Torloni M. R., Zhang J. J., G Lmezoglu A. M.. **WHO statement on caesarean section rates**. *Bjog* (2016) **123** 667-670. DOI: 10.1111/1471-0528.13526 4. Betran A. P., Temmerman M., Kingdon C., Mohiddin A., Opiyo N., Torloni M. R.. **Interventions to reduce unnecessary caesarean sections in healthy women and babies**. *Lancet* (2018) **392** 1358-1368. DOI: 10.1016/S0140-6736(18)31927-5 5. Boerma T., Ronsmans C., Melesse D. Y., Barros A. J. D., Barros F. C., Juan L.. **Global epidemiology of use of and disparities in caesarean sections**. *Lancet* (2018) **392** 1341-1348. DOI: 10.1016/S0140-6736(18)31928-7 6. Butler É M., Chiavaroli V., Derraik J. G. B., Grigg C. P., Wilson B. C., Walker N.. **Maternal bacteria to correct abnormal gut microbiota in babies born by c-section**. *Med. (Baltimore)* (2020) **99**. DOI: 10.1097/MD.0000000000021315 7. Cegolon L., Mastrangelo G., Maso G., Pozzo G. D., Heymann W. C., Ronfani L.. **Determinants of length of stay after cesarean sections in the friuli venezia giulia region (North-Eastern Italy), 2005-2015**. *Sci. Rep.* (2020) **10** 19238. DOI: 10.1038/s41598-020-74161-2 8. Chen H., Boutros P. C.. **VennDiagram: A package for the generation of highly-customizable Venn and Euler diagrams in r**. *BMC Bioinf.* (2011) **12** 35. DOI: 10.1186/1471-2105-12-35 9. Chu D. M., Ma J., Prince A. L., Antony K. M., Seferovic M. D., Aagaard K. M.. **Maturation of the infant microbiome community structure and function across multiple body sites and in relation to mode of delivery**. *Nat. Med.* (2017) **23** 314-326. DOI: 10.1038/nm.4272 10. Dixon P.. **VEGAN, a package of r functions for community ecology**. *J. Vegetation Sci.* (2003) **14** 927-930. DOI: 10.1111/j.1654-1103.2003.tb02228.x 11. Dominguez-Bello M. G., Godoy-Vitorino F., Knight R., Blaser M. J.. **Role of the microbiome in human development**. *Gut* (2019) **68** 1108-1114. DOI: 10.1136/gutjnl-2018-317503 12. Ekstrom L. D., Ahlqvist V. H., Persson M., Magnusson C., Berglind D.. **The association between birth by cesarean section and adolescent cardiorespiratory fitness in a cohort of 339,451 Swedish males**. *Sci. Rep.* (2020) **10** 18661. DOI: 10.1038/s41598-020-75775-2 13. Ferretti P., Pasolli E., Tett A., Asnicar F., Gorfer V., Fedi S.. **Mother-to-Infant microbial transmission from different body sites shapes the developing infant gut microbiome**. *Cell Host Microbe* (2018) **24** 133-145.e5. DOI: 10.1016/j.chom.2018.06.005 14. Fouhy F., Watkins C., Hill C. J., O'shea C. A., Nagle B., Dempsey E. M.. **Perinatal factors affect the gut microbiota up to four years after birth**. *Nat. Commun.* (2019) **10** 1517. DOI: 10.1038/s41467-019-09252-4 15. Hamilton N. E., Ferry M.. **Ggtern: Ternary diagrams using ggplot2**. *J. Stat. Software* (2018) **87** 1-17. DOI: 10.18637/jss.v087.c03 16. He Q., Kwok L. Y., Xi X., Zhong Z., Ma T., Xu H.. **The meconium microbiota shares more features with the amniotic fluid microbiota than the maternal fecal and vaginal microbiota**. *Gut Microbes* (2020) **12** 1794266. DOI: 10.1080/19490976.2020.1794266 17. Korpela K., Helve O., Kolho K. L., Saisto T., Skogberg K., Dikareva E.. **Maternal fecal microbiota transplantation in cesarean-born infants rapidly restores normal gut microbial development: A proof-of-Concept study**. *Cell* (2020) **183** 324-334.e5. DOI: 10.1016/j.cell.2020.08.047 18. Korpela K., Salonen A., Veps L Inen O., Suomalainen M., Kolmeder C., Varjosalo M.. **Probiotic supplementation restores normal microbiota composition and function in antibiotic-treated and in caesarean-born infants**. *Microbiome* (2018) **6** 182. DOI: 10.1186/s40168-018-0567-4 19. Krzywinski M., Schein J., Birol I., Connors J., Gascoyne R., Horsman D.. **Circos: An information aesthetic for comparative genomics**. *Genome Res.* (2009) **19** 1639-1645. DOI: 10.1101/gr.092759.109 20. Ley R. E., Turnbaugh P. J., Klein S., Gordon J. I.. **Microbial ecology: Human gut microbes associated with obesity**. *Nature* (2006) **444** 1022-1023. DOI: 10.1038/4441022a 21. Li H. T., Luo S., Trasande L., Hellerstein S., Kang C., Li J. X.. **Geographic variations and temporal trends in cesarean delivery rates in china 2008-2014**. *JAMA* (2017) **317** 69-76. DOI: 10.1001/jama.2016.18663 22. Luo M., Liu Y., Wu P., Luo D. X., Sun Q., Zheng H.. **Alternation of gut microbiota in patients with pulmonary tuberculosis**. *Front. Physiol.* (2017) **8**. DOI: 10.3389/fphys.2017.00822 23. Magoč T., Salzberg S. L.. **FLASH: fast length adjustment of short reads to improve genome assemblies**. *Bioinformatics* (2011) **27** 2957-2963. DOI: 10.1093/bioinformatics/btr507 24. Martin R., Makino H., Cetinyurek Yavuz A., Ben-Amor K., Roelofs M., Ishikawa E.. **Early-life events, including mode of delivery and type of feeding, siblings and gender, shape the developing gut microbiota**. *PLoS One* (2016) **11**. DOI: 10.1371/journal.pone.0158498 25. Mueller N. T., Bakacs E., Combellick J., Grigoryan Z., Dominguez-Bello M. G.. **The infant microbiome development: Mom matters**. *Trends Mol. Med.* (2015) **21** 109-117. DOI: 10.1016/j.molmed.2014.12.002 26. Mueller N. T., Mao G., Bennet W. L., Hourigan S. K., Dominguez-Bello M. G., Appel L. J.. **Does vaginal delivery mitigate or strengthen the intergenerational association of overweight and obesity? findings from the Boston birth cohort**. *Int. J. Obes. (Lond)* (2017) **41** 497-501. DOI: 10.1038/ijo.2016.219 27. Mueller N. T., Shin H., Pizoni A., Werlang I. C., Matte U., Goldani M. Z.. **Delivery mode and the transition of pioneering gut-microbiota structure, composition and predicted metabolic function**. *Genes (Basel)* (2017) **8**. DOI: 10.3390/genes8120364 28. Mueller N. T., Whyatt R., Hoepner L., Oberfield S., Dominguez-Bello M. G., Widen E. M.. **Prenatal exposure to antibiotics, cesarean section and risk of childhood obesity**. *Int. J. Obes. (Lond)* (2015) **39** 665-670. DOI: 10.1038/ijo.2014.180 29. Perez-Muñoz M. E., Arrieta M. C., Ramer-Tait A. E., Walter J.. **A critical assessment of the "sterile womb" and "**. *Microbiome* (2017) **5** 48. DOI: 10.1186/s40168-017-0268-4 30. Pruesse E., Quast C., Knittel K., Fuchs B. M., Ludwig W., Peplies J.. **SILVA: A comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB**. *Nucleic Acids Res.* (2007) **35** 7188-7196. DOI: 10.1093/nar/gkm864 31. Romero R., Hassan S. S., Gajer P., Tarca A. L., Fadrosh D. W., Nikita L.. **The composition and stability of the vaginal microbiota of normal pregnant women is different from that of non-pregnant women**. *Microbiome* (2014) **2** 4. PMID: 24484853 32. Rusconi F., Zugna D., Annesi-Maesano I., Ba Z N., Barros H., Correia S.. **Mode of delivery and asthma at school age in 9 European birth cohorts**. *Am. J. Epidemiol.* (2017) **185** 465-473. DOI: 10.1093/aje/kwx021 33. Sandall J., Tribe R. M., Avery L., Mola G., Visser G. H., Homer C. S.. **Short-term and long-term effects of caesarean section on the health of women and children**. *Lancet* (2018) **392** 1349-1357. DOI: 10.1016/S0140-6736(18)31930-5 34. Seidu A. A., Hagan J. E., Agbemavi W., Ahinkorah B. O., Nartey E. B., Budu E.. **Not just numbers: beyond counting caesarean deliveries to understanding their determinants in Ghana using a population based cross-sectional study**. *BMC Pregnancy Childbirth* (2020) **20** 114. DOI: 10.1186/s12884-020-2792-7 35. Selma-Royo M., Garc A-Mantrana I., Calatayud M., Parra-Llorca A., Mart Nez-Costa C., Collado M. C.. **Maternal microbiota, cortisol concentration, and post-partum weight recovery are dependent on mode of delivery**. *Nutrients* (2020) **12**. DOI: 10.3390/nu12061779 36. Stearns J. C., Simioni J., Gunn E., Mcdonald H., Holloway A. C., Thabane L.. **Intrapartum antibiotics for GBS prophylaxis alter colonization patterns in the early infant gut microbiome of low risk infants**. *Sci. Rep.* (2017) **7** 16527. DOI: 10.1038/s41598-017-16606-9 37. Su M., Nie Y., Shao R., Duan S., Jiang Y., Wang M.. **Diversified gut microbiota in newborns of mothers with gestational diabetes mellitus**. *PLoS One* (2018) **13**. DOI: 10.1371/journal.pone.0205695 38. **Committee opinion no. 725: Vaginal seeding**. *Obstet Gynecol* (2017) **130** e274-e278. DOI: 10.1097/AOG.0000000000002402 39. Turnbaugh P. J., Ley R. E., Mahowald M. A., Magrini V., Mardis E. R., Gordon J. I.. **An obesity-associated gut microbiome with increased capacity for energy harvest**. *Nature* (2006) **444** 1027-1031. DOI: 10.1038/nature05414 40. Walter J., Hornef M. W.. **A philosophical perspective on the prenatal**. *Microbiome* (2021) **9** 5. DOI: 10.1186/s40168-020-00979-7 41. Wang Q., Garrity G. M., Tiedje J. M., Cole J. R.. **Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy**. *Appl. Environ. Microbiol.* (2007) **73** 5261-5267. DOI: 10.1128/AEM.00062-07 42. Wells J. C., Wibaek R., Poullas M.. **Global epidemiology of use of and disparities in caesarean sections**. *Lancet* (2019) **394** 24-25. DOI: 10.1016/S0140-6736(19)30715-9 43. Wickham H., Chang W., Wickham M. H.. *Package ‘ggplot2’. create elegant data visualisations using the grammar of graphics. version, 2* (2016) 1-189 44. Wilson B. C., Butler É M., Grigg C. P., Derraik J. G. B., Chiavaroli V., Walker N.. **Oral administration of maternal vaginal microbes at birth to restore gut microbiome development in infants born by caesarean section: A pilot randomised placebo-controlled trial**. *EBioMedicine* (2021) **69** 103443. DOI: 10.1016/j.ebiom.2021.103443
--- title: Age and Genetic Risk Score and Rates of Blood Lipid Changes in China authors: - Jianxin Li - Mengyao Liu - Fangchao Liu - Shufeng Chen - Keyong Huang - Jie Cao - Chong Shen - Xiaoqing Liu - Ling Yu - Yingxin Zhao - Huan Zhang - Shujun Gu - Liancheng Zhao - Ying Li - Dongsheng Hu - Jianfeng Huang - Dongfeng Gu - Xiangfeng Lu journal: JAMA Network Open year: 2023 pmcid: PMC10061238 doi: 10.1001/jamanetworkopen.2023.5565 license: CC BY 4.0 --- # Age and Genetic Risk Score and Rates of Blood Lipid Changes in China ## Abstract This cohort study evaluates the association of age and genetic risk score with annual rates of blood lipid changes among adults in China. ## Key Points ### Question Are age and genetic risk associated with rates of blood lipid changes among adults in China? ### Findings In this cohort study of 37 317 participants, the estimated annual changes of blood lipids were associated with age and polygenic risk. Moreover, the associations of the estimated annual lipid changes with age differed significantly between male and female participants. ### Meaning These findings suggest that strategies for precision management of lipid levels should focus on individuals at high genetic risk and in the critical age window. ### Importance Blood lipids are the primary cause of atherosclerosis. However, little is known about relationships between rates of blood lipid changes and age and genetic risk. ### Objective To evaluate associations of blood lipid change rates with age and polygenic risk. ### Design, Setting, and Participants This cohort is from the Prediction for Atherosclerotic Cardiovascular Disease Risk in China, which was established from 1998 to 2008. Participants were followed up until 2020 (mean [SD] follow-up, 13.8 [4.3] years) and received 4 repeated lipid measurements. Data analysis was performed from June to August 2022. A total of 47 691 participants with available genotype data were recruited, and 37 317 participants aged 18 years or older were included in the final analysis after excluding participants who were lost to follow-up or with major chronic diseases, and those without blood lipid measurements at baseline and any follow-up survey. ### Exposures Age and polygenic risk scores based on 126 lipid-related genetic variants. ### Main Outcomes and Measures The estimated annual changes (EAC) of blood lipids in milligrams per deciliter. ### Results This study evaluated 37 317 participants (mean [SD] age of 51.37 [10.82] years; 15 664 [$41.98\%$] were male). The associations of EACs of blood lipids with age differed substantially between male and female participants. Male participants experienced declining change as they got older for total cholesterol (EAC, 0.34 [$95\%$ CI, 0.14 to 0.54] mg/dL for age <40 years vs 0.01 [$95\%$ CI, −0.11 to 0.13] mg/dL for age ≥60 years), triglyceride (EAC, 3.28 [$95\%$ CI, 2.50 to 4.07] mg/dL for age <40 years vs −1.70 [$95\%$ CI, −2.02 to −1.38] mg/dL for age ≥60 years), and low-density lipoprotein cholesterol (LDL-C) (EAC, 0.15 [$95\%$ CI, −0.02 to 0.32] mg/dL for age <40 years vs 0.01 [$95\%$ CI, −0.10 to 0.11] mg/dL for age ≥60 years). Female participants had inverse V-shaped associations and the greatest rate of change appeared in the age group of 40 to 49 years (EAC for total cholesterol, 1.33 [$95\%$ CI, 1.22 to 1.44] mg/dL; EAC for triglyceride, 2.28 [$95\%$ CI, 1.94 to 2.62] mg/dL; and EAC for LDL-C, 0.94 [$95\%$ CI, 0.84 to 1.03] mg/dL). Change in levels of blood lipids were also associated with polygenic risk. Participants at low polygenic risk tended to shift toward lower blood lipid levels, with EACs of −0.16 ($95\%$ CI, −0.25 to −0.07) mg/dL; −1.58 ($95\%$ CI, −1.78 to −1.37) mg/dL; and −0.13 ($95\%$ CI, −0.21 to −0.06) mg/dL for total cholesterol, triglyceride, and LDL-C, respectively. Participants with high polygenic risk had the greatest rates of change for total cholesterol, triglyceride, and LDL-C (EAC, 1.12 [$95\%$ CI, 1.03 to 1.21] mg/dL; EAC, 3.57 [$95\%$ CI, 3.24 to 3.91] mg/dL; and EAC, 0.73 [$95\%$ CI, 0.65 to 0.81] mg/dL, respectively). Similar patterns were also observed across sex and age groups. ### Conclusions and Relevance In this cohort study, EACs of blood lipids were significantly associated with age and polygenic risk, suggesting that prevention strategies for lipids should focus on individuals with high genetic risk and in the critical age window. ## Introduction Blood lipids are the primary cause of atherosclerosis, which contributes most to atherosclerotic cardiovascular disease, the leading cause of death globally.1,2 High low-density lipoprotein cholesterol (LDL-C), the dominant form of atherogenic cholesterol, was responsible for 4.32 million deaths worldwide in 2017.3 Triglyceride (TG) also plays an important role in cardiovascular risk through the atherogenic indicator as TG-rich lipoprotein.1,4,5 Hence guidelines have highlighted that blood lipid control is the foundation of primary prevention of cardiovascular disease.1,5,6,7 Although declining trends in blood lipid levels have occurred in Western countries over the past 2 decades,8 the deteriorating trends persist in low- and middle-income countries due to unfavorable changes of lifestyle and environmental factors, especially in China.9,10 Therefore, it is critical to implement effective strategies for blood lipid management among the Chinese population. Strategies for blood lipid control are typically focused on individuals who currently have high levels.1,7 However, it is hard to reverse the subsequent deleterious cardiovascular effects once the dyslipidemia emerges. Thus, earlier prevention is essential.11 The investigation of the dynamic trends in the blood lipid change profile over age in a population may help us determine the potentially critical period for prevention even when lipid levels are normal. Previous studies have evaluated the relationships between age and lipid levels,12,13,14 but the associations between blood lipid change rates and age are unknown. Therefore, it is urgent to examine the relationships of lipid change rates with age. It has been considered that blood lipid levels could be largely determined by genetic factors under the pattern of polygenic inheritance.5 We assumed that the lipid change profile could be influenced by genetic factors. *Many* genetic loci related to blood lipid levels have been identified by genome-wide association studies.15,16,17,18,19 It provides an opportunity to construct a polygenic risk score (PRS), a quantitative measure of inherited susceptibility by integrating all available lipid-associated genetic loci. The PRS is highly associated with blood lipid levels20,21,22 and is recommended as a potentially useful tool for risk assessment.23,24 Here, we first generated PRSs for 4 lipid levels by incorporating the lipid-related genetic loci from large-scale genome-wide association studies. Then we determined the rate pattern of blood lipid changes and further estimated the associations of the change rates with PRS in a large Chinese population-based longitudinal cohort. ## Study Participants Study participants came from 3 subcohorts of the Prediction for Atherosclerotic Cardiovascular Disease Risk in China (China-PAR) study, a large Chinese population-based longitudinal cohort study. The 3 subcohorts included China Multi-Center Collaborative Study of Cardiovascular Epidemiology (China MUCA) in 1998, International Collaborative Study of Cardiovascular Disease in Asia (InterASIA) from 2000 to 2001, and Community Intervention of Metabolic Syndrome in China and Chinese Family Health Study (CIMIC) from 2007 to 2008. The China MUCA and InterASIA studies were followed up from 2007 to 2008. Subsequently, all 3 subcohorts were followed up from 2012 to 2015 and 2018 to 2020. Details of the cohort have been described in eMethods in Supplement 1 and elsewhere.25 The China-PAR project was approved by the institutional review board at Fuwai Hospital in Beijing, China, and written informed consent was obtained from all participants. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for cohort studies. In summary, among 47 691 participants aged 18 years or older with available genotype data in 3 subcohorts, 46 508 participants completed the follow-up examinations. We further excluded 1803 participants with major chronic diseases (myocardial infarction, stroke, heart failure, kidney failure, and cancer), 726 participants without blood lipid measurements at baseline examination, and 6662 participants without available lipid data at any follow-up survey. Finally, a total of 37 317 participants (mean [SD] follow-up of 13.8 [4.3] years) remained in the current analysis (eFigure 1 in Supplement 1). The included and excluded participants had no substantial difference in baseline characteristics, including blood lipid levels (eTable 1 in Supplement 1). ## Data Collection Four examinations were conducted per the standard protocol, including a baseline and 3 follow-up surveys. During the visits to community clinics, standardized questionnaires were administrated by well-trained interviewers to collect information on personal characteristics, lifestyle, medical history, and anthropometric measurements with stringent quality control. Education level, smoking, alcohol and diet consumption, and physical activity were self-reported by participants. Participants who smoked were those who smoked 400 cigarettes or more or 500 g or more of tobacco leaves, or smoked at least 1 cigarette every day for a year. Individuals who drank alcohol were those who consumed alcohol at least once a week in the previous year. According to guidelines, a modified diet score for Chinese cardiovascular health was defined as the numbers of healthy components (consumption of ≥500 g/d of fruits and vegetables; ≥200 g/week of fish; ≥125 g/d of soybean products; <75 g/d of red meat; and ≥50 g/mo of tea).26,27,28 It was used in this study based on the evidence that the 5 components in the score were all related to blood lipids.5,29,30 Ideal physical activity was defined as having at least 150 minutes per week of moderate physical activity, at least 75 minutes per week of vigorous physical activity, or at least 150 minutes per week of both. Height and weight were measured, and body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. Ten-hour fasting blood samples were drawn from participants. Serum lipid levels, including total cholesterol (TC), TG, and high-density lipoprotein cholesterol (HDL-C), were measured in the laboratory which participated in the Lipid Standardization Program of the United States Centers for Disease Control and Prevention. LDL-C was calculated using the Friedewald formula.31 ## Variant Selection, Genotyping, and PRS We included 130 genetic variants from 3 large genome-wide association studies in East Asia.18,32,33 Subsequently, Illumina Hiseq X Ten sequencer was used to genotype participants’ samples by multiplex polymerase chain reaction targeted amplicon sequencing technology, with a $99.9\%$ call rate and 990× median sequencing depth. We eliminated the correlated variants in high linkage disequilibrium using the pruning technique (r 2 >0.6) for each lipid trait, and 126 genetic variants remained for developing the PRSs of blood lipids (eTable 2 in Supplement 1). The PRS for each lipid trait was estimated as the sum of the number of risk alleles at each variant multiplied by the selected effect sizes according to previous studies.18,33,34 The PRSs for TC, TG, LDL-C, and HDL-C were categorized into 5 groups according to the quintiles among male and female participants, separately. The low, intermediate, and high polygenic risk were defined as the first, second to fourth, and fifth quintiles, respectively. ## Rate of Blood Lipid Change The estimated annual changes (EACs) of lipid levels were used to reflect rates of blood lipid changes in each interval, which were calculated as the difference in blood lipid levels between any 2 adjacent examinations divided by their time interval (year). Positive and negative values represented increase and decrease of lipid levels, respectively. ## Statistical Analysis Sex-specific baseline characteristics of study participants were expressed as means with standard deviations for continuous variables or numbers and percentages for categorical variables and were compared using a t test or χ2 test, respectively. The analytic data set included 37 317 participants without missing data and included EACs of blood lipids for each participant in each interval and risk factors at the beginning of each interval. Associations of EACs of blood lipids with age and polygenic risk were assessed using generalized estimating equations for repeated measures analyses with empirical estimates of standard errors. A term for participant cluster was included in all analyses accounting for nonindependence of participants with an unstructured correlation structure. Potential confounders were first chosen according to previous literature and determined by univariable analyses ($P \leq .05$). Multivariate analyses were then performed to adjust for these confounders at the beginning of 2 adjacent examinations, including region (Northern China or Southern China), area (urban or rural), subcohort, sex, age, education level (high school and above or less than high school), lipid level, smoking, alcohol consumption, BMI (<25 or ≥25), physical activity (ideal or nonideal), diet score (<2 or ≥2), and survey year. The linear trends were tested using the median age (or PRS) in each age (or PRS) category as a continuous variable in the generalized estimating equations. Interactions of polygenetic risk and age on blood lipid changes were examined by including an additional interaction term in the models. Sensitivity analysis was conducted by excluding participants with lipid treatment during the study period. All statistical analyses were performed using SAS 9.4 (SAS Institute). All tests were 2-sided, and $P \leq .05$ was considered statistically significant. Data were analyzed from June to August 2022. ## Characteristics of Study Participants Among 37 317 participants with a mean (SD) age of 51.37 (10.82) years, 15 664 ($41.98\%$) were male (Table 1). Male participants, compared with female participants, were more likely to live in urban areas (3564 [$22.75\%$] vs 4003 [$18.49\%$]), smoke (10 112 [$64.64\%$] vs 651 [$3.02\%$]), drink alcohol (6532 [$41.75\%$] vs 919 [$4.25\%$]). Male participants had higher education levels (3733 [$23.96\%$] vs 3090 [$14.37\%$]), diet scores (11 461 [$74.18\%$] vs 14 209 [$66.67\%$], and a higher prevalence of ideal physical activity (10 291 [$66.43\%$] vs 12 875 ([$60.89\%$]) and lower BMI (23.56 [3.44] vs 24.15 [3.75]), TC (178.55 [35.99] vs 182.27 [36.35] mg/dL), natural log-transformed TG (4.79 [0.56] vs 4.80 [0.54]), LDL-C (101.16 [31.33] vs 102.72 [31.61] mg/dL), and HDL-C (50.11 [13.83] vs 52.08 [12.72] mg/dL) levels. **Table 1.** | Variable | Participants, No. (%) | Participants, No. (%).1 | P value | | --- | --- | --- | --- | | Variable | Male (n = 15 664) | Female (n = 21 653) | P value | | Age, mean (SD), y | 51.57 (10.87) | 51.22 (10.78) | .002 | | North China | 7921 (50.57) | 10 822 (49.98) | .26 | | Urban | 3564 (22.75) | 4003 (18.49) | <.001 | | High school education and above | 3733 (23.96) | 3090 (14.37) | <.001 | | Smoking | 10 112 (64.64) | 651 (3.02) | <.001 | | Alcohol drinking | 6532 (41.75) | 919 (4.25) | <.001 | | Ideal physical activity | 10 291 (66.43) | 12 875 (60.89) | <.001 | | Diet score ≥2 | 11 461 (74.18) | 14 209 (66.67) | <.001 | | BMI, mean (SD) | 23.56 (3.44) | 24.15 (3.75) | <.001 | | TC, mean (SD), mg/dL | 178.55 (35.99) | 182.27 (36.35) | <.001 | | Natural log-transformed TG, mean (SD) | 4.79 (0.56) | 4.80 (0.54) | .01 | | LDL-C, mean (SD), mg/dL | 101.16 (31.33) | 102.72 (31.61) | <.001 | | HDL-C, mean (SD), mg/dL | 50.11 (13.83) | 52.08 (12.72) | <.001 | We also compared sex-specific lipid levels at the beginning of any 2 adjacent examinations. Female participants had lower TC, TG, and LDL-C level than male participants before the age of 50 years, but opposite findings emerged afterward (eFigure 2 in Supplement 1). Female participants had higher HDL-C level than male participants across all age groups. ## Associations of EACs With Age We estimated the associations between age and EACs of blood lipids (Figure 1 and eTable 3 in Supplement 1). There were different association patterns between male and female participants. Male participants experienced declining change as they got older for TC (EAC, 0.34 [$95\%$ CI, 0.14 to 0.54] mg/dL for age <40 years vs 0.01 [$95\%$ CI, −0.11 to 0.13] mg/dL for age ≥60 years), TG (EAC, 3.28 [$95\%$ CI, 2.50 to 4.07] mg/dL for age <40 years vs −1.70 [$95\%$ CI, −2.02 to −1.38] mg/dL for age ≥60 years), and LDL-C (EAC, 0.15 [$95\%$ CI, −0.02 to 0.32] for age <40 years vs 0.01 [$95\%$ CI, −0.10 to 0.11] mg/dL for age ≥60 years) (all P for trend <.05). However, we found inverse V-shaped associations between age and EACs of TC, TG, and LDL-C among female participants, with the greatest rate of change appearng in the age group of 40 to 49 years (EAC for TC, 1.33 [$95\%$ CI, 1.22 to 1.44] mg/dL; EAC for TG, 2.28 [$95\%$ CI, 1.94 to 2.62] mg/dL; and EAC for LDL-C, 0.94 [$95\%$ CI, 0.84 to 1.03] mg/dL). EAC of HDL-C among female participants increased significantly after the age of 60. Female participants had higher EACs of TC, TG, and LDL-C than male participants over 40 years. **Figure 1.:** *Multivariable-Adjusted Estimated Annual Changes of Lipid by Age GroupResults were adjusted for region, area, subcohort, education level, lipid level, smoking, alcohol consumption, body mass index (calculated as weight in kilograms divided by height in meters squared), physical activity, diet, and survey year at the beginning of 2 adjacent examinations. Squares indicate the estimated annual changes of lipid, and error bars represent the 95% CIs.SI conversion factor: To convert TC, LDL-C, and HDL-C to mmol/L, multiply by 0.0259; to convert TG to mmol/L, multiply by 0.0113.HDL-C indicates high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; TC, total cholesterol; TG, triglyceride.* ## Associations of EACs With Polygenic Risk The associations of polygenic risk with EACs of blood lipids were evaluated. Rates of change for TC, TG, LDL-C, and HDL-C increased significantly with elevated polygenic risk in both sexes (all P for trend <.001) (Table 2; eFigure 3 in Supplement 1). Notably, blood lipid levels among participants at low polygenic risk tended to shift toward lower levels, with EACs of −0.16 ($95\%$ CI, −0.25 to −0.07) mg/dL, −1.58 ($95\%$ CI, −1.78 to −1.37) mg/dL, −0.13 ($95\%$ CI, −0.21 to −0.06) mg/dL, and −0.11 ($95\%$ CI,−0.15 to −0.08) mg/dL for TC, TG, LDL-C, and HDL-C, respectively. These decreasing patterns of EACs were more pronounced in male participants at low polygenic risk than those in low-risk female participants. For low-risk male participants, EACs of LDL-C were −0.33 ($95\%$ CI, −0.44 to −0.22) mg/dL, while EACs were 0.02 ($95\%$ CI, −0.07 to 0.12) mg/dL for low-risk female participants. Conversely, individuals at high polygenic risk showed the greatest EACs (TC: 1.12 [$95\%$ CI, 1.03 to 1.21] mg/dL; TG: 3.57 [$95\%$ CI, 3.24 to 3.91] mg/dL; LDL-C, 0.73 [$95\%$ CI, 0.65 to 0.81] mg/dL; and HDL-C, 0.51 [$95\%$ CI, 0.48 to 0.55] mg/dL) (Table 2). **Table 2.** | Blood lipid | Polygenic risk | Polygenic risk.1 | Polygenic risk.2 | P value for trend | | --- | --- | --- | --- | --- | | Blood lipid | Low | Intermediate | High | P value for trend | | Total | Total | Total | Total | Total | | TC | −0.16 (−0.25 to −0.07) | 0.60 (0.55 to 0.66) | 1.12 (1.03 to 1.21) | <.001 | | TG | −1.58 (−1.78 to −1.37) | 0.22 (0.07 to 0.36) | 3.57 (3.24 to 3.91) | <.001 | | LDL-C | −0.13 (−0.21 to −0.06) | 0.42 (0.37 to 0.46) | 0.73 (0.65 to 0.81) | <.001 | | HDL-C | −0.11 (−0.15 to −0.08) | 0.20 (0.18 to 0.22) | 0.51 (0.48 to 0.55) | <.001 | | Male | Male | Male | Male | Male | | TC | −0.59 (−0.72 to −0.46) | 0.21 (0.14 to 0.29) | 0.73 (0.60 to 0.87) | <.001 | | TG | −2.12 (−2.45 to −1.79) | −0.45 (−0.68 to −0.23) | 2.89 (2.37 to 3.41) | <.001 | | LDL-C | −0.33 (−0.44 to −0.22) | 0.14 (0.07 to 0.21) | 0.51 (0.39 to 0.64) | <.001 | | HDL-C | −0.14 (−0.19 to −0.08) | 0.16 (0.12 to 0.19) | 0.48 (0.42 to 0.54) | <.001 | | Female | Female | Female | Female | Female | | TC | 0.17 (0.05 to 0.29) | 0.91 (0.84 to 0.97) | 1.42 (1.29 to 1.54) | <.001 | | TG | −1.17 (−1.44 to −0.90) | 0.74 (0.55 to 0.93) | 4.18 (3.74 to 4.62) | <.001 | | LDL-C | 0.02 (−0.07 to 0.12) | 0.63 (0.57 to 0.69) | 0.89 (0.78 to 1.00) | <.001 | | HDL-C | −0.09 (−0.14 to −0.05) | 0.22 (0.20 to 0.25) | 0.53 (0.49 to 0.58) | <.001 | ## Associations of EACs With Combined Age and Polygenic Risk The associations of EACs with age and polygenic risk were explored with a multivariable-adjusted analysis (Figure 2 and Figure 3). *Positive* genetic associations with EACs of 4 lipid indicators were found across sex and age groups. Lipid levels among participants at low polygenic risk tended to fall gradually or remain steady in each age group, particularly among male participants. EACs of LDL-C among low-risk male participants younger than 40 years were −0.35 ($95\%$ CI, −0.70 to 0.01) mg/dL; aged 40 to 49 years, −0.36 ($95\%$ CI, −0.59 to −0.12) mg/dL; aged 50 to 59 years, −0.21 ($95\%$ CI, −0.41 to −0.01) mg/dL; and aged 60 years or older, −0.45 ($95\%$ CI, −0.66 to −0.24) mg/dL. Conversely, participants at high polygenic risk had the greatest EACs toward higher lipid levels across all age groups. It is worth noting that the genetic association with EAC of TG was modified by age (P for interaction <.001 for both male and female participants). The differences in EAC of TG between low and high polygenic risk groups decreased from 8.47 ($95\%$ CI, 5.81 to 11.13) mg/dL among male participants younger than 40 years to 3.27 ($95\%$ CI, 2.37 to 4.16) mg/dL among those aged 60 years or older, and EACs declined from 7.00 ($95\%$ CI, 5.96 to 8.05) mg/dL among female participants aged 40 to 49 years to 3.73 ($95\%$ CI, 2.70 to 4.75) mg/dL among those aged 60 years or older. No interaction between genetic risk and age was observed for EACs of TC, LDL-C, and HDL-C. **Figure 2.:** *Multivariable-Adjusted Estimated Annual Changes of Lipid According to Polygenic Risk and Age Group Among Male ParticipantsResults were adjusted for region, area, subcohort, education level, lipid level, smoking, alcohol consumption, body mass index (calculated as weight in kilograms divided by height in meters squared), physical activity, diet, and survey year at the beginning of 2 adjacent examinations. The low, intermediate, and high polygenic risk were defined as the first, second to fourth, and fifth quintiles of polygenic risk scores. Bars are the estimated annual changes, squares are differences of the estimated annual changes between low and high polygenic risk, and error bars represent the 95% CIs.SI conversion factor: To convert TC, LDL-C, and HDL-C to mmol/L, multiply by 0.0259; to convert TG to mmol/L, multiply by 0.0113.HDL-C indicates high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; TC, total cholesterol; TG, triglyceride.* **Figure 3.:** *Multivariable-Adjusted Estimated Annual Changes of Lipid According to Polygenic Risk and Age Group Among Female ParticipantsResults were adjusted for region, area, subcohort, education level, lipid level, smoking, alcohol consumption, body mass index (calculated as weight in kilograms divided by height in meters squared), physical activity, diet, and survey year at the beginning of 2 adjacent examinations.The low, intermediate, and high polygenic risk were defined as the first, second to fourth, and fifth quintiles of polygenic risk scores. Bars are the estimated annual changes, squares are differences of the estimated annual changes between low and high polygenic risk, and error bars represent the 95% CIs.SI conversion factor: To convert TC, LDL-C, and HDL-C to mmol/L, multiply by 0.0259; to convert TG to mmol/L, multiply by 0.0113.HDL-C indicates high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; TC, total cholesterol; TG, triglyceride.* ## Sensitivity Analysis To avoid the influence of lipid treatment on blood lipid changes, we excluded all participants with lipid-lowering therapy during the study period and conducted a sensitivity analysis. It demonstrated similar results to the main analysis (eTable 4 and eFigure 4-6 in Supplement 1). ## Discussion Using a large-scale population-based longitudinal cohort with repeated measurements, we assessed the associations of blood lipid changes with age and polygenic risk. Our results suggested that EACs of TC, TG, and LDL-C among male participants decreased with age, whereas female participants had inverse V-shaped associations with peak EACs at age 40 to 49 years. The polygenic risk was also associated with the directions and rates of lipid changes, which was observed in both sexes and all age groups. Several significant findings of this study could have relevant clinical implications. First, we examined the associations of lipid EACs with age. It is well known that blood lipid levels tend to increase with age,12,13,14 but it is unclear whether and to what extent lipid change rates could vary with age. Our study demonstrated that the EACs of TC, TG, and LDL-C declined gradually with age in male participants, and fell sharply in female participants after the age of 40 years, which might be attributed to age-related changes of basal metabolism, the liver sinusoidal endothelium, postprandial lipemia, insulin resistance, growth hormone, and peroxisome proliferator-activated receptor α activity.35 *It is* suggested that lipid interventions could be implemented in the critical age window (before age 40 years for male participants and 40 to 49 years for female participants) when lipid levels deteriorated at the relatively greater rate than others. Guidelines on lipid management are always aimed at population with higher lipid levels,1,7 but younger individuals are more likely to have lower lipid levels, which conceals the fact that they have the greater EACs toward unfavorable lipid profiles and required intensive lifestyle interventions. Consequently, we propose that lipid control should focus more on the critical age window with a higher rate of deterioration, which will yield more health benefits. Second, we identified sex differences in the associations between rates of lipid changes and age. For EACs of TC, TG, and LDL-C with age, there were decreasing trends among male participants, but inverse V-shaped associations among female participants. The similar inverse V-shaped associations among female participants could also be deduced from a Chinese study with nationally representative sample, using differences in mean lipid levels between any 2 adjacent age groups.14 The significant increases of EACs among female participants at age group of 40 to 49 could explain why blood lipid levels among female participants subsequently rose rapidly,13,14 and were consistent with a substantial rise in prevalence of abdominal obesity.36,37 It might be due to the decrease in estrogen among female participants during perimenopause and menopause. Another potential explanation is that their lifestyles tend to change toward overnutrition and physical inactivity during and after pregnancy.38 Interestingly, female participants had higher lipid levels and EACs than male participants beyond the age of 40 years, suggesting that more attention should be paid to lipid management in female participants, especially those who were perimenopausal and menopausal. Third, we found that genetic risk was associated with the directions and rates of changing blood lipids. Although several studies have reported that PRSs were predictive of longitudinal lipid changes, there is limited evidence in China.39,40 A previous study on the Chinese population identified linear associations between the lipid changes and PRSs, which included 20 genetic variants.32 In the current study, we generated the PRSs using 126 significant genetic variants for lipid traits in the East Asian population,18,32,33 further expanded to a larger sample size with repeated lipid measurements, and comprehensively estimated the associations of PRSs with lipid changes, which showed similar results with previous studies. Our study further indicated that lipid levels among individuals at low polygenic risk tended to decrease or remain stable, whereas lipid levels among high-risk individuals accelerated toward unhealthy profiles at high rates. Specifically, people at the highest $20\%$ of genetic risk should be paid more attention to maximize potential health benefits from management of cholesterol and triglycerides. Polygenic risk assessment could motivate those at high risk to adhere to healthy lifestyle modifications,23,41 especially when considering that the treatment and control of dyslipidemia was extremely low in China.14 Moreover, PRS could increase the accuracy of risk estimation for individuals with familial hypercholesterolemia.42 The clinical benefits of polygenic risk should be confirmed by randomized controlled trials. This study has several strengths. First, we estimated the associations of blood lipid changes with age and polygenic risk using a large-scale population-based prospective longitudinal cohort with 4 repeated measurements over a mean follow-up period of 13.8 years. Second, our findings were reliable due to the well-defined phenotypes and rigorous quality control. Third, available repeated measurements of multiple risk factors in our study enable us to better control for potential confounders. ## Limitations Some potential limitations should also be noted. First, stratification based on age and PRS resulted in relatively small sample sizes for some groups, especially among younger individuals at low or high genetic risk. It might decrease statistical power and cause some uncertainties. Second, blood lipids could be influenced by other lifestyle factors not captured in this study, which might affect the associations. But confounding should be limited, given that we have adjusted for major lifestyle factors in the analysis. Third, self-reported confounders, such as smoking, physical activity and diet, might be associated with our results due to recall bias. Fourth, missing data might induce selection bias, while their impact should be slight due to similar characteristics between the included and excluded samples. Finally, our study was conducted in Chinese population, generalization to other ethnicities should be done with caution. ## Conclusions The findings of this cohort study suggests that lipid change rates are associated with age and genetic predisposition in Chinese adults. Therefore, precision prevention strategies for blood lipids should be optimized by taking account of both genetic risk and critical age window, which might offer an opportunity to reduce unhealthy lipid traits and subsequent cardiovascular burden. ## References 1. Grundy SM, Stone NJ, Bailey AL. **2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA guideline on the management of blood cholesterol: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines**. *Circulation* (2019) **139** e1082-e1143. PMID: 30586774 2. **Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980-2017: a systematic analysis for the Global Burden of Disease Study 2017**. *Lancet* (2018) **392** 1736-1788. DOI: 10.1016/S0140-6736(18)32203-7 3. **Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017**. *Lancet* (2018) **392** 1923-1994. DOI: 10.1016/S0140-6736(18)32225-6 4. Nordestgaard BG, Varbo A. **Triglycerides and cardiovascular disease**. *Lancet* (2014) **384** 626-635. DOI: 10.1016/S0140-6736(14)61177-6 5. Mach F, Baigent C, Catapano AL. **2019 ESC/EAS Guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk**. *Eur Heart J* (2020) **41** 111-188. DOI: 10.1093/eurheartj/ehz455 6. Arnett DK, Blumenthal RS, Albert MA. **2019 ACC/AHA guideline on the primary prevention of cardiovascular disease: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines**. *Circulation* (2019) **140** e596-e646. DOI: 10.1161/CIR.0000000000000678 7. Visseren FLJ, Mach F, Smulders YM. **2021 ESC Guidelines on cardiovascular disease prevention in clinical practice**. *Eur Heart J* (2021) **42** 3227-3337. DOI: 10.1093/eurheartj/ehab484 8. Carroll MD, Kit BK, Lacher DA, Shero ST, Mussolino ME. **Trends in lipids and lipoproteins in US adults, 1988-2010**. *JAMA* (2012) **308** 1545-1554. DOI: 10.1001/jama.2012.13260 9. Zhang M, Deng Q, Wang L. **Prevalence of dyslipidemia and achievement of low-density lipoprotein cholesterol targets in Chinese adults: a nationally representative survey of 163,641 adults**. *Int J Cardiol* (2018) **260** 196-203. DOI: 10.1016/j.ijcard.2017.12.069 10. Song PK, Man QQ, Li H. **Trends in lipids level and dyslipidemia among Chinese adults, 2002-2015**. *Biomed Environ Sci* (2019) **32** 559-570. PMID: 31488232 11. German CA, Baum SJ, Ferdinand KC. **Defining preventive cardiology: a clinical practice statement from the American Society for Preventive Cardiology**. *Am J Prev Cardiol* (2022) **12**. DOI: 10.1016/j.ajpc.2022.100432 12. Carroll MD, Lacher DA, Sorlie PD. **Trends in serum lipids and lipoproteins of adults, 1960-2002**. *JAMA* (2005) **294** 1773-1781. DOI: 10.1001/jama.294.14.1773 13. He J, Gu D, Reynolds K. **Serum total and lipoprotein cholesterol levels and awareness, treatment, and control of hypercholesterolemia in China**. *Circulation* (2004) **110** 405-411. DOI: 10.1161/01.CIR.0000136583.52681.0D 14. Yang W, Xiao J, Yang Z. **Serum lipids and lipoproteins in Chinese men and women**. *Circulation* (2012) **125** 2212-2221. DOI: 10.1161/CIRCULATIONAHA.111.065904 15. Teslovich TM, Musunuru K, Smith AV. **Biological, clinical and population relevance of 95 loci for blood lipids**. *Nature* (2010) **466** 707-713. DOI: 10.1038/nature09270 16. Asselbergs FW, Guo Y, van Iperen EP. **Large-scale gene-centric meta-analysis across 32 studies identifies multiple lipid loci**. *Am J Hum Genet* (2012) **91** 823-838. DOI: 10.1016/j.ajhg.2012.08.032 17. Willer CJ, Schmidt EM, Sengupta S. **Discovery and refinement of loci associated with lipid levels**. *Nat Genet* (2013) **45** 1274-1283. DOI: 10.1038/ng.2797 18. Lu X, Peloso GM, Liu DJ. **Exome chip meta-analysis identifies novel loci and East Asian-specific coding variants that contribute to lipid levels and coronary artery disease**. *Nat Genet* (2017) **49** 1722-1730. DOI: 10.1038/ng.3978 19. Varga TV, Kurbasic A, Aine M. **Novel genetic loci associated with long-term deterioration in blood lipid concentrations and coronary artery disease in European adults**. *Int J Epidemiol* (2017) **46** 1211-1222. PMID: 27864399 20. Piccolo SR, Abo RP, Allen-Brady K. **Evaluation of genetic risk scores for lipid levels using genome-wide markers in the Framingham Heart Study**. *BMC Proc* (2009) **3** S46. DOI: 10.1186/1753-6561-3-S7-S46 21. Talmud PJ, Shah S, Whittall R. **Use of low-density lipoprotein cholesterol gene score to distinguish patients with polygenic and monogenic familial hypercholesterolaemia: a case-control study**. *Lancet* (2013) **381** 1293-1301. DOI: 10.1016/S0140-6736(12)62127-8 22. Huo S, Sun L, Zong G. **Genetic susceptibility, dietary cholesterol intake, and plasma cholesterol levels in a Chinese population**. *J Lipid Res* (2020) **61** 1504-1511. DOI: 10.1194/jlr.RA120001009 23. Dron JS, Hegele RA. **The evolution of genetic-based risk scores for lipids and cardiovascular disease**. *Curr Opin Lipidol* (2019) **30** 71-81. DOI: 10.1097/MOL.0000000000000576 24. Lechner K, Kessler T, Schunkert H. **Should we use genetic scores in the determination of treatment strategies to control dyslipidemias?**. *Curr Cardiol Rep* (2020) **22** 146. DOI: 10.1007/s11886-020-01408-9 25. Yang X, Li J, Hu D. **Predicting the 10-Year risks of atherosclerotic cardiovascular disease in chinese population: the China-PAR project (Prediction for ASCVD Risk in China)**. *Circulation* (2016) **134** 1430-1440. DOI: 10.1161/CIRCULATIONAHA.116.022367 26. Lloyd-Jones DM, Hong Y, Labarthe D. **Defining and setting national goals for cardiovascular health promotion and disease reduction: the American Heart Association’s strategic impact goal through 2020 and beyond**. *Circulation* (2010) **121** 586-613. DOI: 10.1161/CIRCULATIONAHA.109.192703 27. Wang SS, Lay S, Yu HN, Shen SR. **Dietary Guidelines for Chinese Residents (2016): comments and comparisons**. *J Zhejiang Univ Sci B* (2016) **17** 649-656. DOI: 10.1631/jzus.B1600341 28. Han C, Liu F, Yang X. **Ideal cardiovascular health and incidence of atherosclerotic cardiovascular disease among Chinese adults: the China-PAR project**. *Sci China Life Sci* (2018) **61** 504-514. DOI: 10.1007/s11427-018-9281-6 29. Park SJ, Kim MS, Choi SW, Lee HJ. **The relationship of dietary pattern and genetic risk score with the incidence dyslipidemia: 14-year follow-up cohort study**. *Nutrients* (2020) **12** 3840. DOI: 10.3390/nu12123840 30. Li M, Duan Y, Wang Y, Chen L, Abdelrahim MEA, Yan J. **The effect of Green green tea consumption on body mass index, lipoprotein, liver enzymes, and liver cancer: an updated systemic review incorporating a meta-analysis**. *Crit Rev Food Sci Nutr* (2022). DOI: 10.1080/10408398.2022.2113 31. Friedewald WT, Levy RI, Fredrickson DS. **Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge**. *Clin Chem* (1972) **18** 499-502. DOI: 10.1093/clinchem/18.6.499 32. Lu X, Huang J, Mo Z. **Genetic susceptibility to lipid levels and lipid change over time and risk of incident hyperlipidemia in Chinese populations**. *Circ Cardiovasc Genet* (2016) **9** 37-44. DOI: 10.1161/CIRCGENETICS.115.001096 33. Spracklen CN, Chen P, Kim YJ. **Association analyses of East Asian individuals and trans-ancestry analyses with European individuals reveal new loci associated with cholesterol and triglyceride levels**. *Hum Mol Genet* (2017) **26** 1770-1784. DOI: 10.1093/hmg/ddx062 34. Kanai M, Akiyama M, Takahashi A. **Genetic analysis of quantitative traits in the Japanese population links cell types to complex human diseases**. *Nat Genet* (2018) **50** 390-400. DOI: 10.1038/s41588-018-0047-6 35. Liu HH, Li JJ. **Aging and dyslipidemia: a review of potential mechanisms**. *Ageing Res Rev* (2015) **19** 43-52. DOI: 10.1016/j.arr.2014.12.001 36. Zhai Y, Fang HY, Yu WT. **Changes in waist circumference and abdominal obesity among Chinese adults over a ten-year period**. *Biomed Environ Sci* (2017) **30** 315-322. PMID: 28549487 37. Zhang L, Wang Z, Wang X. **Prevalence of abdominal obesity in China: results from a cross-sectional study of nearly half a million participants**. *Obesity (Silver Spring)* (2019) **27** 1898-1905. DOI: 10.1002/oby.22620 38. Pan XF, Wang L, Pan A. **Epidemiology and determinants of obesity in China**. *Lancet Diabetes Endocrinol* (2021) **9** 373-392. DOI: 10.1016/S2213-8587(21)00045-0 39. Lutsey PL, Rasmussen-Torvik LJ, Pankow JS. **Relation of lipid gene scores to longitudinal trends in lipid levels and incidence of abnormal lipid levels among individuals of European ancestry: the atherosclerosis risk in communities (ARIC) study**. *Circ Cardiovasc Genet* (2012) **5** 73-80. DOI: 10.1161/CIRCGENETICS.111.959619 40. Varga TV, Sonestedt E, Shungin D. **Genetic determinants of long-term changes in blood lipid concentrations: 10-year follow-up of the GLACIER study**. *PLoS Genet* (2014) **10**. DOI: 10.1371/journal.pgen.1004388 41. Kullo IJ, Jouni H, Austin EE. **Incorporating a genetic risk score into coronary heart disease risk estimates: effect on low-density lipoprotein cholesterol levels (the MI-GENES clinical trial)**. *Circulation* (2016) **133** 1181-1188. DOI: 10.1161/CIRCULATIONAHA.115.020109 42. Fahed AC, Wang M, Homburger JR. **Polygenic background modifies penetrance of monogenic variants for tier 1 genomic conditions**. *Nat Commun* (2020) **11** 3635. DOI: 10.1038/s41467-020-17374-3
--- title: Association of Glucose-6-Phosphate Dehydrogenase Deficiency With Outcomes in US Veterans With COVID-19 authors: - Sarah H. Elsea - Javad Razjouyan - Kyung Min Lee - Julie A. Lynch - Sharyl Martini - Lavannya M. Pandit journal: JAMA Network Open year: 2023 pmcid: PMC10061239 doi: 10.1001/jamanetworkopen.2023.5626 license: CC BY 4.0 --- # Association of Glucose-6-Phosphate Dehydrogenase Deficiency With Outcomes in US Veterans With COVID-19 ## Key Points ### Question Is G6PD deficiency, the most common enzyme deficiency in the world, associated with COVID-19 severity in US veterans? ### Findings In this cohort study of 24 700 veterans, G6PD deficiency was present in $9.4\%$ of veterans with SARS-CoV-2 infection and was associated with a 1.5-fold increased likelihood of severe outcomes in male veterans less than 65 years of age who self-identified as Black, and a 3.6-fold greater likelihood of severe outcomes in male veterans 65 years of age and older who self-identified as White. ### Meaning These results suggest that G6PD deficiency was more prevalent in minoritized racial and ethnic communities and associated with increased likelihood for severe outcomes due to SARS-CoV-2 infection, supporting the need for additional investigations to identify individuals at greatest risk and to define the best approaches for therapeutic intervention. ## Abstract This cohort study examines whether there is an association between glucose-6-phosphate dehydrogenase (G6PD) deficiency and severity of COVID-19 outcomes among US veterans. ### Importance The underlying biological risk factors for severe outcome due to SAR-CoV-2 infection are not well defined. ### Objective To determine the association between glucose-6-phosphate dehydrogenase (G6PD) deficiency and severity of COVID-19. ### Design, Setting, and Participants This retrospective cohort study included analysis of 24 700 veterans with G6PD enzyme testing prior to January 1, 2020, obtained through the US Veterans Health Administration national databases. These veterans were cross-referenced with the Veterans Administration COVID-19 Shared Data Resource for SARS-CoV-2 testing from February 15, 2020, to January 1, 2021. The final study population consisted of 4811 veterans who tested positive for SARS-CoV-2. Statistical analysis was performed from June to December 2021. ### Exposures G6PD deficiency. ### Main Outcomes and Measures COVID-19 severe illness, as defined by the Centers for Disease Control and Prevention: hospitalization, need for mechanical ventilation and/or intensive care unit admission, or in-hospital mortality after a positive SARS-CoV-2 test. ### Results Among 4811 veterans in the Veterans Health Administration who had historical G6PD enzyme activity test results and SARS-CoV-2 positivity included in this study, 3868 ($80.4\%$) were male, 1553 ($32.3\%$) were Black, and 1855 ($39\%$) were White; 1228 ($25.5\%$) were 65 years or older and 3583 ($74.5\%$) were younger than 65 years. There were no significant differences in age, body mass index, or Charlson Comorbidity Index were present between the veterans with G6PD deficiency and without G6PD deficiency. Among these veterans with SARS-CoV-2 infection, G6PD deficiency was more prevalent in Black male veterans (309 of 454 [$68.1\%$]) compared with other racial and ethnic groups. Black male veterans less than 65 years of age with G6PD deficiency had approximately 1.5-fold increased likelihood of developing severe outcomes from SARS-CoV-2 infection compared with Black male veterans without G6PD deficiency (OR, 1.47; $95\%$ CI, 1.03-2.09). In the small subset of White male veterans with G6PD deficiency, we observed an approximately 3.6-fold increased likelihood of developing severe outcomes from SARS-CoV-2 infection compared with White male veterans aged 65 years or older without G6PD deficiency (OR, 3.58; $95\%$ CI, 1.64-7.80). This difference between veterans with and without G6PD deficiency was not observed in younger White male veterans or older Black male veterans, nor in smaller subsets of other male veterans or in female veterans of any age. ### Conclusions and Relevance In this cohort study of COVID-19–positive veterans, Black male veterans less than 65 years of age and White male veterans 65 years of age or older with G6PD deficiency had an increased likelihood of developing severe COVID-19 compared with veterans without G6PD deficiency. These data indicate a need to consider the potential for G6PD deficiency prior to treatment of patients with SARS-CoV-2 infection as part of clinical strategies to mitigate severe outcomes. ## Introduction SARS-CoV-2 has devastated the global community with almost 6 million deaths and expected ongoing deaths with the rise of new variants.1,2 One challenging aspect of treating COVID-19 is understanding why certain infected individuals experience severe, life-threatening complications, while others remain minimally symptomatic. Severity risks associated with SARS-CoV-2 infection are purported to be related to variable factors including patient ancestry, socioeconomic status, health care utilization, and comorbid conditions.3,4,5 Although socioeconomic status and social determinants of health have been discussed as possible causes for the higher burden of increased morbidity and mortality in minoritized racial and ethnic communities in the US, the contribution of biology toward disease severity has been minimally explored.6 *As a* result, despite almost 3 years of observational data, the biologic bases of susceptibility to SARS-CoV-2 infection and risks of severe outcomes after infection are largely unknown. Viral infection triggers massive reactive oxidative species production and oxidative damage. Glutathione (GSH) is essential and protects the body from the harmful effects of oxidative damage from excess reactive oxygen radicals.5 Glucose-6-phosphate dehydrogenase (G6PD) is necessary to prevent the exhaustion and depletion of cellular GSH. G6PD deficiency is a genetic metabolic abnormality and is the most common enzyme deficiency, affecting more than 400 million people worldwide.7 *Although this* X-linked condition is more commonly described in men, hemizygous male individuals and heterozygous female individuals may be affected. In the US, G6PD deficiency has an estimated prevalence of $10\%$ to $14\%$ among Black men.8,9 Acquired deficiency of G6PD is associated with obesity and diabetes, as G6PD enzyme activity is moderated by hyperglycemia, and epidemiological evidence suggests that patients with G6PD deficiency have a higher risk of developing diabetes.5,10,11 A recent study highlighted associations with G6PD deficiency and cardiovascular risk, including hypertension and cardiomyopathy.12 Although the majority of individuals with G6PD deficiency are asymptomatic, a trigger (food, medication, or infection) may lead to hemolytic anemia, hemoglobinuria, and hematuria. Individuals with inherited or acquired G6PD deficiency are vulnerable to oxidative stress and heightened susceptibility to microbial infection.13 Recent publications have outlined several pieces of evidence suggesting that G6PD deficiency may increase susceptibility to, and severity of, COVID-19.5,14,15,16,17,18,19 Further supporting this theory are ex vivo studies in G6PD-deficient cells showing increased susceptibility to infection and cell death by human coronavirus HCoV-229E infection and reduced NF-κB activation in coronavirus-infected G6PD-deficient cells.20,21 Additionally, multiple studies demonstrate that diabetes is independently associated with COVID-19 severity and increased mortality.22,23,24 The compounded effects of diabetes, hyperglycemia, and acquired or inherited G6PD deficiency may increase the susceptibility of patients with G6PD deficiency to worse outcomes from COVID-19. G6PD status is already known for all active military members and many veterans since the US Department of Defense (DOD) has mandated all US Army personnel undergo testing for G6PD deficiency at the time of entry into service.25 The prevalence of G6PD deficiency within the Armed Forces among non-Hispanic Black male individuals and female individuals is $15.9\%$ compared with $2.2\%$ overall.25 This study examined whether the presence of G6PD deficiency in veterans diagnosed with SARS-CoV-2 infection was associated with increased odds of developing severe COVID-19 compared with veterans without G6PD deficiency. Our primary objectives were to report the prevalence of G6PD deficiency among veterans with COVID-19 and to measure the severity of illness (mortality, hospitalization, need for ventilator support, and intensive care unit admission) among those veterans with COVID-19. The purpose of this analysis was to determine if an association exists between the development of severe COVID-19 and G6PD deficiency in US veterans. We also examined whether an association between G6PD deficiency and COVID-19 severity differed by age, sex, and race with consideration of common comorbidities. ## Methods This cohort study was approved by the Baylor College of Medicine institutional review board and Department of Veterans Affairs with a local waiver of consent. Local waiver of consent was granted given the large number of veterans in the database and the preference to keep the individual veteran data anonymous for patient privacy protection. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline. ## Study Design, Setting, and Participants Data supporting this retrospective cohort study were accessed through the national electronic health record (EHR) of veterans enrolled in the Veterans Health Administration (VHA) as active health care users in the 2 years before the study period and defined as those who had received any primary care encounter within VHA facilities including outpatient mental health, screening health, pharmacy, radiology, or laboratory services. Veterans in the VHA who had historical G6PD enzyme activity test results, as standard protocol per the DOD, were further cross-referenced for positive SARS-CoV-2 testing through the VA COVID-19 Shared Data Resource26 (CSDR) ($$n = 24$$ 700) (Figure 1). The CSDR includes demographic and clinical information related to COVID-19 for all patients tested for SARS-CoV-2 within VHA or whose positive test result outside VHA was recorded in VHA clinical notes.14,27 This study analyzed EHR data for veterans in the VHA with historical G6PD test results who had a positive molecular PCR-SARS-CoV-2 test or historical positive test in VHA clinical notes from February 15, 2020, to January 1, 2021 ($$n = 4811$$) (Figure 1). Because the earliest SARS-CoV-2 testing date reported in the CSDR was February 16, 2020, we considered any veteran alive as of February 15, 2020, as eligible for SARS-CoV-2 testing. Although EHR limitations prevent the authors from ascertaining reasons for SARS-CoV-2 testing, which could lend potential biases, the veterans under study who received testing were actively receiving care within the VHA and had equal access to care and testing at all VHA facilities. All data included in this study were generated prior to the widespread availability of SARS-CoV-2 screening of asymptomatic individuals, at-home testing, or vaccination to the veteran population. **Figure 1.:** *Study Population of SARS-CoV-2–Positive US Veterans With or Without G6PD DeficiencyUS veterans with both a prior medical record for G6PD deficiency testing and SARS-CoV-2 testing between February 15, 2020, to January 1, 2021, are shown (n = 24 700). The SARS-CoV-2–positive subgroup (n = 4811) was stratified by sex (male, female) and self-identified race (Black, White, other [self-identified as neither Black nor White or self-identified as Asian, Pacific Islander, or American Indian or Alaska Native]) for further study assessment.* In this study, we used self-identified sex (male or female) and self-identified race (White, Black, or other [self-identified as Asian, Pacific Islander, American Indian or Alaska Native]) to stratify all included veterans within the VHA. Due to the small number within each of the race categories that were not Black nor White, they were considered as a single category because no clear conclusions could be made of any individual race in these groups. Any missing values were assigned as unknown, with exceptions being age (data excluded) and body mass index (BMI [calculated as weight in kilograms divided by height in meters squared]) for which a median BMI was assigned. No sensitivity analysis was performed. ## Variable Exposure and Outcomes G6PD deficiency as the primary variable was determined by quantitative enzyme activity testing, as performed and reported at various VHA US laboratories any time prior to January 1, 2020, with results obtained from the VHA Corporate Data Warehouse, a data repository of national EHR data of all individuals who received care in the VHA. Any veteran who ever had a value falling below the testing laboratory’s reference range was classified as G6PD-deficient. Although the authors acknowledge the challenge of confirming G6PD enzyme test results within the study population, this clinical test was performed at Clinical Laboratory Improvement Amendments–accredited laboratories and rigorously applied throughout the veteran population through protocols established through the DOD.18 The primary outcome measure was COVID-19 severe illness, defined as any of the following clinical scenarios occurring after a positive SARS-CoV-2 test: hospitalization, need for mechanical ventilation, intensive care unit admission or transfer, or in-hospital mortality.28 For odds ratios (ORs) and all combined analyses with regards to the primary outcome measure of severity of illness, individuals were only counted once toward calculation of the composite score. ## Covariates To address potential contributions of modifiers and confounders within the study population, we obtained participants’ demographics and comorbidities from the CSDR. Based upon the Centers for Disease Control and Prevention’s published comorbid medical conditions that confer increased severity of illness from COVID-19 (accessed April 15, 2021),28,29,30 the following medical conditions as modifiers were extracted and curated from VHA EHR: age (dichotomized into <65 years and ≥65 years), BMI (dichotomized into <30 and ≥30), Charlson Comorbidity Index (dichotomized into <2 and ≥2),31,32 self-identified race and sex, and medical history for the presence of the following: diabetes, chronic kidney disease (CKD), coronary atherosclerosis and other heart disease (CAHD), cardiomyopathy, congestive heart failure (CHF), cardiovascular disease including hypertension (CVD), cancer, chronic obstructive pulmonary disease (COPD), human immunodeficiency virus (HIV), chronic liver disease (CLD), cirrhosis, and alcohol dependency, as indicated by International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) codes.29 ## Statistical Analysis G6PD is located on the X chromosome. As an X-linked condition, G6PD deficiency is more prevalent in men, with variable expression in heterozygous female individuals. The prevalence of G6PD deficiency differs significantly among ancestral groups.8 Few female veterans and few men of other high-risk ancestries were present in our cohort (Figure 1 and Figure 2; eTable 1 in Supplement 1). For these reasons, we stratified by racial ancestry and sex to define 4 groups: [1] male-Black, [2] male-White, [3] male-other, and [4] female-all. As $80\%$ of COVID-19-associated deaths were among adults aged at least 65 years, we further stratified by age.28 We performed descriptive and multivariate analyses to compare baseline characteristics across the 4 sex and racial groups. SAS 9.2 (SAS Institute) and MATLAB R2017b (MathWorks) were used for data preparation and statistical analyses from June to December 2021. Two-tailed hypothesis testing was performed using the significance level of $5\%$. To test the association between G6PD deficiency and COVID-19 severity, we applied logistic regression models, comparing the frequency of adverse clinical outcomes between veterans with and without G6PD deficiency, adjusting for specific covariates and comorbid conditions previously determined to modify COVID-19 clinical severity, specifically age, diabetes, CKD, and BMI.33,34 **Figure 2.:** *Prevalence of G6PD Deficiency in US Veteran Population That Tested Positive for SARS-CoV-2 InfectionOf the total male veterans (n = 3868) in this SARS-CoV-2–positive cohort (n = 4811), 10.8% (n = 418) had G6PD deficiency with the following distribution across racial ancestries: Black male veterans: 19.9% (309 of 1553), White male veterans: 3.7% (68 of 1855), and other male veterans (self-identified as neither Black nor White or self-identified as Asian, Pacific Islander, or American Indian or Alaska Native): 8.9% (41 of 460). Of the total female veterans of all racial ancestries (n = 907) in this SARS-CoV-2 positive cohort, 3.8% (36) had G6PD deficiency. These data show that the prevalence of G6PD deficiency in veterans positive for SARS-CoV-2 was higher than expected in the military population. All SARS-CoV-2 testing performed between February 15, 2020, to January 1, 2021. The percentages of US veterans with G6PD deficiency are presented as reported by the US Department of Defense.18* ## Results There were 24 700 veterans in the VHA who had historical G6PD enzyme activity test results. Among the 4811 veterans who tested positive for SARS-CoV-2 infection included in this study, 3868 ($80.4\%$) were male, 1553 ($32.3\%$) were Black, and 1855 ($39\%$) were White; 1228 ($25.5\%$) were 65 years or older and 3583 ($74.5\%$) were younger than 65 years. ## Comorbidities in Veterans with G6PD Deficiency and SARS-CoV-2 Infection A total of 4811 veterans who tested positive for SARS-CoV-2 during the study period were subcategorized by G6PD status, sex, race, and age (Figure 1; eTable 1 in Supplement 1). There were no significant differences in age, BMI, or Charlson Comorbidity Index among the G6PD-deficient and non-G6PD-deficient SARS-CoV-2–positive veterans. ( Figure 3) (eTable 1 and eFigure 1 in Supplement 1). Male veterans composed the majority of the veterans who were SARS-CoV-2 positive across the entire cohort ($80.4\%$ male [3868 of 4811]), with G6PD deficiency documented in $10.8\%$ (418 of 3868). Although the comorbidities present in Black male veterans with or without G6PD deficiency did not differ, several comorbidities were more frequently observed in White male veterans with G6PD deficiency vs the non–G6PD deficiency group, including diabetes, CKD, CAHD, CVD, HIV, CLD, and cirrhosis in White men less than 65 years of age and cardiomyopathy in White men who were at least 65 years of age (Figure 3; eTable 1 in Supplement 1). While some of these associations, including diabetes and hypertension, have been previously reported,10,35,36,37,38 the underlying etiology and possible association with G6PD deficiency with each of these clinical findings requires further investigation. **Figure 3.:** *Clinical Characteristics of US Veterans Testing Positive for SARS-CoV-2 With or Without G6PD DeficiencyHeatmap illustrates the prevalence of comorbidities commonly associated with risk for severe outcomes due to SARS-CoV-2 infection (n = 4811). Veterans were grouped by age (<65 y and ≥65 y) and self-reported sex and race. Percentages for each comorbidity indicated are represented by the heatmap scale shown on the right. BMI indicates body mass index (calculated as weight in kilograms divided by height in meters squared); CAHD, coronary atherosclerosis and other heart disease; CCI, Charlson Comorbidity Index; CHF, congestive heart failure; CKD, chronic kidney disease; CLD, chronic liver disease; COPD, chronic obstructive pulmonary disease; CVD, cardiovascular disease including hypertension; HIV, human immunodeficiency virus; Other, male veterans with other race (did not self-identify as White or Black, or self-identified as Asian, Pacific Islander, or American Indian or Alaska Native).aSevere outcomes include (1) in-hospital mortality, (2) hospitalization, (3) intensive care unit admission, or (4) mechanical ventilation. All data were extracted as indicated by International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) codes.28 See eTable 1 in Supplement 1 for detailed information and raw data and eFigure 1 in Supplement 1 for representation of total cohort data.* Given that G6PD deficiency is X-linked, only $3.8\%$ (36 of 943) of the female veterans in our SARS-CoV-2–positive cohort were found to have G6PD deficiency (Figure 1). G6PD deficiency was documented in $10.8\%$ (418 of 3868) of male veterans in our SARS-COV-2–positive cohort, and most of these veterans were Black (309 of 418 [$73.9\%$]). In comparison, approximately equal numbers of White male veterans [1787] and Black male veterans [1244] were SARS-CoV-2 positive but not G6PD-deficient (Figure 1). Among the male veterans who were G6PD-deficient, only $9\%$ (41 of 454) self-identified as other (not self-identified as Black or White, or self-identified as Asian, Pacific Islander, or American Indian or Alaska Native). We found a higher prevalence of G6PD deficiency ($9.4\%$ [454 of 4811]) (Figure 1) in our SARS-CoV-2–positive cohort than the $2.2\%$ prevalence reported in military statistics (Figure 2). Similarly, $19.9\%$ (309 of 1553) of self-identified Black male veterans in our SARS-CoV-2–positive cohort were G6PD-deficient vs $11.2\%$ of Black men in the military (Figure 2).39 Overall, G6PD deficiency was more prevalent in our cohort of veterans who were SARS-CoV-2 positive across all populations assessed, including men and women, than in the general military population (Figure 2). ## G6PD Deficiency and Outcomes From COVID-19 in Black and White Male Veterans Among younger Black male veterans (aged less than 65 years), the odds of developing severe COVID-19 were greater for veterans with G6PD deficiency than for those without G6PD deficiency (OR 1.47; $95\%$ CI, 1.03-2.09) (Table; eFigure 2 and eTable 2 in Supplement 1). However, the association of G6PD deficiency with severe COVID-19 was not observed in Black male veterans who were older (aged at least 65 years). An approximately 3.6-fold increased likelihood of severe outcomes was, however, observed in the White male veterans with G6PD deficiency aged at least 65 years ($$n = 37$$) when compared with White male veterans aged at least 65 years without G6PD deficiency (OR, 3.58; $95\%$ CI, 1.64-7.80) (eFigure 2 in Supplement 1; Table). Although a significant association between G6PD deficiency and severe COVID-19 was not observed in female veterans or in male veterans from other racial ancestry groups, the smaller numbers of veterans in these demographics (eg, female, Asian) preclude firm conclusions regarding these diverse groups of individuals in which G6PD deficiency may still exert a biologic role in COVID-19 severity. **Table.** | Unnamed: 0 | G6PD deficiency, OR (95% CI) | G6PD deficiency, OR (95% CI).1 | G6PD deficiency, OR (95% CI).2 | G6PD deficiency, OR (95% CI).3 | | --- | --- | --- | --- | --- | | | White male veterans | Black male veterans | Other male veteransb | All female veterans | | Veterans aged <65 y | | | | | | No. | 31 | 205 | 29 | 33 | | Crude | 1.74 (0.70-4.31) | 1.35 (0.97-1.88) | 1.29 (0.47-3.53) | 1.60 (0.64-3.96) | | Adjustedc | 1.25 (0.47-3.34) | 1.47 (1.03-2.09) | 0.75 (0.25-2.30) | 1.55 (0.61-3.93) | | Veterans aged ≥65 y | | | | | | No. | 37 | 104 | 12 | 3 | | Crude | 3.76 (1.74-8.13) | 0.91 (0.60-1.40) | 1.60 (0.46-5.61) | 0.70 (0.06-8.26) | | Adjustedc | 3.58 (1.64-7.80) | 0.95 (0.62-1.47) | 1.65 (0.44-6.17) | 1.06 (0.08-13.19) | ## Discussion Despite clinical and scientific progress made in the last 3 years with regards to COVID-19 treatments, vaccines and enhanced understanding of its mechanisms of infectivity, the biochemical and physiological reasons behind the disproportionate prevalence of serious complications in minoritized racial and ethnic groups remains unclear. This study is the first to present epidemiologic evidence that putatively link a biologic mechanism of impaired cellular responses in G6PD deficiency to increased COVID-19 severity. This finding is supported by in vitro studies and other studies of SARS/MERS prevalence in patients with G6PD deficiency. Our analysis revealed a strong association between G6PD deficiency and COVID-19 severity, modified by race and age. Although G6PD deficiency was associated with an approximately 1.5-fold increased likelihood of severe outcomes in young Black male veterans, associated increased COVID-19 severity may not be measurable in the older Black male population possibly due to other underlying comorbidities in this population (diabetes and chronic kidney disease, for example) that may already confer a ceiling effect on severity, and that ceiling effect may not be additionally altered (increased) by G6PD deficiency. Contrastingly, in the very small subset of White male veterans with G6PD deficiency, we observed an approximately 3.6-fold increased likelihood of developing severe outcomes from COVID-19 in those aged at least 65 years of age compared with White male veterans aged at least 65 years who were not G6PD-deficient. While differences between Black and White male veterans may be contributed by different G6PD-alleles (eg, G6PD-Mediterranean vs G6PD-A), DOD testing only assesses enzyme activity and does not universally determine genetic variants. Future studies to investigate the potential associations of specific G6PD alleles may be informative. Although this study did not find significant association in the subpopulations of female veterans and male veterans from other racial backgrounds (eFigure 2 in Supplement 1), our study cohort was not sufficiently powered to evaluate the impact of G6PD deficiency in these groups; thus, additional studies are required to fully assess the possible underlying risk in these populations. Several potential genetic modifiers related to severity from SARS-CoV-2 infection have been identified, indicating that multiple biochemical and molecular pathways, in addition to G6PD deficiency, are contributing to clinical outcomes.40,41,42,43 Genome-wide association studies (GWAS) have identified several genetic risk variants; however, these studies have focused primarily on genetic data from Northern European populations, excluding populations with admixture, an approach that excludes individuals with diverse and/or complex ancestral lineages.40,42,43,44 For example, multiple studies have linked variants on chromosome 3p21.31 to worse COVID-19 outcomes; however, the chromosome 3p21.31 allele identified in these studies is most common in European populations and less common in Latino and African American populations.40,41,42,43,44,45,46 These and other studies also fail to consider sex in their analyses, limiting the ability to identify specific risks for either sex.47 Although these approaches may be informative for larger populations for which genetic data may be more readily available, the exclusion of populations with genetic diversity can contribute to and perpetuate health disparities. The paucity of biologic and epidemiologic data regarding underserved communities in the United States affected by COVID-19 stem from (with few exceptions) a lack of minoritized racial and ethnic population enrollment in large-scale COVID-19 treatment trials and limited targeted investigations into how genetics and racial ancestry play a role in the inflammatory cascade that is a hallmark of severe infection in COVID-19.48,49,50,51,52 Inflammation and oxidative stress are interconnected processes, one being easily induced by the other and both involved in the pathogenesis of COVID-19.5,7,53 *Glutathione is* a crucial antioxidant that mounts a critical defense against oxidative damage from excess reactive oxygen radicals, and is repleted by G6PD. Glutathione augments the innate and the adaptive immunity, conferring protection against bacterial and viral infections.53 Separate smaller studies have shown that glutathione was significantly reduced in COVID-19 patients, suggesting that COVID-19 infection either depletes glutathione or that glutathione deficiency or insufficiency may exacerbate outcomes.53,54 The results of this present study in combination with other smaller cohort studies and case reports46,55,56,57,58,59 underscore the need for targeted investigations into how genetic risk and racial ancestry may contribute to the inflammatory cascade that appears to be a hallmark of severe COVID-19.49 *These data* provide an important initial step in addressing the limited biologic and epidemiologic data regarding underserved communities affected by COVID-19. Avoidance of health care inequities requires careful consideration of the person, their biological sex and gender, ancestry, and community to define underlying risks and to target effective treatment or prevention of disease. ## Strengths and Limitations This study used a large, national cohort of patients with broad racial and ethnic distribution. Our analysis is among the first and largest with G6PD test records applying a standard retrospective cohort approach to examine the association. This study also had limitations. Clinical outcomes, and demographic factors, such as race, were self-identified, and comorbidities were obtained through ICD-10 codes, which may contain misclassification errors. Mortality was limited to in-hospital mortality due to delays in outpatient death reports. Our study was limited to the treatments within VHA health care system, without access to treatments that patients may have received outside the VHA health care. Additional study is warranted by including Medicare and Medicaid information and additional confounding factors, including health care policy messaging, access to care, and usage of nonprescribed and prescribed medications outside of the VHA. ## Conclusions This cohort study found that younger Black male veterans with G6PD deficiency as a population had an increased likelihood of worse clinical outcomes from SARS-CoV-2 infection. These findings suggest a biologic contribution to these poor outcomes which can be further investigated with targeted efforts in at-risk populations. Appropriate selection of medications and modulation of glutathione levels in patients have the potential to reduce oxidative stress, boost immunity, and reduce the adverse outcomes of COVID-19 infection in the population with G6PD deficiency. This study highlights the need to review and determine possible underlying inherent genetic risks, such as G6PD deficiency, prior to illness or early in treatment course as a strategy to mitigate negative outcomes. ## References 1. 1Johns Hopkins University & Medicine. COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). Accessed September 12, 2021. https://coronavirus.jhu.edu/map.html 2. 2World Health Organization. WHO Coronavirus (COVID-19) Dashboard. Accessed September 12, 2021. https://covid19.who.int/ 3. Shelton JF, Shastri AJ, Ye C. **Trans-ancestry analysis reveals genetic and nongenetic associations with COVID-19 susceptibility and severity**. *Nat Genet* (2021.0) **53** 801-808. DOI: 10.1038/s41588-021-00854-7 4. Guan WJ, Ni ZY, Hu Y. **Clinical characteristics of coronavirus disease 2019 in China**. *N Engl J Med* (2020.0) **382** 1708-1720. DOI: 10.1056/NEJMoa2002032 5. Jain SK, Parsanathan R, Levine SN, Bocchini JA, Holick MF, Vanchiere JA. **The potential link between inherited G6PD deficiency, oxidative stress, and vitamin D deficiency and the racial inequities in mortality associated with COVID-19**. *Free Radic Biol Med* (2020.0) **161** 84-91. DOI: 10.1016/j.freeradbiomed.2020.10.002 6. Owen WF, Carmona R, Pomeroy C. **Failing another national stress test on health disparities**. *JAMA* (2020.0) **323** 1905-1906. DOI: 10.1001/jama.2020.6547 7. Ho HY, Cheng ML, Chiu DT. **Glucose-6-phosphate dehydrogenase–beyond the realm of red cell biology**. *Free Radic Res* (2014.0) **48** 1028-1048. DOI: 10.3109/10715762.2014.913788 8. Nkhoma ET, Poole C, Vannappagari V, Hall SA, Beutler E. **The global prevalence of glucose-6-phosphate dehydrogenase deficiency: a systematic review and meta-analysis**. *Blood Cells Mol Dis* (2009.0) **42** 267-278. DOI: 10.1016/j.bcmd.2008.12.005 9. Gómez-Manzo S, Marcial-Quino J, Vanoye-Carlo A. **Glucose-6-phosphate dehydrogenase: update and analysis of new mutations around the world**. *Int J Mol Sci* (2016.0) **17** 2069. DOI: 10.3390/ijms17122069 10. Lai YK, Lai NM, Lee SW. **Glucose-6-phosphate dehydrogenase deficiency and risk of diabetes: a systematic review and meta-analysis**. *Ann Hematol* (2017.0) **96** 839-845. DOI: 10.1007/s00277-017-2945-6 11. Martini SR, Kent TA. **Hyperglycemia in acute ischemic stroke: a vascular perspective**. *J Cereb Blood Flow Metab* (2007.0) **27** 435-451. DOI: 10.1038/sj.jcbfm.9600355 12. Thomas JE, Kang S, Wyatt CJ, Kim FS, Mangelsdorff AD, Weigel FK. **Glucose-6-phosphate dehydrogenase deficiency is associated with cardiovascular disease in U.S. military centers**. *Tex Heart Inst J* (2018.0) **45** 144-150. DOI: 10.14503/THIJ-16-6052 13. Yen WC, Wu YH, Wu CC. **Impaired inflammasome activation and bacterial clearance in G6PD deficiency due to defective NOX/p38 MAPK/AP-1 redox signaling**. *Redox Biol* (2020.0) **28**. DOI: 10.1016/j.redox.2019.101363 14. Vick DJ. **Evaluation of glucose-6-phosphate dehydrogenase (G6PD) status in US military and VA patients with COVID-19 infection**. *BMJ Mil Health* (2021.0) **167** 144. DOI: 10.1136/bmjmilitary-2020-001706 15. Vick DJ. **Glucose-6-phosphate dehydrogenase deficiency and COVID-19 infection**. *Mayo Clin Proc* (2020.0) **95** 1803-1804. DOI: 10.1016/j.mayocp.2020.05.035 16. Fouad MN, Ruffin J, Vickers SM. **COVID-19 is disproportionately high in African Americans. this will come as no surprise…**. *Am J Med* (2020.0) **133** e544-e545. DOI: 10.1016/j.amjmed.2020.04.008 17. Buinitskaya Y, Gurinovich R, Wlodaver CG, Kastsiuchenka S. **Centrality of G6PD in COVID-19: the biochemical rationale and clinical implications**. *Front Med (Lausanne)* (2020.0) **7**. DOI: 10.3389/fmed.2020.584112 18. Marshall A 19. Al-Abdi S, Al-Aamri M. **G6PD deficiency in the COVID-19 pandemic: ghost within ghost**. *Hematol Oncol Stem Cell Ther* (2021.0) **14** 84-85. DOI: 10.1016/j.hemonc.2020.04.002 20. Hiscott J. **Convergence of the NF-kappaB and IRF pathways in the regulation of the innate antiviral response**. *Cytokine Growth Factor Rev* (2007.0) **18** 483-490. DOI: 10.1016/j.cytogfr.2007.06.002 21. Wu YH, Chiu DT, Lin HR, Tang HY, Cheng ML, Ho HY. **Glucose-6-phosphate dehydrogenase enhances antiviral response through downregulation of NADPH sensor HSCARG and upregulation of NF-κB signaling**. *Viruses* (2015.0) **7** 6689-6706. DOI: 10.3390/v7122966 22. Lim S, Bae JH, Kwon HS, Nauck MA. **COVID-19 and diabetes mellitus: from pathophysiology to clinical management**. *Nat Rev Endocrinol* (2021.0) **17** 11-30. DOI: 10.1038/s41574-020-00435-4 23. Barron E, Bakhai C, Kar P. **Associations of type 1 and type 2 diabetes with COVID-19-related mortality in England: a whole-population study**. *Lancet Diabetes Endocrinol* (2020.0) **8** 813-822. DOI: 10.1016/S2213-8587(20)30272-2 24. Holman N, Knighton P, Kar P. **Risk factors for COVID-19-related mortality in people with type 1 and type 2 diabetes in England: a population-based cohort study**. *Lancet Diabetes Endocrinol* (2020.0) **8** 823-833. DOI: 10.1016/S2213-8587(20)30271-0 25. Lee J, Poitras BT. **Prevalence of glucose-6-phosphate dehydrogenase deficiency, U.S. Armed Forces, May 2004-September 2018**. *MSMR* (2019.0) **26** 14-17. PMID: 31860324 26. 26U.S. Veterans Health Administration. VA COVID-19 shared data resource. Accessed February 16, 2023. https://www.hsrd.research.va.gov/for_researchers/cyber_seminars/archives/3810-notes.pdf 27. Kelly JD, Bravata DM, Bent S. **Association of social and behavioral risk factors with mortality among US veterans with COVID-19**. *JAMA Netw Open* (2021.0) **4**. DOI: 10.1001/jamanetworkopen.2021.13031 28. 28Centers for Disease Control and Prevention. Underlying medical conditions associated with high risk for severe COVID-19. information for healthcare providers. Updated May 13, 2021. Accessed June 2021. https://www.cdc.gov/coronavirus/2019-ncov/hcp/clinical-care/underlyingconditions.html 29. Quan H, Sundararajan V, Halfon P. **Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data**. *Med Care* (2005.0) **43** 1130-1139. DOI: 10.1097/01.mlr.0000182534.19832.83 30. 30Centers for Disease Control and Prevention. Science Brief: Evidence used to update the list of underlying medical conditions that increase a person’s risk of severe illness from COVID-19. Accessed June 2021. https://www.cdc.gov/coronavirus/2019-ncov/science/science-briefs/underlying-evidence-table.html 31. Deyo RA, Cherkin DC, Ciol MA. **Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases**. *J Clin Epidemiol* (1992.0) **45** 613-619. DOI: 10.1016/0895-4356(92)90133-8 32. Ladha KS, Zhao K, Quraishi SA. **The Deyo-Charlson and Elixhauser-van Walraven Comorbidity Indices as predictors of mortality in critically ill patients**. *BMJ Open* (2015.0) **5**. DOI: 10.1136/bmjopen-2015-008990 33. Singh J, Malik P, Patel N. **Kidney disease and COVID-19 disease severity-systematic review and meta-analysis**. *Clin Exp Med* (2022.0) **22** 125-135. PMID: 33891214 34. Bajgain KT, Badal S, Bajgain BB, Santana MJ. **Prevalence of comorbidities among individuals with COVID-19: a rapid review of current literature**. *Am J Infect Control* (2021.0) **49** 238-246. DOI: 10.1016/j.ajic.2020.06.213 35. Karadsheh NS, Quttaineh NA, Karadsheh SN, El-Khateeb M. **Effect of combined G6PD deficiency and diabetes on protein oxidation and lipid peroxidation**. *BMC Endocr Disord* (2021.0) **21** 246. DOI: 10.1186/s12902-021-00911-6 36. Arai Y. **G6PD deficiency: a possible cardiovascular risk factor in older people**. *J Atheroscler Thromb* (2021.0) **28** 586-587. DOI: 10.5551/jat.ED152 37. Parsanathan R, Jain SK. **Glucose-6-phosphate dehydrogenase (G6PD) deficiency is linked with cardiovascular disease**. *Hypertens Res* (2020.0) **43** 582-584. DOI: 10.1038/s41440-020-0402-8 38. Pes GM, Parodi G, Dore MP. **Glucose-6-phosphate dehydrogenase deficiency and risk of cardiovascular disease: a propensity score-matched study**. *Atherosclerosis* (2019.0) **282** 148-153. DOI: 10.1016/j.atherosclerosis.2019.01.027 39. 39Council on Foreign Relations. Demographics of the U.S. military. Updated July 13, 2020. Accessed 2021. https://www.cfr.org/backgrounder/demographics-us-military. (2020.0) 40. **Mapping the human genetic architecture of COVID-19**. *Nature* (2021.0) **600** 472-477. DOI: 10.1038/s41586-021-03767-x 41. Pairo-Castineira E, Clohisey S, Klaric L. **Genetic mechanisms of critical illness in COVID-19**. *Nature* (2021.0) **591** 92-98. DOI: 10.1038/s41586-020-03065-y 42. Thibord F, Chan MV, Chen MH, Johnson AD. **A year of COVID-19 GWAS results from the GRASP portal reveals potential genetic risk factors**. *HGG Adv* (2022.0) **3**. DOI: 10.1016/j.xhgg.2022.100095 43. Peloso GM, Tcheandjieu C, McGeary JE. **Genetic loci associated with COVID-19 positivity and hospitalization in White, Black, and Hispanic veterans of the VA Million Veteran Program**. *Front Genet* (2022.0) **12**. DOI: 10.3389/fgene.2021.777076 44. Marçalo R, Neto S, Pinheiro M. **Evaluation of the genetic risk for COVID-19 outcomes in COPD and differences among worldwide populations**. *PLoS One* (2022.0) **17**. DOI: 10.1371/journal.pone.0264009 45. Hung AM, Shah SC, Bick AG. **APOL1 risk variants, acute kidney injury, and death in participants with African ancestry hospitalized with COVID-19 from the Million Veteran Program**. *JAMA Intern Med* (2022.0) **182** 386-395. DOI: 10.1001/jamainternmed.2021.8538 46. Yang HC, Ma TH, Tjong WY, Stern A, Chiu DT. **G6PD deficiency, redox homeostasis, and viral infections: implications for SARS-CoV-2 (COVID-19)**. *Free Radic Res* (2021.0) **55** 364-374. DOI: 10.1080/10715762.2020.1866757 47. Brady E, Nielsen MW, Andersen JP, Oertelt-Prigione S. **Lack of consideration of sex and gender in COVID-19 clinical studies**. *Nat Commun* (2021.0) **12** 4015. DOI: 10.1038/s41467-021-24265-8 48. Wiltz JL, Feehan AK, Molinari NM. **Racial and ethnic disparities in receipt of medications for treatment of COVID-19 - United States, March 2020-August 2021**. *MMWR Morb Mortal Wkly Rep* (2022.0) **71** 96-102. DOI: 10.15585/mmwr.mm7103e1 49. Bambra C. **Pandemic inequalities: emerging infectious diseases and health equity**. *Int J Equity Health* (2022.0) **21** 6. DOI: 10.1186/s12939-021-01611-2 50. Borno HT, Zhang S, Gomez S. **COVID-19 disparities: an urgent call for race reporting and representation in clinical research**. *Contemp Clin Trials Commun* (2020.0) **19**. DOI: 10.1016/j.conctc.2020.100630 51. Chastain DB, Osae SP, Henao-Martínez AF, Franco-Paredes C, Chastain JS, Young HN. **Racial disproportionality in Covid clinical trials**. *N Engl J Med* (2020.0) **383**. DOI: 10.1056/NEJMp2021971 52. Bischof E, Wolfe J, Klein SL. **Clinical trials for COVID-19 should include sex as a variable**. *J Clin Invest* (2020.0) **130** 3350-3352. DOI: 10.1172/JCI139306 53. Pincemail J, Cavalier E, Charlier C. **Oxidative stress status in COVID-19 patients hospitalized in intensive care unit for severe pneumonia. a pilot study**. *Antioxidants (Basel)* (2021.0) **10** 257. DOI: 10.3390/antiox10020257 54. Polonikov A. **Endogenous deficiency of glutathione as the most likely cause of serious manifestations and death in COVID-19 patients**. *ACS Infect Dis* (2020.0) **6** 1558-1562. DOI: 10.1021/acsinfecdis.0c00288 55. AbouYabis AN, Bell GT. **Hemolytic anemia complicating COVID-19 Infection**. *J Hematol* (2021.0) **10** 221-227. DOI: 10.14740/jh906 56. Kumar N, AbdulRahman A, AlAwadhi AI, AlQahtani M. **Is glucose-6-phosphatase dehydrogenase deficiency associated with severe outcomes in hospitalized COVID-19 patients?**. *Sci Rep* (2021.0) **11** 19213. DOI: 10.1038/s41598-021-98712-3 57. Elalfy M, Adly A, Eltonbary K. **Management of children with glucose-6-phosphate dehydrogenase deficiency presenting with acute haemolytic crisis during the SARs-COV-2 pandemic**. *Vox Sang* (2022.0) **117** 80-86. DOI: 10.1111/vox.13123 58. Aydemir D, Dağlıoğlu G, Candevir A. **COVID-19 may enhance risk of thrombosis and hemolysis in the G6PD deficient patients**. *Nucleosides Nucleotides Nucleic Acids* (2021.0) **40** 505-517. DOI: 10.1080/15257770.2021.1897457 59. Naymagon L, Berwick S, Kessler A, Lancman G, Gidwani U, Troy K. **The emergence of methemoglobinemia amidst the COVID-19 pandemic**. *Am J Hematol* (2020.0) **95** E196-E197. DOI: 10.1002/ajh.25868
--- title: 'Risk perception of cardiovascular disease among Turkish adults: a cross-sectional study' authors: - Sevcan Topçu - Melek Ardahan journal: Primary Health Care Research & Development year: 2023 pmcid: PMC10061271 doi: 10.1017/S1463423623000117 license: CC BY 4.0 --- # Risk perception of cardiovascular disease among Turkish adults: a cross-sectional study ## Abstract ### Aim: The aim of the study was to determine in adults the risk perception for cardiovascular disease (CVD) and the associated factors. ### Background: CVDs are the leading cause of death globally. In adults, perceptions related to the risk for CVDs have a considerable effect on decision-making processes related to one’s own health. ### Methods: A cross-sectional study was conducted with 453 adult people from April to June 2019 in İzmir, Turkey. Data were collected with a sociodemographic characteristics questionnaire, perception of risk of heart disease scale (PRHDS), and health perception. ### Findings: The mean PRHDS score of adults was 48.88 ± 8.12. The risk perception for CVD was influenced by variables that were age, gender, education, marital status, employment status, health perception, familial cardiovascular disease history, chronic disease status, smoking status, and body mass index. Although CVDs are the most prominent cause of disease-related death in the world, risk perception for CVD was found to be low among the individuals included in this study. This finding indicates the importance of informing individuals about CVD risk factors, raising awareness, and training. ## Introduction Non-communicable diseases (NCDs) include chronic conditions that are not caused by an infectious process, are non-transmissible, have prolonged courses, are not readily resolved, and do not have a complete cure available. NCDs kill 41 million people each year, equivalent to $71\%$ of all deaths globally (WHO, 2021). The World Health Organization (WHO) explained in “Ten threats to global health in 2019” that NCDs, such as diabetes, cancer, and cardiovascular disease (CVD), are among one of the ten global health problems (WHO, 2019). In addition, the burden of disability is primarily driven by NCDs, which were responsible for $80\%$ of disabilities in 2017 (IHME, 2018). Low- and middle-income countries carry the greatest share of these premature deaths, as $85\%$ of NCD-mediated early deaths occur in these countries (WHO, 2021). NCDs are estimated to be responsible for $86\%$ of total deaths in Turkey (Üner, Balcılar, Ergüder, 2018). NCDs include CVD, cancer, chronic respiratory diseases, and diabetes. CVD is the most important member of these diseases (WHO, 2021). While CVD-associated deaths worldwide were 17.9 million people annually, this number is estimated to increase up to 22.2 million in 2030 (WHO, 2017). Chronic diseases are gradually increasing due to the aging population and changing lifestyles in our country. According to the Turkish Statistical Institute’s (TSI) data, $37.8\%$ of deaths occur due to circulatory system diseases (TSI, 2020). The deaths due to cardiovascular system diseases are most often seen in individuals within 75–84 age group (TSI, 2020). The most important risk factors that cause death and disability in Turkey are tobacco use, high body-mass index, and high blood pressure (IHME, 2018). Smoking, insufficient physical activity, alcohol consumption, unhealthy nutrition, obesity, hypertension, diabetes, and high blood cholesterol are considered the basic risk factors for CVD (Alissa, 2017; Üner et al., 2018). According to the WHO, unhealthy nutrition, insufficient physical activity, smoking, and alcohol consumption represent the most important behavioral risk factors, with consequent effects including high blood pressure, high blood glucose, high lipid levels, extreme weight gain, and obesity (WHO, 2017). Risk perception is a cognitive process that guides people’s behaviors in the face of situations involving potential risks (Ammouri et al., 2011; Pender et al., 2015). Risk perception can vary from person to person, and it can affect health-related behaviors, as well as many other aspects of life. In terms of health, risk perception is an important consideration that determines an individual’s commitment to a healthy lifestyle (Dayal and Singh, 2017). According to the Health Belief Model, health behaviors are affected by an individual’s values, beliefs, and attitudes (Pender et al., 2015). If individuals believe that a health problem will cause them serious harm, they are aware that the potential for harm will decrease when they take action to reduce the risk. People who perceive themselves to be at risk of negative consequences can regulate risky behavior better than those who do not see themselves as at risk. Risk perception is an important precursor to adopting risk reduction behaviors (Janz and Becker, 1984), and this is affected by different social, cultural, and contextual factors. Therefore, risk perception is a necessary condition for the acquisition of healthy behaviors, but it is not sufficient on its own. In adults, perceptions related to the risk for CVD have a considerable effect on decision-making processes related to one’s own health. CVD risk perception is one of the most important determinants for individuals to develop and maintain a healthy lifestyle; individuals who do not perceive themselves to be at risk for the development of CVD are unlikely to have behaviors related to a healthy lifestyle (Ammouri et al., 2018; Hart, 2005). Individuals with higher CVD risk perception are much more likely to adopt risk-reducing behaviors such as smoking cessation, consistent exercise, and healthy nutrition. A lack of risk perception prevents adults from undertaking protective health behaviors and seeking interventions for the early treatment of CVD (Ammouri et al., 2018; Hart, 2005). Therefore, risk perception of CVD is an important feature that should be more rigorously evaluated. The aim of the study was to determine in adults the risk perception for CVD and the associated factors. ## Study design and sample The population of this study was composed of Turkish adults in the province of Izmir, West Turkey ($$n = 442$$ 839). The sample calculation of this study was performed using the G-Power statistical analysis program (α:0.05, β:0.05, and d:0.5). According to Cohen [1988], a medium effect size was preferred in the sample calculation. The sample size required for this study was determined to be 423. After including a nonrespondent rate of $10\%$, the sample size was calculated as 465. The criteria for participation were as follows: (a) adult age (18 years or older), (b) ability to communicate in Turkish languages, and (c) ability to provide informed consent. A questionnaire was asked to 465 participants. There were 12 participants who did not answer some of the questions and were, therefore, excluded from the survey. The remaining 453 (%97.4) participants completed the questionnaire in full and were included in this study. ## Data collection Data were collected in the Bornova district of İzmir province an area that ranks second in deaths caused by CVD according to Turkish statistics through face-to-face survey method from April to June 2019. This district is an old residential area consisting of 45 neighborhoods where 442 839 people live. The nine neighborhoods where 5102 adults live in (18 years old and over) were randomly selected for this study considering the rate of not responding to the face-to-face surveys and the possibility that potential participants could not be reached for several reasons (not being at home, employment, etc.). Face-to-face surveys were held in households in nine neighborhoods. Face-to-face surveys were preferred to increase the response rate. Data were collected by two researchers after the study’s purpose was explained to the potential participants. Surveys were started with the first adult resident contacted at a random sample of residences for every selected neighborhood and were continued with the nearest household to this. Each survey was completed in 10–15 min. Researchers returned to the same neighborhoods an average of six times until reaching the required sample size. ## Instruments The data collection tools the sociodemographic characteristics questionnaire, the perception of risk of heart disease scale (PRHDS), and health perception were used. The sociodemographic characteristics questionnaire consisted of total 11 questions: age, gender, education status, marital status, employment status, income level, chronic disease status, familial CVD history, smoking status, height, and weight. As recommended by the WHO, participants were classified according to body mass index (BMI) as follows: <18.5 underweight, 18.5–24.9 normal weight, 25.0–29.9 overweight, 30.0–34.0 obesity class I, 35.0–39.0 obesity class II, and above 40.0 obesity class III (WHO, 2010). Ammouri and Neuberger [2008] developed the PRHDS to determine the risk of individual CVD. PRHDS is a 4-point Likert scale consisting of 20 items and three subscales. Dread risk is defined at its high end as perceived dread, catastrophic potential, lack of control, and fatal consequences. Risk is reflected as a hazard that has a few moderate, known outcomes and consequences. Unknown risk is defined at its low end as the perception of hazards judged to be unobservable, unknown, new, and delayed in their manifestation of harm (Ammouri and Neuberger, 2008). The scores that can be taken from the scale vary between 20 and 80. Risk perception level increases as the scores obtained from scale are increased. The scale’s Turkish validity-reliability study was carried out by Toptaner [2013]. Cronbach’s alpha coefficient was 0.80 (Toptaner, 2013). In this study, Cronbach’s alpha coefficient was 0.84. Health perception was rated by adults and measured with five point Likert Scale (very good, good, fair, poor, and very poor). ## Data analyses Data were analyzed using IBM Statistical Package for Social Sciences (SPSS version 20.0). Sociodemographic characteristics reported as frequencies, mean, and percentages. Multivariate regression analysis was utilized for evaluation of PRHDS, dread risk, risk and unknown risk, and affective factors ## Ethical considerations In order to carry out the study, permission was obtained from University Medical Research Ethics Committee (Approval Number:19-4T/45). Written informed consent was obtained from the persons who agreed to participate in the study. ## Results Participants’ age ranged from 18 to 86 ($M = 38.53$ ± 13.42). Of the sample, $53.9\%$ were women, $66.4\%$ were married, $76.2\%$ were employed, and $56.1\%$ had bachelor’s degree (Table 1). Table 1.Sociodemographic characteristicsVariablesParticipants ($$n = 453$$)n% Age (years; M and SD) 38.5313.42 Gender Man20946.1 Woman 244 53.9 Marital Status (%) Married 301 66.4 Single15233.6 Education Status (%) Literate122.6 Elementary School7616.8 Junior High School11124.5 University and above 254 56.1 Vocational Status (%) Working 345 76.2 Non-working10823.8 Income Status (%) Income equal to expense 298 65.8 Income more than expense7516.6 Income less than expense8017.7 Smoking Status (%) Smoke12627.8 Don’t Smoke 282 62.3 Quit smoke459.9 Chronic Disease Status Yes9019.9 No 363 80.1 Familial CVD History Yes18641.1 No 267 58.9 Self-Rated Health Perception Very good 59 13 Good 248 54.7 Fair 137 30.2 Poor 8 1.8 Very poor 1 0.3 Body Mass Index <18.5143.1 18.5–24.920946.1 25.0–29.918540.8 ≥30459.9 The mean PRHDS score of adults was 48.88 ± 8.12, the mean of dread risk subscale score was 16.09 ± 4.34, the mean of risk subscale score was 15.22 ± 3.22, and the mean of unknown risk subscale score was 17.55 ± 3.49 (Table 2). Table 2.PRHDS and Subscale score of participants ($$n = 453$$)X ± SS (min–max)Possible RangePRHDS48.88 ± 8.12 (22–74)20–80Dread Risk16.09 ± 4.34 (7–28)7–28Risk15.22 ± 3.22 (6–23)6–24Unknown Risk17.55 ± 3.49 (7–28)7–28 PRHDS and subscales, age, gender, education, marital status, employment status, health perception, familial CVD history, chronic disease status, smoking status, and BMI were assessed using regression analysis. At the end of regression analysis, a statistically significant was detected between these variables and risk perception for CVD (R2 = 0.27, $P \leq 0.01$), dread risk (R2 = 0.22, $P \leq 0.01$), risk (R2 = 0.17, $P \leq 0.01$), and unknown risk (R2 = 0.19, $P \leq 0.01$). This model showed that the variables were powerful predictors of perception of CVD. The risk perception of CVD was affected by age, education, marital status, employment, health perception, presence of familial CVD history, chronic diseases status, smoking status, and BMI ($P \leq 0.05$). Age, marital status, employment, health perception, presence of familial CVD history, and chronic disease status had a significant association with the dread risk subscale ($P \leq 0.05$). In addition, also the variable of age, gender, education, marital status, employment, health perception, and smoking had affected risk subscale statistically significantly; age, education status, employment, health perception, smoking, and BMI variables had affected unknown risk subscale statistically significantly ($P \leq 0.05$) (Table 3). Table 3.Multiple regression analysis of demographic variables on perception of risk of CVD ($$n = 453$$)VariablesPRHDSDread RiskRiskUnknown RiskβpβpβpβpAge0.240.010.140.020.250.010.160.01Gender0.120.010.080.110.130.010.070.20Education0.120.03−0.020.590.110.040.210.01Marital Status0.150.010.100.040.160.010.090.06Employment Status0.270.010.140.020.240.010.220.01Health Perception−0.210.01−0.220.01−0.130.01−0.110.02Familial CVD History0.140.010.180.010.070.150.040.43Chronic Disease Status0.120.010.140.010.030.560.080.12Smoking Status−0.100.020.040.35−0.120.01−0.170.01BMI−0.110.020.000.950.060.23−0.200.01R0.520.470.420.44R2 0.270.220.170.19F16.1112.539.2110.6p0.010.010.010.01*$p \leq 0.05.$ ## Discussion In this study, the risk perception for CVD and the associated factors was to determine in adults. Previous studies have documented that the risk perception for CVD is not at the desired level. For example, Dayal and Singh [2017] surveyed a group of 20- to 40-year-old adults and found that more than half of the participants assumed themselves at risk of heart disease in the future, with a medium score of risk perception for CVD. In another study conducted on 300 Jordanians adults, Ammouri et al., [ 2011] found that despite the fact that CVD is a major cause of death in Jordan, participants had a medium score of risk perception for CVD. Also, the same study stated that there was a need for heart disease education programs for all adults. Johnson et al. [ 2015] carried out a study among 174 adults ages 40–79 years old who had three or more basic CVD risk factors and found that participants had a high mean score of risk perception for CVD (56 out of 80). Although CVD is the leading cause of death in Turkey, this study found Turkish individuals to have a insufficient level of perceived risk for this disease, which is consistent with similar studies (Ammouri et al., 2011; Dayal and Singh, 2017). An understanding of the participants’ perception of CVD risk is important in reducing the associated morbidity and disability rates. The results of this study show that adults ignore the risk of CVD. The basis of the fight against CVD is the detection and prevention of risk factors, including smoking, being overweight or obese, insufficient physical activity, and an unhealthy diet. Risk perception is an important precursor to engaging in preventive behaviors and making appropriate changes (Janz and Becker, 1984). Several other studies found that the level of perceived risk for CVD increased when individuals were provided with education about risk factors or when these were discussed during individual consultations (Toptaner, 2013; Bustanji and Majali, 2013). Therefore, particular emphasis should be placed on informing individuals about CVD risk factors. Aging is a nonmodifiable risk factor for CVD. That means as age increases, the risk of CVD also increases (Zipes et al., 2018). Dayal and Singh [2017] found that age had a strong and significant association with dread risk and risk subscales. In this study, increased age was associated with higher PRHDS, dread risk, and unknown risk. These results showed that increased age made individuals more aware of the risk of CVD. Malyutina et al. [ 2004] reported that the mortality rates of cardiovascular and coronary heart diseases were higher in single women and men compared to their married counterparts. Many studies have established that CVD risk and mortality rates are lower in married people compared to single individuals and have also found that single men were the population at greatest risk for CVD and associated death (Eaker et al., 2007; Manfredini et al., 2017). In this study, the mean PRHDS, dread, and risk subscale scores were lower in single compared to married individuals. Although single individuals are known to be at greater risk for CVD, this population does not tend to consider themselves at risk. In a study by Tillmann et al. [ 2017], low education level was identified as a causal risk factor for the development of coronary heart disease. Ammouri et al. [ 2011] reported that the risk perception for CVD was higher in individuals with higher education levels. Similarly, in this study found that education level affected risk perception for CVD, and risk, unknown risk subscales, and PRHDS were correlated with education level. These data suggest that individuals with higher education levels are better able to understand and evaluate CVD risk factors. Employment status is another influential factor for risk perception of CVD. Dayal and Singh [2017] reported that employment status was associated with PRHDS score, and risk perception for CVD was higher in retired individuals compared to other groups. In this study, PRHDS, dread risk, unknown risk, and risk were found to be higher in working individuals than other populations. Health perception is a basic concept that determines the realization and maintenance of preventive health behaviors (Pender et al., 2015; Saleh et al., 2019). Several studies showed that individuals who had worse health perception had higher mortality risks related to CVD (Stenholm et al., 2016; Waller et al., 2015). However, health perception is not always compatible with people’s current health status (Pender et al., 2015). Holt et al. [ 2020] found that perceived CVD risk was significantly higher among participants who rated their general health as fair or poor. Similarly, in this study, worse health perception was significantly associated with higher PRHDS and subscale scores. These results suggest that health perception is a very effective predictor for perceived risk of CVD. Therefore, nurses should absolutely evaluate health perception when discussing CVD risk factors with adults. Petitte [2018] found that university students who interview their grandmother/grandfather about their CVD showed increased risk perception scores for CVD. In addition, individuals with a familial CVD history and those having numerous family members with current disease showed a higher risk perception for CVD and increased perception of dread risk compared to other groups. In the present study of adults, the presence of chronic diseases, and knowledge of familial CVD history increased the risk perception of CVD and dread risk. Obesity (Kalyoncuoğlu et al., 2017) and smoking (Lemos and Omland, 2017) are two fundamental CVD risk factors. Smoking doubles the CVD risk (Lemos and Omland, 2017). Smoking cessation is considered to be the most effective protective measure for CVD (Lemos and Omland, 2017). A study seeking to evaluate current factors related to NCDs in Turkey found that $47.8\%$ of participants had 1 or 2 risk factors related to CVD (Üner et al., 2018). In this study, increased BMI was associated with lower PRHDS and unknown risk for CVD, whereas smoking decreased the PRHDS and perception of risk and unknown risk. Individuals who do not consider themselves at risk for CVD do not attempt to prevent or control the disease, even though they may possess some risk factors (Ammouri and Neuberger, 2008). The results of this study demonstrate that many individuals do not believe themselves to be at risk of CVD despite having important risk factors such as smoking and being overweight or obese. Even smokers had a lower level of perceived risk of CVD while also being afraid of it. Recent studies show that underestimating risk factors (Soroush et al., 2017), poor risk perception (Saeidi and Komasi, 2018), and unhealthy lifestyles (Chu et al., 2016) are among the main causes of increased CVD risk. Risk perception plays a prominent role in the prevention of CVD by increasing readiness for lifestyle changes (Barnhart et al., 2009). Ammouri et al. [ 2011] state that when people compare themselves to others with similar characteristics, such as age, sex, eating habits, working conditions, and lifestyle habits, they tend to underestimate their own risk factors. Therefore, participants do not perceive themselves to be at risk despite having certain risk factors. ## Limitations This study has some limitations. Self-reporting was used to evaluate risk perception of CVD and weight–height-related measurements. Therefore, a social desirability bias could exist. The study was a cross-sectional study, and in future studies, should be employed a longitudinal design to confirm the findings, and investigate the causality of relationships. ## Conclusion Although CVD is the most prominent cause of disease-related death in world, risk perception for CVD was found to be low among the individuals included in this study. The risk perception for CVD disease was influenced by diverse variables including age, gender, education, marital status, employment status, health perception, familial CVD disease history, chronic diseases, smoking, and BMI. Even if healthy individuals do not yet have a disease, they may be at risk of certain diseases due to their family history, eating habits, and lifestyle behaviors. An understanding of society’s perception of CVD risk and the variables that affect this is imperative in raising awareness of potential risks and preventing disease. Thus, individuals with a high risk of CVD but a low level of perceived risk can be identified, and the necessary interventions can be implemented. When the economic burden of CVDs is evaluated, the recommended fundamental strategy is primary protection, which involves disease prevention and risk evaluations. More training should be given by nurses to increase community awareness of CVD and its risk factors and to reduce CVD morbidity and mortality rates. Community-based screening programs that include risk factors such as blood pressure and lipid levels should be provided and training opportunities that encourage individuals to undertake protection measures should be implemented by public health nurses who are in direct contact with the community. ## Author contributions Study design: S.T., M.A. Data collection: S.T. Data analysis: S.T., M.A. Manuscript writing and revisions for important intellectual content: S.T., M.A ## Funding statement The author(s) received no financial support for the research, authorship, and/or publication of this article. ## References 1. Alissa N. (2017) 2. Ammouri AA, Neuberger G. **The perception of risk of heart disease scale: Development and psychometric analysis**. *Journal of Nursing Measurement* (2008) **16** 83-97. PMID: 18939714 3. Ammouri AA, Abu Raddaha AH, Natarajan J, D’souza MS. **Perceptions of risk of coronary heart disease among people living with type 2 diabetes mellitus**. *International Journal of Nursing Practice* (2018) **24** 1-9 4. Ammouri AA, Neuberger G, Mrayyan MT, Hamaideh SH. **Perception of risk of coronary heart disease among Jordanians**. *Journal of Clinical Nursing* (2011) **20** 197-203. PMID: 20550620 5. Barnhart JM, Wright ND, Freeman K, Silagy F, Correa N, Walker EA. **Risk perception and its association with cardiac risk and health behaviors among urban minority adults: the Bronx Coronary Risk Perception study**. *American Journal of Health Promotion* (2009) **23** 339-342. PMID: 19445437 6. Bustanji MM, Majali S. **Effect of combined interventions of diet and physical activity on the perceived and actual risk of coronary heart disease among women in north of jordan**. *World Journal of Medical Sciences* (2013) **9** 184-189 7. Chu P, Pandya A, Salomon JA, Goldie SJ, Hunink MM. **Comparative effectiveness of personalized lifestyle management strategies for cardiovascular disease risk reduction**. *Journal of the American Heart Association* (2016) **5** 1-16 8. Cohen J. *Statistical power analysis for the behavioral sciences* (1988) 9. Dayal B, Singh N. **Perception of risk of cardiovascular disease among early adulthood in Lucknow city**. *Al Ameen Journal of Medical Sciences* (2017) **10** 112-118 10. Eaker ED, Sullivan LM, Kelly-Hayes M, D’Agostino EB, Benjamin EJ. **Marital status, marital strain, and risk of coronary heart disease or total mortality: the Framingham Offspring Study**. *Psychosomatic Medicine* (2007) **69** 509-513. PMID: 17634565 11. Hart P. **Women’s perceptions of coronary heart disease: an integrative review**. *Journal of Cardiovascular Nursing* (2005) **20** 170-176. PMID: 15870587 12. Holt EW, Cass AL, Park H, Criss S, Burges M, Isley E, Murr S. **Perceived versus actual risk of cardiovascular disease in college students**. *American Journal of Health Education* (2020) **51** 59-68 13. Institute for Health Metrics and Evaluation (IHME). (2018) Findings from the Global Burden of Disease Study 2017. USA: Seattle.. *Findings from the Global Burden of Disease Study 2017* (2018) 14. Janz NK, Becker MH. **The health belief model: A decade later**. *Health Education Quarterly* (1984) **11** 1-47. PMID: 6392204 15. Johnson JE, Gulanick M, Penckofer S, Kouba J. **Does knowledge of coronary artery calcium affect cardiovascular risk perception, likelihood of taking action, and health-promoting behavior change?**. *Journal of Cardiovascular Nursing* (2015) **30** 15-25. PMID: 24434820 16. Kalyoncuoğlu M, Öztürk S, Durmuş G, Keskin B, Can MM. **Current approach to the chronic ischemic heart disease in the light of the current diagnosis and assessment guidelines**. *Haseki Tip Bulteni* (2017) **55** 85 17. Lemos JD, Omland T. *Chronic coronary artery disease: A companion to Braunwald’s heart disease* (2017) 18. Malyutina S, Bobak M, Simonova G, Gafarov V, Nikitin Y, Marmot M. **Education, marital status, and total and cardiovascular mortality in Novosibirsk, Russia: a prospective cohort study**. *Annals of Epidemiology* (2004) **14** 244-249. PMID: 15066603 19. Manfredini R, De Giorgi A, Tiseo R, Boari B, Cappadona R, Salmi R, Gallerani M, Signani F, Manfredini F, Mikhailidis DP, Fabbian F. **Marital status, cardiovascular diseases, and cardiovascular risk factors: a review of the evidence**. *Journal of Women’s Health* (2017) **26** 624-632 20. Pender NJ., Murdaugh CL, Parsons MA. *Health promotion in nursing practice Harrisonburg* (2015) 21. Petitte SR. (2018) 22. Saeidi M, Komasi S. **A predictive model of perceived susceptibility during the year before coronary artery bypass grafting**. *The Journal of Tehran University Heart Center* (2018) **13** 6. PMID: 29997664 23. Saleh ZT, Connell A, Lennie TA, Bailey AL, Elshatarat RA, Yousef K, Moser DK. **Cardiovascular disease risk predicts health perception in prison inmates**. *Clinical Nursing Research* (2019) **28** 235-251. PMID: 29117723 24. Soroush A, Komasi S, Saeidi M, Heydarpour B, Carrozzino D, Fulcheri M, Marchettini P, Rabboni M, Compare A. **Coronary artery bypass graft patients’ perception about the risk factors of illness: educational necessities of second prevention**. *Annals of Cardiac Anaesthesia* (2017) **20** 303. PMID: 28701594 25. Stenholm S, Kivimäki M, Jylhä M, Kawachi I, Westerlund H, Pentti J, Goldberg M, Zins M, Vahtera JT. **Trajectories of self-rated health in the last 15 years of life by cause of death**. *European Journal of Epidemiology* (2016) **31** 177-185. DOI: 10.1007/s10654-015-0071-0 26. Tillmann T, Vaucher J, Okbay A, Pikhart H, Peasey A, Kubinova R, Pajak A, Tamosiunas A, Malyutina S, Hartwig FP, Fischer K, Veronesi G, Palmer T, Bowden J, Smith GD, Bobak M, Holmes MV. **Education and coronary heart disease: mendelian randomisation study**. *BMJ* (2017) **358** j3542. PMID: 28855160 27. Toptaner NE. (2013) 28. TSI. (2020) Cause of Death Statistics 2019. Available from: https://data.tuik.gov.tr/Bulten/Index?p=Olum-ve-Olum-Nedeni-Istatistikleri-2019-33710. (2020) 29. Üner S, Balcılar M, Ergüder T. *National Household Health Survey – Prevalence of Noncommunicable Disease Risk Factors in Turkey 2017 (STEPS)* (2018) 30. Waller G, Janlert U, Norberg M, Lundqvist R, Forssén A. **Self-rated health and standard risk factors for myocardial infarction: A cohort study**. *British Medical Journal Open* (2015) **5** e006589 31. WHO. (2010) A healthy lifestyle - WHO recommendations. Available from: https://www.who.int/europe/news-room/fact-sheets/item/a-healthy-lifestyle---who-recommendations. (2010) 32. WHO. (2017) Cardiovascular diseases (CVDs) Fact Sheets. Available from: http://www.who.int/en/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds). (2017) 33. WHO. (2019) Ten threats to global health in 2019. Available from: https://www.who.int/news-room/spotlight/ten-threats-to-global-health-in-2019. (2019) 34. WHO. (2021) Noncommunicable diseases. 2021. Available from: https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases. (2021) 35. Zipes DP, Libby P, Bonow RO, Mann DL, Tomaselli GF. *Braunwald’s heart disease E-book: A textbook of cardiovascular medicine* (2018)
--- title: Impact of Prenatal Group Healthy Relationship Education on Postpartum Contraception authors: - Sara E. Mazzoni - Maggie O'Reilly Treter - Jennifer Hyer - Rachel Peña - Galena K. Rhoades journal: Women's Health Reports year: 2023 pmcid: PMC10061321 doi: 10.1089/whr.2022.0104 license: CC BY 4.0 --- # Impact of Prenatal Group Healthy Relationship Education on Postpartum Contraception ## Abstract ### Objective: We aimed to evaluate the impact of an antenatal group healthy relationship education program on the postpartum use of long-acting reversible contraception (LARC). ### Materials and Methods: This is a planned subgroup analysis of a larger randomized controlled trial. Pregnant and newly parenting women were randomized to either group healthy relationship education, “MotherWise,” or no additional services. An evidence-based healthy relationship education program and individual case management sessions were provided. The program did not include any prenatal care or contraception counseling. This subgroup analysis included those participants with a nonanomalous gestation randomized at <40 weeks who received care and delivered at a single safety-net hospital and were discharged home with a live infant(s). ### Results: From September 2, 2016 to December 21, 2018, 953 women were randomized in the larger trial; 507 met inclusion criteria for this study; 278 randomized to program and 229 controls. Participants were mostly young, parous, Hispanic, publicly insured women. Participants randomized to program were more likely to take a prescription medicine and be delivered through cesarean; there were not any other significant differences in baseline, antenatal, or perinatal outcomes. Those randomized to program were more likely to be discharged home with immediate postpartum LARC in place (odds ratio [OR] 1.87; confidence interval [CI] 1.17–3.00), and more likely to be using LARC at the postpartum visit (OR 2.19; CI 1.34–3.56). ### Conclusion: Antenatal group healthy relationship education provided separately from prenatal care is associated with a twofold increase in the use of postpartum LARC. ### Clinical Trial Registration: ClinicalTrials.gov NCT02792309; https://clinicaltrials.gov/ct2/show/NCT02792309?term=NCT02792309&draw=2&rank=1 ## Introduction Pregnancy may be considered a window of opportunity when women are motivated to change their health behaviors. Group prenatal care (GPC) is a model of prenatal care developed with the core principles of education, social support, and empowerment—a model, which lends itself nicely to this theory that antenatal interventions can impact not only immediate birth outcomes, but longer term health outcomes for the mother if health behaviors are changed. It is not surprising then that GPC has been shown to improve some postpartum outcomes, most notably the increased use of family planning services postpartum.1 Based on similar tenets as GPC, healthy relationship education programs are built on the foundation of teaching skills and tools to improve and maintain healthy intimate partner relationships. Among other things, they have been associated with higher relationship happiness, less physical assault, less psychological abuse, and lower psychological distress.2 These programs were traditionally developed and delivered to couples. However, they have also been shown efficacious when offered only to individuals and not both partners.3,4 Within My Reach,5 one such example, is designed to equip individuals with the skills, tools, and resources they need to make the best decisions for themselves and their families and has been shown to improve relationship skills and other family outcomes.6–8 It stands to reason then that an intervention during pregnancy aimed at empowering and educating women about healthy relationships would increase positive health behaviors postpartum—even when those health behaviors are not directly addressed by the intervention. Based on this theory, we developed a novel program combining the principles of GPC and healthy relationship education, “MotherWise.” The MotherWise program provides group healthy relationship education (Within My Reach) and one-on-one case management and does not provide any prenatal care. Our primary objective of this subanalysis was to evaluate the impact of MotherWise—when offered separate from routine prenatal care—at any time during pregnancy on postpartum use of effective contraception. ## Materials and Methods This study is a planned subgroup analysis of a larger randomized controlled trial in which participants were randomized to either a group healthy relationship education program, “MotherWise,” or no additional services from September 2, 2016 to December 21, 2018. Pregnant and newly parenting women were recruited from prenatal care visits at a safety-net hospital, as well as from the community through social media, radio, and social service referrals. Randomization was initially 3:2 to create groups of adequate numbers in the intervention arm, then changed to 1:1 when recruitment was sufficient after 7 months. An evidence-based healthy relationship education curriculum, Within My Reach, as well as brief information on infant care and parenting was provided across six weekly 4-hour group classes (24 hours total of curriculum). The curriculum included information on what healthy relationships are like, ways to leave unsafe relationships, skills for good communication and conflict management, as well as information we developed for MotherWise specifically on connecting with and caring for a newborn. A central theme of Within My *Reach is* “sliding vs. deciding,” a concept9 that encourages making empowered decisions rather than sliding into circumstances that may make a person feel stuck or lead to negative consequences, such as an unintended pregnancy. Groups were offered in English and Spanish and typically had 6 to 12 women. Within My *Reach is* a manualized intervention. Each session includes some lecture, some group discussion, a group activity, and individual workbook-based activities. Groups were cofacilitated by two facilitators who were trained in a 3-day training by the Within My Reach developers. A developer also provided group supervision every other week. Some facilitators were social workers or psychology PhD students, others had no formal training in related fields, but were selected based on their experience with the population and their strong facilitation skills. In addition to the groups, family support coordinators, often the same people who facilitated some groups, provided four one-on-one case management sessions throughout the program to provide referrals to other needed services (e.g., food assistance, housing) and to reinforce the group curriculum. Meals, on-site childcare, and transportation were provided for free. Women earned up to $200 for attending group and case management sessions. Group assignment was not based on gestational age. Most participants remained with their same cohort throughout the program, however, were able to attend other sessions with a different cohort to make up missed classes. The MotherWise program did not include any clinical care or contraception counseling. It was offered separate from routine perinatal care. The parent trial reported on long-term (12 and 30 months following baseline) relationship and family stability outcomes and showed that MotherWise improves relationship skills and decreases the number of relationship transitions.10,11 A separate subanalysis of a smaller sample reported on birth outcomes and showed that the program was associated with a decreased rate of adverse composite obstetrical outcome.12 This subgroup analysis reported here included those participants age 18 or older with a nonanomalous gestation randomized at any time during pregnancy receiving care and delivering at a single safety-net hospital and discharged home with a live infant(s). This study was approved by the Colorado Multiple Institutional Review Board and the University of Denver Institutional Review Board. Demographic and medical information were abstracted from the electronic medical record (EMR) and stored in Research Electronic Data Capture (REDCap) by trained data abstractors. Race and ethnicity were determined by self-report. The clinically determined estimated due date recorded in the EMR was used to ascertain gestational age. Tobacco and nonprescribed drug use were by self-report and dichotomized as yes or no as any use at any time during pregnancy. Sexually transmitted infections were verified by positive results in the patient's EMR. Hypertensive diseases of pregnancy were defined as gestational hypertension and preeclampsia (chronic hypertension excluded). Medical and mental health comorbidities were defined as any preexisting chronic condition documented by the provider in the EMR. Attendance at postpartum visit was defined as attending a scheduled appointment from 4 to 12 weeks postpartum. Postpartum long-acting reversible contraception (LARC) use was defined as LARC in place at the time of discharge or placed at any postpartum visit upto 12 weeks postpartum. All analyses were based on intention to treat. Baseline characteristics and all outcomes were compared with appropriate univariate statistics, either Student's t-test, chi-squared, or Fisher's exact. Multivariable logistic regression was then used for the primary postpartum outcomes first including all variables with p ≤ 0.100 then eliminating those not significant in a backward stepwise fashion. IBM SPSS Statistics 23.0 was used for all data analyses. An a priori power analysis was not performed for the primary outcome of this subanalysis as this was a community-based pragmatic trial enrolling all interested women to investigate other long-term relationship and family stability outcomes. ## Results A total of 953 women were randomized in the parent trial; 507 met inclusion criteria for this study (Fig. 1). Participants were mostly parous ($62\%$), Hispanic ($67\%$), publicly insured ($94\%$) women. There were not any baseline maternal differences in variables measured after randomization (Table 1). More than one-third of each cohort had a medical comorbidity; overall asthma was most common ($15.4\%$), followed by chronic hypertension ($7.9\%$), preexisting diabetes ($5.3\%$), and thyroid disorders ($4.9\%$). More than half of all women had a mental health diagnosis recorded in the medical record, depression being most common ($38.5\%$) followed by anxiety ($24.3\%$), and post-traumatic stress disorder ($9.9\%$). There were not any differences between cohorts in the distribution of the above diagnoses. **FIG. 1.:** *CONSORT diagram.* TABLE_PLACEHOLDER:Table 1. Of those randomized to MotherWise, $\frac{230}{278}$ ($82.7\%$) of women attended at least one class, $\frac{79}{278}$ ($28.4\%$) attended all six classes, and overall, on average, participants attended two-thirds of classes ($65\%$). The median gestational age at first workshop was 26.1 weeks (range 4–40 weeks). There were on average seven women in attendance per each MotherWise class. There were not any differences in antenatal utilization of care between groups (Table 2). Women randomized to MotherWise were more likely to report taking a prescription medicine during pregnancy; selective serotonin reuptake inhibitors were the most common class of medicine in both cohorts ($14.2\%$ of all participants) followed by an asthma control agent ($13.6\%$). Participants randomized to MotherWise were more likely to be delivered through cesarean; there were no differences in any other perinatal outcomes (Table 2). **Table 2.** | Unnamed: 0 | Program, n = 278 | Control, n = 229 | p | | --- | --- | --- | --- | | No. of routine prenatal visits | 11.9 ± 5.4 | 11.7 ± 5.5 | 0.599 | | No. of hospital admissions | 3.1 ± 2.3 | 3.2 ± 2.3 | 0.689 | | No. of emergency room visits | 0.6 ± 0.9 | 0.5 ± 1.0 | 0.483 | | No. of missed appointments | 2.2 ± 2.7 | 2.3 ± 2.7 | 0.654 | | DHS involvement current pregnancy | 35 (12.6) | 31 (13.5) | 0.752 | | STI during pregnancy | 34 (12.2) | 40 (17.5) | 0.096 | | Prescription medicine use | 164 (59) | 106 (46.3) | 0.004 | | Hypertensive disease of pregnancy | 67 (24.1) | 70 (30.6) | 0.103 | | Gestational diabetes | 28 (10.1) | 19 (8.3) | 0.493 | | Cesarean delivery | 78 (28) | 48 (21) | 0.05 | | GA at delivery, weeks | 38.8 ± 2.3 | 38.7 ± 2.0 | 0.704 | | Birthweight, grams | 3155 ± 573 | 3118 ± 520 | 0.45 | | AGA infant | 230 (82.7) | 196 (85.6) | 0.382 | | 5-minute Apgar | 8.7 ± 0.8 | 8.8 ± 0.6 | 0.103 | | NICU admission | 47 (16.9) | 30 (13.1) | 0.235 | On univariate analysis, participants randomized to program were more likely to have LARC in place at the time of discharge from delivery hospitalization (Table 3). They were also more likely to attend their routine postpartum visit ($80.2\%$ vs. $72.9\%$; $p \leq 0.053$). Among only those women who attended their routine postpartum visit, women randomized to program were more likely to have any contraception in place at that visit, and more likely to receive LARC for contraception (Table 4). There were not any differences in breastfeeding rates at any time (at discharge $83\%$ vs. $82\%$; $p \leq 0.759$ and at the postpartum visit $67\%$ vs. $69\%$; $p \leq 0.655$). In multivariate regression analyses controlling for antenatal prescription medication use, hypertensive disease of pregnancy, sexually transmitted infection during pregnancy, and mode of delivery, those randomized to MotherWise remained more likely to be discharged from delivery admission with immediate postpartum LARC (odds ratio [OR] 1.84; confidence interval [CI] 1.15–2.96) but were not more likely to attend a postpartum visit (OR 1.49; CI 0.97–2.28). Among those who attended their postpartum visit, the difference in any contraception was no longer significant (OR 1.28; CI 0.76–2.16), however, the difference in LARC use remained (OR 2.19; CI 1.34–3.56). ## Discussion Our novel prenatal group healthy relationship education program, MotherWise, was associated with double the postpartum LARC use compared with a no-program control group. This effect was seen in the absence of any contraception counseling, without provision of prenatal care or involvement of obstetric providers, only healthy relationship education. Other postpartum behaviors, which might affect a woman's choice of or access to contraception—for example, breastfeeding and attendance at a postpartum visit—were not similarly impacted by the intervention. MotherWise is not a prenatal care intervention since it lacks the clinical care component that is part of the GPC model. At the same time, MotherWise is based on GPC principles of facilitated group learning, education, and social support. In that vein, our findings could be compared with other studies of GPC and postpartum family planning. In retrospective studies, Hale et al. reported an increased use of any postpartum family planning services in women continuously enrolled in Medicaid choosing Centering Pregnancy, one model of GPC1; Trotman et al. reported an increased use of postpartum LARC among adolescents choosing Centering Pregnancy13; and Schellinger et al. found increased postpartum LARC use in Hispanic women with gestational diabetes choosing GPC for their prenatal care.14 In an observational study, DeCesare et al. also found that women choosing Centering Pregnancy were more likely to return for a postpartum contraception visit and choose LARC.15 The results from our randomized trial further strengthens this body of evidence that antenatal group education programs, even those without clinical care, increase the use of effective postpartum LARC. We theorize it is the element of providing education and fostering empowerment in a supportive group setting during pregnancy that led to a greater use of postpartum LARC for those participants in the MotherWise program. A core concept of Within My Reach and the MotherWise program is “deciding versus sliding.” This concept suggests that we can expect better outcomes when we make clear decisions rather than sliding through transitions or experiences we did not plan for. For many, considering pregnancy as a choice may have been a powerful concept. The longer-term follow-up of the larger sample indicated that those assigned to MotherWise were less likely to have an unintended pregnancy in the 12 months following the intervention.10 This finding could be a direct result of greater use of contraception, although future research should replicate these findings and test this association directly. Our study is a robust randomized controlled trial but must be considered in light of some limitations. We did not differentiate between the implant and intrauterine devices, but rather combined all LARC types as one outcome. Furthermore, we did not follow women for longer than the fourth trimester and do not know rates of continuation or subsequent interval of pregnancy rates. Participants were a cohort receiving care from a safety-net hospital, and results may not be generalizable to other populations. All health outcomes, including the primary outcome of LARC uptake, were determined by report in the EMR and therefore quality and quantity of data were limited to what was recorded and available. As mentioned previously, this study was a planned subanalysis of a larger trial, and therefore may not be adequately powered for the primary outcome; however, our sample size was large and our findings were significant. Our study has implications for the future research of both GPC and other antenatal psychosocial interventions. We designed a group healthy relationship program without any direct clinical care and yet had a significant impact on the use of postpartum LARCs. Future work should continue to build on and explore how prepregnancy or antenatal education and fostering empowerment can improve women's health. ## Precis Antenatal group healthy relationship education—without the provision of medical care or contraception counseling—is associated with increased use of postpartum long-acting reversible contraception. ## Disclaimer The contents of this article are solely the responsibility of the authors and do not necessarily represent the views of the Office of Family Assistance, the Administration for Children and Families, or the U.S. Department of Health and Human Services. ## Author Disclosure Statement Galena Rhoades codeveloped the healthy relationship education curriculum used in this study, Within My Reach, and receives royalties when it is purchased and payments for facilitator trainings. The other authors have no potential conflicts of interest to report. ## Funding Information Funding for the MotherWise program was provided by the Office of Family Assistance within the Administration for Children and Families, U.S. Department of Health and Human Services: 90FM0062. Additional support for the research presented was provided by the Fahs-Beck Fund for Research and Experimentation and the University of Denver. The larger randomized controlled trial for which this project collected additional data was conducted by Mathematica Policy Research and funded by the Office of Planning, Research, and Evaluation within the Administration for Children and Families. The sponsors did not have a role in study design; collection, analysis, or interpretation of data; writing of the report; or decision to submit this report. ## References 1. Hale N, Picklesimer AH, Billings DL. **The impact of Centering Pregnancy Group Prenatal Care on postpartum family planning**. *Am J Obstet Gynecol* (2014) **210** 50.e1-e50.e507. DOI: 10.1016/j.ajog.2013.09.001 2. Markman HJ, Rhoades GK. **Relationship education research: Current status and future directions**. *J Marital Fam Ther* (2012) **38** 169-200. DOI: 10.1111/j.1752-0606.2011.00247.x 3. Stanley SM, Carlson RG, Rhoades GK. **Best practices in relationship education focused on intimate relationships**. *Fam Relat* (2020) **69** 497-519 4. Rhoades GK, Stanley SM. **Using individual-oriented relationship education to prevent family violence**. *J Couple Relatsh Ther* (2011) **10** 185-200. DOI: 10.1080/15332691.2011.562844 5. Pearson M, Stanley SM, Rhoades GK. **Within My Reach Instructor Manual**. (2005) 6. Antle B, Sar B, Christensen D. **The impact of the Within My Reach relationship training on relationship skills and outcomes for low-income individuals**. *J Marital Fam Ther* (2013) **39** 346-357. DOI: 10.1111/j.1752-0606.2012.00314.x 7. Antle BF, Karam E, Christensen DN. **An evaluation of healthy relationship education to reduce intimate partner violence**. *J Fam Soc Work* (2011) **14** 387-406 8. Rhoades GK. **The Effectiveness of the Within Our Reach relationship education program for couples: Findings from a federal randomized trial**. *Fam Process* (2015) **54** 672-685. DOI: 10.1111/famp.12148 9. Stanley SM, Rhoades GK, Markman HJ. **Sliding vs**. *deciding: Inertia and the premarital cohabitation effect. Fam Relat* (2006) **55** 499-509 10. Patnaik A, Wood RG. **Healthy Marriage and Relationship Education for Expectant and New Mothers: The One-Year Impacts of MotherWise. In (Vol. OPRE Report #2021–2183)**. (2021) 11. Patnaik A, Gonzalez K, Wood RG. **Healthy Marriage and Relationship Education for Expectant and New Mothers: The 30-Month Impacts of MotherWise. In (Vol. OPRE Report #2022-240)**. (2022) 12. Rhoades GK, Allen MOT, Pena R. **Relationship education for women during pregnancy: The impact of MotherWise on birth outcomes**. *Fam Process* (2022) **61** 1134-1143. DOI: 10.1111/famp.12756 13. Trotman G, Chhatre G, Darolia R. **The effect of centering pregnancy versus traditional prenatal care models on improved adolescent health behaviors in the perinatal period**. *J Pediatr Adolesc Gynecol* (2015) **28** 395-401. DOI: 10.1016/j.jpag.2014.12.003 14. Schellinger MM, Abernathy MP, Amerman B. **Improved outcomes for hispanic women with gestational diabetes using the centering pregnancy group prenatal care model**. *Matern Child Health J* (2017) **21** 297-305. DOI: 10.1007/s10995-016-2114-x 15. DeCesare JZ, Hannah D, Amin R. **Postpartum contraception use rates of patients participating in the centering pregnancy model of care versus traditional obstetrical care**. *J Reprod Med* (2017) **62** 45-49. PMID: 29999281
--- title: 'COVID-19 vaccination in psoriasis patients receiving systemic treatment: A prospective single-center study' authors: - Georg Christian Lodde - Frederik Krefting - Jan-Malte Placke - Lea Schneider - Melanie Fiedler - Ulf Dittmer - Jürgen Christian Becker - Stefanie Hölsken - Dirk Schadendorf - Selma Ugurel - Wiebke Sondermann journal: Frontiers in Immunology year: 2023 pmcid: PMC10061348 doi: 10.3389/fimmu.2023.1107438 license: CC BY 4.0 --- # COVID-19 vaccination in psoriasis patients receiving systemic treatment: A prospective single-center study ## Abstract ### Background The rate of seroconversion after COVID-19 vaccination in patients with moderate to severe psoriasis requiring systemic treatment is poorly understood. ### Objectives The aim of this prospective single-center cohort study performed between May 2020 and October 2021 was to determine the rate of seroconversion after COVID-19 vaccination in patients under active systemic treatment for moderate to severe psoriasis. ### Methods Inclusion criteria were systemic treatment for moderate to severe psoriasis, known COVID-19 vaccination status, and repetitive anti-SARS-CoV-2-S IgG serum quantification. The primary outcome was the rate of anti-SARS-CoV-2-S IgG seroconversion after complete COVID-19 vaccination. ### Results 77 patients with a median age of 55.9 years undergoing systemic treatment for moderate to severe psoriasis were included. The majority of patients received interleukin- ($$n = 50$$, $64.9\%$) or tumor necrosis factor (TNF)-α inhibitors ($$n = 16$$, $20.8\%$) as systemic treatment for psoriasis; nine patients ($11.7\%$) were treated with methotrexate (MTX) monotherapy, and one patient each received dimethyl fumarate ($1.3\%$), respectively apremilast ($1.3\%$). All included patients completed COVID-19 vaccination with two doses over the course of the study. Serum testing revealed that 74 patients ($96.1\%$) showed an anti-SARS-CoV-2-S IgG seroconversion. While all patients on IL-17A, -12 or -$\frac{12}{23}$ inhibitors ($$n = 50$$) achieved seroconversion, three of 16 patients ($18.8\%$) receiving MTX and/or a TNF-α inhibitor as main anti-psoriatic treatment did not. At follow-up, none of the patients had developed symptomatic COVID-19 or died from COVID-19. ### Conclusions Anti-SARS-CoV-2-S IgG seroconversion rates following COVID-19 vaccination in psoriasis patients under systemic treatment were high. An impaired serological response, however, was observed in patients receiving MTX and/or TNF-α inhibitors, in particular infliximab. ## Introduction Psoriasis is a chronic inflammatory disease occurring worldwide which leads to typical erythematosquamous skin plaques and affects about 2-$3\%$ of the total Western population [1, 2]. Already since the 1990s, there has been increasing scientific evidence that psoriasis is a systemic inflammatory disease associated with various comorbidities (3–5). Multiple epidemiological studies have shown an increased prevalence of cardiovascular risk factors, increased prevalence of arterial hypertension (6–11), and cardiovascular diseases like myocardial infarction (12–15) in psoriasis. In addition, a large body of evidence revealed that psoriasis is associated with obesity (7, 16–19), insulin resistance [20] and diabetes mellitus [6, 7, 11, 21, 22]. Accordingly, various studies could show that psoriasis is closely related to metabolic syndrome (7, 16, 23–25). As a result of cardiovascular and cardiometabolic comorbidity, patients with severe psoriasis were shown to have a decreased life expectancy of up to 5 years [26, 27]. Psoriasis is also frequently associated with psychological comorbidities. Altogether psoriasis has a massive negative impact on patients’ quality of life. In a US-based interview study, e.g. $98\%$ of psoriasis patients reported that their emotional lives were impaired by their disease, $94\%$ felt their social life was restricted and $68\%$ perceived their careers to be hindered [28]. Depending on the screening methodology, depressive symptoms are described to be present in up to 28-$55\%$ of psoriasis patients [6, 7]. Social stigmatization due to easily visible skin manifestations is a strong predictor of depressive symptoms in psoriasis patients [29]. However, there is emerging evidence that systemic inflammation may represent a pathophysiologic link between the diseases (30–32). For the above-mentioned reasons, patients with psoriasis, in particular with a severe form of the disease, have an urgent need for efficient therapies. Nowadays, systemic therapy options for moderate to severe psoriasis include conventional systemic agents such as dimethyl fumarate and methotrexate (MTX), the small molecule apremilast (phosphodiesterase-4 inhibitor), and various biologics [33]. With the establishment of targeted cytokine inhibitors, the efficacy and tolerability of systemic therapies for the treatment of psoriasis patients has been massively increased [34]. Unprecedented response rates of about $60\%$ in terms of complete skin clearance are possible today with some of the newer biologics targeting interleukin (IL)-23 or IL-17 [35, 36]. However, the first in group biologics licensed for psoriasis were tumor necrosis factor (TNF)-α inhibitors. In terms of safety profile, the incidence of severe adverse events in psoriasis patients receiving TNF-α inhibitors is low [37]. Though, large cohort studies showed infliximab to be associated with an increased risk of serious infections [38]. In addition, TNF-α inhibitors harbor a risk for reactivation of latent infections such as tuberculosis [39, 40]. The rate of serious infections was shown to be higher especially for new users of infliximab and adalimumab [41]. In contrast, therapy with anti-IL-$\frac{12}{23}$ antibodies and IL-17 inhibitors generally does not seem to increase the risk of serious infections [41]. Only very few reports in the literature so far have addressed the question to which extent immunomodulatory, respectively immunosuppressive therapies, such as those currently used to treat patients with psoriasis, influence the response to COVID-19 vaccines, which were shown to lead to serological responses in over $90\%$ of healthy individuals [42, 43]. For example, a recent study by Mahil et al. investigated psoriasis patients in the UK undergoing systemic therapy with MTX or targeted biological monotherapy. Functional humoral immunity to a single dose of BNT162b2 was shown to be impaired by MTX but not by targeted biologics, whereas cellular responses were unaffected [44, 45]. The aim of the present study was to determine the anti-SARS-CoV-2-S IgG seroconversion rate after COVID-19 vaccination in patients under active systemic treatment for moderate to severe psoriasis in order to expand the knowledge on this highly relevant topic. ## Study design and patient eligibility This prospective single-center study of a consecutive sample of psoriasis patients under systemic treatment was performed from May 2020 until October 2021 at the Department of Dermatology, University Hospital Essen, Germany. Study outcome measures were anti-SARS-CoV-2-S IgG seroconversion and outcome of a potential COVID-19 disease. Data on patient characteristics, concomitant diseases, COVID-19 vaccination status, severity of psoriasis, systemic psoriasis therapy, and immunosuppressive comedication were collected. Leukocyte, neutrophil and lymphocyte counts were assessed at the time of first COVID-19 vaccination. Systemic treatment for psoriasis included biologics such as TNF-α inhibitors, IL-17A inhibitors, IL-23 inhibitors, the IL-$\frac{12}{23}$ inhibitor ustekinumab, the small molecule apremilast, as well as conventional therapies such as MTX and dimethyl fumarate. Concomitant diseases were evaluated using the modified Charlson Comorbidity Index (CCI) [46]. Inclusion criteria were systemic treatment for moderate to severe psoriasis, completed COVID-19 vaccination corresponding to two sequential mRNA or viral vector vaccine applications, and repetitive anti-SARS-CoV-2-S IgG serum quantification (Figure 1). Anti-SARS-CoV-2-S IgG antibodies were measured at each time of consultation in our department. **Figure 1:** *Patient flow chart.* ## Serum antibody testing Measurement of IgG antibodies against SARS-CoV-2 spike protein (anti-SARS-CoV-2-S IgG) in patients’ sera was performed with the chemiluminescence assays SARS-CoV-2 S1/S2 IgG or SARS-CoV-2 TrimericS IgG, DiaSorin, Saluggia, Italy. The chemiluminescence analyzer LIASION-XL (DiaSorin) was used following the manufacturer’s instructions. The first assay is semi-quantitative as at the start of the study no standardized fully quantitative assays were available. The second assay is quantitative and adjusted to the upcoming WHO standard. Values >15 AU/ml corresponding to 39 BAU/ml, and values ≥33.8 BAU/ml were considered positive after first or second vaccination, respectively. Sensitivity/specificity for each assay are $94.4\%$/$98.6\%$ and $96.9\%$/$100\%$, respectively. For this study, only qualitative results were used to document seroconversion. ## Data analysis Descriptive statistical analyses were performed using SPSSv26.0 (IBM, Armonk, NY, USA). The follow up time was defined as the period between first serum testing and last patient visit. The study was approved by the institutional ethics committee of the University Duisburg-Essen (21-10141-BO). It was conducted in accordance with the Declaration of Helsinki. ## Baseline characteristics Within the studied period, 77 of 292 consecutive psoriasis patients presenting at the Department of Dermatology, University Hospital Essen, met the inclusion criteria (Figure 1). At a median age of 55.9 years (range 23.8-86.3 years), the majority of patients had a modified CCI of 0 ($$n = 46$$, $59.7\%$, Table 1). Sixty-six patients ($85.7\%$) were treated with biologics (TNF-α inhibitors ($$n = 16$$), IL-17A inhibitors ($$n = 21$$), IL-23 inhibitors ($$n = 21$$), or an IL-$\frac{12}{23}$ inhibitor ($$n = 8$$); nine patients received MTX ($11.7\%$), one patient was treated with dimethyl fumarate ($1.3\%$), and one patient received the phosphodiesterase-4 inhibitor apremilast. At the end of data collection in October 2021, after a median follow-up time of 12.7 months, none of the patients had developed a symptomatic COVID-19 disease or died from COVID-19. The mean time between the second vaccination and analysis of antibody levels was 3.9 weeks (range 0.4-23.0 weeks). None of the patients had been vaccinated more than 6 months before antibody analysis (mean time period between first vaccination and analysis: 12.2 weeks, range: 4.1-25.9 weeks). **Table 1** | Unnamed: 0 | Total study cohortN (%) | | --- | --- | | Total | 77 (100.0) | | Median age, years (range) | 55.9 (23.8-86.3) | | Sex | | | Female | 39 (50.6) | | Male | 38 (49.4) | | Comorbidities1 | | | Arterial hypertension | 31 (40.3) | | Obesity | 15 (19.5) | | Diabetes mellitus | 10 (13.0) | | Nicotine abuse | 25 (32.5) | | Charlson comorbidity index2 | | | 0 | 46 (59.7) | | 1-2 | 23 (29.9) | | ≥3 | 8 (10.4) | | Severity of psoriasis | | | Moderate (Psoriasis Area and Severity Index <20) | 66 (85.7) | | Severe (Psoriasis Area and Severity Index ≥20) | 11 (14.3) | | Type of systemic treatment | | | Biologics | 66 (85.7) | | TNF-α inhibitors | 16 (20.8) | | Adalimumab | 9 (11.7) | | Infliximab | 3 (3.9) | | Etanercept | 2 (2.6) | | Certolizumab | 1 (1.3) | | Golimumab | 1 (1.3) | | Interleukin inhibitors | 50 (64.9) | | Interleukin-12/23 inhibitor | 8 (10.4) | | Ustekinumab | 8 (10.4) | | Interleukin-17A inhibitors | 21 (27.3) | | Secukinumab | 5 (6.5) | | Ixekizumab | 16 (20.8) | | Interleukin-23 inhibitors | 21 (27.5) | | Tildrakizumab | 9 (11.7) | | Risankizumab | 2 (2.6) | | Guselkumab | 10 (13.0) | | Apremilast (Phosphodiesterase inhibitor) | 1 (1.3) | | Methotrexate | 9 (11.7) | | Dimethyl fumarate | 1 (1.3) | ## Comparison of patients with and without seroconversion After completed COVID-19 vaccination (corresponding to two sequential mRNA or viral vector vaccine applications), $\frac{74}{77}$ patients ($96.1\%$) achieved anti-SARS-CoV-2-S IgG seroconversion (Table 2). Seroconversion was reached in $\frac{64}{66}$ patients treated with biologics. Three of these 64 patients received additional immunosuppressive comedication with MTX 5-10 mg per week in the context of psoriasis treatment (Table 2). COVID-19 vaccination led to seroconversion in $\frac{8}{9}$ patients treated with MTX as main systemic psoriasis treatment, in $\frac{1}{1}$ patient under therapy with dimethyl fumarate, and in $\frac{1}{1}$ patient treated with apremilast (Table 2). **Table 2** | Unnamed: 0 | N (%) | | --- | --- | | Total | 77 (100.0%) | | Vaccination type (first and second vaccination) | | | mRNA (2x mRNA-1273, n=4; 2x BNT162b24, n=52) | 56 (72.7) | | Viral vector (2x AZD1222) | 9 (11.7) | | Mixed (1x viral vector, 1x mRNA) | 12 (15.6) | | Immunosuppressive comedication for psoriasis | | | | 72 (93.5) | | Prednisolon 5mg/daily | 1 (1.3) | | Methotrexate | 4 (5.2) | | Methotrexate 5 mg/week | 2 (2.6) | | Methotrexate 7.5 mg/week | 1 (1.3) | | Methotrexate 10 mg/week | 1 (1.3) | | Anti-SARS-CoV-2-S IgG (serum) | | | Positive prior to vaccination | 0 | | Positive after vaccination (seroconversion) | 74 (96.1%) | | Biologics | 64 | | TNF-α inhibitor | 14 | | TNF-α inhibitor combined with methotrexate 5 mg/week | 1 | | TNF-α inhibitor combined with methotrexate 7.5 mg/week | 1 | | Interleukin inhibitors | 50 | | Interleukin-12/23 inhibitor | 8 | | Ustekinumab | 8 | | Interleukin-17A inhibitor | 21 | | Secukinumab | 5 | | Secukinumab combinded with methotrexate 10 mg/week | 1 | | Ixekizumab | 16 | | Interleukin-23 inhibitor | 21 | | Tildrakizumab | 9 | | Risankizumab | 2 | | Guselkumab | 10 | | Apremilast (Phosphodiesterase inhibitor) | 1 | | Methotrexate | 8 | | Dimethyl fumarate | 1 | | Not positive after vaccination (no seroconversion) | 3 (3.9) | | Biologics | 2 | | TNF-α inhibitors | 2 | | Infliximab | 1 | | Infliximab combined with methotrexate 5mg/week | 1 | | Methotrexate | 1 | In $\frac{3}{77}$ completely vaccinated patients ($3.9\%$) anti-SARS-CoV-2-S IgG antibodies could not be detected in sufficient amount in repeated serological tests after vaccination (anti-SARS-CoV-2-S IgG <33.8 BAU/ml) (Table 2). The first patient without seroconversion, a 56-year-old female, was under therapy with the TNF-α inhibitor infliximab 5mg/kg Q7W i.v. and MTX 5 mg per week p.o. as an additional treatment. At the time of the first COVID-19 vaccination, she received her 46th infliximab infusion. The interval between infliximab treatments was shortened from 8 to 7 weeks due to increasing arthralgia towards the end of the interval. The patient had no further relevant comorbidities (CCI 0). At the time of the first COVID-19 vaccination, laboratory parameters including lymphocytes, leukocytes and neutrophils were within the normal range. Repeated laboratory controls performed until the end of follow-up did not reveal any pathological parameters. After two vaccinations, the patient’s absolute anti-SARS CoV-2-S IgG was 25.7 BAU/ml. The second patient failing seroconversion, a 57-year-old female, also received infliximab 5 mg/kg Q8W. In the past, the patient also had comedication with low-dose MTX, which was discontinued 9 months ago due to lymphopenia. At the time of the first COVID-19 vaccination, the patient received her 59th infliximab infusion. The patient had no further relevant comorbidities (CCI 0). At the time of the first vaccination, the patient had decreased lymphocytes (0.90/nl). The lymphocyte counts remained decreased until the end of follow-up. After two vaccinations, the patient’s absolute anti-SARS CoV-2-S IgG was 26.5 BAU/ml. The third patient failing seroconversion, an 86-year-old female, was treated with MTX 10 mg s.c. weekly. MTX treatment was initiated 4 weeks before the first COVID-19 vaccination. Lymphocytes, leukocytes and neutrophils were within normal ranges. The patient also suffered from diabetes mellitus type II, arterial hypertension and peripheral arterial occlusive disease of the lower legs. After two vaccinations, the patient’s absolute anti-SARS CoV-2-S IgG was 15.1 BAU/ml. In the descriptive statistical comparison of the responder and non-responder group, no substantial differences were found. However, the median age of patients without seroconversion was slightly higher compared to patients with serological response (56.9 vs 55.6 years, Table 3). Of 13 patients receiving MTX as their main treatment, respectively comedication for psoriasis, $15.4\%$ ($$n = 2$$) failed to achieve seroconversion and of 16 patients receiving a TNF-α inhibitor as their main treatment for psoriasis $12.5\%$ ($$n = 2$$) did not reach seroconversion. The median values for leukocytes, neutrophils, and lymphocytes were similar in both groups at the time of the first COVID-19 vaccination (Table 3). However, patients who failed seroconversion ($$n = 3$$) had lower median lymphocyte counts compared to patients who achieved seroconversion (1.6/nL (range 0.9-2.8) vs. 1.8/nL (range 0.6-3.8)). **Table 3** | Unnamed: 0 | SeroconversionN (%) | No seroconversionN (%) | | --- | --- | --- | | Total | 74 (100.0) | 3 (100.0) | | Median age, years (range) | 55.6 (23.8-86.3) | 56.9 (55.3-84.7) | | Sex | | | | Female | 36 (48.6) | 3 (100.0) | | Male | 38 (51.4) | 0 | | Charlson comorbidity index | | | | 0 | 44 (59.5) | 2 (66.7) | | 1-2 | 22 (29.7) | 1 (33.3) | | ≥3 | 8 (10.8) | 0 | | Severity of psoriasis | Severity of psoriasis | Severity of psoriasis | | Moderate (Psoriasis Area and Severity Index <20) | 63 (85.1) | 3 (100.0) | | Severe (Psoriasis Area and Severity Index ≥20) | 11 (14.9) | 0 (0.0) | | Type of psoriasis treatment | Type of psoriasis treatment | Type of psoriasis treatment | | Biologics | 64 (86.5) | 2 (66.7) | | TNF-α inhibitor | 14 (18.9) | 2 (66.7) | | Interleukin inhibitor | 50 (67.6) | 0 | | Interleukin 12-/13 inhibitor | 8 (10.8) | 0 | | Ustekinumab | 8 (10.8) | 0 | | Interleukin 17A inhibitor | 21 (28.4) | 0 | | Secukinumab | 5 (6.8) | 0 | | Ixekizumab | 16 (21.6) | 0 | | Interleukin 23 inhibitor | 21 (28.4) | 0 | | Tildrakizumab | 9 (12.2) | 0 | | Risankizumab | 2 (2.7) | 0 | | Guselkumab | 10 (13.5) | 0 | | Methotrexate 10-15 mg/week | 8 (10.8) | 1 (33.3) | | Apremilast (Phosphodiesterase inhibitor) | 1 (1.4) | 0 | | Dimethyl fumarate | 1 (1.4) | 0 | | Immunosuppressive comedication for psoriasis | Immunosuppressive comedication for psoriasis | Immunosuppressive comedication for psoriasis | | | 70 (94.6) | 2 (66.7) | | Prednisolon 5mg/daily | 1 (1.4) | 0 | | Methotrexate | 3 (4.1) | 1 (33.3) | | Methotrexate 5 mg/week | 1 (1.4) | 1 (33.3) | | Methorexate 7.5 mg/week | 1 (1.4) | 0 | | Methotrexate 10 mg/week | 1 (1.4) | 0 | | Methotrexate as main posriasis treatment or comedication | Methotrexate as main posriasis treatment or comedication | Methotrexate as main posriasis treatment or comedication | | Yes | 11 (14.9) | 2 (66.7) | | No | 63 (85.1) | 1 (33.3) | | Leukocytes (3.6-9.2/nL) | Leukocytes (3.6-9.2/nL) | Leukocytes (3.6-9.2/nL) | | Median (range) | 7.3 (3.8-12.0) | 7.0 (5.8-8.1) | | Decreased | | 0 | | Within the normal range | | 3 (100.0) | | Increased | | 0 | | Not available | 6 (8.1) | 0 | | Neutrophils (1.7-6.2/nL) | Neutrophils (1.7-6.2/nL) | Neutrophils (1.7-6.2/nL) | | Median (range) | 4.4(2.2-8.5) | 4.5 (2.4-6.3) | | Decreased | 0 | 0 | | Within the normal range | 59 (79.7) | 2 (66.7) | | Increased | 8 (10.8) | 1 (33.3) | | Not available | 7 (9.5) | 0 | | Lymphocytes (1.0-3.4/nL) | Lymphocytes (1.0-3.4/nL) | Lymphocytes (1.0-3.4/nL) | | Median (range) | 1.8 (0.6-3.8) | 1.6 (0.9-2.8) | | Decreased | 3 (4.1) | 1 (33.3) | | Within the normal range | 58 (78.4) | 2 (66.7) | | Increased | 6 (8.1) | 0 | | Not available | 7 (9.5) | 0 | ## Discussion One of the main intentions of the presented work was to shed more light on the question whether the systemic agents currently used for the treatment of patients with moderate to severe psoriasis negatively affect the serological response to COVID-19 vaccines. Our results showed that $96.1\%$ of patients responded to COVID-19 vaccination in the sense of a seroconversion. The rate of seroconversion was found to be slightly reduced in patients receiving MTX and/or TNF-α inhibitors compared to those under therapy with dimethyl fumarate, apremilast and biologics targeting IL-17 or IL-$\frac{12}{23.}$ Serological response rates to COVID-19 vaccines have been reported to exceed $90\%$ in healthy individuals [42, 43], and neutralizing antibody levels were shown to be highly predictive of immunologic protection from symptomatic SARS-CoV-2 infection [47]. Yet, neutralizing assays are complex and time-consuming. For reasons of practicability, we did not assess neutralizing antibody levels but IgG antibodies against the SARS-CoV-2 spike protein. However, data from the literature found a strong correlation between IgG and neutralizing antibodies at least within a 6-month period after the second dose of vaccination [48]. In our cohort, none of the patients had been vaccinated more than 6 months before the analysis of antibodies. In addition to the measured high rates of seroconversion, the fact that no patient of our cohort reported an infection by SARS-CoV-2, COVID-19, or even died from COVID-19 confirmed that an effective protection was established by the vaccinations in nearly all cases. To the best of our knowledge, only very few comparable data were published. Mahil et al. [ 44, 49] for example measured the serological response of COVID-19 vaccines in 67 psoriasis patients on active systemic treatment. The authors reported that all patients showed seroconversion after completed vaccination consisting of two doses, which fits in well with our findings [44, 49]. Other studies also presented similar findings (50–56). The main results of other studies that also investigated the serological response to COVID-19 vaccination in psoriasis patients are summarized in Table 4 **Table 4** | Reference | Total number of psoriasis patients and healthy controls finally investigated | Therapies | Antibody response in psoriasis patients under therapy with biologicsN (%) | Antibody response in psoriasis patients under therapy with MTX as main or co-medicationN (%) | | --- | --- | --- | --- | --- | | Mahil et al., Lancet Rheumatol. (49) | 64 patients and 15 controls | MTX n=14TNF-α inhibitor n=18IL-17 inhibitor n=13IL-23 inhibitor n=19 | 50 (100.0)following the second dose of BNT162b2 vaccination | 14 (100.0)following the second dose after BNT162b2 vaccination | | Marovt et al., Clin Exp Dermatol. (50) | 32 patients and 22 controls | TNF-α inhibitor n=7IL-12/23 inhibitor n=11IL-17 inhibitor n=6IL-23 inhibitor n=8 | 32 (100.0)following the second dose of BNT162b2 vaccination | | | Graceffa et al., Front Med (Lausanne). (51) | 45 patients and 45 controls | TNF-α inhibitor + MTX n=4TNF-α inhibitor n=17IL-12/23 inhibitor n=7IL-17 inhibitor n= 5IL-23 inhibitor n=12 | 44 (97.8)1 following the second dose of BNT162b2 vaccination | 3 (75)1 following the second dose of BNT162b2 vaccination | | Piros et al., Dermatol Ther. (52) | 102 patients and 55 controls | TNF-α inhibitor n=57IL-12/23 inhibitor n=28IL-17 inhibitor n=16IL-23 inhibitor n=1 | 102 (100.0)following the second dose of BNT162b2 or mRNA-1273 vaccination | | Two of three patients without seroconversion received MTX. MTX is a widely used immunosuppressant for the treatment of various immune-mediated inflammatory diseases. Its safety profile includes leucopenia/pancytopenia and proneness to infections [57]. In previous studies, it has been shown that the response to COVID-19 vaccination can be negatively affected by MTX (58–60). In our cohort, patients who received MTX as their main systemic psoriasis treatment or as comedication showed an impaired seroconversion rate of only $84.6\%$ after COVID-19 vaccination. Haberman et al. even found a strongly decreased anti-SARS-CoV-2 seroconversion rate of $62.2\%$ in patients with immune-mediated inflammatory diseases receiving MTX [59]. This negative effect of MTX on seroconversion rates is also known from other types of vaccines such as influenza, pneumococcal or tetanus (60–65). Additionally, two of the three patients who did not achieve seroconversion after COVID-19 vaccination were under therapy with TNF-α inhibitors ($$n = 1$$, infliximab monotherapy; $$n = 1$$, infliximab plus MTX low-dose). As already stated above, TNF-α inhibitors, and especially infliximab, have an inferior safety profile in comparison with the newer cytokine inhibitors targeting IL-17 and IL-$\frac{12}{23}$, which is mainly due to a higher number of severe infections under TNF-α inhibitors [37, 38, 41] suggesting an impact on the immunologic surveillance of infectious diseases. The effect of TNF-α inhibitors on the immune system has already been reflected in decreased response rates observed in various studies on different vaccinations in the past (66–71). For example, reduced immune responses could be observed after influenza [68, 69], pneumococcal [70], and hepatitis B vaccinations [71] in patients receiving infliximab in comparison to therapy-naive patients. It should be noted, that one of the serological non-responding patients was 86 years old, raising the question whether age may have an impact on COVID-19 vaccine response in psoriasis patients. Although our study only involved one older-aged patient with failed seroconversion, data from the literature indicate that older patients exhibit weaker or delayed immune responses to COVID-19 vaccines compared to younger patients [72]. The aforementioned patient also had different concomitant diseases such as diabetes mellitus. Evidence regarding the efficacy of COVID-19 vaccines in patients with underlying diseases in general is still limited [73]. In our cohort, patients who failed seroconversion ($$n = 3$$) had a lower median lymphocyte count compared to patients who achieved seroconversion. Avivi et al. reported statistically significantly lower seroconversion rates upon COVID-19 vaccination in patients with lymphopenia [74]. However, it has to be considered that in our study the sample size of patients with failure of seroconversion was very low. General limitations of our study include the relatively small number of patients, a high selection bias due to recruitment of patients from our highly specialized psoriasis center and the monocentric design. Furthermore, this study lacks a control group, neutralizing assays and cellular data such as B cell numbers as well as a quantification of T cell responses. Due to the highly dynamic development of new SARS-CoV-2 diagnostics, two test generations were used in this prospective longitudinal study. While the first assay used was semi-quantitative, the second assay was quantitative and adjusted to the upcoming WHO standard. As a result, it was not possible to present consistent quantitative data of antibody levels. In the meantime, three vaccine doses against SARS-CoV-2 are widely considered as standard immunization and e.g. in *Germany a* fourth dose is even recommended for particular subgroups at risk [75]. Thus, it would be of high interest to also investigate the seroconversion rates of patients with psoriasis under active systemic treatment systematically under these adapted conditions. There is already some data showing that initial non-responders can often achieve seroconversion in response to further vaccination, which is consistent with our own recent clinical experiences [76, 77]. For patients who do not mount antibody responses to COVID-19 despite repeated vaccination, a passive immunization against SARS-CoV-2 with neutralizing monoclonal antibodies is possible [78]. Currently, the combinations of casirivimab/imdevimab (Ronapreve®) and tixagevimab/cilgavimab (Evusheld®) are approved by the European Medicines Agency for passive immunization against COVID-19 [79, 80]. However, in vitro studies show that casirivimab/imdevimab does not result in relevant neutralization against the currently circulating Omicron variants (81–83). For this reason, the currently published German S1 guideline on SARS-CoV-2 pre-exposure prophylaxis recommends only the use of tixagevimab/cilgavimab for passive immunization, as in particular in-vitro studies of cilgavimab demonstrated the ability to neutralize omicron variants (78, 81–83). ## Conclusion The vast majority of psoriasis patients receiving systemic treatment achieved a seroconversion in response to two COVID-19 vaccinations. Seroconversion rate for patients under therapy with modern cytokine inhibitors targeting IL-17 or IL-23 was $100\%$. An impaired serological response was observed in patients who received MTX and/or TNF-α inhibitors, respectively. Larger real-world studies are needed to confirm our preliminary findings. ## Data availability statement The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author. ## Ethics statement The studies involving human participants were reviewed and approved by Ethics committee of the University Duisburg-Essen (21-10141-BO). Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements. ## Author contributions Conceptualization: SU, WS, GL, FK. Methodology: SU, MF, WS, GL, FK. Formal analysis: MF, SU, WS, GL, FK. Resources: UD, DS, WS, SU, GL, FK. Data curation, GL, SU, WS, FK. Writing - original draft preparation: GL, SU, WS, FK. Writing and editing: GL, FK, MF, UD, J-MP, JB, DS, WS, SU. Visualization: SU, GL, WS, FK. Supervision: WS. Project administration: GL, FK, SU, WS. All authors contributed to the article and approved the submitted version. ## Conflict of interest GL has received travel support from Sun Pharma. FK has received travel support and/or personal fees from Novartis and Almirall. MF has given a paid lecture for Dia Sorin. UD reports consulting fees from Biontech and Moderna, and advisory board honoraria from Moderna outside the submitted work. J-MP served as consultant and/or has received honoraria from Bristol-Myers Squibb and Novartis, and received travel support from Bristol-Myers Squibb, Novartis, Pierre Fabre and Therakos. JB declares speaker honoraria from Amgen and Sanofi; advisory board honoraria from 4SC, Almirall, Amgen, MerckSerono, Novartis, InProTher, and Sanofi; research funding from Alcedis, Bristol-Myers Squibb, HTG, IQVIA, and MerckSerono; travel support from 4SC and Incyte. DS reports grants or contracts from Amgen, Array/Pfizer, Bristol-Myers Squibb, MSD, Novartis and Roche; consulting fees from 4SC, Amgen, Array Biopharma, AstraZeneca, Bristol-Myers Squibb, Daiichi Sankyo, Haystick, Immunocore, InFlarX, Innocent, LabCorp, Merck Serono, MSD, Nektar, NeraCare, Novartis, OncoSec, Pfizer, Philogen, Pierre Fabre, Replimune, Roche, Sandoz, Sanofi/Regeneron, Sun Pharma; honoraria from Bristol-Myers Squibb, MSD/Merck, Merck Serono, Novartis, Roche, Sanofi and Sun Pharma; support for attendings meetings or travel support from Bristol-Myers Squibb, MSD, Merck Serono, Novartis, Pierre Fabre and Sanofi; participation on drug safety monitoring or advisory boards for 4SC, Amgen, Array Biopharma, AstraZeneca, Bristol-Myers Squibb, Daiichi Sankyo, Immunocore, InFlarX, Merck Serono, MSD, Nektar, NeraCare, Novartis, OncoSec, Pfizer, Philogen, Pierre Fabre, Replimune, Roche, Sandoz, Sanofi/Regeneron and SunPharma; leadership roles for DeCOG, German Cancer Society, Hiege-Stiftung, Deutsche Hautkrebsstiftung, NVKH e.V. and EuMelaReg. WS reports grants and/or personal fees and/or speaker honoraria from medi GmbH Bayreuth, Abbvie, Almirall, Amgen, Bristol-Myers Squibb, Celgene, GSK, Janssen, LEO Pharma, Lilly, MSD, Novartis, Pfizer, Roche, Sanofi Genzyme und UCB outside the submitted work. SU declares research support from Bristol Myers Squibb and Merck Serono; speakers and advisory board honoraria from Bristol Myers Squibb, Merck Sharp & Dohme, Merck Serono, Novartis and Roche, and travel support from Bristol Myers Squibb, Merck Sharp & Dohme, and Pierre Fabre; outside the submitted work. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be constructed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## References 1. Schafer I, Rustenbach SJ, Radtke M, Augustin J, Glaeske G, Augustin M. **[Epidemiology of psoriasis in Germany–analysis of secondary health insurance data]**. *Gesundheitswesen* (2011) **73**. DOI: 10.1055/s-0030-1252022 2. Griffiths CEM, Armstrong AW, Gudjonsson JE, Barker J. **Psoriasis**. *Lancet* (2021) **397**. DOI: 10.1016/S0140-6736(20)32549-6 3. Mrowietz U, Steinz K, Gerdes S. **Psoriasis: to treat or to manage**. *Exp Dermatol* (2014) **23**. DOI: 10.1111/exd.12437 4. Boehncke WH. **Systemic inflammation and cardiovascular comorbidity in psoriasis patients: Causes and consequences**. *Front Immunol* (2018) **9**. DOI: 10.3389/fimmu.2018.00579 5. Boehncke WH, Boehncke S, Tobin AM, Kirby B. **The 'psoriatic march': a concept of how severe psoriasis may drive cardiovascular comorbidity**. *Exp Dermatol* (2011) **20**. DOI: 10.1111/j.1600-0625.2011.01261.x 6. Kaye JA, Li L, Jick SS. **Incidence of risk factors for myocardial infarction and other vascular diseases in patients with psoriasis**. *Br J Dermatol* (2008) **159** 895-902. DOI: 10.1111/j.1365-2133.2008.08707.x 7. Miller IM, Ellervik C, Yazdanyar S, Jemec GB. **Meta-analysis of psoriasis, cardiovascular disease, and associated risk factors**. *J Am Acad Dermatol* (2013) **69**. DOI: 10.1016/j.jaad.2013.06.053 8. Phan C, Sigal ML, Lhafa M, Barthelemy H, Maccari F, Esteve E. **Metabolic comorbidities and hypertension in psoriasis patients in france. comparisons with French national databases**. *Ann Dermatol Venereol* (2016) **143**. DOI: 10.1016/j.annder.2015.06.024 9. Armesto S, Coto-Segura P, Osuna CG, Camblor PM, Santos-Juanes J. **Psoriasis and hypertension: a case-control study**. *J Eur Acad Dermatol Venereol* (2012) **26**. DOI: 10.1111/j.1468-3083.2011.04108.x 10. Cohen AD, Weitzman D, Dreiher J. **Psoriasis and hypertension: a case-control study**. *Acta Derm Venereol* (2010) **90**. DOI: 10.2340/00015555-0741 11. Qureshi AA, Choi HK, Setty AR, Curhan GC. **Psoriasis and the risk of diabetes and hypertension: a prospective study of US female nurses**. *Arch Dermatol* (2009) **145**. DOI: 10.1001/archdermatol.2009.48 12. Benson MM, Frishman WH. **The heartbreak of psoriasis: a review of cardiovascular risk in patients with psoriasis**. *Cardiol Rev* (2015) **23**. DOI: 10.1097/crd.0000000000000048 13. Coumbe AG, Pritzker MR, Duprez DA. **Cardiovascular risk and psoriasis: beyond the traditional risk factors**. *Am J Med* (2014) **127**. DOI: 10.1016/j.amjmed.2013.08.013 14. Armstrong EJ, Harskamp CT, Armstrong AW. **Psoriasis and major adverse cardiovascular events: a systematic review and meta-analysis of observational studies**. *J Am Heart Assoc* (2013) **2**. DOI: 10.1161/jaha.113.000062 15. Balci DD, Balci A, Karazincir S, Ucar E, Iyigun U, Yalcin F. **Increased carotid artery intima-media thickness and impaired endothelial function in psoriasis**. *J Eur Acad Dermatol Venereol* (2009) **23** 1-6. DOI: 10.1111/j.1468-3083.2008.02936.x 16. Miller IM, Ellervik C, Zarchi K, Ibler KS, Vinding GR, Knudsen KM. **The association of metabolic syndrome and psoriasis: a population- and hospital-based cross-sectional study**. *J Eur Acad Dermatol Venereol* (2015) **29**. DOI: 10.1111/jdv.12595 17. Miller IM, Skaaby T, Ellervik C, Jemec GB. **Quantifying cardiovascular disease risk factors in patients with psoriasis: a meta-analysis**. *Br J Dermatol* (2013) **169**. DOI: 10.1111/bjd.12490 18. Setty AR, Curhan G, Choi HK. **Obesity, waist circumference, weight change, and the risk of psoriasis in women: Nurses' health study II**. *Arch Intern Med* (2007) **167**. DOI: 10.1001/archinte.167.15.1670 19. Correia B, Torres T. **Obesity: a key component of psoriasis**. *Acta Biomed* (2015) **86** 20. Gyldenlove M, Storgaard H, Holst JJ, Vilsboll T, Knop FK, Skov L. **Patients with psoriasis are insulin resistant**. *J Am Acad Dermatol* (2015) **72** 599-605. DOI: 10.1016/j.jaad.2015.01.004 21. Lønnberg AS, Skov L, Skytthe A, Kyvik KO, Pedersen OB, Thomsen SF. **Association of psoriasis with the risk for type 2 diabetes mellitus and obesity**. *JAMA Dermatol* (2016) **152**. DOI: 10.1001/jamadermatol.2015.6262 22. Dubreuil M, Rho YH, Man A, Zhu Y, Zhang Y, Love TJ. **Diabetes incidence in psoriatic arthritis, psoriasis and rheumatoid arthritis: a UK population-based cohort study**. *Rheumatol (Oxford)* (2014) **53**. DOI: 10.1093/rheumatology/ket343 23. Danielsen K, Wilsgaard T, Olsen AO, Eggen AE, Olsen K, Cassano PA. **Elevated odds of metabolic syndrome in psoriasis: a population-based study of age and sex differences**. *Br J Dermatol* (2015) **172**. DOI: 10.1111/bjd.13288 24. Rodriguez-Zuniga MJM, Cortez-Franco F, Quijano-Gomero E. **Association of psoriasis and metabolic syndrome in Latin America: A systematic review and meta-analysis**. *Actas Dermosifiliogr* (2017) **108**. DOI: 10.1016/j.ad.2016.11.009 25. Parodi A, Aste N, Calvieri C, Cantoresi F, Carlesimo M, Fabbri P. **Metabolic syndrome prevalence in psoriasis: a cross-sectional study in the Italian population**. *Am J Clin Dermatol* (2014) **15**. DOI: 10.1007/s40257-014-0074-8 26. Abuabara K, Azfar RS, Shin DB, Neimann AL, Troxel AB, Gelfand JM. **Cause-specific mortality in patients with severe psoriasis: a population-based cohort study in the U.K**. *Br J Dermatol* (2010) **163**. DOI: 10.1111/j.1365-2133.2010.09941.x 27. Gelfand JM, Troxel AB, Lewis JD, Kurd SK, Shin DB, Wang X. **The risk of mortality in patients with psoriasis: results from a population-based study**. *Arch Dermatol* (2007) **143**. DOI: 10.1001/archderm.143.12.1493 28. Pariser D, Schenkel B, Carter C, Farahi K, Brown TM, Ellis CN. **A multicenter, non-interventional study to evaluate patient-reported experiences of living with psoriasis**. *J Dermatolog Treat* (2016) **27** 19-26. DOI: 10.3109/09546634.2015.1044492 29. Łakuta P, Marcinkiewicz K, Bergler-Czop B, Brzezińska-Wcisło L. **The relationship between psoriasis and depression: A multiple mediation model**. *Body Image* (2016) **19**. DOI: 10.1016/j.bodyim.2016.08.004 30. Hölsken S, Krefting F, Schedlowski M, Sondermann W. **Common fundamentals of psoriasis and depression**. *Acta Derm Venereol* (2021) **101** adv00609. DOI: 10.2340/actadv.v101.565 31. Koo J, Marangell LB, Nakamura M, Armstrong A, Jeon C, Bhutani T. **Depression and suicidality in psoriasis: review of the literature including the cytokine theory of depression**. *J Eur Acad Dermatol Venereol* (2017) **31** 1999-2009. DOI: 10.1111/jdv.14460 32. Cohen BE, Martires KJ, Ho RS. **Psoriasis and the risk of depression in the US population: National health and nutrition examination survey 2009-2012**. *JAMA Dermatol* (2016) **152**. DOI: 10.1001/jamadermatol.2015.3605 33. Nast A, Altenburg A, Augustin M, Boehncke WH, Härle P, Klaus J. **German S3-guideline on the treatment of psoriasis vulgaris, adapted from EuroGuiDerm - part 1: Treatment goals and treatment recommendations**. *J Dtsch Dermatol Ges* (2021) **19**. DOI: 10.1111/ddg.14508 34. Kamata M, Tada Y. **Efficacy and safety of biologics for psoriasis and psoriatic arthritis and their impact on comorbidities: A literature review**. *Int J Mol Sci* (2020) **21**. DOI: 10.3390/ijms21051690 35. Reich K, Warren RB, Lebwohl M, Gooderham M, Strober B, Langley RG. **Bimekizumab versus secukinumab in plaque psoriasis**. *N Engl J Med* (2021) **385**. DOI: 10.1056/NEJMoa2102383 36. Warren RB, Blauvelt A, Poulin Y, Beeck S, Kelly M, Wu T. **Efficacy and safety of risankizumab vs. secukinumab in patients with moderate-to-severe plaque psoriasis (IMMerge): results from a phase III, randomized, open-label, efficacy-assessor-blinded clinical trial**. *Br J Dermatol* (2021) **184**. DOI: 10.1111/bjd.19341 37. Kamata M, Tada Y. **Safety of biologics in psoriasis**. *J Dermatol* (2018) **45**. DOI: 10.1111/1346-8138.14096 38. Yiu ZZN, Ashcroft DM, Evans I, McElhone K, Lunt M, Smith CH. **Infliximab is associated with an increased risk of serious infection in patients with psoriasis in the U.K. and republic of Ireland: results from the British association of dermatologists biologic interventions register (BADBIR)**. *Br J Dermatol* (2019) **180**. DOI: 10.1111/bjd.17036 39. Gisondi P, Cazzaniga S, Chimenti S, Maccarone M, Picardo M, Girolomoni G. **Latent tuberculosis infection in patients with chronic plaque psoriasis: evidence from the Italian psocare registry**. *Br J Dermatol* (2015) **172**. DOI: 10.1111/bjd.13539 40. Doherty SD, Van Voorhees A, Lebwohl MG, Korman NJ, Young MS, Hsu S. **National psoriasis foundation consensus statement on screening for latent tuberculosis infection in patients with psoriasis treated with systemic and biologic agents**. *J Am Acad Dermatol* (2008) **59**. DOI: 10.1016/j.jaad.2008.03.023 41. Penso L, Dray-Spira R, Weill A, Pina Vegas L, Zureik M, Sbidian E. **Association between biologics use and risk of serious infection in patients with psoriasis**. *JAMA Dermatol* (2021) **157**. DOI: 10.1001/jamadermatol.2021.2599 42. Demonbreun AR, Sancilio A, Velez MP, Ryan DT, Saber R, Vaught LA. **Comparison of IgG and neutralizing antibody responses after one or two doses of COVID-19 mRNA vaccine in previously infected and uninfected individuals**. *EClinicalMedicine* (2021) **38**. DOI: 10.1016/j.eclinm.2021.101018 43. Dagan N, Barda N, Kepten E, Miron O, Perchik S, Katz MA. **BNT162b2 mRNA covid-19 vaccine in a nationwide mass vaccination setting**. *N Engl J Med* (2021) **384**. DOI: 10.1056/NEJMoa2101765 44. Mahil SK, Bechman K, Raharja A, Domingo-Vila C, Baudry D, Brown MA. **The effect of methotrexate and targeted immunosuppression on humoral and cellular immune responses to the COVID-19 vaccine BNT162b2: a cohort study**. *Lancet Rheumatol* (2021) **3**. DOI: 10.1016/s2665-9913(21)00212-5 45. Boekel L. **Immunity after COVID-19 vaccinations in immunocompromised patients with psoriasis**. *Lancet Rheumatol* (2022) **4**. DOI: 10.1016/S2665-9913(21)00360-X 46. Thomas SJ, Perez JL, Lockhart SP, Hariharan S, Kitchin N, Bailey R. **1558O COVID-19 vaccine in participants (ptcpts) with cancer: Subgroup analysis of efficacy/safety from a global phase III randomized trial of the BNT162b2 (tozinameran) mRNA vaccine**. *Ann Oncol* (2021) **S1129**. DOI: 10.1016/j.annonc.2021.08.1551 47. Khoury DS, Cromer D, Reynaldi A, Schlub TE, Wheatley AK, Juno JA. **Neutralizing antibody levels are highly predictive of immune protection from symptomatic SARS-CoV-2 infection**. *Nat Med* (2021) **27**. DOI: 10.1038/s41591-021-01377-8 48. Levin EG, Lustig Y, Cohen C, Fluss R, Indenbaum V, Amit S. **Waning immune humoral response to BNT162b2 covid-19 vaccine over 6 months**. *N Engl J Med* (2021) **385**. DOI: 10.1056/NEJMoa2114583 49. Mahil SK, Bechman K, Raharja A, Domingo-Vila C, Baudry D, Brown MA. **Humoral and cellular immunogenicity to a second dose of COVID-19 vaccine BNT162b2 in people receiving methotrexate or targeted immunosuppression: a longitudinal cohort study**. *Lancet Rheumatol* (2022) **4**. DOI: 10.1016/S2665-9913(21)00333-7 50. Marovt M, Dezelak P, Ekart R, Marko PB. **Immune response to SARS-CoV-2 mRNA vaccine in patients with psoriasis treated with biologics**. *Clin Exp Dermatol* (2022) **47**. DOI: 10.1111/ced.15347 51. Graceffa D, Sperati F, Bonifati C, Spoletini G, Lora V, Pimpinelli F. **Immunogenicity of three doses of anti-SARS-CoV-2 BNT162b2 vaccine in psoriasis patients treated with biologics**. *Front Med (Lausanne).* (2022) **9**. DOI: 10.3389/fmed.2022.961904 52. Piros EA, Cseprekal O, Gorog A, Hidvegi B, Medvecz M, Szabo Z. **Seroconversion after anti-SARS-CoV-2 mRNA vaccinations among moderate-to-severe psoriatic patients receiving systemic biologicals-prospective observational cohort study**. *Dermatol Ther* (2022) **35**. DOI: 10.1111/dth.15408 53. Al-Janabi A, Littlewood Z, Griffiths CEM, Hunter HJA, Chinoy H, Moriarty C. **Antibody responses to single-dose SARS-CoV-2 vaccination in patients receiving immunomodulators for immune-mediated inflammatory disease**. *Br J Dermatol* (2021) **185**. DOI: 10.1111/bjd.20479 54. Fagni F, Simon D, Tascilar K, Schoenau V, Sticherling M, Neurath MF. **COVID-19 and immune-mediated inflammatory diseases: effect of disease and treatment on COVID-19 outcomes and vaccine responses**. *Lancet Rheumatol* (2021) **3**. DOI: 10.1016/S2665-9913(21)00247-2 55. Simon D, Tascilar K, Fagni F, Kleyer A, Kronke G, Meder C. **Intensity and longevity of SARS-CoV-2 vaccination response in patients with immune-mediated inflammatory disease: a prospective cohort study**. *Lancet Rheumatol* (2022) **4**. DOI: 10.1016/S2665-9913(22)00191-6 56. Simon D, Tascilar K, Fagni F, Kronke G, Kleyer A, Meder C. **SARS-CoV-2 vaccination responses in untreated, conventionally treated and anticytokine-treated patients with immune-mediated inflammatory diseases**. *Ann Rheum Dis* (2021) **80**. DOI: 10.1136/annrheumdis-2021-220461 57. West J, Ogston S, Foerster J. **Safety and efficacy of methotrexate in psoriasis: A meta-analysis of published trials**. *PloS One* (2016) **11**. DOI: 10.1371/journal.pone.0153740 58. Furer V, Eviatar T, Zisman D, Peleg H, Paran D, Levartovsky D. **Immunogenicity and safety of the BNT162b2 mRNA COVID-19 vaccine in adult patients with autoimmune inflammatory rheumatic diseases and in the general population: a multicentre study**. *Ann Rheum Dis* (2021) **80**. DOI: 10.1136/annrheumdis-2021-220647 59. Haberman RH, Herati R, Simon D, Samanovic M, Blank RB, Tuen M. **Methotrexate hampers immunogenicity to BNT162b2 mRNA COVID-19 vaccine in immune-mediated inflammatory disease**. *Ann Rheum Dis* (2021) **80**. DOI: 10.1136/annrheumdis-2021-220597 60. Friedman MA, Curtis JR, Winthrop KL. **Impact of disease-modifying antirheumatic drugs on vaccine immunogenicity in patients with inflammatory rheumatic and musculoskeletal diseases**. *Ann Rheum Dis* (2021) **80**. DOI: 10.1136/annrheumdis-2021-221244 61. Adler S, Krivine A, Weix J, Rozenberg F, Launay O, Huesler J. **Protective effect of A/H1N1 vaccination in immune-mediated disease–a prospectively controlled vaccination study**. *Rheumatol (Oxford)* (2012) **51** 695-700. DOI: 10.1093/rheumatology/ker389 62. Park JK, Lee MA, Lee EY, Song YW, Choi Y, Winthrop KL. **Effect of methotrexate discontinuation on efficacy of seasonal influenza vaccination in patients with rheumatoid arthritis: a randomised clinical trial**. *Ann Rheum Dis* (2017) **76**. DOI: 10.1136/annrheumdis-2017-211128 63. Buhler S, Jaeger VK, Adler S, Bannert B, Brummerhoff C, Ciurea A. **Safety and immunogenicity of tetanus/diphtheria vaccination in patients with rheumatic diseases-a prospective multi-centre cohort study**. *Rheumatol (Oxford)* (2019) **58**. DOI: 10.1093/rheumatology/kez045 64. Nived P, Saxne T, Geborek P, Mandl T, Skattum L, Kapetanovic MC. **Antibody response to 13-valent pneumococcal conjugate vaccine is not impaired in patients with rheumatoid arthritis or primary sjogren's syndrome without disease modifying treatment**. *BMC Rheumatol* (2018) **2** 12. DOI: 10.1186/s41927-018-0019-6 65. Rasmussen SL, Fuursted K, Nielsen KA, Laurberg NP, Sorensen MB, Fagerberg SK. **Pneumococcal antibody protection in patients with autoimmune inflammatory rheumatic diseases with varying vaccination status**. *Scand J Rheumatol* (2020) **49**. DOI: 10.1080/03009742.2020.1732459 66. Wagner N, Assmus F, Arendt G, Baum E, Baumann U, Bogdan C. **Bundesgesundheitsblatt gesundheitsforschung gesundheitsschutz**. *Bundesgesundheitsbl* (2019) **62** 494-515. DOI: 10.1007/s00103-019-02905-1 67. Miehsler W, Novacek G, Wenzl H, Vogelsang H, Knoflach P, Kaser A. **A decade of infliximab: The Austrian evidence based consensus on the safe use of infliximab in inflammatory bowel disease**. *J Crohns Colitis* (2010) **4**. DOI: 10.1016/j.crohns.2009.12.001 68. Hagihara Y, Ohfuji S, Watanabe K, Yamagami H, Fukushima W, Maeda K. **Infliximab and/or immunomodulators inhibit immune responses to trivalent influenza vaccination in adults with inflammatory bowel disease**. *J Crohns Colitis* (2014) **8**. DOI: 10.1016/j.crohns.2013.08.008 69. deBruyn J, Fonseca K, Ghosh S, Panaccione R, Gasia MF, Ueno A. **Immunogenicity of influenza vaccine for patients with inflammatory bowel disease on maintenance infliximab therapy: A randomized trial**. *Inflammation Bowel Dis* (2016) **22**. DOI: 10.1097/MIB.0000000000000615 70. Fiorino G, Peyrin-Biroulet L, Naccarato P, Szabo H, Sociale OR, Vetrano S. **Effects of immunosuppression on immune response to pneumococcal vaccine in inflammatory bowel disease: a prospective study**. *Inflammation Bowel Dis* (2012) **18**. DOI: 10.1002/ibd.21800 71. Andrade P, Santos-Antunes J, Rodrigues S, Lopes S, Macedo G. **Treatment with infliximab or azathioprine negatively impact the efficacy of hepatitis b vaccine in inflammatory bowel disease patients**. *J Gastroenterol Hepatol* (2015) **30**. DOI: 10.1111/jgh.13001 72. Schwarz T, Tober-Lau P, Hillus D, Helbig ET, Lippert LJ, Thibeault C. **Delayed antibody and T-cell response to BNT162b2 vaccination in the elderly, Germany**. *Emerg Infect Dis* (2021) **27**. DOI: 10.3201/eid2708.211145 73. Pal R, Bhadada SK, Misra A. **COVID-19 vaccination in patients with diabetes mellitus: Current concepts, uncertainties and challenges**. *Diabetes Metab Syndr* (2021) **15**. DOI: 10.1016/j.dsx.2021.02.026 74. Avivi I, Balaban R, Shragai T, Sheffer G, Morales M, Aharon A. **Humoral response rate and predictors of response to BNT162b2 mRNA COVID19 vaccine in patients with multiple myeloma**. *Br J Haematol* (2021) **195**. DOI: 10.1111/bjh.17608 75. 75 RKI. Epidemiologisches bulletin. STIKO: 24. aktualisierung der COVID-19-Impfempfehlung (2022). Available at: https://www.rki.de/DE/Content/Infekt/EpidBull/Archiv/2022/Ausgaben/50_22.pdf?:blob=publicationFile (Accessed 27, 2022).. *Epidemiologisches bulletin. STIKO: 24. aktualisierung der COVID-19-Impfempfehlung* (2022) 76. Wieske L, van Dam KPJ, Steenhuis M, Stalman EW, Kummer LYL, van Kempen ZLE. **Humoral responses after second and third SARS-CoV-2 vaccination in patients with immune-mediated inflammatory disorders on immunosuppressants: a cohort study**. *Lancet Rheumatol* (2022) **4**. DOI: 10.1016/s2665-9913(22)00034-0 77. Lee A, Wong SY, Chai LYA, Lee SC, Lee MX, Muthiah MD. **Efficacy of covid-19 vaccines in immunocompromised patients: systematic review and meta-analysis**. *Bmj* (2022) **376**. DOI: 10.1136/bmj-2021-068632 78. 78 AWMF. S1-leitlinie SARS-CoV-2 prä-expositionsprophylaxe 2022 (2022). Available at: https://www.awmf.org/uploads/tx_szleitlinien/092-002l_S1_SARS-CoV-2_Prae-Expositionsprophylaxe_2022-05_01.pdf (Accessed 22, 2022).. *S1-leitlinie SARS-CoV-2 prä-expositionsprophylaxe 2022* (2022) 79. 79 EMA. Ronapreve (casirivimab and imdevimab) an overview of ronapreve and why it is authorised in the EU (2022). Available at: https://www.ema.europa.eu/en/documents/overview/ronapreve-epar-medicine-overview_en.pdf (Accessed 22, 2022).. *Ronapreve (casirivimab and imdevimab) an overview of ronapreve and why it is authorised in the EU* (2022) 80. 80 EMA. Evusheld (tixagevimab / cilgavimab) an overview of evusheld and why it is authorised in the EU (2022). Available at: https://www.ema.europa.eu/en/documents/overview/evusheld-epar-medicine-overview_en.pdf (Accessed 22, 2022).. *Evusheld (tixagevimab / cilgavimab) an overview of evusheld and why it is authorised in the EU* (2022) 81. Iketani S, Liu L, Guo Y, Liu L, Chan JF, Huang Y. **Antibody evasion properties of SARS-CoV-2 omicron sublineages**. *Nature* (2022) **604**. DOI: 10.1038/s41586-022-04594-4 82. Bruel T, Hadjadj J, Maes P, Planas D, Seve A, Staropoli I. **Serum neutralization of SARS-CoV-2 omicron sublineages BA.1 and BA.2 in patients receiving monoclonal antibodies**. *Nat Med* (2022) **28**. DOI: 10.1038/s41591-022-01792-5 83. Takashita E, Kinoshita N, Yamayoshi S, Sakai-Tagawa Y, Fujisaki S, Ito M. **Efficacy of antiviral agents against the SARS-CoV-2 omicron subvariant BA.2**. *N Engl J Med* (2022) **386**. DOI: 10.1056/NEJMc2201933
--- title: Sleeve gastrectomy decreases high-fat diet induced colonic pro-inflammatory status through the gut microbiota alterations authors: - Chong Cao - Xiaozhuo Tan - Hai Yan - Qiwei Shen - Rong Hua - Yikai Shao - Qiyuan Yao journal: Frontiers in Endocrinology year: 2023 pmcid: PMC10061349 doi: 10.3389/fendo.2023.1091040 license: CC BY 4.0 --- # Sleeve gastrectomy decreases high-fat diet induced colonic pro-inflammatory status through the gut microbiota alterations ## Abstract ### Background High-fat diet (HFD) induced obesity is characterized with chronic low-grade inflammation in various tissues and organs among which colon is the first to display pro-inflammatory features associated with alterations of the gut microbiota. Sleeve gastrectomy (SG) is currently one of the most effective treatments for obesity. Although studies reveal that SG results in decreased levels of inflammation in multiple tissues such as liver and adipose tissues, the effects of surgery on obesity related pro-inflammatory status in the colon and its relation to the microbial changes remain unknown. ### Methods To determine the effects of SG on the colonic pro-inflammatory condition and the gut microbiota, SG was performed on HFD-induced obese mice. To probe the causal relationship between alterations of the gut microbiota and improvements of pro-inflammatory status in the colon following SG, we applied broad-spectrum antibiotics cocktails on mice that received SG to disturb the gut microbial changes. The pro-inflammatory shifts in the colon were assessed based on morphology, macrophage infiltration and expressions of a variety of cytokine genes and tight junction protein genes. The gut microbiota alterations were analyzed using 16s rRNA sequencing. RNA sequencing of colon was conducted to further explore the role of the gut microbiota in amelioration of colonic pro-inflammation following SG at a transcriptional level. ### Results Although SG did not lead to pronounced changes of colonic morphology and macrophage infiltration in the colon, there were significant decreases in the expressions of several pro-inflammatory cytokines including interleukin-1β (IL-1β), IL-6, IL-18, and IL-23 as well as increased expressions of some tight junction proteins in the colon following SG, suggesting an improvement of pro-inflammatory status. This was accompanied by changing populations of the gut microbiota such as increased richness of Lactobacillus subspecies following SG. Importantly, oral administrations of broad-spectrum antibiotics to delete most intestinal bacteria abrogated surgical effects to relieve colonic pro-inflammation. This was further confirmed by transcriptional analysis of colon indicating that SG regulated inflammation related pathways in a manner that was gut microbiota relevant. ### Conclusion These results support that SG decreases obesity related colonic pro-inflammatory status through the gut microbial alterations. ## Introduction Obesity is characterized with chronic low-grade inflammation in various tissues and organs [1]. Growing evidence has proposed colon as a critical site that displays pro-inflammatory features in response to high-fat diet (HFD) intake earlier than other metabolic tissues [2]. In rodents, exposure to HFD leads to morphological changes such as shortened colon length and histological changes such as increased macrophage infiltration in the epithelium and lamina propria [2]. This is accompanied by alterations to the inflammation cytokines in the colon including increased levels of pro-inflammatory cytokines tumor necrosis factor α (TNF-α), interleukin-1β (IL-1β), IL-6, IL-18, IL-23, and interferon-γ (IFN-γ), coupled with reductions in anti-inflammatory cytokines TNF-β, IL-10, and IL-33 [3, 4]. In human, several studies have indicated that individuals with excessive HFD consumption have a higher inflammatory tone, which is closely related to the disruption of intestinal immune homeostasis and higher incidence of inflammatory bowel diseases [5, 6]. These results point to an increased inflammation in the colon associated with HFD intake and obesity. The HFD-induced obesity is not only linked with pro-inflammatory changes in the colon but also alterations of the gut microbiota. Colon is the location where the largest population of bacteria reside. A wide range of data have demonstrated significant alterations of the gut microbiota following HFD intake, which is highly associated with pro-inflammatory status in the colon [7, 8]. For instance, patients with obesity display a rise in the abundance of certain microbes in the gut such as Enterobacter and Desulfovibrio which have a property of promoting inflammation (9–11), while they also tend to have a decline in the richness of some inflammation-protective bacteria such as *Akkermansia muciniphila* and Lactobacillus [12]. This is corroborated by evidence from animal experiments showing similar imbalance within the gut microbial community in relation to inflammation changes in the colon following HFD feeding [13]. Importantly, inoculation of the intestinal bacteria from HFD-fed mice promotes inflammation in the large bowel [14], pointing toward gut microbiota being a potential causal mediator linking HFD intake to colonic pro-inflammation. The importance of colonic pro-inflammation associated with the gut microbiota changes following HFD intake is highlighted by its detrimental effects on the gut physiology. One important aspect of colonic physiology affected by local inflammation is the gut barrier function. Literature has shown a strong correlation between colonic pro-inflammation and decreased gut barrier integrity in the context of HFD feeding [4]. Increased inflammatory cytokines such as IL-1β and IL-18 in the colon disrupt expressions of a variety of tight junction proteins that play important roles in the maintenance of barrier function, leading to a defect in gut barrier and an increase in intestinal permeability (15–17). Consequently, the increased colonic permeability allows translocation of aberrant microbes with pro-inflammatory activity into other metabolic tissues, causing systemic low-grade inflammation and worsened metabolic disorders [18]. Given the vital role of colonic pro-inflammation following HFD intake in the disruption of gut barrier and induction of more inflammation in the distant tissues [6], manipulations on the gastrointestinal tract with anti-inflammation potential represent promising and effective therapeutic strategies. Among the possible interventions on the gastrointestinal tract is bariatric surgery such as sleeve gastrectomy (SG). SG is currently one of the most effective treatments for obesity [19]. Both human and rodent studies have recently indicated that SG can restore the disrupted gut microbiota resulting from HFD intake [20, 21], with substantial rises in the abundance of multiple anti-inflammation bacteria such as Lactobacillus [22, 23]. Correspondingly, alterations of the intestinal bacteria following bariatric surgery are associated with improvements in the low-grade inflammation within different tissues such as liver and adipose tissue [24, 25]. However, the effects of SG on colonic pro-inflammation and its relation to the gut microbiota remain unclear. In the present study, using a mouse model of SG, we determined the effects of SG on HFD feeding related colonic pro-inflammation. We found a decrease in the expressions of pro-inflammatory cytokines genes and an upregulation of the genes encoding tight junction proteins in the colon of SG-treated mice, suggesting improvements of colonic pro-inflammation following SG. This was accompanied with significant alterations of the gut microbiota. Further studies using broad-spectrum antibiotics to perturb the gut microbiota changes following SG showed diminished effects of SG to improve pro-inflammation in the colon. Additional colonic transcriptome analysis supported that SG resulted in modifications of inflammatory pathways in a manner that was gut microbiota relevant. Altogether, these results demonstrate that SG leads to improvements in the HFD-induced colonic pro-inflammation, associated with alterations of the gut microbiota. ## Animal studies To investigate the effects of SG on HFD-feeding induced colonic pro-inflammation, twenty 4-week-old male C57BL/6J mice were purchased from Charles River (Beijing, China) and allowed to acclimate in the laboratory for two weeks prior to the start of HFD intake. Before surgery, all mice were fed $60\%$ HFD (Research Diets D12492, New Brunswick, New Jersey, USA.) for twelve weeks. Post that, the HFD-induced obese mice were randomized based on body weight to receive either SG ($$n = 11$$) or sham surgery (SHAM, $$n = 9$$). All mice were maintained on the same HFD for eight weeks following surgery until euthanasia. One mouse died and three mice suffered abdominal abscess after SG and were excluded. To study the role of the gut microbiota in the improvements of HFD-feeding related colonic pro-inflammation following SG, another twenty 4-week-old male C57BL/6J mice (Charles River, Beijing, China) were used. All mice were fed $60\%$ HFD (Research Diets D12492, New Brunswick, New Jersey, USA.) for twelve weeks starting at 6 weeks of age. Two days prior to surgery, all mice were provided with broad-spectrum antibiotics cocktails added in the drinking water to delete most of the intestinal bacteria [26]. Then, these mice were randomized based on body weight to undergo either SG (SG-ABX, $$n = 11$$) or sham surgery (SHAM-ABX, $$n = 9$$). All mice were maintained on the same HFD and antibiotics cocktails for eight weeks following surgery until euthanasia. One mouse died and two mice suffered abdominal abscess after SG and were excluded. All mice were housed under specific pathogen-free conditions at an ambient temperature with a 12-12 light-dark cycle and had ad libitum access to food and water. All animal experiments were conducted in accordance with National Research Council Guide for Care and Use of Laboratory Animals and approved by the Department of Laboratory Animal Science Fudan University. ## Antibiotics treatment The broad-spectrum antibiotics cocktails comprises four types of antibiotics purchased from Sigma Aldrich (Shanghai, China), namely neomycin trisulfate (#N6386), metronidazole (#M1547), vancomycin hydrochloride (#V2002) and ampicillin (#A9518). The antibiotics cocktails were freshly prepared by adding the antibiotics powders into drinking water, reaching a concentration of 1 g/L for neomycin trisulfate, 0.25 g/L for metronidazole, 0.5 g/L for vancomycin hydrochloride and 1 g/L for ampicillin [26]. The antibiotics-containing drinking water was stored in the light-protected bottles and changed three times weekly. ## Surgical procedures SG and sham procedures were performed as previously described [21]. Mice were anesthetized by intraperitoneal injection of pentobarbital sodium (50 mg/kg; Sigma, Shanghai, China). The stomach was gently exposed after dissection of the gastrosplenic ligaments. For SG, the glandular stomach was closed at 4 mm proximal of the pylorus toward the fundus using a 5-mm titanium clip (Ethicon, Somerville, NJ). After that, $80\%$ glandular stomach and entire non-glandular stomach were excised along the outside of the clip, leaving a tubular gastric remnant. The gastric remnant with the clip was then enhanced using interrupted 8-0 Prolene sutures. For sham surgery, gentle pressure was applied on the stomach with nontoothed blunt forceps. Immediately after surgery, mice were placed on a heat mat and subcutaneously administered 1 ml warm $5\%$ Glucose and Sodium Chloride Injection. Mice were fasted for food on the day of surgery and resumed HFD one day after surgery. Body weight following surgery was monitored daily for the first week and then weekly. ## Mixed-meal tolerance test Mixed-meal tolerance test (MMTT) was performed 6 weeks following surgery. All mice were fasted for 4 hours before oral gavage of liquid meal (volume 200 ml Ensure Plus spiked with a 25 mg dextrose). After that, tail vein blood glucose levels were measured using glucometers (Contour TS, Shanghai, China) at 0, 15, 30, 45, 60, 90, and 120 minutes. ## Serum lipids measurement Blood samples were collected when mice were euthanized after 4-hour fasting. Serum levels of triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C) were measured using automatic biochemical analyser (Siemens Healthcare Diagnostics Inc, ADVIA XPT, USA) according to the manufacturer’s instructions. ## Quantitative real-time PCR Total RNA was extracted from colon tissues using TRIzol (BioTNT, Shanghai, China) and then reversed into Complementary DNA by PrimeScript RT kit (Takara RR047, Beijing, China). PCR was performed using the TB Green Premix (Takara RR420, Beijing, China) on a QuantStudio 6 (ThermoFisher) system. Primers of target genes were purchased from Integrated DNA Technologies (Sangon Biotech, Shanghai, China) and verified by melting curve analysis. The expression levels of target genes were normalized to β-actin gene and calculated using the 2–ΔΔCT method. ## Histology and immunohistochemistry The colon segments were fixed in $4\%$ buffered formalin for 48 hours prior to paraffin embedding and hematoxylin and eosin (H&E) staining. Crypt depth of colon of each mouse was measured in three different fields under x 400 high power field (HPF) by the software (K-Viewer 1.5.5.2, China). For evaluation of the macrophage infiltration in the colon, CD68 staining was performed using a rabbit anti-mouse CD68 primary antibody (Abcam ab283654, Shanghai, China) and secondary antibody (Jackson, Philadelphia, USA), and further counted in three different sections under HPF by two blinded observers. ## Isolation of colonic lamina propria and flow cytometric analysis The macrophages from colonic lamina propria were isolated by lamina propria dissociation kit (Miltenyi Biotec mouse130-097-410, Shanghai, China) according to the manufacturer’s instructions. Briefly, intestinal fat was removed, and colon was cut open and washed slowly in PBS (Ca2+ and Mg2+ free). The colon segments were cut into 1 mm pieces and added into Enzyme D, Enzyme R, and Enzyme A for incubation at 37°C for 45 minutes. The supernatant was then filtered through 75 um nylon mesh and centrifuged at 500g for with 10 minutes. After centrifuge, the pellets were collected and re-suspended in $40\%$ Percoll (Solarbio, Beijing, China). The $40\%$ Percoll solution with suspended cells was transferred into $80\%$ Percoll and re-centrifuged at 2000 rpm for 20 minutes at room temperature. The white interphase was collected after centrifuge and washed twice with PBS. Before flow cytometric analysis, single-cell suspensions isolated from the colon were further stained for 30 minutes on ice with fluorophore-conjugated commercial antibodies to F$\frac{4}{80}$ (Invitrogen 11-4801-82, USA), CD11b (Invitrogen 12-0112-82, USA) and CD11c (Invitrogen 17-0114-81, USA). After preparation, those cells were re-suspended in PBS with $0.5\%$ FBS and analyzed using FACSAriaIII (BD Bioscience, USA). The data were analyzed by FlowJo software (Becton, Dickinson and Company, USA). ## 16s rRNA sequencing Fecal and cecal samples were collected when mice were euthanized, and immediately frozen at -80°C upon collection. Total genomic DNA was extracted from samples using the OMEGA Soil DNA Kit (M5635-02) (Omega Bio-Tek, Norcross, GA, USA), following manufacturer’s instructions. Then the DNA was stored at -20°C prior to further analysis. PCR amplicons were purified with Vazyme VAHTSTM DNA Clean Beads (Vazyme, Nanjing, China) and quantified using the Quant-iT PicoGreen dsDNA Assay Kit (Invitrogen, Carlsbad, CA, USA). After quantification, amplicons were pooled in equal amounts. Pair-end 2 x 250 bp sequencing was performed using the Illlumina MiSeq platform with MiSeq Reagent Kit v3 at Shanghai Personal Biotechnology Co., Ltd (Shanghai, China). Sequencing data analyses were performed using QIIME2. Briefly, raw sequences after trimming were analyzed by the cutadapt plugin and the dada2 plugin. After that, non-singleton amplicon sequence variants (ASVs, $100\%$ operational taxonomic units (OTUs)) were generated. Microbial taxonomic classification was performed to ASVs according to the classify-sklearn alignment algorithm [27] against the Greengenes database (Release 13.8) of $99\%$ OTUs reference sequences [28]. Alpha diversity metrics including Chao1 and Shannon calculated by the diversity plugin were performed to estimate richness and diversity respectively. Beta diversity metrics including unweighted UniFrac distance matrix were scaled and visualized through principal coordinates analysis (PCoA), and significance of the clustering between groups was determined via permutational multivariate analysis of variance (PERMANOVA). Random Forest Classifier with 10-fold cross-validations, MetagenomeSeq analysis, and Linear discriminant analysis (LDA) effect size (LEfSe) with default parameters were computed to identify significantly different microbes in abundance between groups at different taxonomic levels. ## RNA sequencing and analysis Total RNA was isolated from colon tissues using Trizol Reagent (Invitrogen Life Technologies, USA) and sequenced on NovaSeq 6000 platform (Illumina, USA) by Shanghai Personal Biotechnology Cp. Ltd. R language Pheatmap (1.0.8, China) software package was used to perform bi-directional clustering analysis to identify all differentially expressed genes (DEGs) between two surgical groups. The top Gene Ontology (GO) was used to perform GO enrichment analysis based on the DEGs. P-value was calculated by hypergeometric distribution method (the standard of significant enrichment is $P \leq 0.05$). Cluster Profiler (3.4.4) software was used to carry out the enrichment analysis of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway of DEGs, focusing on the significant enrichment pathway with $P \leq 0.05.$ ## Statistical analyses Data were presented as mean ± SEM. Differences in body weight and blood glucose levels during MMTT between two surgical groups were evaluated using two-way analysis of variance (ANOVA) with post-hoc Sidak test for multiple comparisons [29]. Other simple comparisons between two surgical groups were assessed with Student’s t-test or non-parametric Mann–Whitney U tests. In addition, comparisons among four surgical groups were assessed using two-way analysis of variance (ANOVA) with post-hoc Tukey’s test for multiple comparisons. All statistical analyses were conducted using GraphPad Prism 8 software (La Jolla, CA). Data were considered statistically significant when $P \leq 0.05$ (2-sided significance testing). ## SG leads to improvements in the HFD-feeding induced colonic pro-inflammatory status Twenty 4-week-old male C57BL/6J mice were fed $60\%$ HFD for 12 weeks and then randomized based on body weight to receiving either SG or SHAM operation. Mice were kept on the same $60\%$ HFD following surgery until euthanasia (Figure 1A). SG led to significant weight loss as compared to SHAM operation (Figure 1B). SG mice also displayed significantly lower glucose levels during mixed meal tolerance test (MMTT) (Figure 1C), suggesting an improved glucose tolerance following SG. Besides, SG-treated mice had significantly decreased levels of TC, LDL-C and HDL-C (Figure 1D). **Figure 1:** *SG led to improvements in the HFD-feeding induced colonic pro-inflammatory status. (A) Experimental design and timeline (SG n=7; SHAM n=9). (B) Body weight. (C) Mixed meal tolerance test (MMTT). (D) Serum lipids. (E) Length of colon. (F) Cecum weight. (G) Representative H&E-staining images of colon (400x; scale bar, 50 um) and quantification of depth of crypt. (H) Gene expressions of inflammatory cytokines in the colon. (I) Gene expressions of tight junction proteins in the colon. n = 7–9/group; Data are presented as means ± SEM. Two-way ANOVA with post hoc Sidak test for multiple comparisons (B, C) and Student’s t-test (D, I) were used for significance assessments. ****P < 0.0001, ***P < 0.001, **P < 0.01, *P < 0.05. SHAM, sham surgery; SG, sleeve gastrectomy.* In terms of colonic morphology, there was no difference in the length of colon and weight of cecum between SG and SHAM groups (Figures 1E, F). However, H & E staining indicated a decreased depth of colonic crypts of SG mice as compared to SHAM mice (Figure 1G). To determine the effects of SG on HFD feeding associated colonic pro-inflammation, we measured mRNA expression levels of different cytokines in the colon using qPCR. The mRNA expression levels of pro-inflammatory cytokines such as IL-6, IL-1β, IL-18, and IL-23 were downregulated in the colon of SG mice relative to SHAM mice, while those anti-inflammatory factors such as IL-10, TNF-β, IL-33 were not impacted by SG (Figure 1H). Given that HFD feeding associated colonic pro-inflammation induces gut barrier defects [30], we measured mRNA expression levels of two common intestinal tight junction proteins zonula occludens 1 (ZO-1) and Occludin. SG increased relative expression levels of ZO-1 and Occludin in the colon as compared to SHAM operation (Figure 1I), implicating a possible improvement in the colonic barrier following SG. Collectively, these data demonstrate that SG results in improvements in the HFD feeding related colonic pro-inflammation. ## HFD-feeding induced macrophage infiltration in the colon is not affected by SG Literature has recently proposed an important role of macrophage infiltration in the HFD feeding associated colonic pro-inflammation [31]. We therefore measured mRNA expression levels of macrophage-related chemokine genes including CCL2, CCL7 and CCL12 and marker genes such as F$\frac{4}{80}$, CD68, CD11b and CD11c in the colon. All these genes except CD11c showed comparable expression levels between SG and SHAM groups (Figures 2A, B). Likewise, immunohistochemistry tests revealed no difference in the numbers of CD68+ macrophages infiltrated in the colon epithelium between SG and SHAM mice (Figure 2C). This was further confirmed by flow cytometry analysis of colonic lamina propria where SG mice had similar numbers of F$\frac{4}{80}$+CD11b+CD11c- sub-population to SHAM mice (Figure 2D). These results indicate that SG has no effect on the HFD-feeding induced macrophage infiltration in the colon. **Figure 2:** *HFD feeding induced macrophage infiltrations in the colon are not affected by SG. (A) Gene expressions of macrophage-related chemokines. (B) Gene expressions of macrophage-related markers. (C) Representative histological images of colon stained with anti-CD68 antibody (400x; scale bar, 50 um) and quantification of CD68 positive cells. (D) Representative FACS analysis of F4/80+CD11b+CD11c- cells in colonic lamina propria. The right panel indicates the percentage of F4/80+CD11b+CD11c- cells among F4/80+ cells in colonic lamina propria. n = 7–9/group; Data are presented as means ± SEM. Student’s t-test was used for significance assessments. *P < 0.05. SHAM, sham surgery; SG, sleeve gastrectomy.* ## SG results in alterations of the gut microbiota Given the association of colonic pro-inflammation upon HFD intake with alterations to the gut microbiota [32], we next characterized the intestinal bacterial changes following SG. The fecal microbiota of SG mice displayed higher richness than that of SHAM mice, as estimated by higher levels of Chao 1 index (Figures 3A, B). For the overall composition of the gut microbiota, unweighted UniFrac PCoA revealed a differential clustering of fecal samples between SG and SHAM mice (Figure 3C). This was confirmed by PERMANOVA which showed significance in unweighted UniFrac distances of samples between SG and SHAM groups (F value = 1.85, $$P \leq 0.003$$), suggesting an alteration in the overall composition of the gut microbiota following SG. In terms of the detailed compositions of the gut microbiota, Firmicutes was the major phylum in the fecal microbial communities of SHAM and SG mice, while Desulfovibrio subspecies showed a decreasing trend in the abundance following SG (Supplementary Figure 1A). Random Forest classifier was used to identify differentially enriched microbes between SHAM and SG groups. It indicated that bacterial subspecies belonging to *Lactobacillus genus* were the main discriminators for SG compared to SHAM gut microbiota, with substantial rises in the relative richness following SG (Figure 3D). This was consistent with results generated from MetagenomeSeq analysis showing that multiple bacteria under Lactobacillales order were abundant following SG relative to SHAM operation (Figure 3E). Additional LDA effect size (LEfSe) analysis revealed more taxonomic differences in the microbial composition between SHAM and SG groups (Supplementary Figure 1D). For instance, SG microbiota were enriched for Blautia subspecies, whereas SHAM microbiota were enriched for Desulfovibrio subspecies. Taken together, these data indicate that SG leads to significant alterations of the gut microbiota, featured by decreases of pro-inflammatory microbes such as Desulfovibrio and increases of anti-inflammatory microbes like Lactobacillus. This suggests a potential association of changed gut microbiota with decreased colonic pro-inflammation following SG. **Figure 3:** *SG resulted in alterations of the gut microbiota. (A, B). Chao1 and Shannon index of the gut microbiota in the fecal samples. Student’s t-test was used for significance assessments. *P < 0.05. (C) Unweighted UniFrac principle coordinates analysis (PCoA). (D) Discriminatory importance scores of top-ranked ASVs identified by the Random Forest analysis. A comparison of the relative abundance of top-ranked ASVs between SG and SHAM gut microbiota. Green and brown indicate the degree of relative abundance. (E) MetagenomeSeq analysis showing significantly enriched microbes following SG relative to SHAM operation. The X and Y axis represent taxonomic order and the -log10(adj-Pvalue) value, respectively. Dots of blue, red, and green represent abundant microbes under Bacteroidales, Lactobacillales, and Clostridiales, respectively. n = 6/group; Data are means ± SEM. SHAM, sham surgery; SG, sleeve gastrectomy.* ## Administration of broad-spectrum antibiotics compromises SG’s ability to improve HFD-feeding induced colonic pro-inflammation We next sought to determine whether the improvements of colonic pro-inflammation following SG were dependent on the alterations of the gut microbiota. Another cohort of twenty 4-week-old male C57BL/6 mice were fed $60\%$ HFD for 12 weeks and then randomized based on body weight to receiving either SG (SG-ABX) or SHAM (SHAM-ABX) operation. All mice were kept on the same $60\%$ HFD following surgery until euthanasia. They were also provided with broad-spectrum antibiotics in the drinking water to delete most intestinal bacteria [26] starting from 2 days before surgery till the end of the study (Figure 4A). **Figure 4:** *Administration of broad-spectrum antibiotics comprises SG’s ability to improve HFD-feeding induced colonic pro-inflammation. (A) Experimental design and timeline (SG-ABX n=8, SHAM-ABX n=9). (B) Body weight. (C) Mixed meal tolerance test (MMTT). (D) Serum lipids. (E) Representative H&E-staining images of colon (400x; scale bar, 50 um) and quantification of depth of crypt. (F) Genes expressions of inflammatory cytokines in the colon. (G) Gene expressions of macrophages markers in the colon. (H) Representative histological images of colon stained with anti-CD68 antibody (400x; scale bar, 50 um) and quantification of CD68 positive cells. (I) Gene expressions of tight junction proteins in the colon. n = 8/group; Data are presented as means ± SEM. Two-way ANOVA with post hoc Sidak test for multiple comparisons (B, C) and Student’s t-test (D–H) were used for significance assessments. **P < 0.01, *P < 0.05. SHAM, sham surgery; SG, sleeve gastrectomy; ABX, antibiotics.* To confirm the efficacy of antibiotics cocktails in the gut microbiota deletion, 16s rRNA sequencing was used to characterize the bacterial changes in the cecum contents of SG and SHAM mice that received antibiotics treatment. Chao1 and Shannon index indicated extremely low levels of richness and diversity of the gut microbiota in the antibiotics-treated groups (Supplementary Figures 2A, B). This was consistent with compositions of the microbial community where Proteobacteria was the main phylum left in the gut, accounting for $90\%$ of the entire bacterial abundance following antibiotics administration (Supplementary Figure 2D). At species level, there were fewer microbes that had much lower richness in the cecum contents (Supplementary Figure 2E). These data suggest that the broad-spectrum antibiotics treatment successfully deleted most microbes in the cecum. In this situation, no differences were observed between SG and SHAM mice in the microbial richness and diversity, as estimated by Chao1 and Shannon index, respectively (Supplementary Figures 2A, B). Likewise, no bacterial taxa were identified as varying significantly in the relative abundance between SG-treated and sham-operated mice using Random Forest classifier, MetagenomeSeq analysis or LEfSe (data not shown given the extremely low richness of the microbes detected). For the overall composition of the gut microbiota, unweighted UniFrac PCoA did not show pronouncedly differential clustering of samples between SG-ABX and SHAM-ABX groups (PERMANOVA F value =0.84, $$P \leq 0.614$$) (Supplementary Figure 2C). Together, these data reveal no marked alterations of the gut microbiota following SG relative to SHAM operation in the context of antibiotics treatment. Although antibiotics cocktails eliminated most bacteria in the gut, SG still led to significant weight loss, improved glucose tolerance and decreased serum levels of TC and LDL-C (Figures 4B–D). Interestingly, weight loss, glucose tolerance and serum lipid levels were all comparable between SG and SG-ABX groups (Supplementary Figures 3A–F). For the histological features of HFD feeding associated colonic pro-inflammation, no difference was observed in the crypt depth between the two surgical groups under antibiotics treatment (Figure 4E). However, unlike what was shown in the Figure 1, SG had no effect on the expressions of various inflammation cytokines in the colon in the absence of the gut microbiota changes, as the pro-inflammatory cytokines including IL-6, IL-1β, IL-18, and IL-23 displayed comparable mRNA expression levels between antibiotics-treated SG and SHAM groups (Figure 4F; Supplementary Figure 4A). Likewise, there were no differences in the relative expression levels of ZO-1 and *Occludin* genes in the colon between antibiotics-treated SG and SHAM mice (Figure 4I; Supplementary Figure 4B), implicating no improvement in the gut barrier integrity following SG when there were no intestinal bacterial changes resulting from oral administration of antibiotics. On the other hand, whereas antibiotics-treated animals had reduced colonic macrophage infiltration, SG-ABX and SHAM-ABX groups showed comparable levels of macrophage infiltration and related genes expressions in the colon (Figures 4G, H, Supplementary Figures 4C, D). Altogether, these results indicate an important role of the gut microbiota in the improvements of HFD related colonic pro-inflammation following SG. ## SG significantly modifies the colonic transcriptome, including pathways linked to inflammation regulation To further probe the relationship between improvements of colonic pro-inflammation and the gut microbiota changes following SG at transcriptional level, we conducted RNA sequencing analysis of colon tissues obtained from both SG and SHAM mice with and without antibiotics treatment. For mice that did not receive antibiotics cocktails, volcano plots illustrated 179 differentially expressed genes (DEGs) in the colon between SG and SHAM groups (Figure 5A). Both Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of these DEGs showed a significant enrichment of multiple pathways that were related to inflammation regulation in the colon including inflammatory response, leukocyte migration and chemotaxis as well as PPAR signaling pathway (Figure 5B; Supplementary Figure 5A). On the other hand, for mice that received antibiotics cocktails, volcano plots identified only 73 DEGs between SG and SHAM groups. Neither GO nor KEGG pathway analyses of these DEGs showed an enrichment of inflammation-associated pathways (Figures 5C, D; Supplementary Figure 5B). Taken together, these findings demonstrate that SG exerts transcriptional modifications of inflammatory pathways in the colon in a manner that was gut microbiota relevant. **Figure 5:** *SG significantly modulated the colonic transcriptome. (A) Volcano plots shows differentially expressed genes (DEGs) of colon between SHAM and SG groups. Blue dots and red dots represent significantly down-regulated and up-regulated genes, respectively (Log2 Fold change >1, Bonferroni-adjusted P < 0.05). (B) Enrichment analysis of Gene Ontology (GO) including Biological Process (BP), Molecular Function (MF), and Cell Component (CC) based on the DEGs between SHAM vs SG. (C) Volcano plots shows DEGs of colon between antibiotics-treated SG and SHAM groups. Blue dots and red dots represent significantly down-regulated and up-regulated genes, respectively (Log2 Fold change >1, Bonferroni-adjusted P < 0.05). (D) Enrichment analysis of GO based on the DEGs between SHAM-ABX vs SG-ABX. n = 6–8/group. SHAM, sham surgery; SG, sleeve gastrectomy; ABX, antibiotics.* ## Discussion Obesity is characterized with chronic low-grade inflammation in various tissues and organs associated with changing compositions of the gut microbiota [9]. Colon has recently emerged as a critical site not only because it is the first place to display inflammatory features in response to HFD intake [2] but also because it is where the bulk of the intestinal bacteria are located. HFD intake induces gut dysbiosis which may in turn initiate pro-inflammatory activities in the colon [3]. The colonic pro-inflammation further damages intestinal barrier which allows bacteria to translocate into distant tissues to cause more inflammation and dysfunction [3, 33]. Bariatric surgery such as SG is currently the most efficacious treatment for obesity [34]. Although SG does not involve anatomical alterations of the intestinal tract, this surgical approach does lead to a wide range of alterations in gut physiology in the distal bowel such as changing bacterial populations [35]. In the present study, using a mouse model of SG, we found that the surgical effects extend to the improvements of pro-inflammatory status in the colon which are gut microbiota relevant. The inflammatory shift in the colon following HFD intake is regarded as low-grade since it is not associated with apparent histological features of active inflammation [3]. However, it is considered as pro-inflammatory because it is characterized with increased levels of macrophage infiltration and expressions of various inflammation cytokines [2]. Here we found that, although mice undergoing SG showed decreased depth of crypt without markedly reduced macrophages infiltration in the colon, they had a significant reduction in the expressions of multiple pro-inflammatory cytokines including IL-1β, IL-6, IL-18, and IL-23. This was supported by transcriptional signatures in colon revealing that SG regulated multiple inflammation-related pathways. Consistent with what we have found, one study in rats recently indicates decreases of inflammatory cytokines IFN-γ, IL-17, and IL-23 in the distal jejunum following SG [36]. The pro-inflammatory cytokines in the colon are associated with gut barrier dysfunction [37]. Literature has previously shown that certain inflammatory cytokines such as IL-1β can directly suppress the expressions of a variety of tight junction proteins which are essential for the gut barrier integrity [16]. In line with the decreased levels of inflammatory cytokines in the colon following SG, we observed increased expressions of two common tight junction proteins in the colon, implicating a potential improved gut barrier following SG. In parallel, recent studies demonstrate that SG can increase gene expressions of intestinal tight junction proteins and improve gut barrier function in obese mice that receive HFD [38]. Given that improved gut barrier function and decreased permeability prevent translocation of inflammation from intestine into circulation and periphery [33], it is therefore possible that the improvements of pro-inflammation and gut barrier function in the colon following SG contribute to an overall relief of inflammatory condition observed in obesity. Interestingly, previous studies have revealed that patients who underwent SG experience decreased levels of pro-inflammatory cytokines in the circulation and liver following surgery [25, 39]. Collectively, these data suggest that SG results in improvements in the HFD feeding associated colonic pro-inflammation. Bariatric surgery has been reported to change the gut microbial populations [22, 23], and alterations in the gut microbiota have been pointed as a potential modulator of the colonic inflammation observed in obesity [40]. Therefore, we next characterized the intestinal bacterial changes following SG. We found that SG led to disparate gut microbial compositions, accompanied by increased richness and diversity of the microbial community. Studies in both human and animals propose that a more diverse and abundant bacterial community is beneficial to intestinal health, in connection to decreased inflammation levels and improved local defense [41, 42]. More importantly, we identified significant alterations in the abundance of various bacterial populations following SG. Notable in mice that received SG was an expansion of Lactobacillus subspecies. We and others have consistently observed that SG led to considerable increases in the richness of Lactobacillus in obese rodents [22, 23, 43]. These microbes are generally regarded as “healthy” bacteria and can be found in a variety of foods and probiotics which are often used to treat intestinal health issues [44, 45]. Previous studies have indicated that administration of probiotics containing multiple Lactobacillus strains reduces inflammation and enhances gut barrier function in obese mice [39, 46]. Additionally, certain bacteria that promote inflammation such as Desulfovibrio [47] were found decreased in abundance following SG. Together, these results suggest a strong association of changed gut microbiota with decreased colonic inflammation following SG. The key question then becomes whether SG improves pro-inflammatory conditions in the colon through the gut microbial changes. We applied broad-spectrum antibiotics cocktails to eliminate the gut microbiota changes following surgery. We found that, in the absence of the gut microbiota alterations upon antibiotics treatment, SG had no effect on HFD feeding induced colonic pro-inflammation, as manifested by unchanged expression levels of inflammatory cytokines such as IL-1β, IL-6, and IL-23. A caveat here is that antibiotics treatment itself appealed to reduce expressions of certain pro-inflammatory cytokines like IL-18 in the colon, and therefore the window for improvements in these parameters is smaller. Nevertheless, the increases in the gene expressions of tight junction proteins in the colon following SG were also absent when antibiotics were orally administrated. Further transcriptomic analysis of colon showed no inflammation pathway that was regulated by SG in the absence of bacterial changes. Together, these results support that SG decreases HFD feeding related colonic pro-inflammation in a gut microbiota dependent way. Given that macrophage plays an important role in HFD-induced colonic inflammation [2], we measured macrophage infiltration in the colonic epithelium to reveal its potential relation to the gut microbiota and colonic inflammation following SG. We found that numbers of macrophages infiltrated in the colonic epithelium were profoundly decreased by antibiotics treatment but not affected by SG. These data indicate that macrophage infiltration in the colon is at least partially dependent on the gut microbiota, but it is not associated with specific alterations of the gut microbiota and improvements of colonic pro-inflammation following SG. The gut microbial changes have impacts on composition and function of various intestinal immune cells [48]. Future studies will need to assess which immune cells other than macrophage are most relevant to the reduced pro-inflammatory levels in the colon following SG. Bariatric surgery exerts profound alterations to gut physiology [35]. While controversy remains, accumulating evidence indicates that many aspects of the physiological changes taking place following surgery are influenced by the gut microbiota [49]. The improvements of pro-inflammation in the colon following SG represent one clear example of changes in gut physiology that involve intestinal bacterial effects. In the present study, we applied broad-spectrum antibiotics for evaluating the potential influence of the gut microbiota following SG. This method induced successful deletion of majority of bacteria in the gut. Surprisingly, metabolic improvements imparted by SG including weight loss, improved glucose tolerance and decreased serum lipid levels were not affected by antibiotics administration. These results suggest that the gut microbiota may be dispensable to improvements in these parameters but still important to other physiological changes resulting from SG such as decreased pro-inflammation levels in the colon. Nonetheless, gut microbial transfer studies are still needed to address a cause-and-effect relationship between the gut microbiota and metabolic benefits of SG. One limitation of the current work is that we could not identify all microbes present in samples using 16s rRNA sequencing due to its limited sequencing depth and power. Future work using metagenomic sequencing will be needed to gain more comprehensive information on alterations of the gut microbiota following SG. Another limitation is that we did not measure related microbial metabolites that potentially regulate intestinal inflammation such as bile acids [50, 51]. Bile acids and bile acid receptors have been proposed as critical molecular underpinnings for the beneficial effects of SG (52–56). Several lines of evidence have demonstrated a strong association of changed gut microbiota with increased bile acids levels and signaling following SG [22, 53, 57]. Importantly, administration of antibiotics cocktails like what we used herein leads to disturbed bile acids metabolism and suppressed bile acids signaling following SG [26]. This disrupted gut microbiota-bile acids interaction impairs SG’s effects to increase gut hormone secretions which is another pivotal example of gut physiological changes occurring after SG [26]. Interestingly, bile acids have been linked with controlling inflammation in mouse models of colitis [58, 59]. It is possible to hypothesize that changes in bile acids metabolism and signaling work as a potential mediator linking the gut microbiota to the improvements of colonic inflammation following SG. Investigations on bile acids metabolism will provide a mechanistic insight on how the gut microbiota reduces intestinal inflammation following SG. In conclusion, our findings demonstrate that SG leads to improvements in the HFD-induced colonic pro-inflammation, associated with alterations of the gut microbiota. Depletion of the gut bacterial changes following SG through administration of broad-spectrum antibiotics compromised SG’s effects to relieve pro-inflammation status in the colon. These results point to changes in the intestinal bacteria as important gut adaptation to surgical manipulations on the gastrointestinal tract that mediate alleviations of inflammation in the colon. ## Data availability statement The original contributions presented in the study are publicly available. This data can be found here: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA915230. ## Ethics statement The animal study was reviewed and approved by the Department of Laboratory Animal Science Fudan University. ## Author contributions Authors QY and YS conceived, designed, and supervised the study; CC, XT, and HY conducted this study; CC and XT analyzed the results; QY, YS, CC, and XT wrote the manuscript; RH and QS provided guide of surgical procedures. All authors approved the final manuscript as submitted and agreed to be accountable for all aspects of the work. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2023.1091040/full#supplementary-material ## References 1. Cox AJ, West NP, Cripps AW. **Obesity, inflammation, and the gut microbiota**. *Lancet Diabetes Endocrinol* (2015) **3**. DOI: 10.1016/S2213-8587(14)70134 2. Kawano Y, Nakae J, Watanabe N, Kikuchi T, Tateya S, Tamori Y. **Colonic pro-inflammatory macrophages cause insulin resistance in an intestinal Ccl2/Ccr2-dependent manner**. *Cell Metab* (2016) **24** 295-310. DOI: 10.1016/j.cmet.2016.07.009 3. Luck H, Tsai S, Chung J, Clemente-Casares X, Ghazarian M, Revelo XS. **Regulation of obesity-related insulin resistance with gut anti-inflammatory agents**. *Cell Metab* (2015) **21**. DOI: 10.1016/j.cmet.2015.03.001 4. Winer DA, Winer S, Dranse HJ, Lam TK. **Immunologic impact of the intestine in metabolic disease**. *J Clin Invest* (2017) **127** 33-42. DOI: 10.1172/JCI88879 5. Marfella R, Esposito K, Siniscalchi M, Cacciapuoti F, Giugliano F, Labriola D. **Effect of weight loss on cardiac synchronization and proinflammatory cytokines in premenopausal obese women**. *Diabetes Care* (2004) **27** 47-52. DOI: 10.2337/diacare.27.1.47 6. Duan Y, Zeng L, Zheng C, Song B, Li F, Kong X. **Inflammatory links between high fat diets and diseases**. *Front Immunol* (2018) **9**. DOI: 10.3389/fimmu.2018.02649 7. Kim KA, Gu W, Lee IA, Joh EH, Kim DH. **High fat diet-induced gut microbiota exacerbates inflammation and obesity in mice**. *PloS One* (2012) **7**. DOI: 10.1371/journal.pone.0047713 8. de La Serre CB, Ellis CL, Lee J, Hartman AL, Rutledge JC, Raybould HE. **Propensity to high-fat diet-induced obesity in rats is associated with changes in the gut microbiota and gut inflammation**. *Am J Physiol Gastrointest Liver Physiol* (2010) **299**. DOI: 10.1152/ajpgi.00098.2010 9. Saad MJ, Santos A, Prada PO. **Linking gut microbiota and inflammation to obesity and insulin resistance**. *Physiol (Bethesda)* (2016) **31**. DOI: 10.1152/physiol.00041.2015 10. Fei N, Zhao L. **An opportunistic pathogen isolated from the gut of an obese human causes obesity in germfree mice**. *ISME J* (2013) **7**. DOI: 10.1038/ismej.2012.153 11. Hiel S, Gianfrancesco MA, Rodriguez J, Portheault D, Leyrolle Q, Bindels LB. **Link between gut microbiota and health outcomes in inulin -treated obese patients: Lessons from the Food4Gut multicenter randomized placebo-controlled trial**. *Clin Nutr* (2020) **39**. DOI: 10.1016/j.clnu.2020.04.005 12. Everard A, Belzer C, Geurts L, Ouwerkerk JP, Druart C, Bindels LB. **Cross-talk between akkermansia muciniphila and intestinal epithelium controls diet-induced obesity**. *Proc Natl Acad Sci U.S.A.* (2013) **110**. DOI: 10.1073/pnas.1219451110 13. Roopchand DE, Carmody RN, Kuhn P, Moskal K, Rojas-Silva P, Turnbaugh PJ. **Dietary polyphenols promote growth of the gut bacterium akkermansia muciniphila and attenuate high-fat diet-induced metabolic syndrome**. *Diabetes* (2015) **64**. DOI: 10.2337/db14-1916 14. Natividad JM, Lamas B, Pham HP, Michel ML, Rainteau D, Bridonneau C. **Bilophila wadsworthia aggravates high fat diet induced metabolic dysfunctions in mice**. *Nat Commun* (2018) **9** 2802. DOI: 10.1038/s41467-018-05249-7 15. Thorburn AN, Macia L, Mackay CR. **Diet, metabolites, and "western-lifestyle" inflammatory diseases**. *Immunity* (2014) **40**. DOI: 10.1016/j.immuni.2014.05.014 16. Al-Sadi RM, Ma TY. **IL-1beta causes an increase in intestinal epithelial tight junction permeability**. *J Immunol* (2007) **178**. DOI: 10.4049/jimmunol.178.7.4641 17. Garidou L, Pomie C, Klopp P, Waget A, Charpentier J, Aloulou M. **The gut microbiota regulates intestinal CD4 T cells expressing RORgammat and controls metabolic disease**. *Cell Metab* (2015) **22**. DOI: 10.1016/j.cmet.2015.06.001 18. Chakaroun RM, Massier L, Kovacs P. **Gut microbiome, intestinal permeability, and tissue bacteria in metabolic disease: Perpetrators or bystanders**. *Nutrients* (2020) **12**. DOI: 10.3390/nu12041082 19. Schauer PR, Bhatt DL, Kirwan JP, Wolski K, Aminian A, Brethauer SA. **Bariatric surgery versus intensive medical therapy for diabetes - 5-year outcomes**. *N Engl J Med* (2017) **376**. DOI: 10.1056/NEJMoa1600869 20. Ikeda T, Aida M, Yoshida Y, Matsumoto S, Tanaka M, Nakayama J. **Alteration in faecal bile acids, gut microbial composition and diversity after laparoscopic sleeve gastrectomy**. *Br J Surg* (2020) **107**. DOI: 10.1002/bjs.11654 21. Shao Y, Shen Q, Hua R, Evers SS, He K, Yao Q. **Effects of sleeve gastrectomy on the composition and diurnal oscillation of gut microbiota related to the metabolic improvements**. *Surg Obes Relat Dis* (2018) **14**. DOI: 10.1016/j.soard.2018.02.024 22. Bozadjieva-Kramer N, Shin JH, Shao Y, Gutierrez-Aguilar R, Li Z, Heppner KM. **Intestinal-derived FGF15 protects against deleterious effects of vertical sleeve gastrectomy in mice**. *Nat Commun* (2021) **12** 4768. DOI: 10.1038/s41467-021-24914-y 23. Shao Y, Evers SS, Shin JH, Ramakrishnan SK, Bozadjieva-Kramer N, Yao Q. **Vertical sleeve gastrectomy increases duodenal lactobacillus spp. richness associated with the activation of intestinal HIF2alpha signaling and metabolic benefits**. *Mol Metab* (2022) **57**. DOI: 10.1016/j.molmet.2022.101432 24. de Groot P, Scheithauer T, Bakker GJ, Prodan A, Levin E, Khan MT. **Donor metabolic characteristics drive effects of faecal microbiota transplantation on recipient insulin sensitivity, energy expenditure and intestinal transit time**. *Gut* (2020) **69**. DOI: 10.1136/gutjnl-2019-318320 25. Cabre N, Luciano-Mateo F, Fernandez-Arroyo S, Baiges-Gaya G, Hernandez-Aguilera A, Fibla M. **Laparoscopic sleeve gastrectomy reverses non-alcoholic fatty liver disease modulating oxidative stress and inflammation**. *Metabolism* (2019) **99**. DOI: 10.1016/j.metabol.2019.07.002 26. Chaudhari SN, Luo JN, Harris DA, Aliakbarian H, Yao L, Paik D. **A microbial metabolite remodels the gut-liver axis following bariatric surgery**. *Cell Host Microbe* (2021) **29** 408-24 e7. DOI: 10.1016/j.chom.2020.12.004 27. Bokulich NA, Kaehler BD, Rideout JR, Dillon M, Bolyen E, Knight R. **Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2's q2-feature-classifier plugin**. *Microbiome* (2018) **6** 90. DOI: 10.1186/s40168-018-0470-z 28. McDonald D, Price MN, Goodrich J, Nawrocki EP, DeSantis TZ, Probst A. **An improved greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea**. *ISME J* (2012) **6**. DOI: 10.1038/ismej.2011.139 29. Alquier T, Poitout V. **Considerations and guidelines for mouse metabolic phenotyping in diabetes research**. *Diabetologia* (2018) **61**. DOI: 10.1007/s00125-017-4495-9 30. Suzuki T. **Regulation of intestinal epithelial permeability by tight junctions**. *Cell Mol Life Sci* (2013) **70**. DOI: 10.1007/s00018-012-1070-x 31. Antonioli L, Caputi V, Fornai M, Pellegrini C, Gentile D, Giron MC. **Interplay between colonic inflammation and tachykininergic pathways in the onset of colonic dysmotility in a mouse model of diet-induced obesity**. *Int J Obes (Lond)* (2019) **43**. DOI: 10.1038/s41366-018-0166-2 32. Hiippala K, Jouhten H, Ronkainen A, Hartikainen A, Kainulainen V, Jalanka J. **The potential of gut commensals in reinforcing intestinal barrier function and alleviating inflammation**. *Nutrients* (2018) **10**. DOI: 10.3390/nu10080988 33. Luck H, Khan S, Kim JH, Copeland JK, Revelo XS, Tsai S. **Gut-associated IgA(+) immune cells regulate obesity-related insulin resistance**. *Nat Commun* (2019) **10** 3650. DOI: 10.1038/s41467-019-11370-y 34. Mingrone G, Panunzi S, De Gaetano A, Guidone C, Iaconelli A, Capristo E. **Metabolic surgery versus conventional medical therapy in patients with type 2 diabetes: 10-year follow-up of an open-label, single-centre, randomised controlled trial**. *Lancet* (2021) **397** 293-304. DOI: 10.1016/S0140-6736(20)32649-0 35. Evers SS, Sandoval DA, Seeley RJ. **The physiology and molecular underpinnings of the effects of bariatric surgery on obesity and diabetes**. *Annu Rev Physiol* (2017) **79**. DOI: 10.1146/annurev-physiol-022516-034423 36. Subramaniam R, Aliakbarian H, Bhutta HY, Harris DA, Tavakkoli A, Sheu EG. **Sleeve gastrectomy and roux-en-Y gastric bypass attenuate pro-inflammatory small intestinal cytokine signatures**. *Obes Surg* (2019) **29**. DOI: 10.1007/s11695-019-04059-0 37. Lee JS, Tato CM, Joyce-Shaikh B, Gulen MF, Cayatte C, Chen Y. **Interleukin-23-Independent IL-17 production regulates intestinal epithelial permeability**. *Immunity* (2015) **43**. DOI: 10.1016/j.immuni.2015.09.003 38. Shin JH, Bozadjieva-Kramer N, Shao Y, Lyons-Abbott S, Rupp AC, Sandoval DA. **The gut peptide Reg3g links the small intestine microbiome to the regulation of energy balance, glucose levels, and gut function**. *Cell Metab* (2022) **34**. DOI: 10.1016/j.cmet.2022.09.024 39. Stephens JW, Min T, Dunseath G, Churm R, Barry JD, Prior SL. **Temporal effects of laparoscopic sleeve gastrectomy on adipokines, inflammation, and oxidative stress in patients with impaired glucose homeostasis**. *Surg Obes Relat Dis* (2019) **15**. DOI: 10.1016/j.soard.2019.04.006 40. Zeng Z, Guo X, Zhang J, Yuan Q, Chen S. **Lactobacillus paracasei modulates the gut microbiota and improves inflammation in type 2 diabetic rats**. *Food Funct* (2021) **12**. DOI: 10.1039/d1fo00515d 41. Le Chatelier E, Nielsen T, Qin J, Prifti E, Hildebrand F, Falony G. **Richness of human gut microbiome correlates with metabolic markers**. *Nature* (2013) **500**. DOI: 10.1038/nature12506 42. Khan S, Luck H, Winer S, Winer DA. **Emerging concepts in intestinal immune control of obesity-related metabolic disease**. *Nat Commun* (2021) **12** 2598. DOI: 10.1038/s41467-021-22727-7 43. Basso N, Soricelli E, Castagneto-Gissey L, Casella G, Albanese D, Fava F. **Insulin resistance, microbiota, and fat distribution changes by a new model of vertical sleeve gastrectomy in obese rats**. *Diabetes* (2016) **65** 2990-3001. DOI: 10.2337/db16-0039 44. Linares DM, Gomez C, Renes E, Fresno JM, Tornadijo ME, Ross RP. **Lactic acid bacteria and bifidobacteria with potential to design natural biofunctional health-promoting dairy foods**. *Front Microbiol* (2017) **8**. DOI: 10.3389/fmicb.2017.00846 45. Yu Q, Yuan L, Deng J, Yang Q. **Lactobacillus protects the integrity of intestinal epithelial barrier damaged by pathogenic bacteria**. *Front Cell Infect Microbiol* (2015) **5**. DOI: 10.3389/fcimb.2015.00026 46. Li Z, Yang S, Lin H, Huang J, Watkins PA, Moser AB. **Probiotics and antibodies to TNF inhibit inflammatory activity and improve nonalcoholic fatty liver disease**. *Hepatology* (2003) **37**. DOI: 10.1053/jhep.2003.50048 47. Zhang X, Monnoye M, Mariadassou M, Beguet-Crespel F, Lapaque N, Heberden C. **Glucose but not fructose alters the intestinal paracellular permeability in association with gut inflammation and dysbiosis in mice**. *Front Immunol* (2021) **12**. DOI: 10.3389/fimmu.2021.742584 48. Shi N, Li N, Duan X, Niu H. **Interaction between the gut microbiome and mucosal immune system**. *Mil Med Res* (2017) **4** 14. DOI: 10.1186/s40779-017-0122-9 49. Mika A, Janczy A, Waleron K, Szymanski M, Kaska L, Sledzinski T. **The impact of the interplay of the intestinal microbiome and diet on the metabolomic and health outcomes of bariatric surgery**. *Obes Rev* (2022) **23**. DOI: 10.1111/obr.13455 50. Lavelle A, Sokol H. **Gut microbiota-derived metabolites as key actors in inflammatory bowel disease**. *Nat Rev Gastroenterol Hepatol* (2020) **17**. DOI: 10.1038/s41575-019-0258-z 51. Wang L, Gong Z, Zhang X, Zhu F, Liu Y, Jin C. **Gut microbial bile acid metabolite skews macrophage polarization and contributes to high-fat diet-induced colonic inflammation**. *Gut Microbes* (2020) **12** 1-20. DOI: 10.1080/19490976.2020.1819155 52. McGavigan AK, Garibay D, Henseler ZM, Chen J, Bettaieb A, Haj FG. **TGR5 contributes to glucoregulatory improvements after vertical sleeve gastrectomy in mice**. *Gut* (2017) **66**. DOI: 10.1136/gutjnl-2015-309871 53. Ryan KK, Tremaroli V, Clemmensen C, Kovatcheva-Datchary P, Myronovych A, Karns R. **FXR is a molecular target for the effects of vertical sleeve gastrectomy**. *Nature* (2014) **509**. DOI: 10.1038/nature13135 54. Ding L, Zhang E, Yang Q, Jin L, Sousa KM, Dong B. **Vertical sleeve gastrectomy confers metabolic improvements by reducing intestinal bile acids and lipid absorption in mice**. *Proc Natl Acad Sci U.S.A.* (2021) **118**. DOI: 10.1073/pnas.2019388118 55. Ding L, Sousa KM, Jin L, Dong B, Kim BW, Ramirez R. **Vertical sleeve gastrectomy activates GPBAR-1/TGR5 to sustain weight loss, improve fatty liver, and remit insulin resistance in mice**. *Hepatology* (2016) **64**. DOI: 10.1002/hep.28689 56. Ding L, Yang Q, Zhang E, Wang Y, Sun S, Yang Y. **Notoginsenoside Ft1 acts as a TGR5 agonist but FXR antagonist to alleviate high fat diet-induced obesity and insulin resistance in mice**. *Acta Pharm Sin B* (2021) **11**. DOI: 10.1016/j.apsb.2021.03.038 57. Chaudhari SN, Harris DA, Aliakbarian H, Luo JN, Henke MT, Subramaniam R. **Bariatric surgery reveals a gut-restricted TGR5 agonist with anti-diabetic effects**. *Nat Chem Biol* (2021) **17**. DOI: 10.1038/s41589-020-0604-z 58. Dong S, Zhu M, Wang K, Zhao X, Hu L, Jing W. **Dihydromyricetin improves DSS-induced colitis in mice**. *Pharmacol Res* (2021) **171**. DOI: 10.1016/j.phrs.2021.105767 59. Song X, Sun X, Oh SF, Wu M, Zhang Y, Zheng W. **Microbial bile acid metabolites modulate gut RORgamma(+) regulatory T cell homeostasis**. *Nature* (2020) **577**. DOI: 10.1038/s41586-019-1865-0
--- title: Acceptance and attitude towards the traditional chinese medicine among asymptomatic COVID-19 patients in Shanghai Fangcang hospital authors: - Bo Pan - Hong-wei Yin - Yue Yu - Xing Xiang - Cui Yu - Xiao-Jie Yan - Xiao-feng Zhai - Yuan Bai - Jing Hong journal: BMC Complementary Medicine and Therapies year: 2023 pmcid: PMC10061361 doi: 10.1186/s12906-023-03922-z license: CC BY 4.0 --- # Acceptance and attitude towards the traditional chinese medicine among asymptomatic COVID-19 patients in Shanghai Fangcang hospital ## Abstract ### Objective The Coronavirus Disease 2019 (COVID-19) has brought severe damage to global health and socioeconomics. In China, traditional Chinese medicine (TCM) is the most important complementary and alternative medicine (CAM) and it has shown a beneficial role in the prevention and treatment of COVID-19. However, it is unknown whether patients are willing to accept TCM treatment. The objective of our study is to investigate the acceptance, attitude, and independent predictors of TCM among asymptomatic COVID-19 patients admitted to Shanghai fangcang hospital during the outbreak of the COVID-19 pandemic in Shanghai in 2022. ### Methods A cross-sectional study was conducted on asymptomatic COVID-19 patients in the largest fangcang hospital in Shanghai, China, from April 22, 2022, to May 25, 2022. Based on the literature review of previous similar studies, a self-report questionnaire was developed to assess the patients’ attitude and acceptance of TCM, and a multivariate logistic regression analysis was conducted to determine the independent predictors of TCM acceptance. ### Results A total of 1,121 patients completed the survey, of whom $91.35\%$ were willing to accept CAM treatment whereas $8.65\%$ of participants showed no willingness. Multivariate logistic regression analysis revealed that the patients who have received two doses of COVID-19 vaccine (OR = 2.069, $95\%$CI: 1.029–4.162, $$P \leq 0.041$$ vs. not received), understood the culture of TCM (OR = 2.293, $95\%$CI: 1.029–4.162, $$P \leq 0.014$$ vs. not understood), thought the TCM treatment is safe (OR = 2.856, $95\%$CI: 1.334–6.112, $$P \leq 0.007$$ vs. not thought), thought the TCM treatment is effective (OR = 2.724, $95\%$CI: 1.249–5.940, $$P \leq 0.012$$ vs. not thought), and those who informed their attending physician if using TCM for treatment (OR = 3.455, $95\%$CI:1.867–6.392, $P \leq 0.001$ vs. not informed) were more likely to accept TCM treatment. However, patients who thought TCM might delay your treatment (OR = 0.256, $95\%$CI: 0.142–0.462, $P \leq 0.001$ not thought) was an independent predictor for unwillingness to accept TCM treatment. ### Conclusion This study preliminarily investigated the acceptance, attitude, and predictors of intention to receive TCM among asymptomatic COVID-19 patients. It is recommended to increase the publicity of TCM, clarify the impact of TCM and communicate with attending doctors that meet the healthcare needs of asymptomatic COVID-19 patients. ## Background At present, the Coronavirus Disease 2019 (COVID-19) is still spreading all over the world, which has brought a heavy burden to the global economic recovery and made the fragile healthcare system of some developing countries fall into a state of being on the verge of collapse. The daily number of confirmed cases presents a rising trajectory across the globe. As of January 27, 2023, the total number of confirmed cases had reached over 752 million with more than 6.80 million deaths worldwide [1]. Complementary and alternative medicine (CAM) is defined as a set of different medical and healthcare systems, practices, and products, which are generally not considered part of traditional medicine [2]. One survey carried out in Iran between April 20, 2020, and August 20, 2020, demonstrated that at least one type of CAM was used by $84\%$ of the participants during the outbreak of the COVID-19 pandemic. In the majority of participants, CAM was adopted to prevent the infection of COVID-19 or reduce the anxiety caused by the COVID-19 pandemic [3]. An anonymous electronic survey was conducted in Ghana showed that $82.5\%$ of the participants applied CAM during COVID-19 [4]. A meta-analysis showed that different CAM interventions such as acupuncture, traditional Chinese medicine (TCM), relaxation, and Gongfa significantly alleviated the psychological symptoms (depression, anxiety, stress, sleep quality, negative emotions, quality of life) and physical symptoms (inflammatory factors, physical activity, chest pain, and respiratory function) in COVID-19 patients [5]. As an important part of CAM in China, TCM has its characteristics in the prevention and treatment of acute infectious diseases [6, 7], which is one of the major therapies recommended in the guideline for prevention, control, diagnosis, and treatment of COVID-19 published by the National Health Commission of China [8]. A clinical study conducted at Wuhan fangcang hospitals found that early treatment with Huashibaidu granule for seven days alleviated the deterioration of symptoms in COVID-19 patients with moderate and mild symptoms [9]. Another study also showed that Lianhua Qingwen capsules could significantly shorten the median time of recovery and improve the improvement rate of chest CT imaging as well as the clinical cure rate. Moreover, the role of TCM in the prevention and control of COVID-19 has been recognized by the World Health Organization [10]. In the last week of February 2022, the Omicron BA 2.2 variant of COVID-19 caused a wave of infection in Shanghai, China [11]. Asymptomatic COVID-19 patients were sent to Fangcang hospitals by the community, which were upgraded to designated hospitals for the treatment of asymptomatic COVID-19 patients. To apply TCM to more asymptomatic COVID-19 patients, it is particularly important to understand their hesitation to take TCM. Up to now, no research data has been published regarding the willingness of asymptomatic COVID-19 patients to accept TCM. Thus, we conducted an online cross-sectional study to record the acceptance and attitude towards TCM by asymptomatic COVID-19 patients and analyzed the predictors influencing the patients’ acceptance of treatment with TCM. ## Study design and participants An online questionnaire survey was conducted among adult asymptomatic COVID-19 patients who were admitted to the largest fangcang hospital (Si Ye Cao Fangcang hospital) in Shanghai, China, from April 22, 2022, to May 25, 2022. This study employed the “Wenjuanxing” platform to distribute and retrieve electronic questionnaires, and export relevant data information. Patients with intellectual and cognitive impairment were excluded from the survey. ## Ethical approval The study was approved by the Ethics Committee of The First Affiliated Hospital of Naval Medical University, China (CHEC2022-056). All methods were carried out in accordance with the Declaration of Helsinki. Oral informed consent was obtained and all of the participants were briefed about the purpose of the study, research procedures, privacy of their identity and any other personal information, and their other relevant rights. ## Questionnaires A self-administered questionnaire was constructed in the Chinese language to evaluate the acceptance and attitude towards TCM. The questionnaire was developed based on the literature review of similar studies and was translated into the Chinese language before distribution to the patients [3, 4, 12–22]. The questionnaire mainly included four parts: [1] Basic information of participants included demographic characteristics (gender, age, body mass index, education level, average monthly income, residence, employment, current smoking status, and current drinking status) and clinical characteristics (comorbidities, vaccination status, and knowledge about culture of TCM). [ 2] Participants’ attitude toward TCM was determined through the following questions: do you think it takes a long time for TCM to exert efficacy, do you think the TCM treatment is safe, do you think the TCM treatment is effective, will you inform your attending physician if you accept TCM for treatment, and do you think TCM might delay your treatment. [ 3] Participants’ willingness to take TCM; first, participants were asked to respond to whether they are willing to accept TCM, and if the answer was “Yes”, they were further asked about the types of TCM they were willing to accept and the reasons for their willingness to accept TCM. If the patients were not willing to accept TCM, they were asked the reasons for their unwillingness to accept TCM. We conducted a pilot testing of the main question assessing the willingness to accept TCM among over 50 respondents and did not detect any problems. ## Sample size estimation and data analysis Based on a previous report [3], the proportion of TCM utilization was assumed as $84\%$ in the calculation of sample size using the following formula: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n = Z_{1 - \alpha /2}^2P(1 - P)/{e^2}$$\end{document} Where n is the minimum number of required patients, Z2 indicates 1.962 for $95\%$ confidence interval (CI), P presented the estimated utilization rate, and e indicated the required accuracy of $4\%$. The non-response rate was estimated as $5\%$ and the minimum sample size was calculated as 339 patients. Data analysis was performed using IBM SPSS Statistics for Windows (version 21.0). Participants’ responses to the questionnaire were treated as classified data, which were expressed by numbers and percentages. Univariate analysis was used to evaluate the relationship between independent variables (participants’ basic information and participants’ attitude towards TCM) and dependent variables (participants’ acceptance towards TCM). The variables with a value of $P \leq 0.25$ in univariate analysis were put into a multivariate logistic regression analysis to determine the Predictors affecting patients’ willingness to accept TCM. Odds ratio (OR) and $95\%$CI were adopted to describe these variables. $P \leq 0.05$ was considered statistically significant. ## Participants’ characteristics and willingness to accept TCM A total of 1,185 asymptomatic patients were invited to participate in the survey. One patient submitted an incomplete questionnaire whereas the response was not received from 63 patients. Finally, responses from a total of 1,121 patients were included in the analysis. The overall effective questionnaire recovery rate was $96.60\%$. In our study, 1024 participants expressed their willingness to receive TCM, the rate of TCM acceptance was $91.35\%$. Only 97 participants represented unwillingness to receive TCM, the rate of TCM hesitancy was $8.65\%$ (Table 1). Table 1Demographic characteristics and clinical features of participantsItemAll participants($$n = 1121$$)Intention to accept TCMTCM hesitancy($$n = 97$$)TCM acceptance($$n = 1024$$)P-valueAge (Year)0.820 < 2061 ($5.44\%$)7 ($7.22\%$)54 ($5.27\%$) 20–35511 ($45.58\%$)41 ($42.27\%$)470 ($45.90\%$) 35–50339 ($30.24\%$)30 ($30.93\%$)309 ($30.18\%$) > 50210 ($18.73\%$)19 ($19.59\%$)191 ($18.65\%$)Gender0.086 Female565 ($50.40\%$)57 ($58.76\%$)516 ($50.39\%$) Male556 ($49.60\%$)40 ($41.24\%$)508 ($49.61\%$)BMI (kg/m2)0.198 < 18.566 ($5.89\%$)10 ($10.31\%$)56 ($5.47\%$) 18.5–24634 ($56.56\%$)49 ($50.52\%$)585 ($57.13\%$) 24–28324 ($28.90\%$)31 ($31.96\%$)293 ($28.61\%$) > 2897 ($8.65\%$)7 ($7.22\%$)90 ($8.79\%$)Residence0.900 Rural571 ($50.94\%$)50 ($51.55\%$)521 ($50.88\%$) Urban550 ($49.06\%$)47 ($48.45\%$)503 ($49.12\%$)Education level0.403 ≤ Senior high school789 ($70.38\%$)65 ($67.01\%$)724 ($70.70\%$) College degree175 ($15.61\%$)14 ($14.43\%$)161 ($15.72\%$) ≥ Bachelor’s degree157 ($14.01\%$)18 ($18.56\%$)139 ($13.57\%$)Occupation0.181 Unemployed257 ($22.93\%$)26 ($26.80\%$)231 ($22.56\%$) Employed788 ($70.29\%$)61 ($62.89\%$)727 ($71.00\%$) Retired76 ($6.98\%$)10 ($10.31\%$)66 ($6.45\%$)Average monthly income (CNY)0.007 < 3000257 ($22.93\%$)34 ($35.05\%$)223 ($21.78\%$) 3000–8000635 ($56.65\%$)42 ($43.30\%$)593 ($57.91\%$) > 8000229 ($20.43\%$)21 ($21.65\%$)208 ($20.31\%$)Current smoking status0.128 No892 ($79.57\%$)83 ($85.57\%$)809 ($79.00\%$) Yes229 ($20.43\%$)14 ($14.43\%$)215 ($21.00\%$)Current drinking status0.265 No1016 ($90.63\%$)91 ($93.81\%$)925 ($90.33\%$) Yes105 ($9.37\%$)6 ($6.19\%$)99 ($9.67\%$)Other underlying diseases0.242 No1029 ($91.79\%$)86 ($88.66\%$)943 ($92.09\%$) Yes92 ($8.21\%$)11 ($11.34\%$)81 ($7.91\%$)Have you received two doses of COVID-19 vaccine? < 0.001 No112 ($9.99\%$)20 ($20.62\%$)92 ($8.98\%$) Yes1009 ($90.01\%$)77 ($79.38\%$)932 ($91.02\%$)Do you understand the culture of TCM? < 0.001 No697 ($62.18\%$)83 ($85.57\%$)614 ($59.96\%$) Yes424 ($37.82\%$)14 ($14.43\%$)410 ($40.04\%$)Data are presented as number (percentage). P-values were calculated through univariate analysis between the “TCM hesitancy” and “TCM acceptance” groups. BMI: body mass index; CNY: China Yuan; COVID-19: Coronavirus Disease 2019; TCM: traditional Chinese medicine The relationship of various characteristics with the acceptance of TCM is presented in Table 1. The willingness to accept TCM differed insignificantly ($P \leq 0.05$) between various demographic characteristics except for the monthly income. Having a monthly income of more than 3,000 yuan, the proportion of participants vaccinated with two doses of COVID-19 vaccines and understanding the culture of TCM were significantly higher for showing a willingness to accept TCM in comparison to those who showed unwillingness. ## Participants’ attitude towards TCM Table 2 shows significant statistical differences in attitude towards TCM between the TCM hesitancy and TCM acceptance groups ($P \leq 0.001$ for all the five related questions). In our study, nearly one-fifth of the respondents thought that TCM needed a longer time to take effect. Interestingly, most respondents believed that TCM treatment was safe ($\frac{1013}{1121}$, $90.37\%$) and effective ($\frac{1019}{1121}$, $90.90\%$), only $11.60\%$ ($\frac{130}{1121}$) of the participants believed that TCM would delay their treatment. Table 2Participants’ attitude towards the traditional Chinese medicineItemAll participants($$n = 1121$$)Intention to accept TCMTCM hesitancy($$n = 97$$)TCM acceptance($$n = 1024$$)P-valueDo you think it takes a long time for TCM to exert efficacy? < 0.001 No213 ($19.00\%$)47 ($48.45\%$)166 ($16.21\%$) Yes908 ($81.00\%$)50 ($51.55\%$)858 ($83.79\%$)Do you think the TCM treatment is safe? < 0.001 No108 ($9.63\%$)47 ($48.45\%$)61 ($5.96\%$) Yes1013 ($90.37\%$)50 ($51.55\%$)963 ($94.04\%$)Do you think the TCM treatment is effective? < 0.001 No102 ($9.10\%$)45 ($46.39\%$)57 ($5.57\%$) Yes1019 ($90.90\%$)52 ($53.61\%$)967 ($94.43\%$)Will you inform your attending physician if you accept TCM for treatment? < 0.001 No128 ($11.42\%$)44 ($45.36\%$)84 ($8.20\%$) Yes993 ($88.58\%$)53 ($54.64\%$)940 ($91.80\%$)Do you think TCM might delay your treatment? < 0.001 No991 ($88.40\%$)67 ($69.07\%$)924 ($90.23\%$) Yes130 ($11.60\%$)30 ($30.93\%$)100 ($9.77\%$)Data are presented as number (percentage). P-values were calculated through univariate analysis between the “TCM hesitancy” and “TCM acceptance” groups. TCM: traditional Chinese medicine ## Independent predictors of TCM acceptance According to univariate analysis, 13 variables with $P \leq 0.25$ were obtained (Tables 1 and 2). Multivariate logistic regression analysis indicated that 6 variables influenced the willingness to accept TCM ($P \leq 0.05$) (Table 3). The participants who have received two doses of COVID-19 vaccine (OR = 2.069, $95\%$CI: 1.029–4.162, $$P \leq 0.041$$ vs. not received), understood the culture of TCM (OR = 2.293, $95\%$CI: 1.029–4.162, $$P \leq 0.014$$ vs. not understood), thought the TCM treatment is safe (OR = 2.856, $95\%$CI: 1.334–6.112, $$P \leq 0.007$$ vs. not thought), thought the TCM treatment is effective (OR = 2.724, $95\%$CI: 1.249–5.940, $$P \leq 0.012$$ vs. not thought), and those who informed their attending physician if using TCM for treatment (OR = 3.455, $95\%$CI:1.867–6.392, $P \leq 0.001$ vs. not informed) were more likely to accept TCM treatment. However, the participants who thought TCM might delay your treatment (OR = 0.256, $95\%$CI: 0.142–0.462, $P \leq 0.001$ not thought) was unwilling to accept TCM treatment. Table 3Predictors of intention to use traditional Chinese medicineVariableOR ($95\%$ CI)P-valueGender FemaleRef/ Male1.787(0.979–3.262)0.058BMI (kg/m2) < 18.5Ref/ 18.5–241.721(0.711–4.167)0.229 24–281.457(0.561–3.782)0.439 > 281.699(0.503–5.740)0.393Occupation UnemployedRef/ Employed0.920(0.466–1.814)0.810 Retired0.838(0.298–2.355)0.738Average monthly income (CNY) < 3000Ref/ 3000–80001.254(0.647–2.431)0.502 > 80000.736(0.325–1.666)0.462Current smoking status NoRef/ Yes1.095(0.512–2.344)0.815Other underlying diseases NoRef/ Yes0.652(0.270–1.572)0.341Have you received two doses of COVID-19 vaccine? NoRef/ Yes2.069(1.029–4.162)0.041Do you understand the culture of TCM? NoRef/ Yes2.293(1.029–4.162)0.014Do you think TCM needs a longer time to exert efficacy? NoRef/ Yes1.607(0.849–3.034)0.145Do you think the TCM treatment is safe? NoRef/ Yes2.856(1.334–6.112)0.007Do you think the TCM treatment is effective? NoRef/ Yes2.724(1.249–5.940)0.012Will you inform your attending physician if you accept TCM for treatment? NoRef/ Yes3.455(1.867–6.392)< 0.001Do you think TCM might delay your treatment? NoRef/ Yes0.256(0.142–0.462)< 0.001P-value indicates whether the adjusted OR of particular sub-category is significant when compared with the reference category. BMI: body mass index; CNY: China Yuan;COVID-19: Coronavirus Disease 2019; TCM: traditional Chinese medicine ## The sources of information about TCM The present study found that the most important source of TCM information for the participants was the media ($\frac{494}{1121}$, $44.07\%$), followed by families and friends ($\frac{459}{1121}$, $40.95\%$) and medical staff ($\frac{427}{1121}$, $38.09\%$) (Fig. 1). Fig. 1The sources of information about traditional Chinese medicine ($$n = 1121$$) ## Reasons for accepting TCM Regarding the reasons why 1,024 participants were willing to accept TCM treatment, $51.86\%$ ($\frac{531}{1024}$) of the participants thought that TCM could improve immunity, and $47.75\%$ ($\frac{489}{1024}$) believed that TCM could alleviate the symptoms of COVID-19. $44.53\%$ ($\frac{456}{1024}$) considered that TCM could cure COVID-19 (Fig. 2). Fig. 2The reasons for willingness to use traditional Chinese medicine among the asymptomatic COVID-19 patients ($$n = 1024$$) ## Types of TCM that the participants were willing to accept Among the 1,024 participants who were willing to accept TCM, $70.51\%$ ($\frac{722}{1024}$) were willing to accept Chinese herbal medicine, $62.79\%$ ($\frac{643}{1024}$) were willing to accept Chinese patent medicine, $34.96\%$ ($\frac{358}{1024}$) were willing to accept massage, $33.20\%$ ($\frac{340}{1024}$) chose moxibustion, and $29.00\%$ ($\frac{297}{1024}$) were willing to accept cupping therapy (Fig. 3). Fig. 3Types of traditional Chinese medicine the asymptomatic COVID-19 patients wanted to accept ($$n = 1024$$) ## Reasons for not accepting TCM For the reasons why 97 respondents were unwilling to accept TCM treatment, $36.08\%$ ($\frac{35}{97}$) of the respondents reported that they did not receive doctors’ recommendations, and $25.77\%$ ($\frac{25}{97}$) of the respondents illustrated that they were afraid of the side effects of TCM, and $24.74\%$ ($\frac{24}{97}$) of respondents showed that they did not need additional burden (Fig. 4). Fig. 4The reasons for reluctance to use traditional Chinese medicine among the asymptomatic COVID-19 patients ($$n = 97$$) ## Discussion At present, the COVID-19 pandemic is still spreading all over the world, with constant mutation, which increases the difficulty of prevention and control. TCM has a long history in the prevention and treatment of acute infectious diseases and the experiences of TCM treatment have been recorded in the Shanghan Lun and Detailed Analysis of Epidemic Warm Diseases [9], which provided a new scheme for treating COVID-19 [23–27]. However, the willingness to accept TCM among asymptomatic COVID-19 patients was not reported in the literature. The present study explored the acceptance of TCM and its independent predictors among asymptomatic COVID-19 patients and found that $91.35\%$ of the patients were willing to accept TCM treatment, while $8.65\%$ of the patients presented unwillingness to accept TCM. Demographic factors including gender, age, education level, income level, and residence were not significantly associated with TCM acceptance in the present study, which was different from the cross-sectional studies reported from other regions [28–31]. National Health Commission of the People’s Republic of China has repeatedly mentioned the promotion of TCM in the prevention and control of COVID-19, we put forward the following suggestions to accelerate the acceptance of TCM based on the results of this study. ## Increasing the publicity of TCM is important to promote the acceptance of TCM TCM is a significant part of Chinese cultural heritage, which ideas and practice methods contain the profound wisdom of Chinese philosophy [32]. The multivariate logistic regression analyses indicated that participants who understood the culture of TCM was one of predictors for TCM acceptance, but more than half of the participants lacked an understanding of the culture of TCM in our study. A cross-sectional study in Australia reported that only $26\%$ of dental students knew about CAM [33]. In Hungary, $12.4\%$ of the subjects showed a better understanding of CAM [34]. In Bangladesh, nearly $45\%$ of pharmaceutical students believed that lack of knowledge was the main obstacle to the application of CAM [35]. Given that TCM theory is difficult for non-professionals to understand, a concise and comprehensive introduction of TCM is necessary. Participants of the present study reported that the most important source of their TCM information was media ($44.07\%$), followed by family members and friends ($40.95\%$) and medical staff ($38.09\%$). In India, the majority of dental practitioners ($73.3\%$) reported that media (internet, newspapers, etc.) was their main source of knowledge about CAM [36]. One study in Silesia, Poland, demonstrated that $60.8\%$ of patients obtained information about CAM from the internet and $38.6\%$ acquired information from television. These results indicated that appropriate media publicity is conducive to guiding and enhancing the public’s receptivity to TCM [37]. This raises requirements for TCM practitioners in China. ## Clarifying the impact of TCM is the premise of advocating TCM According to the results of the present study, among the participants who were willing to accept TCM, $70.51\%$ preferred Chinese herbal medicine, $62.79\%$ chose Chinese patent medicine, and $34.96\%$ favored massage treatment. A survey on patients with Parkinson disease in China illustrated that herbal medicine, rehabilitation exercise, and acupuncture were the most commonly used TCM therapies [38]. Compared to other TCM therapies, Chinese herbal medicine is the most popular treatment in China. The safety and efficacy of the TCM is critical to controlling the COVID-19 and has caused widespread concern. Multivariate logistic regression analysis in the current study showed that safety and efficacy of the TCM were the independent predictors for willingness to accept TCM. These results were higher than data from others countries. A survey on diabetic patients in Jeddah Saudi Arabia showed that $54.2\%$ patients believed CAM have no side effect. A study in Indonesia indicated that $68.31\%$ of diabetic patients considered that CAM products were safe and $63.69\%$ of diabetic patients considered that CAM products were effective. However, considering TCM would delay the treatment was an important independent predictor for hesitation to accept TCM in this study. In fact, the clinical role of TCM in the management of COVID-19 has been verified by clinical trials in China [39, 40]. Therefore, considering the willingness of asymptomatic COVID-19 patients to accept TCM and the effectiveness and safety of the TCM, clarifying the impact of TCM is the premise of advocating TCM for the patients. ## Active communication with attending doctors contribute to acceptance of TCM Communication between doctors and patients plays an important part in healthcare activities [41], which can determine the patients’ self-management behavior and health outcomes [42]. Our study indicated that informing the attending doctor before receiving TCM treatment is an important predictors for willingness to accept TCM treatment. A study conducted in Palestine from April 2018 to March 2019 exhibited that $64.0\%$ of pregnant women believed that doctors should provide patients with advice on commonly used CAM therapies [43]. In Saudi Arabia, $81.11\%$ of the subjects intended to discuss the application of CAM with their doctors [44]. According to the outpatient service in Iran, among 155 patients who accepted CAM voluntarily, 50 patients ($32.2\%$) reported that they had disclosed the usage of CAM to their doctors [45]. These results suggested that patients had a strong interest in receiving CAM with the advice of their physicians. In the present study, when participants were asked the reason behind their willingness to accept TCM treatment, only $26.27\%$ of the participants said that it was due to the recommendation by medical staff. When asked about their hesitation toward TCM treatment, $36.08\%$ of participants replied that it had not been recommended by medical staff. Therefore, healthcare practitioners must have a certain understanding of CAM so that they can actively communicate with patients and recommend the most appropriate treatment according to the patients’ conditions. According to a survey in Germany, more than half of the patients expressed their interest in CAM consultation and more than $80\%$ of patients expected their attending doctor to have a certain knowledge of CAM [46]. In India, $57.5\%$ of dental practitioners reported that health professionals should be able to provide patients with advice on commonly used CAM methods [36]. ## Limitations This study has several limitations. First of all, this study is a self-administered questionnaire-based cross-sectional study. Thus, the causality cannot be directly deduced and further longitudinal research is needed to verify the possible causal relationship. Secondly, the questionnaire was developed based on multiple questionnaires but it could not be verified in a large sample size due to time constraints. Thirdly, the sample had a low representation of the elderly (> 60 years old) owing to few admissions of participants of this age to the fangcang hospital. Fourthly, since our survey was just conducted in the largest fangcang hospital in Shanghai, it cannot fully represent the willingness of asymptomatic COVID-19-infected people in Shanghai and even in the world. Fifthly, as the world is urgently fighting against COVID-19, more outcomes of TCM treatment for COVID-19 will be published, which may affect the willingness of patients to accept TCM. ## Conclusion This study reported the attitude and willingness to accept TCM in asymptomatic COVID-19 patients and their predictors worldwide. In this survey, $91.35\%$ of the participants were willing to accept CAM treatment, while $8.65\%$ of the participants were not willing to accept CAM treatment. Receiving two dose of COVID-19 vaccine, understanding the culture of TCM, thinking TCM is safe and effective, and informing the attending doctor before using TCM were contributors to TCM acceptance, whereas the main contributors to TCM hesitancy was considering that TCM would delay the treatment. Thus, strengthening publicity through the media, especially the emerging network media such as WeChat, microblog, and Zhihu, to generalize the advantages of TCM to the public, inviting the recovering COVID-19 patients who were treated with TCM to give health lectures and broadcasting the clinical practice of TCM in the treatment of COVID-19 in asymptomatic COVID-19 patients, and recommending TCM and teaching some gongfa of TCM (tai chi, qi gong, wuqinxi, etc.) to benefit patients by attending doctors may expand the acceptance of TCM. Further observation will be carried out on this batch of patients to calculate the final prevalence rate of TCM and a long-term follow-up will be conducted to observe the effects of TCM on the quality of life of patients with COVID-19. ## References 1. 1.World Health Organization. WHO Coronavirus (COVID-19) Dashboard. (2023-01-27) [2023-01-27]. https://covid19.who.int/. 2. 2.National Center for Complementary and Integrative Health0. Complementary, alternative, or integrative health: What’s in a name? (2022-05-09) [2022-05-09]. https://www.nccih.nih.gov/health/complementary-alternative-or-integrative-health-whats-in-a-name#hed1. 3. Dehghan M, Ghanbari A, Ghaedi Heidari F, Mangolian Shahrbabaki P, Zakeri MA. **Use of complementary and alternative medicine in general population during COVID-19 outbreak: a survey in Iran**. *J Integr Med* (2022.0) **20** 45-51. DOI: 10.1016/j.joim.2021.11.004 4. Kretchy IA, Boadu JA, Kretchy JP, Agyabeng K, Passah AA, Koduah A. **Utilization of complementary and alternative medicine for the prevention of COVID-19 infection in Ghana: a national cross-sectional online survey**. *Prev Med Rep* (2021.0) **24** 101633. DOI: 10.1016/j.pmedr.2021.101633 5. Badakhsh M, Dastras M, Sarchahi Z, Doostkami M, Mir A, Bouya S. **Complementary and alternative medicine therapies and COVID-19: a systematic review**. *Rev Environ Health* (2021.0) **36** 443-50. DOI: 10.1515/reveh-2021-0012 6. Hong J, Xu XW, Yang J, Zheng J, Dai SM, Zhou J. **Knowledge about, attitude and acceptance towards, and predictors of intention to receive the COVID-19 vaccine among cancer patients in Eastern China: a cross-sectional survey**. *J Integr Med* (2022.0) **20** 34-44. DOI: 10.1016/j.joim.2021.10.004 7. Liu J, Manheimer E, Shi Y, Gluud C. **Chinese herbal medicine for severe acute respiratory syndrome: a systematic review and meta-analysis**. *J Altern Complement Med* (2004.0) **10** 1041-51. DOI: 10.1089/acm.2004.10.1041 8. 8.National Health Commission of the People’s Republic of China. Notice on issuing the diagnosis and treatment plan for pneumonia infected by novel coronavirus (trial ninth edition). (2022-03-15) [2022-05-09]. http://www.nhc.gov.cn/yzygj/s7653p/202203/b74ade1ba4494583805a3d2e40093d88.shtml. 9. Zhao C, Li L, Yang W, Lv W, Wang J, Guo J. **Chinese Medicine Formula Huashibaidu Granule Early treatment for mild COVID-19 patients: an unblinded, cluster-randomized clinical trial**. *Front Med (Lausanne)* (2021.0) **8** 696976. DOI: 10.3389/fmed.2021.696976 10. 10.World Health Organization. WHO Expert Meeting on Evaluation of Traditional Chinese Medicine in the Treatment of COVID-19. (2022-03-15) [2022-05-09]. https://www.who.int/publications/m/item/who-expert-meeting-on-evaluation-of-traditional-chinese-medicine-in-the-treatment-of-covid-19. 11. Zhang X, Zhang W, Chen S. **Shanghai’s life-saving efforts against the current omicron wave of the COVID-19 pandemic**. *Lancet* (2022.0) **S0140–6736** 00838-8 12. Hwang JH, Cho HJ, Im HB, Jung YS, Choi SJ, Han D. **Complementary and alternative medicine use among outpatients during the 2015 MERS outbreak in South Korea: a cross-sectional study**. *BMC Complement Med Ther* (2020.0) **20** 147. DOI: 10.1186/s12906-020-02945-0 13. Pokladnikova J, Park AL, Draessler J, Lukacisinova A, Krcmova I. **The use of complementary and alternative medicine by adults with allergies: a czech national representative survey**. *BMC Complement Med Ther* (2021.0) **21** 171. DOI: 10.1186/s12906-021-03316-z 14. Stub T, Jong MC, Kristoffersen AE. **The impact of COVID-19 on complementary and alternative medicine providers: a cross-sectional survey in Norway**. *Adv Integr Med* (2021.0) **8** 247-55. DOI: 10.1016/j.aimed.2021.08.001 15. Kristoffersen AE, Quandt SA, Stub T. **Use of complementary and alternative medicine in Norway: a cross-sectional survey with a modified norwegian version of the international questionnaire to measure use of complementary and alternative medicine (I-CAM-QN)**. *BMC Complement Med Ther* (2021.0) **21** 93. DOI: 10.1186/s12906-021-03258-6 16. Sari Y, Anam A, Sumeru A, Sutrisna E. **The knowledge, attitude, practice and predictors of complementary and alternative medicine use among type 2 diabetes mellitus patients in Indonesia**. *J Integr Med* (2021.0) **19** 347-53. DOI: 10.1016/j.joim.2021.04.001 17. Ding A, Patel JP, Auyeung V. **Testing the traditional Chinese Medicine Consultation Model for Adherence in complementary and alternative medicine**. *Evid Based Complement Alternat Med* (2020.0) **2020** 8897628. DOI: 10.1155/2020/8897628 18. Owusu S, Gaye YE, Hall S, Junkins A, Sohail M, Franklin S. **Factors associated with the use of complementary and alternative therapies among patients with hypertension and type 2 diabetes mellitus in western Jamaica: a cross-sectional study**. *BMC Complement Med Ther* (2020.0) **20** 314. DOI: 10.1186/s12906-020-03109-w 19. Chang HY, Wallis M, Tiralongo E. **Predictors of complementary and alternative medicine use by people with type 2 diabetes**. *J Adv Nurs* (2012.0) **68** 1256-66. DOI: 10.1111/j.1365-2648.2011.05827.x 20. Loquai C, Dechent D, Garzarolli M, Kaatz M, Kaehler KC, Kurschat P. **Use of complementary and alternative medicine: a multicenter cross-sectional study in 1089 melanoma patients**. *Eur J Cancer* (2017.0) **71** 70-9. DOI: 10.1016/j.ejca.2016.10.029 21. Vlieger AM, van Vliet M, Jong MC. **Attitudes toward complementary and alternative medicine: a national survey among paediatricians in the Netherlands**. *Eur J Pediatr* (2011.0) **170** 619-24. DOI: 10.1007/s00431-010-1331-3 22. Jain L, Vij J, Satapathy P, Chakrapani V, Patro B, Kar SS. **Factors influencing COVID-19 vaccination intentions among College students: a cross-sectional study in India**. *Front Public Health* (2021.0) **9** 735902. DOI: 10.3389/fpubh.2021.735902 23. Lee DYW, Li QY, Liu J, Efferth T. **Traditional chinese herbal medicine at the forefront battle against COVID-19: clinical experience and scientific basis**. *Phytomedicine* (2021.0) **80** 153337. DOI: 10.1016/j.phymed.2020.153337 24. Shi N, Guo L, Liu B, Bian Y, Chen R, Chen S. **Efficacy and safety of chinese herbal medicine versus lopinavir-ritonavir in adult patients with coronavirus disease 2019: a non-randomized controlled trial**. *Phytomedicine* (2021.0) **81** 153367. DOI: 10.1016/j.phymed.2020.153367 25. Chan KW, Wong VT, Tang SCW. **COVID-19: an update on the Epidemiological, Clinical, preventive and therapeutic evidence and guidelines of integrative chinese-western medicine for the management of 2019 Novel Coronavirus Disease**. *Am J Chin Med* (2020.0) **48** 737-62. DOI: 10.1142/S0192415X20500378 26. Lu ZH, Yang CL, Yang GG, Pan WX, Tian LG, Zheng JX. **Efficacy of the combination of modern medicine and traditional chinese medicine in pulmonary fibrosis arising as a sequelae in convalescent COVID-19 patients: a randomized multicenter trial**. *Infect Dis Poverty* (2021.0) **10** 31. DOI: 10.1186/s40249-021-00813-8 27. Xiao M, Tian J, Zhou Y, Xu X, Min X, Lv Y. **Efficacy of Huoxiang Zhengqi dropping pills and Lianhua Qingwen granules in treatment of COVID-19: a randomized controlled trial**. *Pharmacol Res* (2020.0) **161** 105126. DOI: 10.1016/j.phrs.2020.105126 28. Abuelgasim KA, Alsharhan Y, Alenzi T, Alhazzani A, Ali YZ, Jazieh AR. **The use of complementary and alternative medicine by patients with cancer: a cross-sectional survey in Saudi Arabia**. *BMC Complement Altern Med* (2018.0) **18** 88. DOI: 10.1186/s12906-018-2150-8 29. Ciarlo G, Ahmadi E, Welter S, Hübner J. **Factors influencing the usage of complementary and alternative medicine by patients with cancer**. *Complement Ther Clin Pract* (2021.0) **44** 101389. DOI: 10.1016/j.ctcp.2021.101389 30. Ataman H, Aba YA, Güler Y. **Complementary and alternative medicine methods used by turkish infertile women and their effect on quality of life**. *Holist Nurs Pract* (2019.0) **33** 303-11. DOI: 10.1097/HNP.0000000000000339 31. Peltzer K, Pengpid S. **Prevalence and determinants of traditional, complementary and alternative Medicine Provider Use among adults from 32 countries**. *Chin J Integr Med* (2018.0) **24** 584-90. DOI: 10.1007/s11655-016-2748-y 32. Liu C, Gu M. **Protecting traditional knowledge of chinese medicine: concepts and proposals**. *Front Med* (2011.0) **5** 212-8. DOI: 10.1007/s11684-011-0142-x 33. Park JS, Page A, Turner E, Li J, Tennant M, Kruger E. **Dental students’ knowledge of and attitudes towards complementary and alternative medicine in Australia - An exploratory study**. *Complement Ther Med* (2020.0) **52** 102489. DOI: 10.1016/j.ctim.2020.102489 34. Soós S, Jeszenői N, Darvas K, Harsányi L. **Complementary and alternative medicine: attitudes, knowledge and use among surgeons and anaesthesiologists in Hungary**. *BMC Complement Altern Med* (2016.0) **16** 443. DOI: 10.1186/s12906-016-1426-0 35. Saha BL, Seam MOR, Islam MM, Das A, Ahamed SK, Karmakar P. **General perception and self-practice of complementary and alternative medicine (CAM) among undergraduate pharmacy students of Bangladesh**. *BMC Complement Altern Med* (2017.0) **17** 314. DOI: 10.1186/s12906-017-1832-y 36. Suganya M, Vikneshan M, Swathy U. **Usage of complementary and alternative medicine: a survey among indian dental professionals**. *Complement Ther Clin Pract* (2017.0) **26** 26-9. DOI: 10.1016/j.ctcp.2016.11.005 37. Kasprzycka K, Kurzawa M, Kucharz M, Godawska M, Oleksa M, Stawowy M. **Complementary and alternative Medicine Use in Hospitalized Cancer Patients-Study from Silesia, Poland**. *Int J Environ Res Public Health* (2022.0) **19** 1600. DOI: 10.3390/ijerph19031600 38. Pan XW, Zhang XG, Chen XC, Lu Q, Hu YS, Han LY. **A survey of application of complementary and alternative medicine in chinese patients with Parkinson’s Disease: a pilot study**. *Chin J Integr Med* (2020.0) **26** 168-73. DOI: 10.1007/s11655-018-2560-y 39. Xia L, Shi Y, Su J, Friedemann T, Tao Z, Lu Y. **Shufeng Jiedu, a promising herbal therapy for moderate COVID-19:antiviral and anti-inflammatory properties, pathways of bioactive compounds, and a clinical real-world pragmatic study**. *Phytomedicine* (2021.0) **85** 153390. DOI: 10.1016/j.phymed.2020.153390 40. Xiong Y, Tian Y, Ma Y, Liu B, Ruan L, Lu C. **The effect of Huashibaidu formula on the blood oxygen saturation status of severe COVID-19: a retrospective cohort study**. *Phytomedicine* (2022.0) **95** 153868. DOI: 10.1016/j.phymed.2021.153868 41. Ong LM, de Haes JC, Hoos AM, Lammes FB. **Doctor-patient communication: a review of the literature**. *Soc Sci Med* (1995.0) **40** 903-18. DOI: 10.1016/0277-9536(94)00155-M 42. Matusitz J, Spear J. **Effective doctor-patient communication: an updated examination**. *Soc Work Public Health* (2014.0) **29** 252-66. DOI: 10.1080/19371918.2013.776416 43. Quzmar Y, Istiatieh Z, Nabulsi H, Zyoud SH, Al-Jabi SW. **The use of complementary and alternative medicine during pregnancy: a cross-sectional study from Palestine**. *BMC Complement Med Ther* (2021.0) **21** 108. DOI: 10.1186/s12906-021-03280-8 44. Alhawsawi TY, Alghamdi M, Albaradei O, Zaher H, Balubaid W, Alotibi HA. **Complementary and alternative medicine use among ischemic stroke survivors in Jeddah, Saudi Arabia**. *Neurosciences (Riyadh)* (2020.0) **25** 362-8. DOI: 10.17712/nsj.2020.5.20200088 45. Behnood-Rod A, Afzali Poor Khoshkbejari M, Pourzargar P, Hassanzadeh M, Moharamzad Y, Foroughi F. **Complementary and alternative medicine use among iranian patients attending urban outpatient general practices**. *Complement Ther Clin Pract* (2018.0) **30** 58-63. DOI: 10.1016/j.ctcp.2017.12.008 46. Lederer AK, Baginski A, Raab L, Joos S, Valentini J, Klocke C. **Complementary medicine in Germany: a multi-centre cross-sectional survey on the usage by and the needs of patients hospitalized in university medical centers**. *BMC Complement Med Ther* (2021.0) **21** 285. DOI: 10.1186/s12906-021-03460-6
--- title: 'Determination of Intracellular Esterase Activity Using Ratiometric Raman Sensing and Spectral Phasor Analysis' authors: - 'Henry J. Braddick' - William J. Tipping - Liam T. Wilson - Harry S. Jaconelli - Emma K. Grant - Karen Faulds - Duncan Graham - Nicholas C. O. Tomkinson journal: Analytical Chemistry year: 2023 pmcid: PMC10061367 doi: 10.1021/acs.analchem.2c05708 license: CC BY 4.0 --- # Determination of Intracellular Esterase Activity Using Ratiometric Raman Sensing and Spectral Phasor Analysis ## Abstract Carboxylesterases (CEs) are a class of enzymes that catalyze the hydrolysis of esters in a variety of endogenous and exogenous molecules. CEs play an important role in drug metabolism, in the onset and progression of disease, and can be harnessed for prodrug activation strategies. As such, the regulation of CEs is an important clinical and pharmaceutical consideration. Here, we report the first ratiometric sensor for CE activity using Raman spectroscopy based on a bisarylbutadiyne scaffold. The sensor was shown to be highly sensitive and specific for CE detection and had low cellular cytotoxicity. In hepatocyte cells, the ratiometric detection of esterase activity was possible, and the result was validated by multimodal imaging with standard viability stains used for fluorescence microscopy within the same cell population. In addition, we show that the detection of localized ultraviolet damage in a mixed cell population was possible using stimulated Raman scattering microscopy coupled with spectral phasor analysis. This sensor demonstrates the practical advantages of low molecular weight sensors that are detected using ratiometric Raman imaging and will have applications in drug discovery and biomedical research. ## Introduction Carboxylesterases (CEs) are a ubiquitous class of enzymes within the esterase family that hydrolyze exogenous and endogenous carboxylesters to their corresponding carboxylic acids.1 CEs can be divided into five major groups (CE1–CE5), with the majority falling into the CE1 or CE2 families.2 In mammals, liver cells, which play a primary role in metabolism, display the highest levels of CE activity.2 Aberrant CE activity has been directly linked to numerous diseases including obesity,3 cancer,4 and hepatic steatosis,3 and therefore, sensors for the detection of esterase activity are important tools for the study of drug metabolism and disease progression. Although the use of fluorescence microscopy for sensing intracellular esterase has been well established,5 the inherent “on/off” nature and concentration dependency of many fluorescent sensors makes ratiometric analyses difficult, and the broad linewidth of fluorescent emission signals (∼1500 cm–1) results in a color barrier that can prevent multiplex analysis of different intracellular targets.6 In addition, the use of fluorescent probes in live cells and tissues has been impractical due to the short excitation wavelengths (<400 nm) required for some scaffolds, which result in photodamage and short tissue penetrating depth,7 while the photobleaching of these probes can render repeat analysis impossible. A promising esterase sensor based on two-photon excitation has been recently reported, which overcomes many of these limitations.8 However, the complex scaffold requires either long synthetic routes or expensive starting materials to prepare, limiting accessibility. Raman microscopy is a powerful tool for the non-destructive visualization of biomolecules, cells, and tissues.9 Vibrational-tag Raman imaging has enabled the study of the intracellular interactions of a variety of exogenous probes, with the flagship method being alkyne-tag Raman imaging (ATRI).10 Alkyne groups exhibit a strong vibration within the cell-silent region of a *Raman spectrum* (1800–2800 cm–1), which allows for their straightforward detection within biological samples.11 Since the initial application of ATRI in the study of nucleic acids,12 the technique has been used to visualize the metabolism and distribution of proteins,13,14 lipids,13−15 and drug molecules,16−19 among other species.20 The narrow linewidth of Raman bands (<20 cm–1) has enabled the development of Raman sensors. Recent examples have included the detection of ionic species,21,22 intracellular hydrogen sulfide,23 and pH (Figure 1).24−26 In each case, probe molecules contain a suitably reactive sensing group in conjugation with, or affixed to, an alkyne or nitrile moiety (Figure 1A), and reaction of the sensing group with the analyte of interest yields a change in the vibrational properties of the sensor. Ratiometric sensors of this nature are advantageous due to the intrinsic referencing ability and inherent quantification benefits that accompany ratiometric methods.27 **Figure 1:** *Examples of intracellular sensing using ATRI. (A) General structure of ATRI-based sensors. (B) Bisarylbutadiyne sensor for hydrogen sulfide 1.23 (C) Reversible sensor for the quantification of pH 3.26 (D) Detection of intracellular esterase activity using sensor 4.* Vibrational-tag Raman imaging has previously been applied to enzyme sensing as an alternative approach to multiplex detection, with Fujioka et al. simultaneously detecting four unique enzymes using electronic pre-resonance stimulated Raman scattering (EPR–SRS).6 Xanthene derivatives targeted to different enzyme substrates, together with isotopic editing (12C/13C and 14N/15N) of a conjugated nitrile moiety, enabled the specific detection of each enzyme simultaneously at discrete wavenumbers. The Raman sensors were activated when the molecular absorption of the xanthene core was shifted from the visible (electronic non-resonant condition) to the near infrared (NIR and EPR condition) upon reaction with the target biomolecule.28 Herein, we describe the first ratiometric Raman sensor for intracellular imaging of esterase activity using SRS microscopy. Synthesized using an accessible strategy, 4 is a low-molecular-weight (<350 Da) bisarylbutadiyne probe that is detected using NIR irradiation. The sensor contains an acetoxymethyl (AM) group, which, upon cleavage, yields an acidic phenol group that results in a red-shifting of the diyne stretching frequency at physiological pH. Our method allows for the sensitive and selective detection of esterase enzyme activity and represents an adaptable strategy for the sensing of different enzyme classes. Finally, we show that regions of damage within a cell population can be identified using spectral phasor analysis within a single experiment, providing a novel platform to assess cell viability. ## Procedure for Spontaneous Raman Spectroscopy Mapping Experiments Cells were plated on glass-bottomed culture dishes (35 mm high, Ibidi) at a concentration of 5 × 105 cells per well and incubated at $5\%$ CO2 and 37 °C for 24 h prior to compound treatment. For live cell imaging, cells were treated with compound 4, 5, or 6 (10 μM, diluted from a 20 mM stock solution in DMSO) in media and incubated at $5\%$ CO2 and 37 °C for 30 min. The dishes were then aspirated and washed with PBS (3 × 2 mL) before the cells were imaged in PBS. To simulate dead cells, cells were pre-treated with PFA ($4\%$ v/v) and Triton X-100 ($0.05\%$ v/v) in PBS for 2 h before being washed with PBS (3 × 2 mL), treated with 4, 5, or 6 (10 μM, diluted from a 20 mM stock solution in DMSO) in media, and incubated at $5\%$ CO2 and 37 °C for 30 min. The dishes were then aspirated and washed with PBS (3 × 2 mL) before imaging in PBS. Raman maps were acquired using λex = 532 nm with a Nikon 60×/NA 1.0 NIR Apo water immersion objective, 5 μm step size in x and y, 0.5 s acquisition time, a laser power of $100\%$ (36 mW), and a spectral center of 2800 cm–1. Three replicate maps were acquired from different culture plates for each condition. Average spectra were calculated for each cell map in MatLab R2022a, from which intensity ratios at 2212 and 2226 cm–1 were extracted. ## General Procedure for SRS Imaging Experiments Cells were plated in six-well plates containing high-precision glass coverslips (#1.5 H, 22 × 22 mm; Thorlabs) at a concentration of 5 × 105 cells per well and incubated in media at $5\%$ CO2 and 37 °C for 24 h prior to compound treatment. For live cell imaging, cells were treated with 4 or 5 (10 μM, diluted from a 20 mM stock solution in DMSO) in media and incubated at $5\%$ CO2 and 37 °C for 30 min. The wells were then aspirated and washed with PBS (3 × 2 mL). The coverslips were then removed from the wells and affixed to microscope slides for imaging with a PBS boundary. To simulate dead cells, cells were pre-treated with PFA ($4\%$ v/v) and Triton X-100 ($0.05\%$ v/v) in PBS for 2 h. The wells were then aspirated and washed with PBS (3 × 2 mL), treated with 4 or 5 (10 μM, diluted from a 20 mM stock solution in DMSO) in media, and incubated at $5\%$ CO2 and 37 °C for 30 min. The wells were then aspirated and washed with PBS (3 × 2 mL). The coverslips were then removed from the wells and affixed to microscope slides for imaging with a PBS boundary (see the Supporting Information for full experimental details). ## Results For a Raman-based probe to fully benefit from ratiometric sensing, the Raman peak of interest must undergo a discernible spectroscopic shift (>7.5 cm–1) following interaction with the analyte. Previous work has shown that in the case of a bisarylbutadiyne scaffold, it was possible to induce a large Raman alkyne shift (Δνalkyne) by introducing a formal charge in conjugation with the oligoyne chain.25 This phenomenon was exploited to generate a library of pH sensors with a range of pKa values (2–10), and we postulated that this concept could be applied to enzymatic sensing. We envisaged an esterase sensitive probe that, upon reaction with an enzyme, liberated a compound that was ionized under physiological conditions (37 °C, pH 7.4) and thereby induced a significant Raman alkyne shift. Therefore, difluorophenol 5 (Figure 2A) was selected as the scaffold for our sensor. With a pKa of 6.2, the phenol group is deprotonated at physiological pH, forming the conjugate base 5–.25 We targeted the corresponding acetate [6] and AM ester [4] (Figure 2B), which are effective and stable esterase-sensitive head groups in “pro-fluorophore” approaches to sensing intracellular esterase activity.8,29,30 Compounds 4 and 6 were prepared from commercial starting materials through four-step syntheses (see the Supporting Information for full synthetic procedures). **Figure 2:** *Development of a bisarylbutadiyne esterase sensor. (A) Deprotonation of the difluorophenol scaffold 5 at physiological pH. (B) Esterase-sensitive compounds 4 and 6 synthesized as part of this work. (C) Overlaid Raman alkyne peaks of difluorophenol 5 (blue), AM ester 4 (orange), and acetate 6 (green) [100 μM, PBS/DMSO (pH 7.4, 8:2 v/v), 532 nm, 1 × 20 s exposure, 50× lens. Spectra were acquired after 1 h of incubation at 37 °C]. Peak centers were determined using a non-linear Gauss fitting function (Orgin2021). (D) LoD study of esters 4 and 6 using PLE (100 μM, PBS/DMSO (8:2 v/v), 532 nm, 1 × 20 s exposure, 50× lens. Spectra were acquired after 1 h of incubation at 37 °C).* The efficacies of 4 and 6 as esterase sensors were assessed by comparing their sensitivity, selectivity, and Δνalkyne upon incubation with the commercially available mammalian esterase, porcine liver esterase (PLE). First, it was deemed that a larger Δνalkyne value between the probe molecule and 5– was desirable in order to facilitate ratiometric sensing. Compounds 4–6 were analyzed in a mixture of PBS (pH 7.4) and DMSO (8:2 v/v), and the alkyne peak centers were determined (Figure 2C). The AM ester 4 showed a greater Δνalkyne value than that of acetate 6, with values of 7.8 and 5.7 cm–1, respectively, indicating the potential of 4 to function as a ratiometric sensor. The in vitro enzymatic reactivity of both compounds was then assessed using PLE (Figure 2D). The hydrolysis of each ester in the presence of PLE was deduced using the ratio of the signal intensities at 2218 and 2225 cm–1, and the AM ester 4 was identified as the more effective esterase substrate due to its lower limit of detection toward PLE. Partial conversion of 4 to 5 was observed using only 0.025 U/mL of PLE after 1 h of incubation, providing a promising esterase probe with a large Δνalkyne value and high sensitivity, comparable to a recent fluorescent esterase sensor.31 *As a* control experiment, PLE was denatured by heating at 90 °C for 3 h prior to the addition of 4 (100 μM, 30 min). In this case, no hydrolysis of 4 to 5 was observed (Figure S1). The reactivity of 4 and 6 toward PLE over a 90 min period was also assessed, with both compounds found to hydrolyze to phenol 5 at similar rates (Figure S2). We assessed the specificity of both sensors by incubating each compound with a variety of amino acids, salts, and biomolecules. AM ester 4 was found to be more stable than acetate 6 in the presence of the interference agents used (Figure S3A/S3B), demonstrating the specificity of 4 toward esterase-catalyzed hydrolysis. In addition, the pH stability of 4 was investigated by dissolving the probe in Britton–Robinson buffers at fixed pH values of 5.31 and 9.43 and PBS (pH 7.4) (Figure S4). Each solution was analyzed repeatedly over a 2 h period at room temperature using spontaneous Raman spectroscopy. In each case, no conversion to phenol 5 was observed. In addition, 4 showed no photodegradation over the same time period. Finally, the cytotoxicity of esters 4 and 6 and phenol 5 was investigated against HepG2 cells, with all compounds found to have no effect on cell viability after incubation at up to 20 μM for 8 h (Figure S5). These results were consistent with other bisarylbutadiyne compounds of this nature and demonstrated the suitability of 4, 5, and 6 for cell-based studies.25 While the precise reasons for its superior performance are unknown, based on the higher sensitivity, stability, and larger Δνalkyne of 4, this compound was taken forward for cellular studies. The efficacy of 4 for intracellular esterase sensing was next assessed (Figure 3). We selected HepG2 (hepatocellular carcinoma) cells for the analysis of our sensor due to the high level of CEs present in mammalian hepatocytes.2 Live HepG2 cells were treated with 4 (10 μM, 30 min), and the average *Raman spectrum* was plotted from the mapping data acquired (Figure 3A). In live cells, the alkyne Raman shift was detected at 2215.1 cm–1, which was concordant with difluorophenol 5 and suggested that the expected ester hydrolysis had occurred after just 30 min of treatment. To validate this result, dead HepG2 cells were simulated by fixing with paraformaldehyde (PFA, $4\%$ v/v) and Triton X-100 ($0.05\%$ v/v) in PBS and were then treated with 4 (10 μM, 30 min). In these cells, the observed Raman alkyne shift was 2222.8 cm–1, indicating an absence of esterase activity likely due to the denaturation of cellular proteins. **Figure 3:** *Assessment of 4 as an intracellular esterase sensor. (A) Overlaid alkyne peaks of the average spectra of difluorophenol 5 in live HepG2 cells (blue), AM ester 4 in live HepG2 cells (red), and 4 in fixed HepG2 cells (orange). [532 nm, 1 × 0.5 s exposure, 60× lens, 1 μm step size. Maps were acquired after treatment with 5 or 4 (10 μM) in media for 30 min. To fix, cells were pre-treated with PFA (4% v/v) and Triton X-100 (0.05% v/v) in PBS for 2 h prior to addition of 5 or 4]. (B) Ratio of peak intensities at 2212 and 2226 cm–1 taken from the average spectra of maps of difluorophenol 5 in live HepG2 cells and AM ester 4 in live HepG2 cells or fixed HepG2 cells. [532 nm, 1 × 0.5 s exposure, 60× lens, 5 μm step size. Maps were acquired after treatment with 5 or 4 (10 μM) in media for 30 min. To fix, cells were pre-treated with PFA (4% v/v) and Triton X-100 (0.05% v/v) in PBS for 2 h prior to addition of 5 or 4]. ****T test p ≤ 1 × 10–4. (C) Pseudo-Raman spectra generated from SRS spectral sweeps (2248–2185 cm–1, 14 images) of 4 in live HepG2 cells and in fixed HepG2 cells. All images were acquired at 512 × 512 pixels and a 9–48 μs pixel dwell time. Images were acquired after treatment with 4 (10 μM) in media for 30 min. To fix, cells were pre-treated with PFA (4% v/v) and Triton X-100 (0.05% v/v) for 2 h prior to addition of 4. (D) Tandem SRS–fluorescence imaging of live HepG2 cells treated with a solution of 4 (10 μM) and appropriate working concentrations of organelle stains (MitoTracker red 250 nM; LysoTracker green 62.5 nM; ER-Tracker green 1 μM) in media. Fluorescence images were acquired initially (MitoTracker red λex = 633 nm, λem = 640–750 nm; LysoTracker green λex = 488 nm, λem = 495–600 nm; ER-Tracker green λex = 488 nm, λem = 495–600 nm) before SRS images at 2923 cm–1 (CH3, protein) and 2218 cm–1 (alkyne). All images were acquired at 512 × 512 pixels and a 9–48 μs pixel dwell time. False colors and scale bars representing 10 μm were applied in ImageJ. Merged images of 4 and the organelle stains were generated in ImageJ and the Pearson’s R values were calculated using the Coloc2 tool.* When the spectra of 4 in live and fixed cells are overlaid, it becomes apparent that the ratiometric ability of the probe can be further enhanced by analyzing the Raman intensity at wavenumber values located on the shoulders of the peaks of interest. This approach has previously been shown to facilitate ratiometric measurements using SRS microscopy.23 An increase in Δνalkyne to 14 cm–1 could be achieved by adopting this strategy and measuring the 2212 cm–$\frac{1}{2226}$ cm–1 signal intensity ratio. To demonstrate this, the ratio of the Raman signal intensities at 2212 and 2226 cm–1 was extracted from three different mapping repeats of 4 in live and fixed cells and compared to the ratio values of phenol 5 in live cells, which was used as a control (Figure 3B). In live cells treated with 4, the ratio $\frac{2212}{2226}$ cm–1 was ∼2.5, consistent with the ratio observed in live cells treated with phenol 5. The ratio was also determined in fixed cells treated with 4 and was found to be ∼0.3, significantly different to the live cell sample, indicating a clear potential for differentiating live/fixed cell populations using probe 4. After demonstrating the ability of 4 to act as a ratiometric esterase sensor with a suitable Δνalkyne value using spontaneous Raman spectroscopy, we sought to use SRS for the high-resolution visualization of esterase activity within cells. Live and fixed HepG2 cells were treated with 4 (10 μM, 30 min) before imaging with SRS microscopy. Pseudo-*Raman spectra* were generated from SRS spectral sweeps between 2248 and 2185 cm–1 from the live and fixed HepG2 populations, and the overlaid spectra indicated that shoulder analysis was also possible with SRS (Figure 3C), albeit with blue-shifted wavenumber values when compared to spontaneous Raman spectroscopy due to an inherent offset within the SRS equipment. This effect has been observed in previous work.26 Analysis of Figure 3C indicated that 2232 and 2219 cm–1 represented suitable wavenumber values for the ratiometric shoulder analysis of 4 and the corresponding phenolate 5– after esterase-catalyzed hydrolysis. We next sought to determine the intracellular localization of 4. Figure 3D shows false-color tandem SRS–fluorescence microscopy images of HepG2 cells treated with 4 (10 μM, 30 min) and different organelle stains (MitoTracker red, 250 nM; LysoTracker green, 62.5 nM; ER-Tracker green, 1 μM, each 30 min). Imaging at 2923 and 2218 cm–1 enabled the visualization of intracellular protein and the distribution of 4 within the populations, respectively. Co-localization analyses of the signal at 2218 cm–1 and the fluorescence signal of the organelle stains revealed that our sensor strongly localizes to the endoplasmic reticulum, with a Pearson’s R value of 0.84, as expected for lipophilic compounds such as 4.32 It was also found that the distribution of 4 poorly correlated to the lysosomal ($R = 0.22$) and mitochondrial ($R = 0.55$) compartments. An advantage of Raman-based imaging is that it is compatible with other imaging modalities including fluorescence. To demonstrate the ability of 4 to act as an intracellular esterase sensor using SRS and as a means of determining cell viability, live and fixed HepG2 cells were treated with 4 (10 μM, 30 min) and the cell viability stains ethidium homodimer (EthD-1, 4 μM, 30 min) and calcein AM (2 μM, 30 min) (Figure 4A). The ethidium homodimer is a DNA-binding, membrane-impermeable stain used to visualize dead cells, while calcein AM is a pro-fluorophore that acts as a live cell stain following an esterase-mediated hydrolysis to activate the fluorophore. SRS spectral sweeps between 2253 and 2181 cm–1 enabled the ratiometric comparison of signal intensities at 2232 and 2219 cm–1. These wavenumber values were chosen as they displayed the greatest difference in ratio between live and fixed cells, therefore best facilitating ratiometric esterase sensing. In live cells, as confirmed by a positive calcein AM fluorescent signal and a lack of signal in the EthD-1 channel, the ratio of the signal intensities at 2219 and 2232 cm–1 across a number of cells (>3) was 1.03 ± 0.08. In contrast, cells fixed with PFA ($4\%$ v/v) and Triton X-100 ($0.05\%$ v/v) showed a fluorescent signal arising from EthD-1 and an absence of signal in the calcein AM channel, confirming that the cells were not viable and the ratio of the signal intensities at 2219 and 2232 cm–1 was significantly different to the live cell value, with a value of 0.51 ± 0.11 (Figure 4B). As a control experiment, live and fixed HepG2 cells were treated with phenol 5 (10 μM, 30 min), and in each case, the observed $\frac{2219}{2232}$ cm–1 ratio was ∼1.09, consistent with the value from 4 in live cells (Figure S6). To demonstrate the applicability of the ratiometric sensor, detection in a series of cell lines (HeLa, U-87, and SK-BR-3) was performed (Figure S7). In each case, the ratio of the signal intensities at 2219 and 2232 cm–1 in live and fixed populations was significantly different, consistent with our findings in HepG2 cells. As such, 4 represents an effective tool for the determination of cell viability and ratiometric sensing of esterase enzyme activity across a range of cell lines, facilitated by the sensitivity, stability, and spectroscopic profile of the compound. **Figure 4:** *Ratiometric and phasor analysis of 4 as an intracellular esterase sensor. (A) Ratiometric study of 4 in live and fixed HepG2 cells treated with cell viability stains ethidium homodimer (EthD-1) and calcein AM [to fix, cells were pre-treated with PFA (4% v/v) and Triton X-100 (0.05% v/v) in PBS for 2 h prior to addition of 4 and cell viability stains]. Images were acquired after treatment with 4 (10 μM), EthD-1 (4 μM), and calcein AM (2 μM) in media for 30 min. Fluorescence images were acquired initially (EthD-1 λex = 514 nm, λem = 540–650 nm; calcein AM λex = 488 nm, λem = 493–526 nm) before SRS images at 2923 cm–1 (CH3, protein) and SRS spectral sweeps (2253–2181 cm–1, 18 images). Images at 2232 and 2219 cm–1 were taken from the corresponding images of the SRS spectral sweeps. All images were acquired at 512 × 512 pixels, 9–48 μs pixel dwell time. False colors and scale bars representing 10 μm were applied in ImageJ. Ratio bars show the Fire LUT scaled between values of 0 and 2. (B) Ratio of the intensities at 2219 and 2232 cm–1 in live and fixed HepG2 cells. Pseudo-Raman spectra were generated from >3 cells in each spectral sweep (2253–2181 cm–1, 18 images), and the intensities at 2219 and 2232 cm–1 were extracted. ****T test p ≤ 1 × 10–4. (C) Spectral phasor analysis of the SRS spectral sweeps (2253–2181 cm–1, 18 images) of live and fixed HepG2 cells treated with 4 as seen in (A). SRS spectral sweeps were background-subtracted on ImageJ, and phasor plots were generated using an ImageJ plugin. The corresponding images of live and fixed cells were then generated from appropriate ROIs on the spectral phasor plot. (D) Overlaid pseudo-Raman spectra of the live and fixed HepG2 cells taken from the spectral phasor output maps.* Spectral phasor analysis of SRS images is a powerful technique for cellular segmentation based directly on the SRS spectrum at each pixel location within the image. Pioneered by Fu et al. ,33 it has recently been applied to monitoring intracellular lipid abundance in response to treatment with statins and for SRS-based imaging cytometry.34,35 Hyperspectral SRS data can be processed with spectral phasor analysis to form a phasor plot; a two-dimensional map consisting of spectral phasor data points. Each spectral phasor represents a unique *Raman spectrum* from within the 3D input SRS data set (with axes of xyλ), and the proximity of spectral phasors to one another on the phasor plot gives an indication as to the spectroscopic similarity of the input data points. Regions of the phasor plot containing tightly clustered spectral phasors can then be mapped to visualize segmented regions of the original data that possess similar Raman spectra.33,34 To demonstrate the application of 4 to studying mixed cell populations, we applied spectral phasor analysis to the SRS spectral sweeps (2253–2181 cm–1, 18 images) of 4 within live and fixed HepG2 cells (Figure 4C). We observed that SRS images of 4 in live and fixed cells occupy unique and different regions of the phasor plot as a result of the Δνalkyne between 4 and phenolate 5– that is formed upon esterase-mediated hydrolysis. To validate this analysis, pseudo-*Raman spectra* of the live and fixed output maps were generated and overlaid (Figure 4D). It was found that these pseudo-*Raman spectra* mimic the original spectra generated from the raw SRS images of 4 in live and fixed HepG2 cells, thus confirming the suitability of a spectral phasor approach for the determination of esterase activity. Further, SRS spectral sweeps in the high wavenumber region (3050–2803 cm–1, 40 images) of live and fixed cells treated with 4 (10 μM, 30 min) and subsequent spectral phasor analysis enabled visualization of various cellular components (Figure S8). The SRS sweeps of live and fixed cells occupy similar regions of the phasor plot, resulting in output images displaying minimal differences in the cellular structure of live and fixed cells, thereby confirming that the observations in Figure 4C arise from 4 and its hydrolysis in live cells. Having demonstrated the applicability of spectral phasor analysis for investigating single-cell populations, we aimed to demonstrate this application in mixed cell populations as a means of simultaneously visualizing the active and denatured esterase enzyme (Figure 5). To stimulate localized UV damage, we selected a small group of HepG2 cells (yellow dashed marker) within a live population on a perfusion chamber, which were irradiated with UV light (405 nm, ∼5 mW laser power, 40 min). The population of cells was then treated with 4 (10 μM, in media) and incubated at 37 °C for 10 min. SRS imaging at 2923 cm–1 revealed blebbing of the UV-irradiated cells, an effect associated with cell death (Figure S10).36,37 An SRS spectral sweep (2253–2181 cm–1, 18 images) allowed comparison of the signal intensities at 2219 and 2232 cm–1 between live and UV-irradiated cells. We observed that the signal intensity at 2232 cm–1 was greatest in the UV-irradiated cells, indicating that these cells contained the greatest proportion of intact AM ester 4 compared to the non-irradiated cells (green dashed marker), which possessed a greater signal intensity at 2219 cm–1. We compared the ratio of the intensities at 2232 and 2219 cm–1 in live and UV-irradiated cells and saw a significant difference between the two groups of cells (Figure 5B). The $\frac{2219}{2232}$ cm–1 ratio in live cells was 3.25 ± 0.92, and the same ratio in UV-irradiated cells was 1.62 ± 0.28. The significant difference between these ratios demonstrates the disabling effect UV irradiation has on intracellular enzymatic activity, as evidenced by others.31 We also noted that for both the live and UV-irradiated cells, this ratio was greater than we had seen in our previous analyses (Figure 4B). This was attributed to a shift in the peak center (and subsequently the phasor plots) due to imaging these cells under physiological conditions (37 °C, in media), where previously, the images were captured at room temperature in PBS. **Figure 5:** *Localized UV irradiation experiment and subsequent phasor analysis. (A) Study of 4 in live and UV-irradiated cells. Following UV irradiation, images were acquired after treatment with 4 (10 μM) in media for 30 min. Images at 2232 and 2219 cm–1 were taken from the corresponding images of SRS spectral sweeps (2253–2181 cm–1, 18 images). All images were acquired at 512 × 512 pixels, 9–48 μs pixel dwell time. False colors and scale bars representing 10 μm were applied in ImageJ. (B) Ratio of the intensities at 2219 and 2232 cm–1 in live and UV-irradiated HepG2 cells. Pseudo-Raman spectra were generated from >3 cells in each of the live and UV-irradiated areas of the spectral sweep (2253–2181 cm–1, 18 images), and the intensities at 2219 and 2232 cm–1 were extracted. ****T test p ≤ 1 × 10–4. (C) Spectral phasor analysis of the SRS spectral sweep (2253–2181 cm–1, 18 images) of live and UV-irradiated HepG2 cells as seen in (A). The SRS spectral sweep was background-subtracted on ImageJ, and a phasor plot was generated using an ImageJ plugin. The corresponding images of live and UV-irradiated cells were then generated from appropriate ROIs on the spectral phasor plot (using Figure 4C as the reference).* Finally, we applied a spectral phasor analysis to the SRS spectral sweep of the whole field of view (FOV) containing live and UV-irradiated cells (Figure 5C). The resulting phasor plot contained regions that are characteristic of both live and fixed cells as identified in the phasor plots acquired from the single-cell populations presented in Figure 4C. Selecting a region of interest (ROI) within the “live region” of the phasor plot (Figure 5Ci, green dashed marker) resulted in a segmented spectral image that was localized to the non-UV-irradiated cells as expected but also displayed regions within the UV-irradiated cells, suggesting that these regions still contained the active esterase enzyme that had successfully hydrolyzed 4 to phenol 5. Further, by selecting an ROI within the phasor region associated with fixed cells (Figure 5Ci, red dashed marker), it was seen that the distribution of 4 is confined largely to the UV-irradiated cells. These observations suggest that after 40 min of UV irradiation, the cells are severely damaged (as evidenced by blebbing and reduced esterase activity) but still possess metabolically active regions containing the functional esterase enzyme. Using SRS, both live and damaged cells can be studied within the same FOV. Our findings highlight the potential of the use of 4 in conjunction with SRS and spectral phasor analysis to study these different cell types in the same experiment. ## Conclusions We have reported the synthesis and application of the first low-molecular-weight (<350 Da), ratiometric esterase sensor for detection using spontaneous Raman spectroscopy and SRS microscopy. The synthetically accessible AM ester 4 is a highly selective, pH-stable, and non-cytotoxic probe for the sensing of intracellular esterase. A clear advantage of 4 compared to similar fluorescent sensors is the ratiometric output it provides, enabling the detection of the probe before and after interaction with the esterase enzyme. As such, unlike commercial live/dead stains based on mixtures of EthD-1 and calcein AM, 4 is self-referencing, meaning that a single probe is required for assessing cell viability, and ratiometric analyses are possible independent of the probe concentration. This offers further potential for multiplexing with other Raman and/or fluorescent probes for the simultaneous sensing of other intracellular species. After determining the localization of 4 within the endoplasmic reticulum of HepG2 cells, we showed that live and localized UV-damaged regions of cells could be simultaneously visualized by SRS and spectral phasor analysis. Due to the lower pKa of 5 relative to physiological pH, the general structure of 4 represents an exciting scaffold for the sensing of alternative enzyme classes through modular design of the enzyme-sensitive group. Further, the narrow Raman linewidths exhibited by 5 and 4 hold obvious potential for the multiplex analysis of 4 with other enzyme sensors through 13C labeling of the alkyne groups to generate analogous enzymatic probes. ## References 1. Redinbo M. R., Potter P. M.. **Keynote review: Mammalian carboxylesterases: From drug targets to protein therapeutics**. *Drug Discovery Today* (2005) **10** 313-325. DOI: 10.1016/S1359-6446(05)03383-0 2. Hosokawa M.. **Structure and Catalytic Properties of Carboxylesterase Isozymes Involved in Metabolic Activation of Prodrugs**. *Molecules* (2008) **13** 412-431. DOI: 10.3390/molecules13020412 3. Quiroga A. D., Li L., Trötzmüller M., Nelson R., Proctor S. D., Köfeler H., Lehner R.. **Deficiency of carboxylesterase 1/esterase-x results in obesity, hepatic steatosis, and hyperlipidemia**. *Hepatology* (2012) **56** 2188-2198. DOI: 10.1002/hep.25961 4. Na K., Lee E. Y., Lee H. J., Kim K. Y., Lee H., Jeong S. K., Jeong A. S., Cho Y. C., Kim S. A., Song Y. S., Kim S. K., Cho W. C., Kim H., Paik Y. K.. **Human plasma carboxylesterase 1, a novel serologic biomarker candidate for hepatocellular carcinoma**. *Proteomics* (2009) **9** 3989-3999. DOI: 10.1002/pmic.200900105 5. Chyan W., Raines R. T.. **Enzyme-Activated Fluorogenic Probes for Live-Cell and in Vivo Imaging**. *ACS Chem. Biol.* (2018) **13** 1810-1823. DOI: 10.1021/acschembio.8b00371 6. Fujioka H., Shou J., Kojima R., Urano Y., Ozeki Y., Kamiya M.. **Multicolor Activatable Raman Probes for Simultaneous Detection of Plural Enzyme Activities**. *J. Am. Chem. Soc.* (2020) **142** 20701-20707. DOI: 10.1021/jacs.0c09200 7. Mao Y., Ma M., Wei P., Zhang P., Liu L., Guan T., Zhang X., Yi T.. **A sensitive and rapid “off-on” fluorescent probe for the detection of esterase and its application in evaluating cell status and discrimination of living cells and dead cells**. *Analyst* (2020) **145** 1408-1413. DOI: 10.1039/c9an02085c 8. Park S. J., Lee H. W., Kim H. R., Kang C., Kim H. M.. **A carboxylesterase-selective ratiometric fluorescent two-photon probe and its application to hepatocytes and liver tissues**. *Chem. Sci.* (2016) **7** 3703-3709. DOI: 10.1039/c5sc05001d 9. Tipping W. J., Lee M., Serrels A., Brunton V. G., Hulme A. N.. **Stimulated Raman scattering microscopy: an emerging tool for drug discovery**. *Chem. Soc. Rev.* (2016) **45** 2075-2089. DOI: 10.1039/C5CS00693G 10. Yamakoshi H., Dodo K., Palonpon A., Ando J., Fujita K., Kawata S., Sodeoka M.. **Alkyne-tag Raman imaging for visualization of mobile small molecules in live cells**. *J. Am. Chem. Soc.* (2012) **134** 20681-20689. DOI: 10.1021/ja308529n 11. Palonpon A. F., Sodeoka M., Fujita K.. **Molecular imaging of live cells by Raman microscopy**. *Curr. Opin. Chem. Biol.* (2013) **17** 708-715. DOI: 10.1016/j.cbpa.2013.05.021 12. Yamakoshi H., Dodo K., Okada M., Ando J., Palonpon A., Fujita K., Kawata S., Sodeoka M.. **Imaging of EdU, an Alkyne-Tagged Cell Proliferation Probe, by Raman Microscopy**. *J. Am. Chem. Soc.* (2011) **133** 6102-6105. DOI: 10.1021/ja108404p 13. Wei L., Hu F., Shen Y., Chen Z., Yu Y., Lin C. C., Wang M. C., Min W.. **Live-cell imaging of alkyne-tagged small biomolecules by stimulated Raman scattering**. *Nat. Methods* (2014) **11** 410. DOI: 10.1038/nmeth.2878 14. Hong S., Chen T., Zhu Y., Li A., Huang Y., Chen X.. **Live-cell stimulated Raman scattering imaging of alkyne-tagged biomolecules**. *Angew. Chem., Int. Ed.* (2014) **53** 5827-5831. DOI: 10.1002/anie.201400328 15. Jamieson L. E., Greaves J., McLellan J. A., Munro K. R., Tomkinson N. C. O., Chamberlain L. H., Faulds K., Graham D.. **Tracking intracellular uptake and localisation of alkyne tagged fatty acids using Raman spectroscopy**. *Spectrochim. Acta, Part A* (2018) **197** 30-36. DOI: 10.1016/j.saa.2018.01.064 16. Tipping W. J., Lee M., Serrels A., Brunton V. G., Hulme A. N.. **Imaging drug uptake by bioorthogonal stimulated Raman scattering microscopy**. *Chem. Sci.* (2017) **8** 5606-5615. DOI: 10.1039/C7SC01837A 17. Sepp K., Lee M., Bluntzer M. T. J., Helgason G. V., Hulme A. N., Brunton V. G.. **Utilizing Stimulated Raman Scattering Microscopy To Study Intracellular Distribution of Label-Free Ponatinib in Live Cells**. *J. Med. Chem.* (2020) **63** 2028-2034. DOI: 10.1021/acs.jmedchem.9b01546 18. Tipping W. J., Merchant A. S., Fearon R., Tomkinson N. C. O., Faulds K., Graham D.. **Temporal imaging of drug dynamics in live cells using stimulated Raman scattering microscopy and a perfusion cell culture system**. *RSC Chem. Biol.* (2022) **3** 1154-1164. DOI: 10.1039/D2CB00160H 19. Gaschler M. M., Hu F., Feng H., Linkermann A., Min W., Stockwell B. R.. **Determination of the Subcellular Localization and Mechanism of Action of Ferrostatins in Suppressing Ferroptosis**. *ACS Chem. Biol.* (2018) **13** 1013-1020. DOI: 10.1021/acschembio.8b00199 20. Bakthavatsalam S., Dodo K., Sodeoka M.. **A decade of alkyne-tag Raman imaging (ATRI): applications in biological systems**. *RSC Chem. Biol.* (2021) **2** 1415-1429. DOI: 10.1039/D1CB00116G 21. Tipping W. J., Wilson L. T., Blaseio S. K., Tomkinson N. C. O., Faulds K., Graham D.. **Ratiometric sensing of fluoride ions using Raman spectroscopy**. *Chem. Commun.* (2020) **56** 14463-14466. DOI: 10.1039/D0CC05939K 22. Takemura S., Watanabe H., Nishihara T., Okamoto A., Tanabe K.. **Monitoring intracellular metal ion complexation with an acetylene-tagged ligand by Raman spectroscopy**. *RSC Adv.* (2020) **10** 36119-36123. DOI: 10.1039/D0RA06329K 23. Zeng C., Hu F., Long R., Min W.. **A ratiometric Raman probe for live-cell imaging of hydrogen sulfide in mitochondria by stimulated Raman scattering**. *Analyst* (2018) **143** 4844-4848. DOI: 10.1039/C8AN00910D 24. Yamakoshi H., Palonpon A. F., Dodo K., Ando J., Kawata S. S., Fujita K., Sodeoka M.. **Simultaneous imaging of protonated and deprotonated carbonylcyanide**. *Chem. Commun.* (2014) **50** 1341-1343. DOI: 10.1039/C3CC48587K 25. Wilson L. T., Tipping W. J., Jamieson L. E., Wetherill C., Henley Z., Faulds K., Graham D., Mackay S. P., Tomkinson N. C. O.. **A new class of ratiometric small molecule intracellular pH sensors for Raman microscopy**. *Analyst* (2020) **145** 5289-5298. DOI: 10.1039/D0AN00865F 26. Wilson L. T., Tipping W. J., Wetherill C., Henley Z., Faulds K., Graham D., Mackay S. P., Tomkinson N. C. O.. **Mitokyne: A Ratiometric Raman Probe for Mitochondrial pH**. *Anal. Chem.* (2021) **93** 12786-12792. DOI: 10.1021/acs.analchem.1c03075 27. Jamieson L. E., Wetherill C., Faulds K., Graham D.. **Ratiometric Raman imaging reveals the new anti-cancer potential of lipid targeting drugs**. *Chem. Sci.* (2018) **9** 6935-6943. DOI: 10.1039/C8SC02312C 28. Kawatani M., Spratt S. J., Fujioka H., Shou J., Misawa Y., Kojima R., Urano Y., Ozeki Y., Kamiya M.. **9-Cyano-10-telluriumpyronin Derivatives as Red-light-activatable Raman Probes**. *Chem.–Asian J.* (2022) **18**. DOI: 10.1002/asia.202201086 29. Lavis L. D., Chao T. Y., Raines R. T.. **Synthesis and utility of fluorogenic acetoxymethyl ethers**. *Chem. Sci.* (2011) **2** 521-530. DOI: 10.1039/C0SC00466A 30. Levine S. R., Beatty K. E.. **Synthesis of a far-red fluorophore and its use as an esterase probe in living cells**. *Chem. Commun.* (2016) **52** 1835-1838. DOI: 10.1039/C5CC08764C 31. Wang J., Teng Z., Zhang L., Yang Y., Qian J., Cao T., Cao Y., Qin W., Liu Y., Guo H.. **Multifunctional Near-Infrared Fluorescent Probes with Different Ring-Structure Trigger Groups for Cell Health Monitoring and In Vivo Esterase Activity Detection**. *ACS Sens.* (2020) **5** 3264-3273. DOI: 10.1021/acssensors.0c01734 32. Lee Y., Park N., Bin Park Y., Jeong Hwang Y., Kang C., Seung Kim J.. **Organelle-selective fluorescent Cu**. *Chem. Commun.* (2014) **50** 3197-3200. DOI: 10.1039/C4CC00091A 33. Fu D., Xie X. S.. **Reliable Cell Segmentation Based on Spectral Phasor Analysis of Hyperspectral Stimulated Raman Scattering Imaging Data**. *Anal. Chem.* (2014) **86** 4115-4119. DOI: 10.1021/ac500014b 34. Tipping W. J., Wilson L. T., An C., Leventi A. A., Wark A. W., Wetherill C., Tomkinson N. C. O., Faulds K., Graham D.. **Stimulated Raman scattering microscopy with spectral phasor analysis: applications in assessing drug-cell interactions**. *Chem. Sci.* (2022) **13** 3468-3476. DOI: 10.1039/D1SC06976D 35. Huang K. C., Li J., Zhang C., Tan Y., Cheng J. X.. **Multiplex Stimulated Raman Scattering Imaging Cytometry Reveals Lipid-Rich Protrusions in Cancer Cells under Stress Condition**. *iScience* (2020) **23** 100953. DOI: 10.1016/j.isci.2020.100953 36. Coleman M. L., Sahai E. A., Yeo M., Bosch M., Dewar A., Olson M. F.. **Membrane blebbing during apoptosis results from caspase-mediated activation of ROCK I**. *Nat. Cell Biol.* (2001) **3** 339-345. DOI: 10.1038/35070009 37. Gala de Pablo J., Chisholm D. R., Ambler C. A., Peyman S. A., Whiting A., Evans S. D.. **Detection and time-tracking activation of a photosensitiser on live single colorectal cancer cells using Raman spectroscopy**. *Analyst* (2020) **145** 5878-5888. DOI: 10.1039/D0AN01023E
--- title: 'Relationship between fibrinogen level and advanced colorectal adenoma among inpatients: A retrospective case-control study' authors: - Huijie Wang - Huanwei Zheng - Xu Cao - Ping Meng - Jinli Liu - Zhichao Wang - Teng Zhang - Haiying Zuo journal: Frontiers in Medicine year: 2023 pmcid: PMC10061582 doi: 10.3389/fmed.2023.1140185 license: CC BY 4.0 --- # Relationship between fibrinogen level and advanced colorectal adenoma among inpatients: A retrospective case-control study ## Abstract ### Objective This study was to explore the relationship between fibrinogen and advanced colorectal adenoma among inpatients. ### Methods From April 2015 to June 2022, 3738 participants (566 case subjects and 3172 control subjects) who underwent colonoscopies enrolled, and smooth curve fitting and logistic regression models were applied to explore the association between fibrinogen and advanced colorectal adenoma. In addition, sensitivity and subgroup analyses were performed to assess the stability of the results. ### Results Compared with lower fibrinogen quantile 1 (< 2.4 g/L), the adjusted OR values for fibrinogen and advanced colorectal adenoma in quantile 2 (2.4–2.75 g/L), quantile 3 (2.76–3.15 g/L), and quantile 4 (≥3.16 g/L) were 1.03 ($95\%$ confidence interval [CI]: 0.76–1.41), 1.37 ($95\%$ CI: 1.01–1.85), and 1.43 ($95\%$ CI: 1.06–1.94), respectively. A linear relationship between fibrinogen and advanced colorectal adenoma was observed. Sensitivity and subgroup analyses showed stable results. ### Conclusion Complements the evidence that fibrinogen was positively associated with advanced adenomas, suggesting that fibrinogen may play a role in the adenoma-carcinoma sequence. ## Introduction A colorectal adenoma is the precursor to colorectal cancer (CRC) [1]. The adenoma-carcinoma sequence is central to the pathogenesis of CRC [2]. Although most CRCs originate from colorectal adenoma (3–5), most common adenomas cannot develop into cancer [5, 6], and advanced colorectal adenomas are instead more likely to become cancerous. Some coagulation indicators have been recognized as potential biomarkers for CRC (7–10). However, research on risk factors for advanced adenomas, a critical stage in the adenoma-carcinoma pathway, is limited (11–13), especially for the components of the hemostatic system (14–16). Fibrinogen is a glycoprotein produced in the liver [10]. The interaction of fibrinogen with the perivascular environment influences the progression of the disease beyond its conventional role in the acute hemostatic cascade and is associated with the disease that has inflammatory components, particularly with pro-inflammatory effects in several types of cancer [17]. The relationship between fibrinogen and CRC has been studied. Preoperative elevated fibrinogen levels were associated with poor prognosis/disease-free survival and worst response to treatment and tumor size (18–21). Based on the theory of adenoma-carcinoma sequence and the existing studies suggesting that fibrinogen may influence the progression from adenoma to CRC, we hypothesized that fibrinogen is associated with advanced adenoma. Electronic medical records generate a wealth of clinical data that can describe conditions in detail and are the hallmark of modern healthcare systems. Clinical data can be extracted and integrated, cleaned and transformed, and converted into a data format to create a database that can be used for disease management, early diagnosis, or treatment decisions [22]. Studies targeting advanced colorectal adenoma are more critical to understand the adenoma-carcinoma sequence [12] and be beneficial for developing strategies to prevent CRC [12]. Given that the association between fibrinogen plasma levels and advanced adenoma has not been studied, we conducted this case–control study to assess the association between fibrinogen plasma levels and advanced adenoma. ## Study population The present study was conducted among 3,738 consecutive inpatients who underwent colonoscopies from April 2015 to June 2022. Subjects with advanced colorectal adenoma who underwent pathological examination to confirm the diagnosis were considered eligible cases. Subjects with normal colonoscopies during the same period were considered controls. Each patient was analyzed only once. Exclusion criteria: history of colorectal surgery, an incomplete colonoscopy (no cecum reached), inadequate bowel preparation, younger than 18, incomplete medical records, history of any cancer treatment within 3 years, combined with malignancies, and fibrinogen data missing. A total of 3,738 subjects (566 cases and 3,172 controls) were included. Shown in Figure 1. **Figure 1:** *Flow diagram of the screening and enrollment of study participants.* ## Definition of results and indicators For the current study, individuals with advanced colorectal adenoma, defined in another piece of literature [23], comprised the case group. Plasma fibrinogen was measured using a Mindray CX-9000 automatic coagulation analyzer (Shenzhen, China) and its associated reagents. As soon as possible after collection, all blood samples were processed within 4 h. In the present study, the median time between colonoscopy and fibrinogen testing is 2 [1, 5] days. The baseline variables were extracted from electronic medical records, including age, sex, weight, marital status, past medical history, family history (including colorectal and digestive system malignancy), drinking status, smoking status, and co-morbidities. Marital status is divided into three categories: married, single/divorced, and other. The history of smoking and drinking were classified into four categories, respectively: never, former, current, and NA (not recorded). A current drinker is defined as an individual who consumes any type of alcohol per week (self-reported) [24]. An ex-smoker or ex-drinker is defined as someone who has smoked or used alcohol in the previous period, although the smoking has now ceased. Co-morbidities included hypertension, ischemic cerebrovascular disease, coronary heart disease (CHD), hyperlipidemia (HLP), liver diseases (including liver cirrhosis, fatty liver, and hepatitis), and diabetes mellitus (DM). Individuals with DM were diagnosed with one of the following criteria: HbA1c ≥$6.5\%$, fasting glucose ≥7.0 mmol/L [25, 26], or medical records. In addition, we extracted the data of participants from the laboratory information system and electronic medical records during their hospitalization, including fibrinogen, albumin (ALB), aspartate aminotransferase (AST), alanine aminotransferase (ALT), glucose (GLU), alkaline phosphatase (ALP), creatinine (CREA), urea, uric acid (UA), platelets (PLT), thrombin time (TT), activated partial thromboplastin time (APTT), the international normalized ratio of prothrombin time (PT-INR), prothrombin time (PT), activated partial thromboplastin time (APTT). If there are multiple eligible test reports for the same test during the hospitalization period, the first result within this period of hospitalization will prevail (before the colonoscopy). ## Statistical analysis Continuous variables are expressed as mean ± standard deviation or median (quartile 1–quartiles 3) values according to the normality of the distribution. Comparisons between groups were performed by the chi-squared test or Fisher exact test and Student’s t-test or the Mann–Whitney U-test for categorical variables or continuous variables, respectively, as appropriate. We evaluated the relationship between fibrinogen and advanced colorectal adenoma using smooth curve fitting and logistic regression analysis. To evaluate the impact of fibrinogen, fibrinogen level was categorized into quartiles (quartile 1, <2.40 g/L; quartile 2, 2.40–2.75 g/L; quartile 3, 2.76–3.15 g/L; quartile 4, ≥3.16 g/L). We constructed three models: (i) crude model, no other covariates were adjusted; (ii) adjustment by age and sex; and (iii) adjustment by age, sex, hypertension, DM, APTT, PLT, ALP, and CREA. These potential confounders were chosen based on previous scientific literature, or a more than $10\%$ change in effect estimates. Subgroup analyses were performed using a binary logistic regression model and then performed an interaction test. In the subgroup analyses, considering the small number of ex-smokers and ex-drinkers, we integrated ex-smokers and ex-drinkers with non-smokers and non-drinkers as a group, respectively. Multiple imputations (five replications), based on a chained equation approach method in the R mice procedure, were performed to maximize statistical power and reduce bias to account for missing data. In addition, sensitivity analysis was conducted using all complete cases. All statistical analyses were conducted by packages R 3.3.2 (http://www.R-project.org, The R Foundation) and Free Statistics software version 1.7.1, $p \leq 0.05$ (two-tailed test) was considered statistically significant. The retrospective study was approved by the Ethics Review Board of Shijiazhuang Traditional Chinese Medicine Hospital (NO.20220919029). Requirements for informed consent were waived due to the retrospective nature. ## Study population characteristics A total of 3,738 subjects, including 566 subjects with advanced colorectal adenoma and 3,172 with normal colonoscopies, were included in this study. The subjects’ baseline characteristics were shown in Table 1. Some differences existed between groups concerning various covariates, including age, sex, weight, smoking status, drinking status, fibrinogen, ALB, ALP, UA, GLU, CREA, Urea, PLT, hypertension, ischemic cerebrovascular disease, CHD, DM (all $p \leq 0.05$). **Table 1** | Variables | Total | Case | Control | p-value | | --- | --- | --- | --- | --- | | Variables | n = 3,738 | n = 566 | n = 3,172 | p-value | | Age, (year) | 52.3 ± 13.0 | 61.4 ± 10.2 | 50.7 ± 12.7 | <0.001 | | Sex, male, n (%) | 1,559 (41.7) | 366 (64.7) | 1,193 (37.6) | <0.001 | | Marital status, n (%) | | | | 0.105 | | Single/divorced | 160 (4.3) | 145 (4.6) | 15 (2.7) | | | Married | 3,376 (90.3) | 2,854 (90) | 522 (92.2) | | | Others | 202 (5.4) | 173 (5.5) | 29 (5.1) | | | Weight, (kg) | 67.2 ± 12.4 | 70.6 ± 11.8 | 66.6 ± 12.4 | <0.001 | | Smoking status, n (%) | | | | <0.001 | | Non-smoker | 2,331 (62.4) | 317 (56) | 2014 (63.5) | | | Current smoker | 142 (3.8) | 52 (9.2) | 90 (2.8) | | | Ex-smoker | 28 (0.7) | 7 (1.2) | 21 (0.7) | | | | 1,237 (33.1) | 190 (33.6) | 1,047 (33) | | | Drinking status, n (%) | | | | <0.001 | | Non-drinker | 2,319 (62.0) | 317 (56) | 2002 (63.1) | | | Current drinker | 181 (4.8) | 55 (9.7) | 126 (4) | | | Ex-drinker | 17 (0.5) | 5 (0.9) | 12 (0.4) | | | | 1,221 (32.7) | 189 (33.4) | 1,032 (32.5) | | | FIB, (g/L) | 2.9 ± 0.7 | 3.0 ± 0.8 | 2.8 ± 0.7 | <0.001 | | ALB, (g/L) | 44.3 ± 3.7 | 43.7 ± 4.1 | 44.5 ± 3.6 | <0.001 | | ALT, (U/L) | 17.0 (13.0, 26.0) | 18.0 (13.0, 25.0) | 17.0 (13.0, 26.0) | 0.071 | | AST, (U/L) | 19.5 (16.9, 24.0) | 19.2 (17.0, 23.0) | 19.6 (16.7, 24.0) | 0.777 | | ALP, (U/L) | 75.1 ± 24.6 | 80.4 ± 24.9 | 74.1 ± 24.5 | <0.001 | | UA, (μmol/L) | 303.3 ± 94.5 | 322.7 ± 90.3 | 299.8 ± 94.8 | <0.001 | | GLU, (mmol/L) | 6.0 ± 1.7 | 6.4 ± 2.0 | 5.9 ± 1.7 | <0.001 | | CREA, (μmol/L) | 64.0 ± 21.6 | 71.4 ± 38.2 | 62.6 ± 16.6 | <0.001 | | Urea, (mmol/L) | 4.8 ± 1.5 | 5.1 ± 1.9 | 4.7 ± 1.4 | <0.001 | | PLT, (×109/L) | 236.1 ± 60.2 | 223.5 ± 60.4 | 238.4 ± 59.9 | <0.001 | | APTT, (s) | 29.3 ± 5.5 | 29.0 ± 5.8 | 29.4 ± 5.5 | 0.109 | | TT, (s) | 16.3 ± 1.8 | 16.2 ± 1.7 | 16.4 ± 1.8 | 0.104 | | PT-INR | 1.0 ± 0.1 | 1.0 ± 0.1 | 1.0 ± 0.1 | 0.989 | | PT, (s) | 12.0 ± 1.1 | 12.0 ± 1.1 | 12.0 ± 1.1 | 0.621 | | Family history, n (%) | | | | | | Colorectal cancer | 54 (1.4) | 11 (1.9) | 43 (1.4) | 0.28 | | Digestive system malignancy | 189 (5.1) | 34 (6) | 155 (4.9) | 0.262 | | Co-morbidities, n (%) | | | | | | Hypertension | 926 (24.8) | 234 (41.3) | 692 (21.8) | <0.001 | | Ischemic cerebrovascular disease | 370 (9.9) | 72 (12.7) | 298 (9.4) | 0.015 | | CHD | 419 (11.2) | 89 (15.7) | 330 (10.4) | <0.001 | | HLP | 357 (9.6) | 59 (10.4) | 298 (9.4) | 0.443 | | Previous History of cancer | 59 (1.6) | 14 (2.5) | 45 (1.4) | 0.064 | | Liver disease | 402 (10.8) | 51 (9) | 351 (11.1) | 0.146 | | DM | 640 (17.1) | 158 (27.9) | 482 (15.2) | <0.001 | ## Effects of fibrinogen on advanced colorectal adenoma In multivariable logistic regression analyses, a positive relationship was found between fibrinogen and advanced colorectal adenoma. In the crude model, fibrinogen was found to be positively related to advanced colorectal adenoma [odds ratio (OR), 1.36; $95\%$ confidence interval (CI), 1.21–1.53]. The results were similar after adjusting for age and gender (OR, 1.17; $95\%$ CI, 1.03–1.33). After adjustment for sex, age, hypertension, DM, APTT, PLT, CREA, ALP, and ALB, the OR value was also stable (OR, 1.14; $95\%$ CI, 0.99–1.3). When fibrinogen was performed as a quartile for analysis, a positive association between them was shown even after adjustment for potential confounders. Compared to participants with low fibrinogen in quartile 1 (<2.40 g/L), the adjusted OR values for fibrinogen and advanced colorectal adenoma in quartile 2 (2.40–2.75 g/L), quartile 3 (2.76–3.15 g/L), and quartile 4 (≥3.16 g/L) were 1.03 ($95\%$ CI: 0.76–1.41, $$p \leq 0.834$$), 1.37 ($95\%$ CI:1.01–1.85, $$p \leq 0.04$$), and 1.43 ($95\%$ CI: 1.06–1.96, $$p \leq 0.019$$; Table 2), respectively. The quartile 3 and quartile 4 groups (≥2.76 g/L) had the higher advanced colorectal adenoma incidence. To further explore the association between fibrinogen and advanced colorectal adenoma, smooth curve fitting was performed, and the results present a linear association between them (only $99\%$ of the data was shown, P for non-linearity: 0.536, Figure 2). ## Sensitivity analysis The subgroup analyses were conducted to explore the relationship between fibrinogen and advanced colorectal adenoma among different layers. None of the variables, including age (<65 years, or ≥ 65 years), sex (male and female), smoking status, drinking status, history of cancer, DM, CHD, HLP, hypertension, ischemic cerebrovascular disease, significantly affected the relationship between fibrinogen and advanced colorectal adenoma (Figure 3). In subgroup analyses, we performed more indicators that have a significant difference in Table 1 (Supplementary Table S2). The results were stable as well. **Figure 3:** *Subgroup analyses of the fibrinogen and advanced colorectal adenoma among inpatients. Except for the stratification component itself, each stratification factor was adjusted for age, sex, albumin, alkaline phosphatase, creatinine, platelets, activated partial thromboplastin time, hypertension, and diabetes mellitus. NA, not recorded.* In addition, sensitivity analysis was performed using the complete cases. All patients with missing data were excluded, and 3,738 individuals were left. And the association between fibrinogen and advanced colorectal adenoma remained stable [Model I: OR ($95\%$ CI), 1.17 (1.02–1.35); Model II: OR ($95\%$ CI), 1.13 (0.98–1.37)]. When serum GGT as a categorized variable, compared with individuals with lower fibrinogen in quartile 1 (<2.40 g/L), the adjusted OR values for fibrinogen and advanced colorectal adenoma in quartile 2 (2.40–2.74 g/L), quartile 3 (2.75–3.15 g/L), and quartile 4 (≥3.16 g/L) were 1.02 ($95\%$ CI: 0.73–1.42, $$p \leq 0.903$$), 1.39 ($95\%$ CI:1.01–1.9, $$p \leq 0.042$$), and 1.41 ($95\%$ CI: 1.02–1.94, $$p \leq 0.036$$; Model II, Supplementary Table SI), respectively. Furthermore, we used the fibrinogen-Z score in the multivariable logistic regression analyses (Supplementary Table S3) and found that with an elevated FIB-Z Score, the risk of advanced colorectal adenoma increased. These findings indicated the results were stable. ## Discussion This study explored the association between fibrinogen and advanced colorectal adenoma among inpatients. The fibrinogen was independently associated with advanced colorectal adenoma with linear association curves among inpatients. The present study found that the incidence of advanced colorectal adenoma increased as fibrinogen increased. The relationship between fibrinogen and advanced colorectal adenoma was stable between layers. To our knowledge, there are no clinical studies demonstrating the correlation between fibrinogen and advanced adenoma. However, some literature may substantiate the relationship between them. Several studies have analyzed the plasma proteome of patients with advanced adenoma or colorectal cancer and found elevated fibrinogen α and β chains in the adenoma-cancer sequence, suggesting that fibrinogen may play a critical role in tumor progression (27–29). In addition, only two studies have associated plasma fibrinogen levels with CRC. Kristine et al. found elevated fibrinogen to be associated with an increased risk of CRC in 84,000 subjects, and this relationship was greatest during the early follow-up years. In addition, the authors suggest that the possible role of fibrinogen in CRC may be interpreted through its part in the inflammatory reaction [30]. This finding supports the chronic low-level inflammation as a latent contributor of cancer development [30, 31]. A nested case-cohort study indicated that fibrinogen (≥400 mg/dl) was positively correlated with CRC, suggesting that it may be a latent biomarker of CRC and supporting the “common soil” hypothesis [32]. Advanced adenoma, a precursor of CRC, also showed an increased risk associated with elevated fibrinogen levels in our study. Complements the evidence that fibrinogen was positively associated with advanced adenomas. Fibrinogen in the adenoma-carcinoma progression can be interpreted in terms of both hemostatic and inflammatory factors, and several hypotheses have been proposed. ( i) Fibrinogen can bind growth factors [33], and therefore, fibrinogen residing on the cell matrix may act as a reservoir to control the bioavailability and accessibility of growth factors, and affect cancer cell proliferation, inhibit apoptosis, angiogenesis, and metastasis [34]. ( ii) Fibrinogen can bind to several types of cells. Fibrinogen-mediated cell bridging can exert traction for adhesion, shape change, motility, and invasion potential of cancer cells [35]. ( iii) Fibrinogen contributes to the protection of neoplastic cells from natural killer cell cytotoxicity through the interaction of β3 integrin’s with platelets, thus allowing escape from host immune surveillance [36]. ( iv) *Fibrinogen is* a critical regulator of inflammation in diseases, including cancer [17], and may have a role in cellular signaling by interaction with cellular receptors [37]. Such interactions may promote inflammation, angiogenesis, and cell proliferation [33]. Overall, fibrinogen effects and favors the adenoma-carcinoma progression, whether as an inflammatory or hemostatic factor. Our study also had several limitations. First, the present research had limitations inherent to retrospective studies. We collected the participant’s data from the laboratory information system and electronic medical records, there were missing data for some indicators. However, we performed a sensitivity analysis and found that the results were stable with or without multiple imputations of the data. Second, in a present retrospective study, we did not collect repeated measurements for fibrinogen, which may not represent their long-term levels and their association with advanced colorectal adenoma. Third, even though multivariable logistic regression, subgroup analyses, and sensitivity analysis were performed, residual confounding effects from unmeasured or unknown factors could not be completely excluded. Finally, the findings were analyzed in a single-center database from China, and the generalizability may be limited for populations with different demographic characteristics. Studying risk factors for advanced colorectal adenoma could help develop more comprehensive and non-invasive screening recommendations and possibly provide a clearer understanding of the mechanism of colon cancer. Studies on the epidemiology of advanced adenomas may be crucial in revealing why only a fraction of common adenomas progresses to CRC. ## Conclusion A linear association between fibrinogen and advanced colorectal adenoma was found among inpatients. With an increase in plasma fibrinogen, the risk of advanced adenoma increased. These findings provide evidence that fibrinogen may be a potential high-risk factor for colorectal screening. ## Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. ## Ethics statement The studies involving human participants were reviewed and approved by Ethics Review Board of Shijiazhuang Traditional Chinese Medicine Hospital (NO.20220919029). Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements. ## Author contributions HW and HuZ conceived and designed the study and wrote and revised the manuscript. HW, XC, PM, JL, ZW, TZ, and HaZ collected and analyzed the data. All authors contributed to the article and approved the submitted version. ## Funding This work was supported by the Hebei Administration of Traditional Chinese Medicine (grant number 2023145). ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmed.2023.1140185/full#supplementary-material ## References 1. Chen YX, Gao QY, Zou TH, Wang BM, Liu SD, Sheng JQ. **Berberine versus placebo for the prevention of recurrence of colorectal adenoma: a multicentre, double-blinded, randomised controlled study**. *Lancet Gastroenterol Hepatol* (2020) **5** 267-75. DOI: 10.1016/s2468-1253(19)30409-1 2. Vogelstein B, Fearon ER, Hamilton SR, Kern SE, Preisinger AC, Leppert M. **Genetic alterations during colorectal-tumor development**. *N Engl J Med* (1988) **319** 525-32. DOI: 10.1056/NEJM198809013190901 3. Fearon ER, Vogelstein B. **A genetic model for colorectal tumorigenesis**. *Cells* (1990) **61** 759-67. DOI: 10.1016/0092-8674(90)90186-i 4. Hill MJ, Morson BC, Bussey HJ. **Aetiology of adenoma--carcinoma sequence in large bowel**. *Lancet* (1978) **1** 245-7. DOI: 10.1016/s0140-6736(78)90487-7 5. Neugut AI, Jacobson JS, De Vivo I. **Epidemiology of colorectal adenomatous polyps**. *Cancer Epidemiol Biomark Prev* (1993) **2** 159-76. DOI: 10.1016/j.bpg.2017.06.004 6. Peipins LA, Sandler RS. **Epidemiology of colorectal adenomas**. *Epidemiol Rev* (1994) **16** 273-97. DOI: 10.1093/oxfordjournals.epirev.a036154 7. Falanga A, Santoro A, Labianca R, De Braud F, Gasparini G, D’alessio A. **Hypercoagulation screening as an innovative tool for risk assessment, early diagnosis and prognosis in cancer: the hypercan study**. *Thromb Res* (2016) **140** S55-9. DOI: 10.1016/s0049-3848(16)30099-8 8. Lima LG, Monteiro RQ. **Activation of blood coagulation in cancer: implications for tumour progression**. *Biosci Rep* (2013) **33** e00064. DOI: 10.1042/bsr20130057 9. Mosesson MW. **Fibrinogen and fibrin structure and functions**. *J Thromb Haemost* (2005) **3** 1894-904. DOI: 10.1111/j.1538-7836.2005.01365.x 10. Vilar R, Fish RJ, Casini A, Neerman-Arbez M. **Fibrin(ogen) in human disease: both friend and foe**. *Haematologica* (2020) **105** 284-96. DOI: 10.3324/haematol.2019.236901 11. Stegeman I, De Wijkerslooth TR, Stoop EM, Van Leerdam ME, Dekker E, Van Ballegooijen M. **Colorectal cancer risk factors in the detection of advanced adenoma and colorectal cancer**. *Cancer Epidemiol* (2013) **37** 278-83. DOI: 10.1016/j.canep.2013.02.004 12. Terry MB, Neugut AI, Bostick RM, Potter JD, Haile RW, Fenoglio-Preiser CM. **Reliability in the classification of advanced colorectal adenomas**. *Cancer Epidemiol Biomark Prev* (2002) **11** 660-3. PMID: 12101114 13. Wark PA, Wu K, van’t Veer P, Fuchs CF, Giovannucci EL. **Family history of colorectal cancer: a determinant of advanced adenoma stage or adenoma multiplicity?**. *Int J Cancer* (2009) **125** 413-20. DOI: 10.1002/ijc.24288 14. Niederseer D, Stadlmayr A, Huber-Schönauer U, Plöderl M, Schmied C, Lederer D. **Cardiovascular risk and known coronary artery disease are associated with colorectal adenoma and advanced neoplasia**. *J Am Coll Cardiol* (2017) **69** 2348-50. DOI: 10.1016/j.jacc.2017.02.065 15. Park J, Han JS, Jo HJ, Kim HY, Yoon H, Shin CM. **Resting heart rate is associated with colorectal advanced adenoma**. *PLoS One* (2021) **16** e0254505. DOI: 10.1371/journal.pone.0254505 16. Yang SY, Kim YS, Chung SJ, Song JH, Choi SY, Park MJ. **Association between colorectal adenoma and coronary atherosclerosis detected by ct coronary angiography in korean men; a cross-sectional study**. *J Gastroenterol Hepatol* (2010) **25** 1795-9. DOI: 10.1111/j.1440-1746.2010.06330.x 17. Davalos D, Akassoglou K. **Fibrinogen as a key regulator of inflammation in disease**. *Semin Immunopathol* (2012) **34** 43-62. DOI: 10.1007/s00281-011-0290-8 18. Li M, Wu Y, Zhang J, Huang L, Wu X, Yuan Y. **Prognostic value of pretreatment plasma fibrinogen in patients with colorectal cancer: a systematic review and meta-analysis**. *Medicine* (2019) **98** e16974. DOI: 10.1097/md.0000000000016974 19. Lin Y, Liu Z, Qiu Y, Zhang J, Wu H, Liang R. **Clinical significance of plasma d-dimer and fibrinogen in digestive cancer: a systematic review and meta-analysis**. *Eur J Surg Oncol* (2018) **44** 1494-503. DOI: 10.1016/j.ejso.2018.07.052 20. Moik F, Posch F, Grilz E, Scheithauer W, Pabinger I, Prager G. **Haemostatic biomarkers for prognosis and prediction of therapy response in patients with metastatic colorectal cancer**. *Thromb Res* (2020) **187** 9-17. DOI: 10.1016/j.thromres.2020.01.002 21. Sun Y, Han W, Song Y, Gao P, Yang Y, Yu D. **Prognostic value of preoperative fibrinogen for predicting clinical outcome in patients with nonmetastatic colorectal cancer**. *Cancer Manag Res* (2020) **12** 13301-9. DOI: 10.2147/cmar.S275498 22. Wu WT, Li YJ, Feng AZ, Li L, Huang T, Xu AD. **Data mining in clinical big data: the frequently used databases, steps, and methodological models**. *Mil Med Res* (2021) **8** 44. DOI: 10.1186/s40779-021-00338-z 23. Baron JA, Cole BF, Sandler RS, Haile RW, Ahnen D, Bresalier R. **A randomized trial of aspirin to prevent colorectal adenomas**. *N Engl J Med* (2003) **348** 891-9. DOI: 10.1056/NEJMoa021735 24. Chen SB, Liu DT, Chen YP. **Prognostic value of body mass index stratified by alcohol drinking status in patients with esophageal squamous cell carcinoma**. *Front Oncol* (2022) **12** 769824. DOI: 10.3389/fonc.2022.769824 25. Alberti KG, Zimmet PZ. **Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation**. *Diabet Med* (1998) **15** 539-53. DOI: 10.1002/(SICI)1096-9136(199807)15:7<539::AID-DIA668>3.0.CO;2-S 26. Kerner W, Brückel J. **Definition, classification and diagnosis of diabetes mellitus**. *Exp Clin Endocrinol Diabetes* (2014) **122** 384-6. DOI: 10.1055/s-0034-1366278 27. Agatea L, Crotti S, Ragazzi E, Bedin C, Urso E, Mammi I. **Peptide patterns as discriminating biomarkers in plasma of patients with familial adenomatous polyposis**. *Clin Colorectal Cancer* (2016) **15** e75-92. DOI: 10.1016/j.clcc.2015.12.002 28. Bedin C, Crotti S, Ragazzi E, Pucciarelli S, Agatea L, Tasciotti E. **Alterations of the plasma peptidome profiling in colorectal cancer progression**. *J Cell Physiol* (2016) **231** 915-25. DOI: 10.1002/jcp.25196 29. Fayazfar S, Zali H, Arefi Oskouie A, Asadzadeh Aghdaei H, Rezaei Tavirani M, Nazemalhosseini Mojarad E. **Early diagnosis of colorectal cancer via plasma proteomic analysis of crc and advanced adenomatous polyp**. *Gastroenterol Hepatol Bed Bench* (2019) **12** 328-39. PMID: 31749922 30. Allin KH, Bojesen SE, Nordestgaard BG. **Inflammatory biomarkers and risk of cancer in 84,000 individuals from the general population**. *Int J Cancer* (2016) **139** 1493-500. DOI: 10.1002/ijc.30194 31. Grivennikov SI, Greten FR, Karin M. **Immunity, inflammation, and cancer**. *Cells* (2010) **140** 883-99. DOI: 10.1016/j.cell.2010.01.025 32. Parisi R, Panzera T, Russo L, Gamba S, De Curtis A, Di Castelnuovo A. **Fibrinogen levels in relation to colorectal cancer onset: a nested case-cohort study from the moli-sani cohort**. *Front Cardiovasc Med* (2022) **9** 1009926. DOI: 10.3389/fcvm.2022.1009926 33. Kabat GC, Salazar CR, Zaslavsky O, Lane DS, Rohan TE. **Longitudinal association of hemostatic factors with risk for cancers of the breast, colorectum, and lung among postmenopausal women**. *Eur J Cancer Prev* (2016) **25** 449-56. DOI: 10.1097/CEJ.0000000000000193 34. Terry MB, Neugut AI, Bostick RM, Sandler RS, Haile RW, Jacobson JS. **Risk factors for advanced colorectal adenomas: a pooled analysis**. *Cancer Epidemiol Biomark Prev* (2002) **11** 622-9 35. Simpson-Haidaris PJ, Rybarczyk B. **Tumors and fibrinogen. The role of fibrinogen as an extracellular matrix protein**. *Ann N Y Acad Sci* (2001) **936** 406-25. DOI: 10.1111/j.1749-6632.2001.tb03525.x 36. Zheng S, Shen J, Jiao Y, Liu Y, Zhang C, Wei M. **Platelets and fibrinogen facilitate each other in protecting tumor cells from natural killer cytotoxicity**. *Cancer Sci* (2009) **100** 859-65. DOI: 10.1111/j.1349-7006.2009.01115.x 37. Ten Cate H, Falanga A. **Overview of the postulated mechanisms linking cancer and thrombosis**. *Pathophysiol Haemost Thromb* (2008) **36** 122-30. DOI: 10.1159/000175150
--- title: 'The non-linear correlation between the volume of cerebral white matter lesions and incidence of bipolar disorder: A secondary analysis of data from a cross-sectional study' authors: - Hui Du - Bing Yang - Hui Wang - Yaqing Zeng - Jianpin Xin - Xiaoqiang Li journal: Frontiers in Psychiatry year: 2023 pmcid: PMC10061585 doi: 10.3389/fpsyt.2023.1149663 license: CC BY 4.0 --- # The non-linear correlation between the volume of cerebral white matter lesions and incidence of bipolar disorder: A secondary analysis of data from a cross-sectional study ## Abstract Cerebral white matter lesions (WML) are major risk factors for bipolar disorder (BD). However, studies on the association between cerebral WML volume and BD risk are limited. This study aimed to investigate the relationship between cerebral WML volume and BD incidence. This is a secondary retrospective analysis of patients ($$n = 146$$, 72 males, 74 females, mean age = 41.77 years) who have previously undergone magnetic resonance imaging examinations. Information was obtained from the Dryad database. Univariate analysis, piecewise linear regression model, and multivariable logistic regression model were used for statistical analysis. A non-linear relationship was recognized between the cerebral WML volume and BD incidence, in which the inflection point of the WML volume was 6,200 mm3. The effect sizes and confidence intervals on the left and right sides of the emphasis point were 1.0009 (1.0003, 1.0015) and 0.9988 (0.9974, 1.0003), respectively. Subgroup analysis (WML volume < 6,200 mm3) showed that the cerebral WML volume (for 0.1 mm3 increase) was positively related to the BD incidence (OR = 1.11, $95\%$ confidence interval [CI] (1.03, 1.21)). Here we show that the cerebral WML volume is positively and non-linearly correlated to the BD risk. Volumetric analysis of WML provide a better understanding of the association between WML and the BD risk, and thereby the pathophysiological mechanisms of BD. ### Graphical abstract A non-linear relationship between the volume of cerebral white matter lesions (WML) and bipolar disorder (BD) incidence is shown. The cerebral WML volume is positively and non-linearly correlated to the BD risk. The correlation is stronger when the cerebral WML volume was <6,200 mm3. **Graphical Abstract:** *A non-linear relationship between the volume of cerebral white matter lesions and bipolar disorder incidence is shown after adjusting for age; sex; lithium, atypical antipsychotic, antiepileptic, and antidepressant drug use; BMI; migraine; smoking; hypertension; diabetes mellitus; substance and alcohol dependency; and anxiety disorder.* ## Introduction Emotional fluctuations are very common in life. However, when mood swings are violent and persistent or lead to significant pain or damage, emotional disorders may be a potential cause. Bipolar disorder (BD) is a disease of high heritability characterized by repetitive episodes of elation and depression, combined with periods of normal mood in most cases [1, 2]. BD is the sixth leading cause of disability worldwide and has a lifetime predominance of approximately 1–$3\%$ within the common population [3, 4]. The real causes of BD likely vary among people, and its precise underlying mechanism remains unclear [5]. There are many studies on the causes and mechanisms of BD. Neuroimaging studies of bipolar disorder have revealed an association to abnormalities in the neural circuitry which regulate emotion and reward processing (6–8). The ventral system, which regulates emotional perception, includes brain structures such as the amygdala, the insula, the ventral striatum, the ventral anterior cingulate cortex, and the prefrontal cortex [9]. The dorsal system, responsible for emotional regulation, includes the hippocampus, the dorsal anterior cingulate cortex, and other parts of the prefrontal cortex [9, 10]. Phillips et al. [ 9] describe a model of BD that mood swings may occur when the ventral system is overactivated and the dorsal system is underactivated [9, 10]. Studies have shown that mitochondrial impairment and oxidative stress may be involved in the development and progression of BD (11–13). A mate-analysis concluded that mitochondrial modulators have a significant antidepressant effect in BD patients [14]. Another study showed that a deficiency in Glucose-6-phosphate dehydrogenase activity has been linked to bipolar disorder and has a positive relationship with mitochondrial impairment [15]. Charles Okanda Nyatega’ findings imply that BD may be linked to striatal functional brain alterations and structural dysconnectivity [16]. In addition, decreased levels of L-tryptophan, which causes increased pain sensitivity and cognitive impairment, were found in patients with BD [17]. As the mechanisms for BD remain unclear, and there are currently no validated biological markers, mental health professionals continue to rely on a phenomenology-based diagnostic system to diagnose BD (The Diagnostic and Statistical Manual of Mental Disorders, DSM). There is often a long period of inadequate or incorrect treatment before the official BD diagnosis is made [18]. Therefore, challenges remain in identifying stable biomarkers for BD, to better understand the neurobiology and reach a more accurate diagnosis and treatment. The development of neuroimaging techniques, in particular the non-invasive measurement of brain structures from magnetic resonance imaging (MRI) scans, represents a unique approach to identify the brain structural variations associated with BD. Neuroimaging studies have found differences in the volume of various brain regions between BD and healthy control [6]. Accumulating evidence links BD to cerebral white matter lesions (WML) observed by MRI (19–22). Furthermore, WML is associated with poor clinical course of patients with BD [23]. However, the association between BD and WML has not been clearly characterized, as WML at different regions of the central nervous system and detected with different methods have been used for this matter. Volumetric analysis is common within the field of structural neuroimaging [24, 25]. However, few relevant studies have assessed the relationship between cerebral WML volume and incidence of BD [26, 27]. Here, we investigated the relationship between WML volume and BD risk in order to find a validated biological marker for BD and address the limitations of previous studies. This is a secondary analysis based on previously published data. ## Data source We used data published in the “Dryad” database.1 The database allows users to freely download raw data. Based on the Dryad Terms of Service, the exposed data of the paper can be used to re-analyze different scientific hypotheses [28, 29]. We cite the Dryad dataset in the present study. The database records included the following variables for secondary analysis: age; illness duration; WML volume; sex group; lithium, atypical antipsychotic, antiepileptic, and antidepressant drug use; body mass index (BMI); migraine; smoking; hypertension; diabetes mellitus; substance and alcohol dependency; and anxiety disorder. ## Study design and population The original research, BIPFAT study, was designed as a retrospective cohort study of 154 subjects (75 males, 79 females, mean age = 42.95 years) enrolled at the Medical University of Graz in Austria [26]. The data used in this research can be downloaded from Dryad (Supplementary material) [30]. ## Participants Inclusion Criteria: Patients that took part in the single-center BIPFAT study in Austria as inpatients or outpatients of the Medical University of Graz and were diagnosed of BD I or BD II according to the DSM-IV criteria. Participants were required to be in a euthymic state (Hamilton Rating Scale for Depression (HAM-D) and Young Mania Rating Scale (YMRS) scores: <11 and <9, respectively) and to have signed a written informed consent form. The ethics committee of the Medical University of Graz approved this study. The exclusion criteria were the presence of systemic lupus erythematosus, rheumatoid arthritis, hemodialysis, inflammatory bowel disease, chronic obstructive pulmonary disease, and neurodegenerative and neuroinflammatory disorders (i.e., Alzheimer’s disease, Huntington’s disease, multiple sclerosis, and Parkinson’s disease). More details on the study exclusion criteria are detailed in the original study [26]. An additional exclusion criterion for this study was the presence of a lifelong psychiatric diagnosis. Eight participants with WML larger than 9,000 mm3 were excluded [31, 32], and the remaining 146 participants entered the final analysis. ## Data collection and measurements The original study database included demographic parameters, complete actual and lifetime psychiatric history using the Structured Clinical Interview for DSM Disorders (SCID), anthropometric measures, medication history, fasting blood, blood pressure, electroencephalogram (EEG), different lifestyle questionnaires, and MRI of the cerebral cortex. All participants were former inpatients or outpatients of the Medical University of Graz and had a diagnosis of BD based on the DSM-IV criteria. ## Statistical analysis Continuous variables were presented as mean ± standard deviation. Categorical variables were expressed as frequencies and percentages. Continuous variables were compared using the two-sample t-test or Wilcoxon rank-sum, and categorical variables were compared using the χ2 test and Fisher’s exact test. *The* generalized additive model (GAM) was used to confirm a non-linear relationship between the cerebral WML volume and BD incidence. Next, the saturation effect of cerebral WML volume on BD was calculated based on smoothing curves using a two-stage logistic regression model. Univariate and multivariate Cox proportional risk models were used to assess the relationship between the cerebral WML volume and BD risk. We used three models: model 1 (crude model), model 2 (adjusted for age and sex), and model 3 (adjusted for age; sex; lithium, atypical antipsychotic, antiepileptic, and antidepressant drug use; BMI; migraine; smoking; hypertension; diabetes mellitus; substance and alcohol dependency; and anxiety disorder). Differences were considered statistically significant when the calculated p-value was <0.05. All analyses were performed using the statistical software R (https://www. R-project. Org, The R Foundation) and EmpowerStats (http://www.empowerstats.com, X&Y Solutions Inc., Boston, MA, United States) (33–35). ## Characteristics of study participants Overall, 74 women ($50.68\%$) and 72 men ($49.32\%$) were retrospectively analyzed. The baseline characteristics of the patients with BD (BD group) and healthy participants (control group) are shown in Table 1. The mean patient age was 41.77 years (SD, 13.81). Overall, the average WML volume was 3499.24 mm3. The BD group had a higher mean WML volume (3948.86 mm3) compared to the control group (2686.30 mm3; $p \leq 0.01$). **Table 1** | Variables | Total | Bipolar disorder | Healthy controls | p-value | | --- | --- | --- | --- | --- | | No. of participants | 146 | 94 | 52 | | | Age (years) | 41.77 ± 13.81 | 42.91 ± 13.05 | 39.70 ± 15.00 | 0.179 | | Sex (male) | 72 (49.32%) | 50 (53.19%) | 22 (42.31%) | 0.208 | | BMI (kg/m2) | 26.80 ± 4.86 | 27.89 ± 4.66 | 24.38 ± 4.62 | <0.001 | | WML volume(mm3) | 3499.24 ± 1903.22 | 3948.86 ± 1841.33 | 2686.30 ± 1751.93 | <0.001 | | Illness duration (years) | / | 17.74 ± 11.84 | / | | | Depression | / | 12.93 ± 12.25 | / | | | Mania | / | 8.36 ± 8.88 | / | | | Age First Episode (years) | / | 24.83 ± 10.58 | / | | | Lithium (n, %) | 16 (10.96%) | 16 (17.02%) | 0 (0.00%) | 0.002 | | Antiepileptic (n, %) | 17 (11.64%) | 17 (18.09%) | 0 (0.00%) | 0.001 | | Atypical Antipsychotic (n, %) | 39 (26.71%) | 39 (41.49%) | 0 (0.00%) | <0.001 | | Antidepressive drug (n, %) | 34 (23.29%) | 34 (36.17%) | 0 (0.00%) | <0.001 | | Migraine (n, %) | 37 (25.52%) | 22 (23.40%) | 15 (29.41%) | 0.428 | | Smoking (n, %) | 61 (41.78%) | 48 (51.06%) | 13 (25.00%) | 0.002 | | Hypertension (n, %) | 37 (25.34%) | 26 (27.66%) | 11 (21.15%) | 0.387 | | DM (n, %) | 7 (4.79%) | 7 (7.45%) | 0 (0.00%) | 0.044 | | Substance dependency (n, %) | 11 (7.53%) | 11 (11.70%) | 0 (0.00%) | 0.010 | | Alcohol dependency (n, %) | 17 (11.64%) | 17 (18.09%) | 0 (0.00%) | 0.001 | | Anxiety disorder (n, %) | 22 (15.17%) | 22 (23.66%) | 0 (0.00%) | <0.001 | ## Non-linear relationship between WML volume and BD incidence We aimed to characterize the actual relationship between WML volume and BD incidence. As WML volume is a continuous variable, the GAM was used to identify a non-linear relationship between the two variables. We found that this relationship was non-linear after adjusting for sex; age; lithium, atypical antipsychotic, antiepileptic, and antidepressant drug use; BMI; migraine; smoking; hypertension; diabetes mellitus; substance and alcohol dependency; and anxiety disorder. We used a two-piecewise linear regression model to calculate the inflection point of the WML volume, which was found to be 6,200 (log-rank test, $p \leq 0.05$; Table 2). A positive relationship between the WML volume and BD incidence was observed on the left side of the inflection point (OR: 1.0009, $95\%$ CI: 1.0003, 1.0015, $p \leq 0.01$), whereas saturation was observed toward the right of the inflection point (OR: 0.9988, $95\%$ CI: 0.9974, 1.0003, $$p \leq 0.1217$$; Table 2). Thus, to analyze the positive relationship between the WML volume and BD incidence, we selected patients with WML volume < 6,200 mm3. **Table 2** | Unnamed: 0 | Incidence of BD (OR, 95%CI) | p-value | | --- | --- | --- | | Fitting model by standard linear regression | 1.0004 (1.0000, 1.0008) | 0.0382 | | Fitting model by two-stage linear regression | | | | The inflection point of WML volume(mm3) | 6200 | | | <6,200 | 1.0009 (1.0003, 1.0015) | 0.0048 | | >6,200 | 0.9988 (0.9974, 1.0003) | 0.1217 | | p for log likelihood ratio test | 0.025 | | ## Univariate analysis for BD incidence (WML volume < 6,200 mm3) In order to address which factors are related to BD incidence, we performed univariate analysis (Table 3). Using the univariate Cox proportional risk model, we found that WML volume (OR = 1.10, $95\%$ CI: 1.05, 1.14, per 0.1cm3), BMI (OR = 1.16, $95\%$ CI: 1.06, 1.28), and smoking (OR = 3.86, $95\%$ CI: 1.75, 8.49) were positively correlated with BD, whereas age, sex, migraine, and hypertension were not associated with BD. **Table 3** | Unnamed: 0 | Statistics | OR (95% CI) | p-value | | --- | --- | --- | --- | | WML volume (0.1 cm3) | 29.50 ± 12.55 | 1.10 (1.05, 1.14) | <0.0001 | | Age | 39.90 ± 13.09 | 1.02 (0.99, 1.05) | 0.2012 | | BMI | 26.69 ± 4.85 | 1.16 (1.06, 1.28) | 0.0016 | | Sex | Sex | Sex | Sex | | Male | 61 (47.66%) | 1.0 | | | Female | 67 (52.34%) | 0.78 (0.38, 1.60) | 0.4934 | | Migraine | Migraine | Migraine | Migraine | | No | 95 (74.80%) | 1.0 | | | Yes | 32 (25.20%) | 0.81 (0.36, 1.85) | 0.6244 | | Smoking | Smoking | Smoking | Smoking | | No | 71 (55.47%) | 1.0 | | | Yes | 57 (44.53%) | 3.86 (1.75, 8.49) | 0.0008 | | Hypertension | Hypertension | Hypertension | Hypertension | | No | 98 (76.56%) | 1.0 | | | Yes | 30 (23.44%) | 1.05 (0.45, 2.45) | 0.9142 | ## Multivariate analysis between WML volume and BD incidence (WML volume < 6,200 mm3) To explore the relationship between WML volume and BD incidence, we used WML volume (0.1 mm3) as the independent variable, BD risk as the dependent variable, and age; sex; lithium, atypical antipsychotic, antiepileptic, and antidepressant drug use; BMI; migraine; smoking; hypertension; diabetes mellitus; substance and alcohol dependency; and anxiety disorder were adjusted for multivariate regression analysis. In model 1, the WML volume was positively correlated with BD risk (odds ratio (OR) = 1.10, $95\%$ CI: 1.05, 1.14, $p \leq 0.001$; Table 4), and the same positive correlation was observed in the minimum adjusting model (model 2: OR: 1.10, $95\%$ CI: 1.05, 1.15, $p \leq 0.0001$). In model 3, the incidence of BD increased by 1.11 times per 0.1 mm3 increase in WML (OR = 1.11, $95\%$ CI: 1.03, 1.21, $p \leq 0.001$). **Table 4** | Variable | Crude model (OR,95% CI, p) | Model I (OR,95% CI, p) | Model II (OR,95% CI, p) | | --- | --- | --- | --- | | WML volume (for 0.1 cm3 increase) | 1.10 (1.05, 1.14) <0.0001 | 1.10 (1.05, 1.15) <0.0001 | 1.11 (1.03, 1.21) 0.0067 | ## Discussion Our study shows a significant association between cerebral WML volume and BD risk, and this relationship is independent of other risk factors (OR: 1.11, $95\%$ CI: 1.03, 1.21, $p \leq 0.001$ for 0.1 mm3 WML increase). Other studies have reported similar results [27, 36, 37]. Our study not only evaluated the independent impact of cerebral WML volume and BD risk but also explored the non-linear relationship between them. The inflection point of the cerebral WML volume was calculated to be 6,200 mm3. When the inflection point was <6,200 mm3, the cerebral WML volume was positively correlated with the BD occurrence (OR: 1.0009, $95\%$ CI: 1.0003, 1.0015, $p \leq 0.001$), indicating that patients with larger WML volume have a higher risk of BD. When the inflection point was >6,200 mm3, no positive relationship was found between the cerebral WML volume and BD risk (OR: 0.99988, $95\%$ CI: 0.9974, 1.0003, $$p \leq 0.1217$$). The measurements of WML are different. Some studies indicate that patients with BD are likely more vulnerable (almost twice) to developing WML changes than healthy participants [38]. However, the degree of white matter and paraventricular hyperintensities was quantified according to the scoring method described by Coffey et al. [ 39] and was grouped into two categories (mild and more extensive) [38]. Another study found that adolescent patients with BD had significantly increased numbers of WML compared with healthy individuals [40]. However, WML incidence was evaluated on a four-point ordinal scale (none, mild, moderate, and severe) [40]. The WML measurements were based on a qualitative rating scale, and the standards were confusing. In this study, 3 T MRI was performed [26], while some previous studies primarily report use of the 1.5 T MRI (41–44). 3T MRI is more sensitive to WML than 1.5 T MRI, which means our study may have found more WML than that found with 1.5 T MRI [45, 46]. Volumetric analysis has rarely been applied to the study of WML in BD [23, 27, 47], although this approach is common in structural neuroimaging. Through voxel-based morphometry for T1-weighted images (MRI), some researchers found that patients with BD have a greater cluster size than healthy people [37]. Another study found that the WML volume of male patients with BD is closely related to the number of manic episodes [26]. Also, WML volume seems to be correlated with familiarity and type of BP [27]. However, we found a non-linear relationship between cerebral WML volume and BD incidence when the volumetric analysis was performed. White matter may play an important role in the neurobiology of BD [48]. One study found changes to bilateral white matter connectivity (i.e., decreased fractional anisotropy) during emotion regulation and sensory processing in participants with BD when compared with healthy controls [49]. Global abnormalities in white matter tracts (seen by MRI) might account for the characteristic mood lability of BD [50]. McDonald’s study showed that white matter reduction in the left frontal and temporoparietal regions was associated with the genetic risk of bipolar disorder [51]. Karlsgodt et al. found that the white matter microstructural disruptions were predictive of functional outcomes in youth at high risk of developing psychosis [52]. Lower fractional anisotropy, which reflects the collinearity and/or integrity of the fibers, has been consistently reported in white matter tracts involved in emotional processing and regulation in youth and adults with BD (53–56). Higher incidence rates of BD are also associated with more abnormalities in white matter tracts [21]. However, the presence of some factors such as obesity, metabolic syndrome, and cardiovascular risk factors, such as hypertension, diabetes, and age are associated with cerebral white matter lesions (57–59). In our study, age, hypertension, and diabetes were adjusted for in the multivariate regression analysis, and we found that the BD incidence did not increase when the cerebral WML volume increased to >6,300 mm3. We speculate that other white matter lesions-related factors (such as cerebral infarction, vasculitis, inflammation, and multiple sclerosis) are responsible for the increase in cerebral WML volume after a certain volume limit, showing no association with BD occurrence. This study had several limitations. First, from a statistical point of view, the number of patients of the present study is insufficient. However, the study population included in this study was adequate from the perspective of studies on WML volume and BD, as most studies on BD collect data from less than 100 participants (23, 60–64). Therefore, future studies should further verify our results by expanding the sample size. Second, the literature suggests that different parts of white matter degeneration (deep or periventricular WML) affect the occurrence of BD. However, in our study, the WML volume does not distinguish the location of white matter degeneration, and further research is required to clarify this. Finally, our study was retrospective, cross-sectional and showed a non-linear association between WML volume and BD risk. Therefore, prospective basic and clinical studies are required to confirm this causal relationship. Concerning the future directions, our study was only focused on the most used T2 MRI, thus future studies should attempt to perform resting state functional MRI (RS-fMRI) and diffusion tensor imaging (DTI), which could help in obtaining more detailed information about altered functional connectivity in brain areas that are impaired in BD. In conclusion, this cross-sectional study demonstrated a non-linear relationship between WML volume and BD risk. Moreover, the relationship between WML volume and BD risk was positive when the WML volume was <6,200 mm3. In contrast, no positive relationship was observed between WML volume and BD risk when the WML volume was >6,200 mm3. Neuroimaging and subsequent volumetric analysis of WML led to a better understanding of the association between WML and the BD risk, which, in turn, provide further insights into the pathophysiological mechanisms of BD. However, larger sample size studies should further verify our results to confirm WML volume as a stable biomarker of BD incidence. ## Data availability statement The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author. ## Ethics statement Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article. ## Author contributions XL and HD contributed to conception and design of the study and wrote the first draft of the manuscript. BY and YZ organized the database. JX and HW performed the statistical analysis. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyt.2023.1149663/full#supplementary-material ## References 1. Johansson V, Kuja-Halkola R, Cannon TD, Hultman CM, Hedman AM. **A population-based heritability estimate of bipolar disorder - in a Swedish twin sample**. *Psychiatry Res* (2019) **278** 180-7. DOI: 10.1016/j.psychres.2019.06.010 2. Fabbri C. **The role of genetics in bipolar disorder**. *Curr Top Behav Neurosci* (2020) **48** 41-60. DOI: 10.1007/7854_2020_153 3. Schmitt A, Malchow B, Hasan A, Falkai P. **The impact of environmental factors in severe psychiatric disorders**. *Front Neurosci* (2014) **8** 19. DOI: 10.3389/fnins.2014.00019 4. Boland EM, Alloy LB. **Sleep disturbance and cognitive deficits in bipolar disorder: toward an integrated examination of disorder maintenance and functional impairment**. *Clin Psychol Rev* (2013) **33** 33-44. DOI: 10.1016/j.cpr.2012.10.001 5. Nierenberg AA, Kansky C, Brennan BP, Shelton RC, Perlis R, Iosifescu DV. **Mitochondrial modulators for bipolar disorder: a Pathophysiologically informed paradigm for new drug development**. *Aust N Z J Psychiatry* (2013) **47** 26-42. DOI: 10.1177/0004867412449303 6. Phillips ML, Swartz HA. **A critical appraisal of neuroimaging studies of bipolar disorder: toward a new conceptualization of underlying neural circuitry and a road map for future research**. *Am J Psychiatry* (2014) **171** 829-43. DOI: 10.1176/appi.ajp.2014.13081008 7. Sepede G, De Berardis D, Campanella D, Perrucci MG, Ferretti A, Salerno RM. **Neural correlates of negative emotion processing in bipolar disorder**. *Prog Neuro-Psychopharmacol Biol Psychiatry* (2015) **60** 1-10. DOI: 10.1016/j.pnpbp.2015.01.016 8. Battaglia S, Cardellicchio P, Di Fazio C, Nazzi C, Fracasso A, Borgomaneri S. **Stopping in (E)motion: reactive action inhibition when facing valence-independent emotional stimuli**. *Front Behav Neurosci* (2022) **16** 998714. DOI: 10.3389/fnbeh.2022.998714 9. Phillips ML, Drevets WC, Rauch SL, Lane R. **Neurobiology of emotion perception I: the neural basis of Normal emotion perception**. *Biol Psychiatry* (2003) **54** 504-14. DOI: 10.1016/s0006-3223(03)00168-9 10. Chen C-H, Suckling J, Lennox BR, Ooi C, Bullmore ET. **A quantitative meta-analysis of Fmri studies in bipolar disorder**. *Bipolar Disord* (2011) **13** 1-15. DOI: 10.1111/j.1399-5618.2011.00893.x 11. Barnett R. **Bipolar Disorder**. *Lancet* (2018) **392** 1510. DOI: 10.1016/s0140-6736(18)32548-0 12. Kato T. **Current understanding of bipolar disorder: toward integration of biological basis and treatment strategies**. *Psychiatry Clin Neurosci* (2019) **73** 526-40. DOI: 10.1111/pcn.12852 13. Morris G, Walder K, McGee SL, Dean OM, Tye SJ, Maes M. **A model of the mitochondrial basis of bipolar disorder**. *Neurosci Biobehav Rev* (2017) **74** 1-20. DOI: 10.1016/j.neubiorev.2017.01.014 14. Liang L, Chen J, Xiao L, Wang Q, Wang G. **Mitochondrial modulators in the treatment of bipolar depression: a systematic review and meta-analysis**. *Transl Psychiatry* (2022) **12** 4. DOI: 10.1038/s41398-021-01727-7 15. Puthumana JS, Regenold WT. **Glucose-6-phosphate dehydrogenase activity in bipolar disorder and schizophrenia: relationship to mitochondrial impairment**. *J Psychiatr Res* (2019) **112** 99-103. DOI: 10.1016/j.jpsychires.2019.03.004 16. Okanda Nyatega C, Qiang L, Jajere Adamu M, Bello KH. **Altered striatal functional connectivity and structural Dysconnectivity in individuals with bipolar disorder: a resting state magnetic resonance imaging study**. *Front Psychol* (2022) **13** 1054380. DOI: 10.3389/fpsyt.2022.1054380 17. Tanaka M, Vécsei L. **Monitoring the kynurenine system: concentrations, ratios or what Else?**. *Adv Clin Exp Med* (2021) **30** 775-8. DOI: 10.17219/acem/139572 18. Goldberg JF, Ernst CL. **Features associated with the delayed initiation of mood stabilizers at illness onset in bipolar disorder**. *J Clin Psychiatry* (2002) **63** 985-91. DOI: 10.4088/jcp.v63n1105 19. Duarte JA, Massuda R, Goi PD, Vianna-Sulzbach M, Colombo R, Kapczinski F. **White matter volume is decreased in bipolar disorder at early and late stages**. *Trends Psychiatry Psychother* (2018) **40** 277-84. DOI: 10.1590/2237-6089-2017-0025 20. Beyer JL, Young R, Kuchibhatla M, Krishnan KRR. **Hyperintense Mri lesions in bipolar disorder: a meta-analysis and review**. *Int Rev Psychiatry* (2009) **21** 394-409. DOI: 10.1080/09540260902962198 21. Demir A, Ozkan M, Ulug AM. **A macro-structural dispersion characteristic of brain white matter and its application to bipolar disorder**. *IEEE Trans Biomed Eng* (2021) **68** 428-35. DOI: 10.1109/TBME.2020.3002688 22. Jabbi M, Weber W, Welge J, Nery F, Tallman M, Gable A. **Frontolimbic brain volume abnormalities in bipolar disorder with suicide attempts**. *Psychiatry Res* (2020) **294** 113516. DOI: 10.1016/j.psychres.2020.113516 23. Regenold WT, Hisley KC, Phatak P, Marano CM, Obuchowski A, Lefkowitz DM. **Relationship of cerebrospinal fluid glucose metabolites to Mri deep white matter Hyperintensities and treatment resistance in bipolar disorder patients**. *Bipolar Disord* (2008) **10** 753-64. DOI: 10.1111/j.1399-5618.2008.00626.x 24. Wilde EA, Bigler ED, Huff T, Wang H, Black GM, Christensen ZP. **Quantitative structural neuroimaging of mild traumatic brain injury in the chronic effects of Neurotrauma consortium (Cenc): comparison of volumetric data within and across scanners**. *Brain Inj* (2016) **30** 1442-51. DOI: 10.1080/02699052.2016.1219063 25. Bigler ED. **Volumetric Mri findings in mild traumatic brain injury (Mtbi) and neuropsychological outcome**. *Neuropsychol Rev* (2021) **2021** 1-37. DOI: 10.1007/s11065-020-09474-0 26. Birner A, Seiler S, Lackner N, Bengesser SA, Queissner R, Fellendorf FT. **Cerebral white matter lesions and affective episodes correlate in male individuals with bipolar disorder**. *PLoS One* (2015) **10** e0135313. DOI: 10.1371/journal.pone.0135313 27. Tighe SK, Reading SA, Rivkin P, Caffo B, Schweizer B, Pearlson G. **Total white matter Hyperintensity volume in bipolar disorder patients and their healthy relatives**. *Bipolar Disord* (2012) **14** 888-93. DOI: 10.1111/bdi.12019 28. Walport M, Brest P. **Haring research data to improve public health**. *Lancet* (2011) **377** 537-9. DOI: 10.1016/s0140-6736(10)62234-9 29. Miller GW. **Making data accessible: the dryad experience**. *Toxicol Sci* (2016) **149** 2-3. DOI: 10.1093/toxsci/kfv238 30. 30.DRYAD (2015). Cerebral white matter lesions and affective episodes correlate in male individuals with bipolar disorder. Available at: https://datadryad.org/stash/dataset/. (2015) 31. Sowell ER, Peterson BS, Thompson PM, Welcome SE, Henkenius AL, Toga AW. **Mapping cortical change across the human life span**. *Nat Neurosci* (2003) **6** 309-15. DOI: 10.1038/nn1008 32. Melazzini L, Vitali P, Olivieri E, Bolchini M, Zanardo M, Savoldi F. **White matter Hyperintensities quantification in healthy adults: a systematic review and meta-analysis**. *J Magn Reson Imaging* (2021) **53** 1732-43. DOI: 10.1002/jmri.27479 33. Lu J, Li H, Wang S. **The kidney reabsorption-related magnesium depletion score is associated with increased likelihood of abdominal aortic calcification among us adults**. *Nephrol Dial Transplant* (2022) **gfac218**. DOI: 10.1093/ndt/gfac218 34. Huang R, Song L, Zhao J, Lei Y, Li T. **Differential influences of serum vitamin C on blood pressure based on age and sex in normotensive individuals**. *Front Nutr* (2022) **9** 986808. DOI: 10.3389/fnut.2022.986808 35. Zhou R, Zhang X, Dong M, Huang L, Zhu X, Wang S. **Association between endogenous Lh level prior to progesterone administration and live birth rate in artificial frozen-thawed blastocyst transfer cycles of ovulatory women**. *Hum Reprod* (2021) **36** 2687-96. DOI: 10.1093/humrep/deab172 36. El-Badri SM, Cousins DA, Parker S, Ashton HC, McAllister VL, Ferrier IN. **Magnetic resonance imaging abnormalities in Young euthymic patients with bipolar affective disorder**. *Br J Psychiatry* (2006) **189** 81-2. DOI: 10.1192/bjp.bp.105.011098 37. Lee D-K, Lee H, Park K, Joh E, Kim C-E, Ryu S. **Common gray and white matter abnormalities in schizophrenia and bipolar disorder**. *PLoS One* (2020) **15** e0232826. DOI: 10.1371/journal.pone.0232826 38. Silverstone T, McPherson H, Li Q, Doyle T. **Deep white matter Hyperintensities in patients with bipolar depression, unipolar depression and age-matched control subjects**. *Bipolar Disord* (2003) **5** 53-7. DOI: 10.1034/j.1399-5618.2003.01208.x 39. Coffey CE, Wilkinson WE, Weiner RD, Parashos IA, Djang WT, Webb MC. **Quantitative cerebral anatomy in depression. A controlled magnetic resonance imaging study**. *Arch Gen Psychiatry* (1993) **50** 7. DOI: 10.1001/archpsyc.1993.01820130009002 40. Pillai JJ, Friedman L, Stuve TA, Trinidad S, Jesberger JA, Lewin JS. **Increased presence of white matter Hyperintensities in adolescent patients with bipolar disorder**. *Psychiatry Res* (2002) **114** 51-6. DOI: 10.1016/S0925-4927(01)00129-9 41. Lorenzetti V, Allen NB, Whittle S, Yücel M. **Amygdala volumes in a sample of current depressed and remitted depressed patients and healthy controls**. *J Affect Disord* (2010) **120** 112-9. DOI: 10.1016/j.jad.2009.04.021 42. Meisenzahl EM, Seifert D, Bottlender R, Teipel S, Zetzsche T, Jäger M. **Differences in hippocampal volume between major depression and schizophrenia: a comparative neuroimaging study**. *Eur Arch Psychiatry Clin Neurosci* (2010) **260** 127-37. DOI: 10.1007/s00406-009-0023-3 43. Burke J, McQuoid DR, Payne ME, Steffens DC, Krishnan RR, Taylor WD. **Amygdala volume in late-life depression: relationship with age of onset**. *Am J Geriatr Psychiatry* (2011) **19** 771-6. DOI: 10.1097/JGP.0b013e318211069a 44. Kanellopoulos D, Gunning FM, Morimoto SS, Hoptman MJ, Murphy CF, Kelly RE. **Hippocampal volumes and the brain-derived neurotrophic factor Val66met polymorphism in geriatric major depression**. *Am J Geriatr Psychiatry* (2011) **19** 13-22. DOI: 10.1097/JGP.0b013e3181f61d62 45. Kamada K, Kakeda S, Ohnari N, Moriya J, Sato T, Korogi Y. **Signal intensity of motor and sensory cortices on T2-weighted and flair images: Intraindividual comparison of 1.5t and 3t Mri**. *Eur Radiol* (2008) **18** 2949-55. DOI: 10.1007/s00330-008-1069-8 46. Neema M, Guss ZD, Stankiewicz JM, Arora A, Healy BC, Bakshi R. **Normal findings on brain fluid-attenuated inversion recovery Mr images at 3t**. *AJNR Am J Neuroradiol* (2009) **30** 911-6. DOI: 10.3174/ajnr.A1514 47. Regenold WT, Hisley KC, Obuchowski A, Lefkowitz DM, Marano C, Hauser P. **Relationship of white matter Hyperintensities to cerebrospinal fluid glucose polyol pathway metabolites-a pilot study in treatment-resistant affective disorder patients**. *J Affect Disord* (2005) **85** 341-50. DOI: 10.1016/j.jad.2004.10.010 48. Mahon K, Burdick KE, Szeszko PR. **A role for white matter abnormalities in the pathophysiology of bipolar disorder**. *Neurosci Biobehav Rev* (2010) **34** 533-54. DOI: 10.1016/j.neubiorev.2009.10.012 49. Versace A, Almeida JRC, Quevedo K, Thompson WK, Terwilliger RA, Hassel S. **Right orbitofrontal Corticolimbic and left Corticocortical white matter connectivity differentiate bipolar and unipolar depression**. *Biol Psychiatry* (2010) **68** 560-7. DOI: 10.1016/j.biopsych.2010.04.036 50. Cardoso de Almeida JR, Phillips ML. **Distinguishing between unipolar depression and bipolar depression: current and future clinical and neuroimaging perspectives**. *Biol Psychiatry* (2013) **73** 111-8. DOI: 10.1016/j.biopsych.2012.06.010 51. McDonald C, Bullmore ET, Sham PC, Chitnis X, Wickham H, Bramon E. **Association of Genetic Risks for schizophrenia and bipolar disorder with specific and generic brain structural Endophenotypes**. *Arch Gen Psychiatry* (2004) **61** 974-84. DOI: 10.1001/archpsyc.61.10.974 52. Karlsgodt KH, Niendam TA, Bearden CE, Cannon TD. **White matter integrity and prediction of social and role functioning in subjects at ultra-high risk for psychosis**. *Biol Psychiatry* (2009) **66** 562-9. DOI: 10.1016/j.biopsych.2009.03.013 53. Sarrazin S, Poupon C, Linke J, Wessa M, Phillips M, Delavest M. **A multicenter Tractography study of deep white matter tracts in bipolar I disorder: psychotic features and interhemispheric Disconnectivity**. *JAMA Psychiatry* (2014) **71** 388-96. DOI: 10.1001/jamapsychiatry.2013.4513 54. Emsell L, Leemans A, Langan C, Van Hecke W, Barker GJ, McCarthy P. **Limbic and Callosal white matter changes in euthymic bipolar I disorder: an advanced diffusion magnetic resonance imaging Tractography study**. *Biol Psychiatry* (2013) **73** 194-201. DOI: 10.1016/j.biopsych.2012.09.023 55. Linke J, King AV, Poupon C, Hennerici MG, Gass A, Wessa M. **Impaired anatomical connectivity and related executive functions: differentiating vulnerability and disease marker in bipolar disorder**. *Biol Psychiatry* (2013) **74** 908-16. DOI: 10.1016/j.biopsych.2013.04.010 56. Foley SF, Bracher-Smith M, Tansey KE, Harrison JR, Parker GD, Caseras X. **Fractional anisotropy of the Uncinate fasciculus and cingulum in bipolar disorder type I, type ii, unaffected siblings and healthy controls**. *Br J Psychiatry* (2018) **213** 548-54. DOI: 10.1192/bjp.2018.101 57. Fiedorowicz JG, Palagummi NM, Forman-Hoffman VL, Miller DD, Haynes WG. **Elevated prevalence of obesity, metabolic syndrome, and cardiovascular risk factors in bipolar disorder**. *Ann Clin Psychiatry* (2008) **20** 131-7. DOI: 10.1080/10401230802177722 58. Portet F, Brickman AM, Stern Y, Scarmeas N, Muraskin J, Provenzano FA. **Metabolic syndrome and localization of white matter Hyperintensities in the elderly population**. *Alzheimers Dement* (2012) **8** S88-95.e1. DOI: 10.1016/j.jalz.2011.11.007 59. Habes M, Erus G, Toledo JB, Zhang T, Bryan N, Launer LJ. **White matter Hyperintensities and imaging patterns of brain ageing in the general population**. *Brain J Neurol* (2016) **139** 1164-79. DOI: 10.1093/brain/aww008 60. Kozicky J-M, McGirr A, Bond DJ, Gonzalez M, Silveira LE, Keramatian K. **Neuroprogression and episode recurrence in bipolar I disorder: a study of gray matter volume changes in first-episode mania and association with clinical outcome**. *Bipolar Disord* (2016) **18** 511-9. DOI: 10.1111/bdi.12437 61. Moorhead TWJ, McKirdy J, Sussmann JED, Hall J, Lawrie SM, Johnstone EC. **Progressive gray matter loss in patients with bipolar disorder**. *Biol Psychiatry* (2007) **62** 894-900. DOI: 10.1016/j.biopsych.2007.03.005 62. Doris A, Belton E, Ebmeier KP, Glabus MF, Marshall I. **Reduction of cingulate gray matter density in poor outcome bipolar illness**. *Psychiatry Res* (2004) **130** 153-9. DOI: 10.1016/j.pscychresns.2003.09.002 63. Moore PB, Shepherd DJ, Eccleston D, Macmillan IC, Goswami U, McAllister VL. **Cerebral white matter lesions in bipolar affective disorder: relationship to outcome**. *Br J Psychiatry* (2001) **178** 172-6. DOI: 10.1192/bjp.178.2.172 64. Krabbendam L, Honig A, Wiersma J, Vuurman EF, Hofman PA, Derix MM. **Cognitive dysfunctions and white matter lesions in patients with bipolar disorder in remission**. *Acta Psychiatr Scand* (2000) **101** 274-80. PMID: 10782546
--- title: REal-world treatment outcomes after delayed intRavitreal therapy in center-involving diabetic macular edema – RETORT study authors: - Sai Prashanti Chitturi - Ramesh Venkatesh - Rubble Mangla - Yash Parmar - Rohini Sangoram - Naresh Kumar Yadav - Jay Chhablani journal: International Journal of Retina and Vitreous year: 2023 pmcid: PMC10061690 doi: 10.1186/s40942-023-00463-y license: CC BY 4.0 --- # REal-world treatment outcomes after delayed intRavitreal therapy in center-involving diabetic macular edema – RETORT study ## Abstract ### Purpose To compare real-life data on delayed intravitreal treatment of diabetic macular edema (DME) patients to early treatment. ### Methods In this single-centre, retrospective, interventional, comparative study, DME patients were divided into two groups based on when they received treatment: Group 1 - received treatment within 24 weeks and Group 2 - at or after 24 weeks from the time of treatment advice. Visual acuity and central subfield thickness (CSFT) changes were compared at various time points. Reasons for delaying treatment were noted. ### Results The study included 109 (Group 1–94; Group 2–15) eyes. When treatment was advised, demographic profile, diabetes duration, glucose control and VA between two groups were comparable. At this point, CSFT was higher in Group 1 than in Group 2 ($$p \leq 0.036$$). At injection time, Group 2 had better VA and lower CSFT than Group 1 ($p \leq 0.05$). Group 2’s VA (53.4 ± 12.67) was significantly lower than Group 1’s (57.38 ± 20.01) after 1-year treatment. At 1-year, CSFT decreased in Group 1 and increased in Group 2. Group 1 had mean improvement of + 7.6 letters and Group 2 had a decline of -6.9 letters. Group 2 required more intravitreal anti-VEGF (median – 3; IQR: 2–4), steroid injections (median – 4; IQR: 2–4) and focal laser sessions (median – 4; IQR: 2–4). ### Conclusion Late-treated DME eyes needed more injections and focal laser sessions than early treated eyes. Adherence to early treatment of DME in real-life will help prevent long-term vision loss. ## Introduction Diabetic retinopathy (DR) is the leading cause of blindness in the world, and diabetic macular edema (DME) plays a major role in vision loss [1, 2]. All patients with DR are at a risk of developing DME. DME usually appears gradually and causes mild to moderate vision loss [3, 4]. DME develops as a result of prolonged hyperglycaemia, which damages the retinal endothelial cell tight junctions and promotes fluid leakage from retinal capillaries, resulting in the accumulation of subretinal and intraretinal fluid and macular edema [5]. A number of pathogenetic pathways related to inflammation and vascular endothelial growth factor (VEGF) have been identified in the development of DME [6]. Thus, in the current era, intravitreal anti-VEGF and steroid therapy are the leading treatment options for center involving DME (CI-DME). The presenting visual acuity (VA) has been an important parameter for initiating intravitreal therapy in the management of DME [7]. According to the diabetic retinopathy clinical research network (DRCR.net) protocol V trial, patients with good VA (≥ $\frac{20}{32}$) can be observed [8]. According to a number of landmark clinical trials published by the DRCR.net writing group, patients presenting with vision loss (< $\frac{20}{32}$), as soon as the DME is diagnosed, early treatment with intravitreal anti-VEGF agents is recommended [9, 10]. If left untreated, DME can cause chronic edema and irreversible changes in the retina, rendering the patient visually impaired. Studies have shown that if the disease is not treated, 20-$30\%$ of DME patients will lose at least three lines of vision within three years [11]. As a result, untreated DME has a poor long-term prognosis, and treatment should begin as soon as a patient is diagnosed. In most instances, the protocol used in clinical trials conducted in an investigator-controlled clinical setting is incompatible to a real-world patient-controlled clinical setting [12]. In the real world, a number of patients either refuse or postpone treatment with intravitreal agents for DME management. There is little information in the literature about the effects of delayed DME management on VA in a real-world clinical setting. The real-world treatment outcomes of observation and treatment in patients with DME with very good VA were published in the OBTAIN study [13]. The study concluded that the majority of DME patients with very good VA (≥ $\frac{20}{25}$) maintained vision at 12 months, regardless of whether the DME was treated or not, suggesting that DME eyes with very good VA be closely monitored and treatment considered when a one-line drop in vision is observed. The study findings were consistent with those published in Protocol V of the DRCR.net study [13]. However, there is a lack of information in the literature about real-world experiences with the timing of intravitreal therapy for patients with CI-DME and moderate visual loss (< $\frac{20}{30}$). With this background, we aimed to compare the treatment outcomes of early (< 24 weeks) and delayed (≥ 24 weeks) treatment groups in patients with treatment naïve CI-DME with a presenting VA worse than $\frac{20}{30}$ over a 1-year follow-up after treatment initiation in a real-world scenario. ## Methods In this single-centre retrospective study, clinical databases were searched for all type 2 diabetes mellitus cases with non-tractional treatment-naive CI-DME who visited the retina clinic of a tertiary eye care hospital between June 2017 and December 2020. The presence of retinal edema/thickening within the 1-mm Early Treatment Diabetic Retinopathy Study (ETDRS) circle seen on the retinal thickness map of a macular volume OCT scans obtained on the Heidelberg Spectralis device was defined as CI-DME. The medical records of these cases were reviewed to determine the presenting VA, the time when the intravitreal injection was recommended, and the time when the patient received the first intravitreal injection. Two groups were identified based on the time it took to receive the intravitreal injection from the time of treatment advice: Group 1 (early): eyes treated within 24 weeks of advice and Group 2 (delayed): eyes treated at or after 24 weeks of advice. Eyes with clinical and OCT details at the time of the injection and one year after treatment began were studied. Thus, only non-tractional CI-DME eyes with clinical and OCT details at the time of treatment advice, injection and one year after starting treatment were considered for the study. The study excluded all other causes of maculopathies mimicking DME and DME caused by traction from vitreomacular interface abnormalities such as epiretinal membrane, taut posterior hyaloid, and vitreomacular traction syndrome. Patients with glaucoma and visual field loss, retinal or optic nerve lesions, or any other factor (e.g., significant cataract) that could impact the visual outcome were omitted from the study. Patients with insufficient systemic or ocular information were excluded as well. The study excluded eyes that had recently undergone pan retinal photocoagulation within the previous three months. Eyes with poor quality OCT images were not included in the study. All eligible patients’ medical records were reviewed, and the following information was gathered: age, gender, affected eye, duration of diabetes mellitus, associated medical conditions, VA at presentation, at the time of injection, and one year after starting therapy, severity of DR, and treatment received by the patient. The International Clinical Disease Severity Scale was used to categorize DR severity into mild, moderate and severe non-proliferative DR and proliferative DR [14]. VA was recorded in Snellen units in the study. The Spectralis, Heidelberg machine provided the OCT images (Heidelberg Engineering, Germany). A 25-line horizontal raster macular volumetric scan centered at the fovea was performed and used for the study, with 512 A-scans per line and a 30° scanning area. The following information was obtained from the OCT images: the presence of CI-DME at presentation and central subfield thickness (CSFT), which was retinal thickness measured in the 1 mm ETDRS circle in an automated manner. The findings in the OCT scans were recorded at the time of initial presentation, just prior to the intravitreal therapy and at 1-year after starting the therapy. Changes in the outer retinal layers, such as external limiting membrane (ELM) and ellipsoid zone (EZ) layer discontinuity, and changes in the retinal pigment epithelium (RPE) layer at the fovea, were given special attention in eyes where VA did not correlate with central retinal thickness and other ocular findings. The treatments both groups received during the one-year follow-up period following the initiation of intravitreal therapy were documented. During the course of the study, retina specialists treated eyes at their discretion. The choice of intravitreal anti-VEGF (Avastin®, Accentrix®, Razumab®, or Eylea®) and/or steroid (Ozurdex® implant or triamcinolone acetonide) agent, the treatment protocol to be followed, the time gap between two consecutive intravitreal injections, the decision to switch from one treatment modality to another, and the decision to discontinue therapy were entirely at the discretion of the treating clinician. The protocol for repeating intravitreal injections was based on the intravitreal medication administered during the previous treatment session. For intravitreal anti-VEGF agents such as Avastin®, Accentrix®, and Razumab®, injections were repeated after four weeks, whereas injections for Eylea® were repeated after eight weeks. The eyes were retreated with intravitreal Ozurdex® and triamcinolone acetonide after a minimum of 12 and 8 weeks, respectively. On the OCT, intravitreal agents were discontinued when the macular edema resolved completely and the foveal contour returned to normal. In most instances, focal thermal laser therapy was reserved for DME cases that did not involve the foveal center. The focal laser treatment protocol followed was in accordance with the modified ETDRS treatment guidelines, with a burn size of 50 μm, a burn duration of 0.05 to 0.1 s, and a mild grey-white burn treating all areas of diffuse capillary leakage or non-perfusion at a distance of 500 to 300 μm superiorly, nasally, and inferiorly from the centre of the macula and at 500 to 3500 μm temporally from the centre of the macula. There were no burns within 500 μm of the optic disc. The study’s outcome measures were as follows: A)The proportion of patients who postponed treatment for ≥ 24 weeks after receiving treatment advice. B)Differences in VA and CSFT between the two groups at the time of injection and one year after starting therapy. C)The percentage of patients in both groups who lost 5 ETDRS or more and the reasons for this at the 1-year follow-up visit. D)Intravitreal treatments received in both groups during the one-year follow-up period. All data were collected and analysed in accordance with the policies and procedures of the local Institutional Review Board as well as the principles outlined in the Helsinki Declaration. ## Statistical analysis All data were analysed using GraphPad Prism version 9.4.1 [681] for Windows, GraphPad Software, San Diego, California USA, www.graphpad.com. The Kolmogorov-Smirnov normality test showed the data sets to be of the non-parametric variety and hence only non-parametric statistical tests were used in this study. Snellen’s VA was converted to approx. ETDRS letters using the formula 85 + 50 x log (Snellen fraction) [15]. Quantitative variables between the 2 groups (Group 1 and Group 2) were analysed using the Mann-Whitney U test. Chi-square test was used to compare the categorical data between the 2 groups. The Wilcoxon signed-rank test was used to compare changes in VA, CSFT, and treatments received in the two groups at various points throughout the study. P values < 0.05 were considered statistically significant. ## Results One hundred and nine eyes of 93 patients who met the inclusion criteria were considered for the study. The average age of the study participants was 65.34 ± 8.90 years and there were 74 males and 19 females in the study. Mean duration of diabetes mellitus of 16.39 ± 7.88 years in the study. The mean HbA1C level was $8.4\%$ and $42\%$ of the patients ($$n = 39$$) did not have any associated systemic illness. Table 1 shows the DR severity grading in the study participants. In the study, the mean approximate ETDRS VA was 47.14 ± 17.1 letters and the mean CSFT was 516.0 ± 141.8 μm. Treatment with intravitreal agents was administered within 24 weeks in 94 ($86\%$) of the eyes and after 24 weeks in 15 ($14\%$) of the eyes from the time of treatment advice in the study. Table 1Demographic, clinical and optical coherence tomography (OCT) findings of the study participants:VariableValueNo. of patients (n)93No. of eyes (n)109Age (years)65.34 ± 8.90Males: Females74:19Duration of DM (years)16.39 ± 7.88HbA1c (%)8.4 ± 0.974Coronary artery disease (n, %)7 [8]Hypertension (n, %)49 [53]Hypercholesterolemia (n, %)3 [3]Chronic kidney disease (n, %)4 [4]Cerebrovascular accident (n, %)2 [2]No systemic illness (n, %)39 [42]Moderate NPDR (n, %)26 [24]Severe NPDR (n, %)38 [35]PDR (n, %)45 [41]Visual acuity (ETDRS letters)47.14 ± 17.1Central subfield thickness (microns)516.0 ± 141.8No. of eyes who received intravitreal injections for DME before 24 weeks from treatment advice (n, %)94 [86]No. of eyes who received intravitreal injections for DME at or after 24 weeks from treatment advice (n, %)15 [14]Abbreviations: DM – diabetes mellitus; HBA1C - glycosylated hemoglobin; NPDR – non-proliferative diabetic retinopathy; PDR – proliferative diabetic retinopathy; ETDRS – Early Treatment Diabetic Retinopathy Study; DME – diabetic macular edema Table 2 compares the demographic, clinical, and OCT findings between the two groups of patients with CI-DME when intravitreal therapy was recommended. The average time between recommending treatment and receiving treatment in Group 1 (early) was 2.237 ± 3.607 weeks and 31.24 ± 7.456 weeks in Group 2 (late). Table 2Demographic, clinical and optical coherence tomography (OCT) finding comparisons at the time of treatment advice between the two study groups:Early treatment groupDelayed treatment groupP valueNo of eyes (n)9415No. of patients (n)8112Males: Females (n)63:189:3> 0.999Age (years)65.93 ± 9.13462.20 ± 9.5360.148Duration of DM (years)16.99 ± 8.2915.40 ± 5.060.795HbA1c (%)8.453 ± 1.038.213 ± 0.6210.565Moderate NPDR (n, %)18 [20]8 [53]0.008Severe NPDR (n, %)38 [40]0 [0]0.001PDR (n, %)38 [40]7 [47]0.779Visual acuity (ETDRS letters)50.05 ± 12.158.23 ± 11.10.314Central subfield thickness (microns)527.2 ± 143.7445.3 ± 107.80.036Average time interval between recommending treatment and receiving treatment (weeks)2.237 ± 3.60731.24 ± 7.456Abbreviations: DM – diabetes mellitus; HBA1C - glycosylated hemoglobin; NPDR – non-proliferative diabetic retinopathy; PDR – proliferative diabetic retinopathy; ETDRS – Early Treatment Diabetic Retinopathy Study; DME – diabetic macular edema The demographic profile, duration of diabetes mellitus and glycaemic control between the two groups were comparable. There was no statistical significance between the two groups in terms of VA ($$p \leq 0.314$$) at the time of treatment advice. However, at this time point in the study, the CSFT in group 1 eyes was significantly higher than in group 2 eyes ($$p \leq 0.036$$). Table 3 compares the clinical grading of DR, VA, and OCT measurements between the two groups at the time of treatment and one year after starting the therapy. Between the two groups, eyes in Group 2 had significantly better VA and lower CSFT than eyes in Group [1] However, at the 1-year follow-up time point after starting treatment, Group 2’s VA (53.4 ± 12.67) was significantly lower than Group 1’s (57.38 ± 20.01). At the 1-year follow-up visit, the CSFT in Group 1 decreased while it increased in Group [2] After one year of treatment, there was an average improvement of + 7.6 ETDRS letters in Group 1 and a decline of -6.9 ETDRS letters in Group 2. Table 3Demographic, clinical and optical coherence tomography (OCT) finding comparisons between the two study groups at the time of injection and at 1 year post commencement of treatment:Early treatment group($$n = 94$$)Delayed treatment group($$n = 15$$)P valueWorsening in DR severity at least by 1 step at the end of 1 year (n, %)13 [14]4 [27]0.247Mean VA (ETDRS letters) at the time of injection49.74 ± 16.356.27 ± 15.20.003Mean CSFT at the time of injection527.2 ± 143.7462.5 ± 73.57< 0.001Mean VA (ETDRS letters) 1-year after treatment57.38 ± 20.0151.4 ± 12.670.170Mean change in VA (ETDRS letters) at the end of 1 year7.638 ± 17.96-6.867 ± 11.650.001Mean CSFT 1-year after treatment422.6 ± 158.9477.8 ± 92.940.846Abbreviations: DR – diabetic retinopathy; VA – visual acuity; CSFT – central subfield thickness; ETDRS – Early Treatment Diabetic Retinopathy Study Table 4 compares changes in VA and CSFT between groups at the one-year follow-up visit. At the 1-year follow-up visit after starting the therapy, there was a significant improvement in VA and a reduction in CSFT in group 1 and vice versa in group 2 ($p \leq 0.05$). Table 4Changes in visual acuity and CSFT in eyes following treatment in the prompt and delayed treatment groups at the 1 year follow up visit:Early treatment group($$n = 94$$)Delayed treatment group ($$n = 15$$)P value#VA (ETDRS letters)At the time of injection49.74 ± 16.360.27 ± 15.20.003After 1 year post treatment57.38 ± 20.0153.4 ± 12.670.170P value*< 0.0010.042CSFTAt the time of injection527.2 ± 143.7462.5 ± 73.57< 0.001After 1 year post treatment422.6 ± 158.9477.8 ± 92.940.846P value*< 0.0010.021Abbreviations: VA – visual acuity; CSFT – central subfield thickness; ETDRS – Early Treatment Diabetic Retinopathy Study; P# - P value calculated between early and delayed treatment groups; P* - P value calculated at the time of injection and after 1 year-post treatment When compared to eyes where treatment was started early, eyes where treatment was delayed required a greater number of intravitreal anti-VEGF and steroid injections and focal/grid thermal laser photocoagulation sessions. This is noted in Table 5. Table 5Comparisons in the treatments received during the 1-year study period between both groups:Early treatment group($$n = 94$$)Delayed treatment group($$n = 15$$)P valueMedian number of Anti-VEGF injections taken during 1 year (25 – $75\%$ percentile interquartile range)1 (1–3)3 (2–4)0.182Median no of intraocular steroid injections taken during 1 year (25 – $75\%$ percentile interquartile range)1 (0-1.25)4 (2–4)< 0.001No. of eyes treated with focal/grid thermal laser photocoagulation12 [13]8 [53]0.001Median no. of focal/grid thermal laser photocoagulation sessions1 (0-1.25)4 (2–4)< 0.001Abbreviations: VEGF – vascular endothelial growth factor ## Patients with ≥ 5 ETDRS letter loss The current study found that 18 of the 94 ($19\%$) eyes in the early treatment group and 8 of the 15 ($53\%$) eyes in the deferred treatment group lost ≥ 5 ETDRS letters at year 1. This was significantly higher in the delayed treatment group than in the early treatment group ($$p \leq 0.008$$). In the early treatment group, the most common causes of vision loss were worsening macular edema in 10 ($56\%$) eyes, cataract development in 6 ($33\%$) eyes, and ELM-EZ discontinuity in 2 ($11\%$) eyes. Vision loss in the delayed treatment group was primarily attributed to ELM-EZ discontinuity in 5 ($62\%$) eyes, worsening macular edema in 2 ($25\%$) eyes, and cataract development in one ($13\%$) eye. ## Summarizing the characteristics of the delayed treatment group This group included 15 eyes of 12 patients who had CI-DME treatment 24 weeks after receiving treatment advice. This group consisted of 9 ($75\%$) males and 3 ($25\%$) females. The average age of the patients in this group was 62.20 ± 9.536 years, and the average duration of diabetes was 15.40 ± 5.06 years. The average time it took patients to receive intravitreal injections after receiving treatment advice was 31.24 ± 7.456 weeks. Patients cited the following reasons for not considering intravitreal therapy at the time of treatment recommendation: (a) absence of visual symptoms and good presenting VA in eight of twelve ($66\%$) patients, (b) inability to achieve optimal sugar levels for intravitreal injection in two ($17\%$) cases, and (c) high treatment costs in two ($17\%$) additional cases. Between the different time points (i.e., at the time of treatment advice, treatment injection and 1-year follow up) in the study, the delayed treatment group showed progressive VA worsening and CSFT thickening. More than $50\%$ of the eyes in the delayed treatment group showed a drop of ≥ 5 ETDRS letters in the study. The primary reason for the reduction of VA was the worsening of DME. In the study, the delayed treatment group required a greater median number of anti-VEGF and steroid intravitreal injections. The type of anti-VEGF agent used did not differ between the two groups. During the one-year follow-up, patients in the delayed treatment group required more focal thermal laser treatment sessions. ## Discussion This study described the treatment outcomes of a small but extremely important group of patients who received delayed treatment for DME for the first time in a real-world clinical setting. Over a one-year follow-up period after starting treatment, eyes with delayed therapy required significantly more intravitreal injections, had worsening of the central retinal thickness, and poor VA improvements when compared to eyes with early disease treatment. Furthermore, in the delayed treatment group, the proportion of patients who lost 5 ETDRS letters or more was higher than in the early treatment group. According to the findings of this study, approximately $14\%$ of the patients who needed DME treatment failed to adhere to the management protocol. There are a variety of reasons for denying treatment by the patient, including mild-moderate vision loss, fear of repeated injections and complications associated with the procedure, inability to come for regular, timely follow-up visits, deranged glucose level, and, most importantly, treatment costs [16, 17]. At the time of treatment advice, the VA and CSFT in the delayed treatment group were significantly better than those in the early treatment group in this study. The delayed treatment group’s VA had decreased further by the time they received treatment. Thus, the absence of significant vision loss during the initial disease course and further worsening over time could be one of the primary reasons for patients to postpone early treatment and seek treatment later. Fluid accumulation in DME is caused by hyperpermeable deep retinal capillaries as well as poor resorption by the RPE at the fovea. This causes fluid to accumulate within the intraretinal layers and beneath the fovea in DME [18]. There is a weak relationship between OCT-measured CSFT and VA [19]. Short-term changes in the CSFT cannot be used as surrogate markers to predict long-term changes in VA. Several studies have clearly demonstrated that the continuity and integrity of the outer retinal layers, namely the ELM, EZ, and RPE, determine long-term VA changes. Chronic untreated or persistent DME is linked to discontinuity and damage to the outer retinal layers, resulting in permanent vision loss [20–22]. In the current study, we observed that patients in the delayed treatment group had poor VA improvement over a 1-year follow-up period after initiation of therapy. Most patients in the delayed treatment group with reduced VA showed discontinuity of the ELM-EZ layers ($$n = 5$$, $62\%$). This confirms that persistent edema over a long period is responsible for outer retinal layer changes at the fovea and poor VA. Thus, early treatment of DME with intensive therapy prior to the development of changes in the outer retinal layers may benefit long-term VA. A study by Angermann et al. emphasized the significance of adherence to treatment protocol in DME patients [17]. They discovered that patients with good therapy adherence had better visual outcomes and a lower risk of disease progression than patients with poor therapy adherence. Furthermore, visual outcomes were poor in DME cases that were lost to follow-up over a 48-week follow-up period. This study highlights the importance of early therapy in DME cases, as well as good adherence to treatment, to maximize visual benefits. In the treatment of DME, focal/grid laser photocoagulation, VEGF inhibitors and corticosteroids have remained the mainstay. In the current study, we discovered that patients in the early treatment group required fewer intravitreal steroid injections over a one-year period. In contrast, the delayed treatment group required a greater proportion of intravitreal anti-VEGF agents and steroids and focal laser treatment sessions than the early treatment group. The DRCR.net Protocol K evaluated the outcomes of patients with treatment-naive CI-DME who were treated with focal/grid laser photocoagulation [23]. According to the study, a significant number of eyes did not show a reduction in CSFT of more than $10\%$ at the 16-week follow-up visit. As a result, the limited role of focal/grid laser photocoagulation in CI-DME cases is highlighted. In the management of DME, poor response or resistance to routine anti-VEGF agents is a reality. A recent study by Elnahry et al. found that OCT and OCT-angiography markers, such as a higher CSFT and a smaller foveal avascular zone area, as well as increased vessel densities in superficial parafovea and deep fovea, were associated with a better response to monthly intravitreal bevacizumab injections than in non-responders [24]. Patients who do not respond adequately to standard anti-VEGF medications may benefit from treatment with new and potent anti-VEGF molecules such as brolucizumab and faricimab [25, 26]. Long-term, chronic, untreated DME appears to develop resistance and a poor treatment response to routine antiangiogenetic drugs in general, according to studies [27, 28]. These eyes can also be treated with potent anti-VEGF molecules like faricimab [26]. Corticosteroid therapy, which controls the crucial role of inflammation in DME, is used to treat persistent, chronic DME [29]. Increased intraocular corticosteroid use puts patients at risk for intraocular hypertension and cataract formation [29]. Therefore, early treatment with a VEGF inhibitor appears to be the optimal method for treating DME. In our study, the delayed untreated group could have had a higher proportion of anti-angiogenic therapy-resistant cases. These eyes were treated with intravitreal corticosteroid therapy as opposed to the standard anti-VEGF treatment. For the treatment of resistant cases, newer and more potent anti-VEGF molecules, such as brolucizumab and faricimab, were not available at the time the study was conducted. This emphasizes the fact that early treatment of DME with routinely available anti-VEGF agents such as bevacizumab, ranibizumab, and aflibercept may result in a favourable response and eliminate the need for costly and less frequently available drugs such as brolucizumab and faricimab. Additionally, complications caused by intraocular corticosteroid use could be avoided. In comparison to other macular pathologies that require frequent intravitreal injections, such as neovascular age-related macular degeneration, compliance and adherence to the DME treatment protocol was poor, which affects the final visual outcome [30]. As a result, strategies to bind DME patients to regular intravitreal injections must be identified from the start, based on the causes of poor compliance and adherence, particularly the social determinants. These may include strategies such as raising awareness about the disease and the importance of early intervention, policies to increase the number of patients covered by medical insurance, forcing insurance companies to cover treatment with biosimilar anti-VEGF agents, more widespread use of less expensive biosimilar anti-VEGF agents, and reducing hospital visits by providing home vision monitoring devices and home-based OCT scans. This study has several limitations. To begin, the sample size of the delayed treatment group was smaller. The disproportionate number of cases between the two groups was caused by the strict inclusion criteria. Second, the initial visit itself lacked information regarding the duration of DME. Patients in the early treatment group who exhibited a suboptimal response to therapy may have had a longer duration of DME. Third, the treatment of DME lacked a step-by-step approach, particularly in the delayed treatment group. The treatment was entirely at the discretion of the retina specialist who administered it. Anti-VEGF therapy as the initial treatment, followed by corticosteroids and/or focal laser in non-responsive cases, could have provided additional evidence that chronic DME cases are poor anti-VEGF drug responders. Other factors, such as social and economic health determinants, were not considered in this research. Fifth, a questionnaire could have helped determine the reasons for DME patients’ poor compliance and adherence to therapy. Future plans should include a multicentric study with a larger sample size, well-defined treatment strategies, and a focus on identifying the causes of poor compliance and adherence to intravitreal drug therapy. To improve visual outcomes in DME eyes, efforts should be focused on decreasing the proportion of patients who receive delayed treatment. Finally, it can be concluded that a collaborative effort should be made by clinicians, pharmaceutical and insurance companies, as well as the hospital administrative team, to encourage and motivate patients to begin intensive therapy early in the disease course in order to reduce the burden of a greater number of intravitreal injections and VA loss over time. ## References 1. Sabanayagam C, Banu R, Chee ML, Lee R, Wang YX, Tan G. **Incidence and progression of diabetic retinopathy: a systematic review**. *Lancet Diabetes Endocrinol* (2019.0) **7** 140-9. DOI: 10.1016/S2213-8587(18)30128-1 2. Lee R, Wong TY, Sabanayagam C. **Epidemiology of diabetic retinopathy, diabetic macular edema and related vision loss**. *Eye Vis Lond Engl* (2015.0) **2** 17. DOI: 10.1186/s40662-015-0026-2 3. Duh EJ, Sun JK, Stitt AW. **Diabetic retinopathy: current understanding, mechanisms, and treatment strategies**. *JCI Insight* (2017.0) **2** 93751. DOI: 10.1172/jci.insight.93751 4. Musat O, Cernat C, Labib M, Gheorghe A, Toma O, Zamfir M. **DIABETIC MACULAR EDEMA**. *Romanian J Ophthalmol* (2015.0) **59** 133-6 5. Romero-Aroca P, Baget-Bernaldiz M, Pareja-Rios A, Lopez-Galvez M, Navarro-Gil R, Verges R. **Diabetic Macular Edema Pathophysiology: vasogenic versus inflammatory**. *J Diabetes Res* (2016.0) **2016** 1-17. DOI: 10.1155/2016/2156273 6. Das A, McGuire PG, Rangasamy S. **Diabetic Macular Edema: pathophysiology and novel therapeutic targets**. *Ophthalmology* (2015.0) **122** 1375-94. DOI: 10.1016/j.ophtha.2015.03.024 7. Giridhar S, Verma L, Rajendran A, Bhend M, Goyal M, Ramasamy K. **Diabetic macular edema treatment guidelines in India: all India Ophthalmological Society Diabetic Retinopathy Task Force and Vitreoretinal Society of India consensus statement**. *Indian J Ophthalmol* (2021.0) **69** 3076. DOI: 10.4103/ijo.IJO_1469_21 8. Baker CW, Glassman AR, Beaulieu WT, Antoszyk AN, Browning DJ, Chalam KV. **Effect of initial management with Aflibercept vs Laser Photocoagulation vs Observation on Vision loss among patients with Diabetic Macular Edema Involving the Center of the Macula and Good Visual Acuity: a Randomized Clinical Trial**. *JAMA* (2019.0) **321** 1880. DOI: 10.1001/jama.2019.5790 9. Bressler SB, Glassman AR, Almukhtar T, Bressler NM, Ferris FL, Googe JM. **Five-year outcomes of Ranibizumab with prompt or deferred laser Versus Laser or Triamcinolone Plus Deferred Ranibizumab for Diabetic Macular Edema**. *Am J Ophthalmol* (2016.0) **164** 57-68. DOI: 10.1016/j.ajo.2015.12.025 10. **Aflibercept, Bevacizumab, or Ranibizumab for Diabetic Macular Edema**. *N Engl J Med* (2015.0) **372** 1193-203. DOI: 10.1056/NEJMoa1414264 11. Gangnon RE, Davis MD, Hubbard LD, Aiello LM, Chew EY, Ferris FL. **A severity scale for diabetic macular edema developed from ETDRS data**. *Invest Ophthalmol Vis Sci* (2008.0) **49** 5041-7. DOI: 10.1167/iovs.08-2231 12. Kim H-S, Lee S, Kim JH. **Real-world evidence versus Randomized Controlled Trial: Clinical Research based on Electronic Medical Records**. *J Korean Med Sci* (2018.0) **33** e213. DOI: 10.3346/jkms.2018.33.e213 13. Busch C, Fraser-Bell S, Zur D, Rodríguez-Valdés PJ, Cebeci Z, Lupidi M. **Real-world outcomes of observation and treatment in diabetic macular edema with very good visual acuity: the OBTAIN study**. *Acta Diabetol* (2019.0) **56** 777-84. DOI: 10.1007/s00592-019-01310-z 14. Wilkinson CP, Ferris FL, Klein RE, Lee PP, Agardh CD, Davis M. **Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales**. *Ophthalmology* (2003.0) **110** 1677-82. DOI: 10.1016/S0161-6420(03)00475-5 15. Gregori NZ, Feuer W, Rosenfeld PJ. **Novel method for analyzing snellen visual acuity measurements**. *Retina Phila Pa* (2010.0) **30** 1046-50. DOI: 10.1097/IAE.0b013e3181d87e04 16. Kao C-C, Hsieh H-M, Lee DY, Hsieh K-P, Sheu S-J. **Importance of medication adherence in treatment needed diabetic retinopathy**. *Sci Rep* (2021.0) **11** 19100. DOI: 10.1038/s41598-021-98488-6 17. 17.Angermann R, Hofer M, Huber AL, Rauchegger T, Nowosielski Y, Casazza M et al. The impact of compliance among patients with diabetic macular oedema treated with intravitreal aflibercept: a 48-month follow‐up study. Acta Ophthalmol (Copenh) [Internet]. 2022 [cited 2022 Sep 16];100. Available from: https://onlinelibrary.wiley.com/doi/10.1111/aos.14946 18. Browning DJ, Stewart MW, Lee C. **Diabetic macular edema: evidence-based management**. *Indian J Ophthalmol* (2018.0) **66** 1736-50. DOI: 10.4103/ijo.IJO_1240_18 19. **Coherence tomography–measured Central Retinal Thickness and Visual Acuity in Diabetic Macular Edema**. *Ophthalmology* (2007.0) **114** 525-36. DOI: 10.1016/j.ophtha.2006.06.052 20. Muftuoglu IK, Mendoza N, Gaber R, Alam M, You Q, Freeman WR. *Retina Phila Pa* (2017.0) **37** 2015-24. DOI: 10.1097/IAE.0000000000001459 21. Uji A, Murakami T, Unoki N, Ogino K, Horii T, Yoshitake S. **Parallelism for quantitative image analysis of photoreceptor-retinal pigment epithelium complex alterations in diabetic macular edema**. *Invest Ophthalmol Vis Sci* (2014.0) **55** 3361-7. DOI: 10.1167/iovs.14-13948 22. Shin HJ, Lee SH, Chung H, Kim HC. **Association between photoreceptor integrity and visual outcome in diabetic macular edema**. *Graefes Arch Clin Exp Ophthalmol* (2012.0) **250** 61-70. DOI: 10.1007/s00417-011-1774-x 23. **The course of response to focal/grid photocoagulation for diabetic macular edema**. *Retina Phila Pa* (2009.0) **29** 1436-43. DOI: 10.1097/IAE.0b013e3181bcef6b 24. Elnahry AG, Noureldine AM, Abdel-Kader AA, Sorour OA, Ramsey DJ. **Optical coherence tomography angiography biomarkers predict anatomical response to Bevacizumab in Diabetic Macular Edema**. *Diabetes Metab Syndr Obes Targets Ther* (2022.0) **15** 395-405. DOI: 10.2147/DMSO.S351618 25. Brown DM, Emanuelli A, Bandello F, Barranco JJE, Figueira J, Souied E. **KESTREL and KITE: 52-Week results from two phase III pivotal trials of Brolucizumab for Diabetic Macular Edema**. *Am J Ophthalmol* (2022.0) **238** 157-72. DOI: 10.1016/j.ajo.2022.01.004 26. Rush RB, Rush SW. **Faricimab for Treatment-Resistant Diabetic Macular Edema**. *Clin Ophthalmol Auckl NZ* (2022.0) **16** 2797-801. DOI: 10.2147/OPTH.S381503 27. Cai S, Bressler NM. **Aflibercept, bevacizumab or ranibizumab for diabetic macular oedema: recent clinically relevant findings from DRCR.net Protocol T**. *Curr Opin Ophthalmol* (2017.0) **28** 636-43. DOI: 10.1097/ICU.0000000000000424 28. Sophie R, Lu N, Campochiaro PA. **Predictors of functional and anatomic outcomes in patients with Diabetic Macular Edema treated with Ranibizumab**. *Ophthalmology* (2015.0) **122** 1395-401. DOI: 10.1016/j.ophtha.2015.02.036 29. Chawan-Saad J, Wu M, Wu A, Wu L. **Corticosteroids for Diabetic Macular Edema**. *Taiwan J Ophthalmol* (2019.0) **9** 233-42. DOI: 10.4103/tjo.tjo_68_19 30. Weiss M, Sim DA, Herold T, Schumann RG, Liegl R, Kern C. *Retina Phila Pa* (2018.0) **38** 2293-300. DOI: 10.1097/IAE.0000000000001892
--- title: Klebsiella quasipneumoniae in intestine damages bile acid metabolism in hematopoietic stem cell transplantation patients with bloodstream infection authors: - Guankun Yin - Yifan Guo - Qi Ding - Shuai Ma - Fengning Chen - Qi Wang - Hongbin Chen - Hui Wang journal: Journal of Translational Medicine year: 2023 pmcid: PMC10061697 doi: 10.1186/s12967-023-04068-9 license: CC BY 4.0 --- # Klebsiella quasipneumoniae in intestine damages bile acid metabolism in hematopoietic stem cell transplantation patients with bloodstream infection ## Abstract ### Background Bloodstream infection (BSI) is a serious hematopoietic stem cell transplantation (HSCT) complication. The intestinal microbiome regulates host metabolism and maintains intestinal homeostasis. Thus, the impact of microbiome on HSCT patients with BSI is essential. ### Methods Stool and serum specimens of HSCT patients were prospectively collected from the pretransplant conditioning period till 4 months after transplantation. Specimens of 16 patients without BSI and 21 patients before BSI onset were screened for omics study using 16S rRNA gene sequencing and untargeted metabolomics. The predictive infection model was constructed using LASSO and the logistic regression algorithm. The correlation and influence of microbiome and metabolism were examined in mouse and Caco-2 cell monolayer models. ### Results The microbial diversity and abundance of Lactobacillaceae were remarkably reduced, but the abundance of Enterobacteriaceae (especially Klebsiella quasipneumoniae) was significantly increased in the BSI group before onset, compared with the non-BSI group. The family score of microbiome features (Enterobacteriaceae and Butyricicoccaceae) could highly predict BSI (AUC = 0.879). The serum metabolomic analysis showed that 16 differential metabolites were mainly enriched in the primary bile acid biosynthesis pathway, and the level of chenodeoxycholic acid (CDCA) was positively correlated with the abundance of K. quasipneumoniae ($R = 0.406$, $$P \leq 0.006$$). The results of mouse experiments confirmed that three serum primary bile acids levels (cholic acid, isoCDCA and ursocholic acid), the mRNA expression levels of bile acid farnesol X receptor gene and apical sodium-dependent bile acid transporter gene in K. quasipneumoniae colonized mice were significantly higher than those in non-colonized mice. The intestinal villus height, crypt depth, and the mRNA expression level of tight junction protein claudin-1 gene in K. quasipneumoniae intestinal colonized mice were significantly lower than those in non-colonized mice. In vitro, K. quasipneumoniae increased the clearance of FITC-dextran by Caco-2 cell monolayer. ### Conclusions This study demonstrated that the intestinal opportunistic pathogen, K. quasipneumoniae, was increased in HSCT patients before BSI onset, causing increased serum primary bile acids. The colonization of K. quasipneumoniae in mice intestines could lead to mucosal integrity damage. The intestinal microbiome features of HSCT patients were highly predictive of BSI and could be further used as potential biomarkers. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12967-023-04068-9. ## Background Hematopoietic stem cell transplantation (HSCT) is used to treat various benign and malignant hematologic diseases. Complications, including graft-versus-host disease (GVHD) and infection, are common and life-threatening. Bloodstream infection (BSI) is closely associated with high morbidity and mortality in HSCT patients [1, 2]. Bacterial or fungal infections after HSCT are found in many patients [3]. Therefore, early BSI detection is of great significance for clinical treatment. Recently, the connection between the microbiome and microbiome-related metabolites and HSCT complications has been increasing [4]. The characteristics and relationship between the intestinal microbiome and host metabolome before BSI remain to be investigated. The intestinal microbiome is crucial for maintaining and promoting human health, maintaining intestinal immune homeostasis, resisting exogenous microorganism invasion, and protecting intestine from damage [5]. However, intestinal microbiome destruction is also associated with various diseases [6–11]. Recently, microbiome characterisation has made it possible to better understand the complex interactions between the microbiome and HSCT [4]. Studies have shown that obligate anaerobic symbiotic bacteria in the intestine play a key role in maintaining the normal intestinal environment balance [3]. HSCT patients usually manifest with the loss of obligate anaerobic symbionts, pathogen expansion, and overall microbial diversity reduction [4]. Studies have reported that intestinal microbiome is related to infection; for example, Enterococcus or Proteobacteria increase bacteraemia risk [12, 13]. Gram-negative dominance in the intestine is also associated with BSI [14]. Furthermore, a single-centre observational study found that *Gammaproteobacteria is* a predictor of pulmonary complications after HSCT [15, 16]. Changes in host metabolites caused by alterations in the intestinal microbiome’s structure and density were associated with GVHD occurrence [17, 18]. Butyrate is related to intestinal microbiome diversity in patients with GVHD [19, 20]. A high abundance of butyrate-producing bacteria reduces the risk of lower respiratory tract virus infection in patients after allo-HSCT [10]. Bile acids and plasmalogens vary at acute GVHD onset [18]. However, there has been little evidence of metabolic features in HSCT patients with BSI. This study aimed to investigate the biological characteristics preceding BSI onset by analysing intestinal microbiome and serum metabolome, and further determine their potential associations with BSI onset. ## Study design, patient and specimen collection A total of 130 HSCT patients at Peking University People's Hospital from June 2020 to February 2021 were enrolled. Specimens (stool and serum) from each patient were prospectively collected from the pretransplant conditioning period to 4 months after transplantation. According to the BSI diagnostic criteria [21], 33 BSI and 17 non-BSI patients were screened. The remaining 80 patients were excluded because their blood culture results were negative, but infections were not ruled out clinically (Fig. 1). Finally, 21 BSI patients with paired specimens (stool and serum) within 30 days after transplantation and within 14 days before BSI onset were included, and 16 non-BSI patients with paired specimens within 30 days after transplantation were screened (Fig. 1 and Additional file 2: Table S1). The specimens of BSI and non-BSI group were 29 and 16 pairs, respectively. Fig. 1Experimental design process. The experimental design process included the sample collection process, patient screening and sample detection. Brown bar represented that the samples were within 14 days before BSI onset BSI diagnostic criteria were based on laboratory-confirmed bloodstream infection criteria from the Centers for Disease Control and Prevention’s National Healthcare Safety Network, and BSI onset was defined as the time of collecting the first positive blood culture [21]. The study was approved by the ethics committees of Peking University People's Hospital (No. 2021PHB414-002) and all remaining clinical samples were obtained consent from patients. ## 16S rRNA gene sequencing in human stool and data analysis All clinical residual stool specimens were collected and refrigerated at − 80 ℃ until tested. Genomic DNA of the stool specimens was extracted using the DNeasy PowerSoil Pro Kit (QIAGEN, Germany). The extracted DNA from each specimen was used as a template to amplify the V3–V4 regions of 16S rRNA genes using PCR. The PCR products were detected using agarose gel electrophoresis, and the target products were purified using a Gel Extraction Kit (QIAGEN, Germany). The library was constructed using a TruSeq® DNA PCR-Free Sample Preparation Kit (Illumina, USA). The purified products were sequenced using the Illumina NovaSeq6000 platform (Illumina, USA), and 250 bp paired-end reads were generated. The barcodes and primer sequences were removed from the raw data. Paired-end reads were assembled using FLASH (version 1.2.7) [22]. QIIME (version 1.9.1) [23] was used to filter high-quality clean tags, and chimeric sequences were removed. Operational taxonomic units (OTU) clustering was performed using UPARSE software (version 7.0.1001) [24]. Species annotation was performed using the Silva database based on the Mothur algorithm [25]. OTUs abundance information were normalized using a standard of sequence number corresponding to the sample with the least sequences. The subsequent alpha and beta diversity analyses were based on the normalized data (see Additional file 1: Supplementary Methods for further details). ## Construction of infection prediction model The following steps were completed for the family score calculation using the R statistical software (version 4.2.0). The “createDataPartition” package was used to divide the BSI and non-BSI groups into the training and validation sets randomly (training set: validation set = 7:3) to perform Least absolute shrinkage and selection operator (LASSO) algorithm (Additional file 7: Fig. S2). The “glmnet” package was used to perform the LASSO algorithm as previously described [26]. Coefficients reduced to zero were excluded. The remaining coefficients were analysed using logistic regression. The "glm" and "predict" functions were used to perform logistic regression and calculate the prediction value called "family score" in this study, respectively. The "boot" package was used to complete the bootstrap method to validate the regression model (Sampling with replacement). The number of bootstrap replicates was set at 1000. The coefficients after bootstrapping were listed in Additional file 3: Table S2. ## Untargeted metabolomic analysis in human serum The remaining clinical serum specimens were refrigerated at − 80 °C until tested. Ice-cold methanol was added to the serum. The mixture was then incubated and centrifuged. The supernatant was collected for LC–MS/MS analysis. LC–MS/MS analysis was performed using a QTOF/MS-6545 (Agilent, USA) and 1290 Infinity LC (Agilent, USA). The HPLC conditions were as follows: UPLC: column, ACQUITY UPLC HSS T3 C18, 1.8 µm, 2.1 mm × 100 mm (Waters, USA); column temperature, 40 ℃; flow rate, 0.4 mL/min; injection volume, 2 μL; solvent system, water ($0.1\%$ formic acid) and acetonitrile ($0.1\%$ formic acid). Information on the specimens was acquired using the LC–MS system, followed by machine orders. The original data were transformed into the mzML format using ProteoWizard software (version 3.0). Peak extraction, alignment, and retention time correction were performed using the XCMS program [27]. Metabolic identification information was obtained by searching the Pubchem database, KEGG database, the Human Metabolome database [27]. The differential metabolites were filtered according to P value < 0.05, |log2FC| > 1, and VIP ≥ 1 (see Additional file 1: Supplementary Methods for details). ## Mice Female C57BL/6N mice (4–8 weeks old) were purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd (Beijing, China). All experimental mice were no more than five in a cage. Twelve-hour light–dark cycles were carried out at 20–22 ℃. The feed and bedding were sterile. The Peking University People's Hospital Animal Care Committee approved all procedures. ## Intestinal microbiome depletion, chemotherapy and bacterial colonization in mice The mice were administered broad-spectrum antibiotics (metronidazole 1 g/L, neomycin sulfate 1 g/L, ampicillin 1 g/L and vancomycin 0.5 g/L (MNVA)) in drinking water for 14 days to deplete the microbiome, as described previously [28]. Chemotherapy was improved according to the previous method by continuous intraperitoneal injection of cytarabine 120 mg kg−1 d−1 and cyclophosphamide 100 mg kg−1 d−1 for 4 days [29]. The mice were administered suspension of K. quasipneumoniae or supernatant of stool suspension by gavage for 3 days. The amount of K. quasipneumoniae was 8 × 108 CFU per mouse per day. Five ($\frac{5}{21}$) BSI and 3 ($\frac{3}{16}$) non-BSI patients’ stool specimens were randomly selected to prepare the stool supernatant, respectively. Each patient’s stool (0.5 g) was placed in 1 × PBS and resuspended. The stool suspension was filtered with cell strainers and then centrifuged (600×g, 5 min). The supernatant was separated and added to $50\%$ glycerol to freeze-store until transplantation. ## 16S rRNA gene copy numbers detection of mice stools The mice stool samples before and after administering broad-spectrum antibiotics were collected and kept at − 80 ℃. DNA from 20 to 25 mg of mouse stools was extracted using a DNeasy PowerSoil Pro Kit (QIAGEN, Germany). DNA concentrations were measured using a Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific, USA). The 16S rRNA gene was quantified using real-time PCR, and the copy numbers were calculated [30]. The 16S rRNA gene primer sequences were used as previously described [30]. Forward primer: (5′ to 3′) TCCTACGGGAGGCAGCAGT; reverse primer: (5′ to 3′) GGACTACCAGGGTATCTAATCCTGTT. Real-time PCR was performed using the ABI Prism 7500 system (Applied Biosystems, USA) with TB Green® Premix Ex Taq™ II (Takara, China). ## Quantitative real-time reverse transcription-PCR of mice small intestine Total RNA in mice small intestine was extracted using Quick-RNA Miniprep Plus Kit (ZYMO Research, USA) and synthesized cDNA using PrimeScript RT Master Mix (Takara, China). The primers used were shown in Additional file 6: Table S5 and β-actin was used as internal reference gene. Real-time PCR was performed using the ABI Prism 7500 system (Applied Biosystems, USA) with TB Green® Premix Ex Taq™ II (Takara, China). ## Bile acid detection in mice serum Mouse serum (50 μL) was added with 200 μL methanol/acetonitrile. Ten microlitres of an internal standard mixed solution (1 μg/mL) were added to the extract as an internal standard for quantification. Samples were taken at − 20 °C for 10 min. After centrifuging for 10 min (12,000 r/min, 4 °C), the supernatant was evaporated to dryness and reconstituted in 100 μL of $50\%$ methanol for further LC–MS/MS analysis. The analysis was performed using an LC–ESI–MS/MS system (UHPLC, ExionLC™ AD; MS, Applied Biosystems 6500 Triple Quadrupole). ## Histology of small intestine in mice The ileum (approximately 0.5 cm) was collected, washed with 1 × PBS, and placed in $4\%$ paraformaldehyde. Intestinal samples in $4\%$ paraformaldehyde were dehydrated and embedded in paraffin wax. Paraffin-embedded tissues were sectioned (approximate thickness 5 μm) and stained with haematoxylin and eosin (H&E). Three parts of each sample were randomly selected to measure villus height and crypt depth. Each part included at least three villi and crypts. For the goblet cells, five random vision fields at 40× magnification were determined to calculate the cell number, and each field contained three villi. ## Cell culture and fluorescein isothiocyanate (FITC)-dextran transport Caco-2 cells were cultured in Dulbecco's Modified Eagle Medium/Nutrient Mixture F-12 medium (DMEM/F-12) supplemented with $20\%$ (v/v) fetal bovine serum and non-essential amino acids at 37 ℃ and $5\%$ CO2. Caco-2 cells were transferred on transwell inserts (pore size = 0.4 μm; Thermo Scientific™ Nunc™, Denmark) in 6-well plates (5 × 105 cells/transwell) and cultured for 21 days to construct monolayers. The transepithelial electrical resistance (TEER) of monolayers were measured by Epithelial Volt-Ohm Meter (Millicell ERS-2, USA). The monolayers were incubated with 1 × 109 CFU/mL K. quasipneumoniae or *Enterococcus faecium* for 1 h and with 100 mM chenodeoxycholic acid (CDCA; Sigma, Germany) for 24 h [31, 32]. After incubations, the paracellular transport was measured by the clearance of FITC-dextran (4 kDa, 100 μg/mL) (Sigma, Germany) [31]. The fluorescence was measured every hour for 4 h (λexc: 493 nm; λem: 520 nm). ## Statistics Statistical analysis of all data was performed using R statistical software (version 4.2.0) and GraphPad Prism (version 8.0). The Shapiro–Wilk test was used to test normal distribution, and Fisher's exact test was used to test homogeneous variance. For data with normal distribution and homogeneous variance, one-way ANOVA was used to compare multiple data groups; an unpaired two-tailed Student's t-test was used to compare two data groups. For data with abnormal distribution or uneven variance, the Kruskal–Wallis test was used to compare multiple data groups; the Mann–Whitney U test was used to compare the two data groups. P value < 0.05 was considered statistically significant. ## Patient characteristics According to the BSI diagnosis criteria and sample availability, 21 BSI (BSI group) and 16 non-BSI (non-BSI group) HSCT patients were observed (Fig. 1), and the characteristics were shown in Table 1. No significant differences in age or gender were found between the two groups. The primary disease of patients in BSI group was mainly acute leukemia, followed by lymphoma. In the non-BSI group, the primary diseases of patients were mainly acute leukemia and multiple myeloma. The number of allo-HSCT and auto-HSCT patients was matched in the two groups, and there was no statistical difference. Furthermore, the BSI patients’ liver function was weaker than that of the non-BSI patients in some extent, which suggested that BSI patients may have severe liver dysfunction. In addition, the pathogens in the BSI group were mainly Gram-negative bacteria, most of which were Enterobacteriaceae ($38.1\%$, $\frac{8}{21}$), followed by *Pseudomonas aeruginosa* ($28.6\%$, $\frac{6}{21}$). Gram-positive bacteria were mainly coagulase negative staphylococcus epidermidis ($19.0\%$, $\frac{4}{21}$), which is a common pathogen in HSCT patients. Table 1Patient characteristicsBSI ($$n = 21$$)non-BSI ($$n = 16$$)P valuedAge at diagnosis (year)a39.8 ± 15.841.6 ± 18.90.746Gender (M/F)M 15F 6M 10F 60.726Primary disease– AML102 ALL23 CMML–1 MDS22 Lymphoma4– MM16 AA11 POEMS syndrome1– PNH–1Types of HSCTallo-HSCT 19auto-HSCT 2allo-HSCT 10auto-HSCT 60.055Liver function ALT (U/L)a70.4, 78.419.3, 11.50.001 AST (U/L)a40.9, 38.719.0, 9.80.057 GGT (U/L)a144.4, 151.942.1, 40.8< 0.001Total bilirubin (µmol/L)a22.2, 28.612.2, 5.20.058Direct bilirubin (µmol/L)a13.24, 22.84.8, 2.30.003BSI related pathogensb–Coagulase-Negative Staphylococcusc4–Enterococcus faecium1–Streptococcus spp.2–Enterobacter cloacae2–Escherichia coli5–Klebsiella pneumoniae1–Pseudomonas aeruginosa6–Corynebacterium jeikeium1–a Mean ± SD; b One patient had a mixed *Staphylococcus epidermidis* and *Streptococcus oral* infection; c Eliminate possible contamination according to BSI diagnostic criteria; d Students' t-test was used for data with a homogeneous variance; otherwise, Mann–Whitney U test was used; Fisher's exact test was used for countsAML: acute myeloid leukaemia; ALL: acute lymphoblastic leukaemia; CMML: chronic myelomonocytic leukemia; MDS: myelodysplastic syndromes; MM: multiple myeloma; AA: aplastic anemia; PNH: paroxysmal nocturnal hemoglobinuria ## Alterations of intestinal microbiome preceding BSI in HSCT patients To investigate intestinal microbiome alterations in HSCT patients before BSI onset, 16S rRNA gene sequencing was performed on stool specimens. The difference between the specimens in the BSI and non-BSI groups was shown using non-metric multidimensional scaling (NMDS) based on the family level (Fig. 2a). The Shannon diversity in BSI group was significantly decreased at different taxonomic levels, compared with the non-BSI group, except species level (Fig. 2b). There was no significant difference in richness between the BSI and non-BSI groups (Additional file 7: Fig. S1). The relative abundance of Firmicutes and Proteobacteria in the BSI group was $21.2\%$ lower and $25.3\%$ higher than that in the non-BSI group on average, respectively (Fig. 2c and Additional file 2: Table S1). The relative abundances of Lactobacillaceae ($$P \leq 0.031$$) and Tannerellaceae ($$P \leq 0.007$$) were decreased in the BSI group, while Enterobacteriaceae abundance ($P \leq 0.001$) was increased significantly (Fig. 2c). The relative abundances of Enterobacter ($P \leq 0.001$) and Klebsiella ($$P \leq 0.002$$) were increased and the Blautia abundance was decreased significantly ($$P \leq 0.027$$), among the top 10 genera in the BSI group (Fig. 2c). The abundance of *Enterococcus faecium* was no changed, while the abundance of *Escherichia coli* ($$P \leq 0.041$$) was increased significantly in BSI group, compared with non-BSI group (Fig. 2d). Interestingly, K. quasipneumoniae was significantly increased ($P \leq 0.001$) in the BSI group, compared with non-BSI group (Fig. 2d). These results suggested that intestinal microbiome may be altered before BSI onset, mainly manifesting reduced probiotics and increased potential pathogens. Fig. 2Characteristics of the intestine microbiome in BSI and non-BSI patients. a Analysis of non-metric multidimensional scaling (NMDS) of samples from BSI and non-BSI groups at family level. b Shannon diversity at the phylum, family, genus, and species levels c Stacked column charts of the relative abundance of the top 10 phyla, families and genera. The red and blue boxes represented a significant increase and decrease in relative abundance in the BSI group, respectively, compared with the non-BSI group d Boxplot of relative abundance at species level. The student’s t-test was used for data with a homogeneous variance; otherwise, the Mann–Whitney test was used. Multiple groups were compared using one-way ANOVA ## Microbiome features at the family level predicting BSI onset To observe the possible predictability of microbiome features in the early post-HSCT period, patients in the BSI and non-BSI groups were randomly divided into the training and validation sets to perform the LASSO algorithm (Additional file 7: Fig. S2). A total of 7 features were selected from 414 families using the LASSO logistic regression model (Additional file 3: Table S2). Because Enterobacteriaceae and Butyricicoccaceae could be detected in more than $90\%$ of samples, these two features were selected for subsequent logical regression and the total score (family score) was then obtained (Fig. 3a and Additional file 3: Table S2). After validating the model using the bootstrap method, the mean AUC value was 0.869 ($95\%$ CI 0.823–0.884), and the percentage of AUC greater than 0.85 was $91.9\%$ ($\frac{919}{1000}$) (Fig. 3b). The agreement between the prediction and observation was demonstrated by the calibration curve (Fig. 3c). The family score was significantly different between the BSI and non-BSI groups (Fig. 3d). The family score (sensitivity, 0.828; specificity, 0.875; AUC, 0.879) was showed higher predictive performance than the Shannon diversity at family level (sensitivity, 1.000; specificity, 0.563; AUC, 0.815) through confusion matrix and receiver operating characteristics (ROC) curves (Fig. 3e, f).Fig. 3The prediction model for BSI. a Developed nomogram. The first and third lines represented the score and the total score scales, respectively; the last line represented the probability of BSI corresponding to the total score; and the peaks represented density. b Histogram of AUC frequency under 1000 sampling. c Calibration curves. AUC, area under the curve; CI, confidence interval. d The family scores’ boxplot in the BSI and non-BSI groups. e Prediction results’ confusion matrix. f Receiver operating characteristic (ROC) curves of family score and Shannon diversity at the family level. The student’s t-test was used for data with a homogeneous variance; otherwise, the Mann–Whitney test was used. Multiple groups were compared using one-way ANOVA ## Elevated serum bile acid related to alterations of intestinal microbiome Since intestinal microbiome is closely related to host metabolism, an untargeted metabolomic assay of serum samples was performed. A remarkable difference in samples between the BSI and non-BSI groups was displayed by OPLS-DA (Fig. 4a). There were 16 differential metabolites, including 4 downregulated and 12 upregulated metabolites (Fig. 4b and Additional file 4: Table S3). KEGG enrichment analysis revealed that the differential metabolites were mainly enriched in the primary bile acid biosynthesis pathway (Fig. 4c).Fig. 4Serum metabolome and its relationship with the microbiome in HSCT patients. a Partial least squares-discriminant analysis combined with orthogonal signal correction (OPLS-DA) of samples from the BSI and non-BSI groups. R2X = 0.134; R2Y = 0.849; Q2 = 0.416. b Metabolites’ volcano plot. FC, fold change; VIP, variable importance projection. c Bubble diagram of the KEGG enrichment pathway of the differential metabolites. d Chord diagram of correlation between differential metabolites and top 10 species. The relationships were shown according to correlation coefficient (R) > 0.4 and P values (P) < 0.05. e Two dimensional correlation scatter plot of *Klebsiella quasipneumonae* (relative abundance) and CDCA (intensity). The student’s t-test was used for data with a homogeneous variance; otherwise, the Mann–Whitney test was used. Multiple groups were compared using one-way ANOVA Next, we attempted to investigate the effect of microbiome on host metabolism. The correlations between the intensity of differential metabolites and the relative abundance of top 10 species were analysed using spearman correlation coefficient. The results showed that there was a certain correlation between 5 species and 6 different metabolites (R > 0.4) (Fig. 4d). Only chenodeoxycholic acid (CDCA) (M0115) was screened among the differential primary bile acid metabolites, and its intensity was positively correlated with the relative abundance of K. quasipneumoniae ($R = 0.406$, $$P \leq 0.006$$) and negatively correlated with the relative abundance of *Blautia obeum* (R = -0.413, $$P \leq 0.005$$) (Fig. 4d, e). These findings suggested that the increased relative abundance of K. quasipneumoniae may lead to the elevated level of serum primary bile acid. ## Increased primary bile acid levels and its transport in mice colonized with K. quasipneumoniae To verify whether the relative abundance of K. quasipneumoniae and the level of bile acid was related, mice fed with broad-spectrum antibiotics (ABX) were treated with chemotherapy to simulate HSCT patients’ intestinal conditions and then subjected to bacterial transplantation by gavage (Additional file 7: Fig. S3). The serum samples from mice were collected on the first day after transplantation, and main bile acids were detected (Fig. 5a and Additional file 5: Table S4). Primary bile acids, including CA ($$P \leq 0.001$$), isoCDCA ($$P \leq 0.031$$), and UCA ($$P \leq 0.005$$), in mice colonized with K. quasipneumoniae (MT-K.q group) were significantly increased compared with the control group (MT-PBS group) (Fig. 5b). However, except isoCDCA, for the other two primary bile acids, no significant differences were observed between the mice transplanted with BSI and non-BSI patients’ microbiome (MT-BSI and MT-nonBSI groups) (Fig. 5b). It’s all known that unconjugated bile acids, like CA and CDCA, can activated bile acid signalling farnesoid X receptor (FXR), and the majority of them are then reabsorbed by apical sodium-dependent BA transporter (ASBT) into the enterocytes and transported back to the liver [33]. Our results found that the Fxr and Asbt mRNA expression in MT-K.q group were significantly higher than that in MT-PBS groups ($$P \leq 0.042$$ and $$P \leq 0.001$$, respectively), suggesting that the increased primary bile acids caused by the colonization of K. quasipneumoniae promoted the activation of FXR and the transport of primary bile acids (Fig. 5c, d). Furthermore, the bile acid levels (including primary and secondary) on the first (1d) and third day (3d) were also compared after bacterial transplantation (Additional file 7: Fig. S4). The primary bile acids at 3d in the MT-K.q group decreased compared with that at 1d, may suggesting that the time of colonization was insufficient (Additional file 7: Fig. S4c).Fig. 5Mouse experiment with bacteria colonization. a Flow chart for verifying the correlation between K. quasipneumoniae/microbiome and bile acids in mice. ABX, MNVA in drinking water; Chemo, chemotherapy; MT, K. quasipneumoniae or microbiome transplantation for colonization; sampling on the first day after transplantation. b Concentration of serum primary bile acids in mice after transplantation. CA, cholic acid; isoCDCA, isochenodeoxycholic acid; UCA, ursocholic acid. MT-PBS, gavage with PBS; MT-K.q, gavage by K. quasipneumoniae; MT-BSI, gavage by microbiome from BSI patients; MT-nonBSI, gavage by microbiome from non-BSI patients. c and d Relative mRNA expression of Fxr and Asbt in MT-PBS and MT-K.q groups. e The pathological results of the ileum obtained by H&E staining after transplantation. f and g *The villus* height and crypt depth of the mice small intestine after transplantation. h Relative mRNA expression of Cldn1, Ocln and ZO1 in MT-PBS and MT-K.q groups. i Relationship between TEER of Caco-2 monolayers and culture time. j FITC-dextran paracellular transport in Caco-2 monolayers. ** represented significant difference between K.q group and CDCA group ($$P \leq 0.005$$), & represented significant difference between group K.q group and E.f group ($$P \leq 0.014$$), and ## represented significant difference between K.q group and PBS group ($$P \leq 0.003$$). Bars represent the mean ± SD. The student’s t-test was used for data with a homogeneous variance; otherwise, the Mann–Whitney test was used. Multiple groups were compared using one-way ANOVA ## Severe intestinal mucosa injury in mice colonized with K. quasipneumoniae Next, the mice intestinal mucosa was observed because of its direct relatedness to the microbiome. The villi in MT-K.q, MT-BSI, and MT-nonBSI groups were pathologically damaged to a certain degree compared with the PBS control group at 1d, including atrophy, height decrease, and rupture (Fig. 5e). Simultaneously, the hypertrophic crypts were also observed (Fig. 5e). Compared with the MT-nonBSI group, more serious villi loss and damage were shown in the MT-BSI group (Fig. 5e). In contrast to the MT-PBS group, the small intestinal villus height and crypt depth in the MT-K.q and MT-BSI groups decreased significantly (Fig. 5f,g and Additional file 5: Table S4). These results demonstrated that the colonization of K. quasipneumoniae and the microbiome of BSI patients caused serious mice intestinal mucosa damage. However, there was no difference in villus height and crypt depth between MT-K.q and MT-BSI groups, indicating that K. quasipneumoniae may play a major role in intestinal mucosal injury (Fig. 5f, g). Then, the mRNA expression of intestinal epithelial tight junction protein relative genes were measured between MT-K.q and MT-PBS groups to analyse the integrity of intestinal mucosa, including claudins, occludin and zonula occludens (ZOs) genes. The claudin-1 gene (Cldn1) relative mRNA expression was significantly decreased in MT-K.q group, compared with MT-PBS group ($$P \leq 0.014$$) (Fig. 5h), showing that the colonization of K. quasipneumoniae destroyed the integrity of intestinal mucosa in mice by reducing the Cldn1 expression. Moreover, compared with 1d, the 3d results showed that the small intestinal mucosa was recovered in the three transplant groups (Additional file 7: Figs. S5 and S6). ## Caco-2 cell monolayers permeabilization induced by K. quasipneumoniae In order to observe the factors causing intestinal mucosal damage, Caco-2 cell monolayers was constructed and incubated with different substances to observe permeabilization. CDCA (CDCA group) and K. quasipneumoniae (K.q group) were selected to incubate with Caco-2 cell monolayers, respectively. Simultaneously, a co-incubation group of Caco-2 cell monolayers and E. faecium was set up (E.f group). In the above analysis, the relative abundance of E. faecium was found to have no significant difference between BSI and non-BSI patients. On the 12th to 21st days of culture, the TEER tended to be stable, proving compact monolayers successfully constructed (Fig. 5i). The FITC-dextran clearance experiment results showed that FITC clearance in K.q group at 1 h was significantly increased compared with PBS group ($$P \leq 0.003$$), CDCA group ($$P \leq 0.005$$) and E.f group ($$P \leq 0.014$$) (Fig. 5j). Subsequently, the fluorescence clearance of K.q group tended to be stable and consistent with that of the PBS group (Fig. 5j). The possible reason was that the concentration difference of fluorescein in the upper and lower compartments decreased rapidly after 1 h. The results proved that K. quasipneumoniae, instead of CDCA, significantly induced the permeability of intestinal cell monolayers. ## Discussion HSCT is a common method for treating benign and malignant hematological diseases. However, infections, especially BSI, are associated with high mortality in adult HSCT patients [1]. Recently, microbiome analyses have provided a basis for better interpreting the complicated relationship between microbiome and HSCT [4]. One study found that the intestinal microbiome characteristics could predict the risk of infection in patients with acute myeloid leukaemia and HSCT [11, 12]. Alterations in microbiome-related metabolites are associated with HSCT complications [18]. Investigations on intestinal microbiome and host metabolome in patients with BSI are yet to be undertaken to discover the biological characteristics preceding BSI. Through stool 16S rRNA gene sequencing analysis, we found that the intestinal microbiome α diversity increased gradually from the phylum to the genus level. However, the diversity decreased at the species level. This may have resulted from the 16S rRNA gene sequencing technology limitations, causing low species detection [34]. Previous studies have shown that the higher Gammaproteobacteria (including Enterobacteriaceae) abundance, the higher mortality associated with pulmonary complications after HSCT [16]. A previous study found that moderate or severe aGVHD patients prior to transplantation have high abundance of Lactobacillaceae, exhibiting high mortality [35]. However, the abundance of Lactobacillaceae was lower and Enterobacteriaceae was higher in neonates with necrotizing enterocolitis (NEC), relative to non-NEC patients [36]. The high abundance of Lactobacillaceae was also observed in patients with end-stage renal disease treated with dietary fiber, proving its benefits [37]. Another study found that Bacilli, Erysipelotrichaceae, and Enterobacteriaceae (Klebsiella) relative abundance were increased, and Prevotella, Ruminococcaceae, and Akkermansia relative abundance were decreased in children before HSCT compared with the healthy controls [38]. Consistent with previous studies, our results also found that Enterobacteriaceae relative abundance was increased, but Lactobacillaceae was decreased before BSI onset. Therefore, the negative effects of Enterobacteriaceae and positive effects of Lactobacillaceae may be indicated [38]. In previous studies, Enterococcus, Streptococcus, and various Proteobacteria were dominant during HSCT. Among them, enterococcal dominance increased vancomycin-resistant *Enterococcus bacteremia* risk by nine folds, whereas proteobacterial domination increased Gram-negative rod bacteremia risk by five folds [13, 39]. Although Enterococcus was not significantly different between the BSI and non-BSI groups in our study, the increased potentially harmful bacteria (especially Enterobacteriaceae reported) and decreased beneficial bacteria under serious disease conditions were proven, and its predictability for BSI was confirmed. A marked difference in K. quasipneumoniae between the BSI and non-BSI groups was also observed. This opportunistic pathogen species could cause gastrointestinal tract infections [40]. We also found that the colonization of K. quasipneumoniae can induce intestinal mucosal barrier damage in mice. Therefore, further studies are needed to explore whether the intestinal barrier damage caused by high abundance of K. quasipneumoniae in HSCT patients is a related to BSI occurrence. Due to the close correlation between the microbiome and host metabolism [8], serum metabolome detection and its association with the microbiome were determined in our study. Differential metabolites were mainly enriched in the primary bile acid biosynthetic pathway. Abnormal bile acid metabolism was consistent with liver injury in HSCT patients with BSI in this study. These findings suggested that HSCT patients with elevated bile acid levels may be related to subsequent BSI onset. Bile acid metabolites could modulate immune cells to regulate host immunity [39, 41]. Altered host immunity caused by bile acids might be a possible reason for BSI occurrence in HSCT patients. In our results, although primary bile acid did not injury the intestinal mucosal barrier, its impact on host immunity remains to be confirmed in the future. Moreover, bile acid biotransformation results from the host and intestinal microbiome interaction. Primary bile acid deconjugation occurs via bile salt hydrolases (BSH), widespread in the microbiome. Firmicutes, Bacteroidetes, and Actinobacteria with BSH have been identified in metagenomic studies [42]. This seems to be a possible reason to explain why the primary bile acids in HSCT patients with BSI were elevated in our study, because the abundance of Firmicutes and Actinobacteria was decreased. Furthermore, the physiological function of the host can be altered by microbiome-related secondary bile acids [42]. LCA and DCA may harm the intestine and contribute to intestinal diseases, including membrane damage and colon cancer. However, ursodeoxycholic acid (UDCA) protects colon cells from apoptosis and oxidative damage [42–44]. Although other major secondary bile acids in our results did not show differences between the infection and non-infection groups except for 7-KDCA (Additional file 7: Fig. S4b), the secondary bile acids’ role in HSCT patient needs to be explored because of intestinal susceptibility. The increased serum primary bile acids might be associated with increased K. quasipneumoniae according to the correlation analysis in this study. Therefore, K. quasipneumoniae and primary bile acids were believed to be related and subsequently verified in mice. Since intensive chemotherapy could destroy the microbiome composition and further affect bile acid production, a chemotherapy mouse model was constructed to simulate the intestinal conditions of HSCT patients [45]. We confirmed that the intestinal colonization of K. quasipneumoniae in mice increased serum primary bile acids. However, an important point that needs to be explored is whether bile acid is catabolized or anabolized by K. quasipneumoniae. Wei Jia et al. reviewed that alternative bile acid (BA) synthetic pathway may be manipulated by gut microbiota [46]. Therefore, it is necessary to explore the genes related to bile acid metabolism in *Klebsiella quasipneumoniae* and set up the K. quasipneumoniae colonization group with inhibitors to deeply analyse the specific relationship between them. Furthermore, the mice transplanted with BSI patients’ microbiome showed increased primary bile acids compared with those transplanted with non-BSI patients’ microbiome to a certain extent. This was consistent with our findings in HSCT patients. Interestingly, CDCA was elevated in BSI patients, whereas isoCDCA was increased in mice transplanted with BSI patients’ microbiome. IsoCDCA is a CDCA stereoisomer [47, 48]. Therefore, the effect of interaction between K. quasipneumoniae and other bacteria within the microbiome on metabolism still need to be studied and discussed further. A limitation of this study is the small number of final cases. As it was impossible to predict which patient with HSCT would have BSI, collecting samples prospectively on a large scale was necessary. Only $25\%$ of HSCT patients have BSI. Among these patients, those with available samples were further filtered. Although $75\%$ of patients did not meet the BSI diagnostic criteria, most of them did not rule out the possibility of infection clinically or had no samples available. Therefore, only 21 BSI and 16 non-BSI patients were finally included in this study. In a previous prospective single-centre study, 19 eligible HSCT children were included [49]. Twenty-two out of 113 patients developed bacteraemia in an allogeneic HSCT were studied [13]. Therefore, the difficulty in collecting samples before BSI may be indirectly explained. However, the sample size must be expanded to improve relevant studies in the future. ## Conclusions This study demonstrated that the microbial diversity and probiotics were decreasing, and potential pathogens (especially K. quasipneumonae) were increasing the HSCT patient before BSI onset. This study also revealed the host metabolic profile before BSI, confirmed the relationship between K. quasipneumonae and serum primary bile acid, and emphasized the effect of K. quasipneumonae on the damage of mucosal barrier. Furthermore, the microbiome features at family level were highly predictive of BSI and could be further used as potential biomarkers. ## Supplementary Information Additional file 1. Supplementary Materials and Methods. Method details of human stool 16S rRNA gene sequencing and serum untargeted metabolomics analysis. Additional file 2: Table S1. The relative abundance and microbial diversity in different taxonomy. Additional file 3: Table S2. The results of LASSO and logisitic regression. Additional file 4. Table S3. The differential metabolites of human serum. Additional file 5. Table S4. The detection of serum bile acids and small intestinal in mice. Additional file 6. Table S5. The list of all primers for PCR.Additional file 7: Supplementary figures. Fig. S1 to Fig. S6. ## References 1. Almyroudis NG, Fuller A, Jakubowski A, Sepkowitz K, Jaffe D, Small TN. **Pre- and post-engraftment bloodstream infection rates and associated mortality in allogeneic hematopoietic stem cell transplant recipients**. *Transpl Infect Dis* (2005.0) **7** 11-17. DOI: 10.1111/j.1399-3062.2005.00088.x 2. 2.Auletta JJ. Current uses and outcomes of hematopoietic cell transplantation (HCT): CIBMTR Summary Slides. In: Current uses and outcomes of hematopoietic cell transplantation (HCT): CIBMTR Summary Slides. Center for International Blood & Marrow Transplant esearch (CIBMTR). 2021. https://cibmtr.org/CIBMTR/Resources/Summary-Slides-Reports. Accessed 2022. 3. Daikeler T, Tichelli A, Passweg J. **Complications of autologous hematopoietic stem cell transplantation for patients with autoimmune diseases**. *Pediatr Res* (2012.0) **71** 439-444. DOI: 10.1038/pr.2011.57 4. Chang CC, Hayase E, Jenq RR. **The role of microbiota in allogeneic hematopoietic stem cell transplantation**. *Expert Opin Biol Ther* (2021.0) **21** 1121-1131. DOI: 10.1080/14712598.2021.1872541 5. Belkaid Y, Harrison OJ. **Homeostatic immunity and the microbiota**. *Immunity* (2017.0) **46** 562-576. DOI: 10.1016/j.immuni.2017.04.008 6. Liu R, Hong J, Xu X, Feng Q, Zhang D, Gu Y. **Gut microbiome and serum metabolome alterations in obesity and after weight-loss intervention**. *Nat Med* (2017.0) **23** 859-868. DOI: 10.1038/nm.4358 7. Gurung M, Li Z, You H, Rodrigues R, Jump DB, Morgun A. **Role of gut microbiota in type 2 diabetes pathophysiology**. *EBioMedicine* (2020.0) **51** 102590. DOI: 10.1016/j.ebiom.2019.11.051 8. Lacroix V, Cassard A, Mas E, Barreau F. **Multi-omics analysis of gut microbiota in inflammatory bowel diseases: what benefits for diagnostic, prognostic and therapeutic tools?**. *Int J Mol Sci* (2021.0). DOI: 10.3390/ijms222011255 9. Peled JU, Devlin SM, Staffas A, Lumish M, Khanin R, Littmann ER. **Intestinal microbiota and relapse after hematopoietic-cell transplantation**. *J Clin Oncol* (2017.0) **35** 1650-1659. DOI: 10.1200/JCO.2016.70.3348 10. Haak BW, Littmann ER, Chaubard J-L, Pickard AJ, Fontana E, Adhi F. **Impact of gut colonization with butyrate-producing microbiota on respiratory viral infection following allo-HCT**. *Blood* (2018.0) **131** 2978-2986. DOI: 10.1182/blood-2018-01-828996 11. Galloway-Peña JR, Shi Y, Peterson CB, Sahasrabhojane P, Gopalakrishnan V, Brumlow CE. **Gut microbiome signatures are predictive of infectious risk following induction therapy for acute myeloid leukemia**. *Clin Infect Dis* (2020.0) **71** 63-71. DOI: 10.1093/cid/ciz777 12. Montassier E, Al-Ghalith GA, Ward T, Corvec S, Gastinne T, Potel G. **Pretreatment gut microbiome predicts chemotherapy-related bloodstream infection**. *Genome Med* (2016.0) **8** 49. DOI: 10.1186/s13073-016-0301-4 13. Taur Y, Xavier JB, Lipuma L, Ubeda C, Goldberg J, Gobourne A. **Intestinal domination and the risk of bacteremia in patients undergoing allogeneic hematopoietic stem cell transplantation**. *Clin Infect Dis* (2012.0) **55** 905-914. DOI: 10.1093/cid/cis580 14. Stoma I, Littmann ER, Peled JU, Giralt S, van den Brink MRM, Pamer EG. **Compositional flux within the intestinal microbiota and risk for bloodstream infection with gram-negative bacteria**. *Clin Infect Dis* (2020.0). DOI: 10.1093/cid/ciaa068 15. Shono Y, van den Brink MRM. **Gut microbiota injury in allogeneic haematopoietic stem cell transplantation**. *Nat Rev Cancer* (2018.0) **18** 283-295. DOI: 10.1038/nrc.2018.10 16. Harris B, Morjaria SM, Littmann ER, Geyer AI, Stover DE, Barker JN. **Gut microbiota predict pulmonary infiltrates after allogeneic hematopoietic cell transplantation**. *Am J Respir Crit Care Med* (2016.0) **194** 450-463. DOI: 10.1164/rccm.201507-1491OC 17. Bou Zerdan M, Niforatos S, Nasr S, Nasr D, Ombada M, John S. **Fecal microbiota transplant for hematologic and oncologic diseases: principle and practice**. *Cancers* (2022.0) **14** 691. DOI: 10.3390/cancers14030691 18. Michonneau D, Latis E, Curis E, Dubouchet L, Ramamoorthy S, Ingram B. **Metabolomics analysis of human acute graft-versus-host disease reveals changes in host and microbiota-derived metabolites**. *Nat Commun* (2019.0) **10** 5695. DOI: 10.1038/s41467-019-13498-3 19. Lin D, Hu B, Li P, Zhao Y, Xu Y, Wu D. **Roles of the intestinal microbiota and microbial metabolites in acute GVHD**. *Exp Hematol Oncol* (2021.0) **10** 49. DOI: 10.1186/s40164-021-00240-3 20. Galloway-Peña JR, Peterson CB, Malik F, Sahasrabhojane PV, Shah DP, Brumlow CE. **Fecal microbiome, metabolites, and stem cell transplant outcomes: a single-center pilot study**. *Open Forum Infect Dis* (2019.0) **6** e173. DOI: 10.1093/ofid/ofz173 21. 21.Centers for Disease Control and Prevention. Bloodstream infection event (central line-associated bloodstream infection and non-central line associated bloodstream infection). In: National Healthcare Safety Network (NHSN). 2019, https://www.cdc.gov/nhsn/pdfs/pscmanual/4psc_clabsurrent.pdf. Accessed 4 Feb 2019. 22. Magoč T, Salzberg SL. **FLASH: fast length adjustment of short reads to improve genome assemblies**. *Bioinformatics* (2011.0) **27** 2957-2963. DOI: 10.1093/bioinformatics/btr507 23. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK. **QIIME allows analysis of high-throughput community sequencing data**. *Nat Methods* (2010.0) **7** 335-336. DOI: 10.1038/nmeth.f.303 24. Haas BJ, Gevers D, Earl AM, Feldgarden M, Ward DV, Giannoukos G. **Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons**. *Genome Res* (2011.0) **21** 494-504. DOI: 10.1101/gr.112730.110 25. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P. **The SILVA ribosomal RNA gene database project: improved data processing and web-based tools**. *Nucleic Acids Res* (2013.0) **41** D590-D596. DOI: 10.1093/nar/gks1219 26. Han L, Zhao K, Li Y, Han H, Zhou L, Ma P. **A gut microbiota score predicting acute graft-versus-host disease following myeloablative allogeneic hematopoietic stem cell transplantation**. *Am J Transplant* (2020.0) **20** 1014-1027. DOI: 10.1111/ajt.15654 27. Zou W, She J, Tolstikov VV. **A comprehensive workflow of mass spectrometry-based untargeted metabolomics in cancer metabolic biomarker discovery using human plasma and urine**. *Metabolites* (2013.0) **3** 787-819. DOI: 10.3390/metabo3030787 28. Sequeira RP, McDonald JAK, Marchesi JR, Clarke TB. **Commensal bacteroidetes protect against**. *Nat Microbiol* (2020.0) **5** 304-313. DOI: 10.1038/s41564-019-0640-1 29. Panoskaltsis-Mortari A, Tram KV, Price AP, Wendt CH, Blazar BR. **A new murine model for bronchiolitis obliterans post-bone marrow transplant**. *Am J Respir Crit Care Med* (2007.0) **176** 713-723. DOI: 10.1164/rccm.200702-335OC 30. Liu HB, Lv QQ, Dai L. **Quantitative analysis of 16S rRNA gene copies in mouse fecal sample**. *Bio-Protoc* (2020.0). DOI: 10.21769/BioProtoc.2003368 31. Wang Z, Litterio MC, Müller M, Vauzour D, Oteiza PI. **(-)-Epicatechin and NADPH oxidase inhibitors prevent bile acid-induced Caco-2 monolayer permeabilization through ERK1/2 modulation**. *Redox Biol* (2020.0) **28** 101360. DOI: 10.1016/j.redox.2019.101360 32. Gadaleta RM, van Erpecum KJ, Oldenburg B, Willemsen EC, Renooij W, Murzilli S. **Farnesoid X receptor activation inhibits inflammation and preserves the intestinal barrier in inflammatory bowel disease**. *Gut* (2011.0) **60** 463-472. DOI: 10.1136/gut.2010.212159 33. Jia W, Xie G, Jia W. **Bile acid-microbiota crosstalk in gastrointestinal inflammation and carcinogenesis**. *Nat Rev Gastroenterol Hepatol* (2018.0) **15** 111-128. DOI: 10.1038/nrgastro.2017.119 34. Zhernakova A, Kurilshikov A, Bonder MJ, Tigchelaar EF, Schirmer M, Vatanen T. **Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity**. *Science* (2016.0) **352** 565-569. DOI: 10.1126/science.aad3369 35. Ingham AC, Kielsen K, Cilieborg MS, Lund O, Holmes S, Aarestrup FM. **Specific gut microbiome members are associated with distinct immune markers in pediatric allogeneic hematopoietic stem cell transplantation**. *Microbiome* (2019.0) **7** 131. DOI: 10.1186/s40168-019-0745-z 36. Yan XL, Liu XC, Zhang YN, Du TT, Ai Q, Gao X. **Succinate aggravates intestinal injury in mice with necrotizing enterocolitis**. *Front Cell Infect Microbiol* (2022.0) **12** 1064462. DOI: 10.3389/fcimb.2022.1064462 37. Li Y, Han M, Song J, Liu S, Wang Y, Su X. **The prebiotic effects of soluble dietary fiber mixture on renal anemia and the gut microbiota in end-stage renal disease patients on maintenance hemodialysis: a prospective, randomized, placebo-controlled study**. *J Transl Med* (2022.0) **20** 599. DOI: 10.1186/s12967-022-03812-x 38. Ingham AC, Kielsen K, Mordhorst H, Ifversen M, Aarestrup FM, Müller KG. **Microbiota long-term dynamics and prediction of acute graft-versus-host disease in pediatric allogeneic stem cell transplantation**. *Microbiome* (2021.0) **9** 148. DOI: 10.1186/s40168-021-01100-2 39. Fujiwara H. **Crosstalk between intestinal microbiota derived metabolites and tissues in allogeneic hematopoietic cell transplantation**. *Front Immunol* (2021.0) **12** 703298. DOI: 10.3389/fimmu.2021.703298 40. Karaliute I, Ramonaite R, Bernatoniene J, Petrikaite V, Misiunas A, Denkovskiene E. **Reduction of gastrointestinal tract colonization by**. *Gut Pathog* (2022.0) **14** 17. DOI: 10.1186/s13099-022-00492-2 41. Song X, Sun X, Oh SF, Wu M, Zhang Y, Zheng W. **Microbial bile acid metabolites modulate gut RORγ(+) regulatory T cell homeostasis**. *Nature* (2020.0) **577** 410-415. DOI: 10.1038/s41586-019-1865-0 42. Winston JA, Theriot CM. **Diversification of host bile acids by members of the gut microbiota**. *Gut Microbes* (2020.0) **11** 158-171. DOI: 10.1080/19490976.2019.1674124 43. Barrasa JI, Olmo N, Lizarbe MA, Turnay J. **Bile acids in the colon, from healthy to cytotoxic molecules**. *Toxicol In Vitro* (2013.0) **27** 964-977. DOI: 10.1016/j.tiv.2012.12.020 44. Kayama H, Okumura R, Takeda K. **Interaction between the microbiota, epithelia, and immune cells in the intestine**. *Annu Rev Immunol* (2020.0) **38** 23-48. DOI: 10.1146/annurev-immunol-070119-115104 45. Rashidi A, Kaiser T, Shields-Cutler R, Graiziger C, Holtan SG, Rehman TU. **Dysbiosis patterns during re-induction/salvage versus induction chemotherapy for acute leukemia**. *Sci Rep* (2019.0) **9** 6083. DOI: 10.1038/s41598-019-42652-6 46. Jia W, Wei M, Rajani C, Zheng X. **Targeting the alternative bile acid synthetic pathway for metabolic diseases**. *Protein Cell* (2021.0) **12** 411-425. DOI: 10.1007/s13238-020-00804-9 47. Fiorucci S, Distrutti E. **Bile acid-activated receptors, intestinal microbiota, and the treatment of metabolic disorders**. *Trends Mol Med* (2015.0) **21** 702-714. DOI: 10.1016/j.molmed.2015.09.001 48. Doden HL, Wolf PG, Gaskins HR, Anantharaman K, Alves JMP, Ridlon JM. **Completion of the gut microbial epi-bile acid pathway**. *Gut Microbes* (2021.0) **13** 1-20. DOI: 10.1080/19490976.2021.1907271 49. Bekker V, Zwittink RD, Knetsch CW, Sanders IMJG, Berghuis D, Heidt PJ. **Dynamics of the gut microbiota in children receiving selective or total gut decontamination treatment during hematopoietic stem cell transplantation**. *Biol Blood Marrow Transplant* (2019.0) **25** 1164-1171. DOI: 10.1016/j.bbmt.2019.01.037
--- title: 'Mortality trends and geographic distribution of kidney cancer in Peru: a secondary analysis' authors: - J. Smith Torres-Roman - Gabriel De la Cruz-Ku - Valeria Juárez-Leon - Delahnie Calderón-Solano - Janina Bazalar-Palacios - Carlo La Vecchia - Paulo S. Pinheiro journal: BMC Urology year: 2023 pmcid: PMC10061714 doi: 10.1186/s12894-023-01208-7 license: CC BY 4.0 --- # Mortality trends and geographic distribution of kidney cancer in Peru: a secondary analysis ## Abstract ### Background The incidence of kidney cancer has been increasing worldwide, with variable patterns in mortality due to improved diagnostic techniques and increased survival. The mortality rates, geographical distribution and trends of kidney cancer in South America remain poorly explored. This study aims to illustrate mortality by kidney cancer in Peru. ### Methods A secondary data analysis of the Deceased Registry of the Peruvian Ministry of Health database, from 2008 to 2019 was conducted. Data for kidney cancer deaths were collected from health facilities distributed throughout the country. We estimated age-standardized mortality rates (ASMR) per 100,000 persons and provided an overview of trends from 2008 to 2019. A cluster map shows the relationships among 3 regions. ### Results A total of 4221 deaths by kidney cancer were reported in Peru between 2008 and 2019. ASMR for Peruvian men ranged from 1.15 to 2008 to 1.87 in 2019, and from 0.68 to 2008 to 0.82 in 2019 in women. The mortality rates by kidney cancer rose in most regions, although they were not significant. Callao and Lambayeque provinces reported the highest mortality rates. The rainforest provinces had a positive spatial autocorrelation and significant clustering ($p \leq 0.05$) with the lowest rates in Loreto and Ucayali. ### Conclusion Mortality by kidney cancer has increased in Peru, being a trend that disproportionally affects more men than women. While the coast, especially Callao and Lambayeque, present the highest kidney cancer mortality rates, the rainforest has the lowest rates, especially among women. Lack of diagnosis and reporting systems may confound these results. ## Background Kidney cancer is the 13th most common cancer worldwide, with more than 430,000 new cases ($2.2\%$ of all new cancers) and 179,000 deaths ($1.8\%$ of all cancer mortality) estimated in 2020 [1]. Renal cell carcinoma is the most frequent histological type of kidney cancer and is more frequent in men, in overweight and obese individuals, and in persons with a history of smoking, hypertension, and chronic kidney disease [2]. Recent reports have raised concern of the increasing rates of kidney cancer and its associated mortality rates in Latin America [3]. In 2017, Bai et al. [ 4] reported that the mortality rate of kidney cancer in Latin America was high with 4.28 deaths per 100,000. Similarly, other reports in the region have demonstrated that the incidence of kidney cancer has been increasing over the last 10 years, creating the need for greater understanding of this phenomenon [4]. Moreover, some reports from the Lima Metropolitan Cancer Registry [5–7], in Peru, have described an increase in the incidence and mortality rates for this disease. For example, from 2004 to 2005 to 2013–2015, the mortality rates for men ranged from 2.4 to 3.9, whereas in women they ranged between 1.0 and 1.5, respectively [7–9]. The incidence of urological cancers and chronic kidney disease has been identified as high in Peru, but little is known about the epidemiological distribution of kidney cancer in this country [10, 11]. Moreover, in some provinces, such as Huancavelica, mercury levels are high, being an important factor for the development of renal diseases [12] and the need for further studies. Therefore, we sought to examine the geographic distribution of mortality by kidney cancer and identify gender-related differences in Peru. ## Design and study setting A secondary analysis was conducted using the National Informatic System of Deaths (SINADEF in Spanish). SINADEF is a collaboration of the Ministry of Health, the National Institute of Statistics and Informatics and the National Registry of Identification and Civil Status, which registers the data of deceased persons, the origin of the death certificate and the statistical report [13]. Cases of kidney cancer-related deaths reported between 2008 and 2019 were included in the study. The diagnosis of kidney cancer was identified by code C64 according to the International Classification of Diseases (ICD), 10th Revision [14]. The demographic data were collected by the Network of the Ministry of Health comprised by health care facilities distributed in the 24 provinces of Peru. The information is available through its online platform: http://www.minsa.gob.pe/portada/transparencia/solicitud/. Peru is located in the Andean region of South America, South of the equator and on the coast of the Pacific Ocean. It is divided into 24 provinces, grouped into 3 geographical regions: coast, highlands, and rainforest and has 31 million inhabitants unequally distributed among its 3 regions. While the coast only covers $12\%$ of the national territory, it is the most populated region with approximately $56\%$ of the total (around 17 million inhabitants). In contrast, the highlands cover approximately $28\%$ of the national territory and include $30\%$ of the total population (around 9 million inhabitants). The rainforest (Peruvian Amazon) is the largest region of the country; accounting for $60\%$ of the national territory but only contains $14\%$ of the total population (around 5 million inhabitants) [15]. Peru is a low/middle income country, with a life expectancy at birth of 72.5 years for men and 77.7 years for women and an infant mortality rate of 17 per 1,000 live births, which has significantly improved over the last two decades. Over the same period, Peru has experienced strong economic growth; accompanied by marked migration to urban centers (mainly on the coast), a reduction of the population living in extreme poverty and a shift in mortality by infectious to non-communicable diseases. Along this period of transition, overweight and obesity has been increasing [16]. However, advances in health care delivery systems and dissemination of health access have been slow, unequally affecting people living in rural areas including the highlands and rainforest [17]. ## Ethical considerations This manuscript is based on administrative databases and does not use any personal identifiable information. ## Statistical analysis Age-standardized mortality rates (ASMR) were estimated per 100,000 person-years using the direct method and the world SEGI standard population, as indicated by the World Health Organization [18]. For the denominator, we used the population in five-year age groups, provided by the National Statistics Institute [19]. We analyzed the mortality rates for kidney cancer by sex in the last five-years per each province, with the objective of reporting the provinces with the highest mortality rates in recent years. Trends in mortality were analyzed using the Joinpoint regression Program Version 4.7.0 [20], to identify the occurrence of possible Joinpoints, i.e., significant changes in slopes. The final model selected was the Annual Percentage Change based on the trend of each segment, estimating whether these values were significant ($p \leq 0.05$). The significance levels utilized herein are based on the Monte Carlo permutation method [21] and on the calculation of the average propensity to consume of the ratio, using the logarithm of the ratio. The spatial analysis was conducted with GeoDA software [22]. The spatial analysis was performed using Moran’s I statistic. The map results in a spatial typology consisting of five categories of health regions: (i) ‘high–high’ (positive autocorrelation), (ii) ‘low–high’ (negative autocorrelation), (iii) ‘low–low’ (positive autocorrelation), (iv) ‘high–low’ (negative spatial autocorrelation), and (v) ‘not significant’ indicating that there was no spatial autocorrelation. The value of the Moran index varies between − 1 and + 1, where negative values indicate a spatial conglomerate of territorial units with different values of analysis and positive values indicate a spatial conglomerate of territorial units with similar values of analysis. We used a reference distribution using 999 random permutations to indicate statistical significance. ## Results A total of 4221 kidney cancer deaths (2706 men and 1515 women) were registered in Peru during the study period. Figure 1 shows the provincial ASMRs per 100,000 person-years for kidney cancer by gender in the last 5 years. The coastal provinces had the highest mortality rates for men, mainly in Lambayeque, La Libertad, Callao e Ica (≥ 2.5 deaths per 100,000), whereas the rainforest provinces had the lowest mortality rates (0 to 0.99 deaths per 100,000). In relation to women, Lambayeque and Callao reported the highest mortality rates for kidney cancer (≥ 1.6 per 100,000), whereas the rainforest and highlands provinces showed the lowest rates (0 to 0.99 deaths per 100,000). Fig. 1Provincial age-standardized mortality rates per 100,000 person-years for kidney cancer by sex, 2015–2019 Table 1 reports the joinpoint analysis for kidney cancer in Peru and its regions. Mortality by kidney cancer increased, albeit not significantly, in the coastal and highlands regions of Peru during the study period. However, mortality in the rainforest region could not be evaluated because some years showed zero deaths. Table 1Joinpoint analysis for kidney cancer from Peru and its regions between 2008 and 2019Geographical areaMenWomenYearsAPCYearsAPCYearsAPC Peru 2008–20192.2(− 0.6,5.1)2008–20191.8(0, 3.7) Coast 2008–20192.0(− 1.0,5.1)2008–20191.8(− 0.5,4.1) Highlands 2008–20192.7(− 1.8,7.4)2008–20192.4(− 2.6,7.7) Rainforest 2008–2019NA2008–2019NANA: Not applicable Figure 2 shows national and regional ASMRs per 100,000 person-years for kidney cancer per year and sex. The men in the coast and highlands regions had the highest mortality rates compared to women. Mortality rates for Peruvian men ranged from 1.15 to 2008 to 1.87 in 2019, and ranged from 0.68 to 2008 to 0.82 in 2019 in women. The rates in men residing in the coastal region ranged from 1.5 to 2008 to 2.39 in 2019, with the highest rate in 2012 (2.84 per 100,000). The rates in men living in the highlands ranged from 0.53 to 2008 to 0.80 in 2019, with 0.43 deaths per 100,000 in women in both 2008 and 2019. Fig. 2National and regional age-standardized mortality rates per 100,000 person-years for kidney cancer per year and sex, 2008–2019 The ASMR (per 100,000 person-years) was estimated in three sub-periods: 2008–2011, 2012–2015, and 2016–2019. Overall, mortality rates in men increased from 1.52 (2008–2011) to 1.76 (2016–2019), representing an increase of $16.1\%$. Among women, mortality rates increased from 0.79 (2008–2011) to 0.89 (2013–2016), being an increase of $12.7\%$ (Table 2). According to regions, the coast reported the highest mortality rates in all sub-periods among men (2.03 to 2.47 per 100,000) and women (0.97 to 1.15 per 100,000). On the coast, the increases in mortality rates were similar in both men and women. In the highlands, an increase of $19\%$ was reported among men, whereas in women the increase was $30.1\%$. The lowest rates were reported in the rainforest region; however, men showed the greatest increase ($66.9\%$) between the first (2008–2011) and last subperiod (2016–2019) in this region. Table 2Age-standardized mortality rates per 100,000 person-year for kidney cancer in men and women in 2008–2011, 2012–2015 and 2016–2019, and the corresponding percentage changeMenWomen2008–20112012–20152016–$2019\%$change(2016-$\frac{19}{2008}$-12)2008–20112012–20152016–$2019\%$change(2016-$\frac{19}{2008}$-12)Peru1.521.831.7616.10.790.900.8912.7Coast2.032.472.3214.40.971.151.0811.1Highlands0.560.590.6619.00.400.360.5230.1Rainforest0.290.240.4866.90.310.230.20-33.5Amazonas0.110.420.00-100.00.350.580.00-100.0Ancash0.350.811.72395.00.470.620.7458.0Apurimac0.610.731.1080.80.100.240.48360.6Arequipa2.121.382.319.00.810.841.0327.6Ayacucho0.260.110.57116.50.700.370.34-51.2Cajamarca0.280.480.3730.70.390.200.6257.2Callao2.414.523.5246.31.501.661.8724.1Cusco0.490.230.6941.30.140.160.53268.7Huancavelica0.411.280.6252.00.160.390.70342.7Huanuco0.620.540.37-40.70.560.140.50-10.5Ica1.921.672.9755.00.830.941.2550.4Junin1.021.001.3431.00.750.790.52-30.4La Libertad2.012.672.3818.41.071.441.2719.1Lambayeque2.162.602.8732.50.911.961.7389.5Lima2.192.672.232.01.021.060.96-5.3Loreto0.700.370.733.40.320.150.10-68.5Madre de Dios0.000.000.24NA0.420.000.34-18.4Moquegua1.811.791.14-37.10.900.250.89-1.4Pasco0.581.130.49-15.80.000.360.58NAPiura2.032.352.030.20.961.510.93-3.5Puno0.550.630.36-34.40.360.480.4731.3San Martin0.000.180.60NA0.140.240.28108.3Tacna1.782.872.2224.30.550.380.9776.3Tumbes2.420.671.13-53.41.090.330.26-76.4Ucayali0.190.000.2529.80.510.090.39-23.4NA: Not applicable Figure 3 shows a spatial cluster map of kidney cancer mortality rates. From 2008 to 2019 men showed a positive spatial autocorrelation and significant clustering (Moran’s I: 0.20, $$P \leq 0.04$$) with the lowest rates being in the Peruvian North-East (Loreto, Ucayali, and Madre de Dios). For women, a positive autocorrelation was also reported (Moran’s I: 0.26, $$P \leq 0.02$$) with the lowest rates in Loreto, Ucayali and Cusco. Fig. 3Spatial cluster map of kidney cancer mortality rates by sex for the period 2008–2019 ## Discussion This study is based on an analysis of the kidney cancer death registry in Peru. Using estimations based on ASMR, we found that kidney cancer mortality rates have been increasing in Peru, with variations according to sex and geographical area. While in women, the ASMR increased in the coast and highlands, it decreased in the rainforest. In contrast, the ASMR increased in men, being more notable in the rainforest region. Furthermore, the coast presented the highest ASMR in both sexes, whereas the rainforest region had the lowest mortality rates in both sexes. Our study identified and quantified an increase in the ASMRs for kidney cancer in both sexes. It is therefore important to consider early action for this disease. In Peru there are around 2 deaths per 100,000 men and around 1 death per 100,000 women. In 2020, GLOBOCAN reported mortality rates close to those reported in our study. For example, the reported rates for men were greater than 2 deaths per 100,000, being greater than 1 death per 100,000 for women [11]. Although these rates are close to those reported by Puerto Rico, Cuba, Panama, Colombia, and Nicaragua, they are low compared to Chile, Argentina, and Uruguay (rates from 4.9 to 7.2 per 100,000 for men and from 1.9 to 2.3 per 100,000 for women) [11]. It is therefore important to control the risk factors for the development of kidney cancer, such as smoking, obesity, hypertension, and chronic kidney disease [23, 24]. In fact, the region with the highest mortality rate is the coast, which has the highest prevalence of the aforementioned risk factors [10, 25, 26]. In high income countries the diagnosis, management and treatment of kidney cancer have substantially improved over the last few decades [27], although with appreciable socio-economic differentials [28]. Nonetheless, Peruvian mortality rates remain lower than those in most high-income countries, likely indicating under registration of deaths. Moreover, Peru does not have incidence registries for cancer or studies of survival in this cancer, making it difficult to demonstrate the real status of this neoplasm. Our study also reports a clear difference in kidney cancer mortality by sex in Peru and its regions. The coast region had the highest mortality rates among men and women across all subperiods. This can be explained by the fact that cities in Peru’s coastal region have higher levels of economic development than other cities. With greater development comes a higher prevalence of risk factors such as smoking, having a high BMI, being inactive, and having hypertension, which may be more prevalent in developed areas [29]. Furthermore, increases in kidney cancer incidence may be due in part to a lack of health staff, limited access to health services, and limitations in diagnostic and treatment options such as diagnostic imaging. On the other hand, the increase in mortality was not as severe in the Sierra and Selva regions, where it is known that the harmful effects of illegal mining, the main occupation of its Inhabitants, and its short- and long-term consequences are a concern [30]. However, when compared to the coastal region, this region has a high certification rate or data loss. As a result, civil registration and vital statistics systems are an important source of information and evidence for monitoring population health, identifying health priorities, and planning interventions to reduce disease mortality. Finally, as we see an increase in the incidence and mortality of kidney cancer, we need to focus on developing strategies for early detection. Additional campaigns and medical care are needed to reduce the prevalence of smoking, obesity, and hypertension, all of which are major risk factors for kidney cancer. It would improve the situation further without impeding equal treatment access, particularly through new targeted therapies. The present study has several limitations, such as a loss of data or underreporting of deaths in some departments. In addition, inaccurate knowledge of the number of deaths in each department makes it impossible to determine the real incidence of renal cancer. Moreover, no study has analyzed the overall survival of this disease, thereby making it impossible to show the reality of this disease in Peru. Some departments such as Pasco, San Martin, or Madre de Dios reported zero deaths in some of the years studied, perhaps due to loss of data in these departments, and thus, we could not identify the percentage change that occurred in these geographic areas. The main interest of our study is that this is the first study to report mortality by renal cancer in Peru, in addition to studying deaths by regions and departments by sex. It also allowed identification of geographic areas in which epidemiological surveillance of risk factors for this disease is needed. ## Conclusion Our study describes trends in kidney cancer mortality in Peru and its geographical areas. Mortality by kidney cancer has increased in Peru, mainly in men compared to women. The highest mortality rates by kidney cancer were observed in the coastal region, whereas the rainforest had the lowest mortality rates. ## References 1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. **Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries**. *CA: Cancer J Clin* (2021.0) **71** 209-49. PMID: 33538338 2. Scelo G, Larose TL. **Epidemiology and risk factors for kidney cancer**. *J Clin Oncol* (2018.0) **36** 3574. DOI: 10.1200/JCO.2018.79.1905 3. Cai Q, Chen Y, Qi X, Zhang D, Pan J, Xie Z, Xu C, Li S, Zhang X, Gao Y. **Temporal trends of kidney cancer incidence and mortality from 1990 to 2016 and projections to 2030**. *Transl Androl Urol* (2020.0) **9** 166. DOI: 10.21037/tau.2020.02.23 4. Bai X, Yi M, Dong B, Zheng X, Wu K. **The global, regional, and national burden of kidney cancer and attributable risk factor analysis from 1990 to 2017**. *Exp Hematol Oncol* (2020.0) **9** 1-15. DOI: 10.1186/s40164-020-00181-3 5. Toft N, Schmiegelow K, Klausen TW, Birgens H. **Adult acute lymphoblastic leukaemia in Denmark. A national population-based retrospective study on acute lymphoblastic leukaemia in Denmark 1998–2008**. *Br J Haematol* (2012.0) **157** 97-104. DOI: 10.1111/j.1365-2141.2011.09020.x 6. Gupta S, Pole JD, Baxter NN, Sutradhar R, Lau C, Nagamuthu C, Nathan PC. **The effect of adopting pediatric protocols in adolescents and young adults with acute lymphoblastic leukemia in pediatric vs adult centers: an IMPACT cohort study**. *Cancer Med* (2019.0) **8** 2095-103. DOI: 10.1002/cam4.2096 7. 7.Ministerio de Salud. Instituto Nacional de Enfermedades Neoplásicas. Registro de Cáncer de Lima Metropolitana 2013–2015.Vol VI, 2021. 8. 8.Ministerio de Salud. Instituto Nacional de Enfermedades Neoplásicas. Registro de Cáncer de Lima Metropolitana 2004–2005.Vol IV, 2013. 9. 9.Ministerio de Salud. Instituto Nacional de Enfermedades Neoplásicas. Registro de Cáncer de Lima Metropolitana 2010–2012.Vol V, 2016. 10. Atamari-Anahui N, Ccorahua-Rios MS, Condori-Huaraka M, Huamanvilca-Yepez Y, Amaya E, Herrera-Añazco P. **Epidemiology of chronic kidney disease in Peru and its relation to social determinants of health**. *Int health* (2020.0) **12** 264-71. DOI: 10.1093/inthealth/ihz071 11. 11.International Agency for Research on Cancer. Cancer Today [Internet]. WHO. ; 2020. Available from: https://gco.iarc.fr/today/home. 12. Hodgson S, Nieuwenhuijsen MJ, Elliott P, Jarup L. **Kidney disease mortality and environmental exposure to mercury**. *Am J Epidemiol* (2007.0) **165** 72-7. DOI: 10.1093/aje/kwj345 13. Miki J, Rampatige R, Richards N, Adair T, Cortez-Escalante J, Vargas-Herrera J. **Saving lives through certifying deaths: assessing the impact of two interventions to improve cause of death data in Perú**. *BMC Public Health* (2018.0) **18** 1-11. DOI: 10.1186/s12889-018-6264-1 14. 14.World Health Organization. International classification of disease and related health problems: 10th revision. Geneva. Volume 1. World Health Organization; 1992. 15. Torres-Roman JS, Ruiz EF, Martinez-Herrera JF, Mendes Braga SF, Taxa L, Saldaña-Gallo J, Pow-Sang MR, Pow-Sang JM, La Vecchia C. **Prostate cancer mortality rates in Peru and its geographical regions**. *BJU Int* (2019.0) **123** 595-601. DOI: 10.1111/bju.14578 16. Torres-Roman JS, Urrunaga-Pastor D, Avilez JL, Helguero-Santin LM, Malaga G. **Geographic differences in overweight and obesity prevalence in peruvian children, 2010–2015**. *BMC Public Health* (2018.0) **18** 353. DOI: 10.1186/s12889-018-5259-2 17. Kristiansson C, Gotuzzo E, Rodriguez H, Bartoloni A, Strohmeyer M, Tomson G, Hartvig P. **Access to health care in relation to socioeconomic status in the amazonian area of Peru**. *Int J Equity Health* (2009.0) **8** 1-8. DOI: 10.1186/1475-9276-8-11 18. 18.Ahmad OB, Boschi-Pinto C, Lopez Christopher AD, Murray JL, Lozano R, Inoue M.AGE STANDARDIZATION OF RATES: A NEW WHO STANDARD. In.; 2001. 19. 19.Instituto Nacional de Estadistica e Informatica. Boletín de Análisis Demográfico Nº 37. Perú: Estimaciones y Proyecciones de Población por departamento, sexo y grupos quinquenales de edad, 1995–2025. [http://proyectos.inei.gob.pe/web/biblioineipub/bancopub/Est/Lib0846/index.htm] 20. 20.National Cancer Institute. Joinpoint regression program. [Accesed 4 April,2020].Availablein: https://surveillance.cancer.gov/help/joinpoint. 21. Kim HJ, Fay MP, Feuer EJ, Midthune DN. **Permutation tests for joinpoint regression with applications to cancer rates**. *Stat Med* (2000.0) **19** 335-51. DOI: 10.1002/(SICI)1097-0258(20000215)19:3<335::AID-SIM336>3.0.CO;2-Z 22. Anselin L, Syabri I, Kho Y. **GeoDa: an introduction to spatial data analysis**. *Geogr Anal* (2006.0) **38** 5-22. DOI: 10.1111/j.0016-7363.2005.00671.x 23. Cumberbatch MG, Rota M, Catto JW, La Vecchia C. **The role of tobacco smoke in bladder and kidney carcinogenesis: a comparison of exposures and meta-analysis of incidence and mortality risks**. *Eur Urol* (2016.0) **70** 458-66. DOI: 10.1016/j.eururo.2015.06.042 24. Dal Maso L, Zucchetto A, Tavani A, Montella M, Ramazzotti V, Talamini R, Canzonieri V, Garbeglio A, Negri E, Tonini A. **Renal cell cancer and body size at different ages: an italian multicenter case-control study**. *Am J Epidemiol* (2007.0) **166** 582-91. DOI: 10.1093/aje/kwm108 25. Villena Chávez JE. **Prevalencia de sobrepeso y obesidad en el Perú**. *Rev Peru Ginecol Obstet* (2017.0) **63** 593-8. DOI: 10.31403/rpgo.v63i2034 26. Torres-Roman JS, Valcarcel B, Martinez-Herrera JF, Bazalar-Palacios J, La Vecchia C, Raez LE. **Mortality Trends for Lung Cancer and Smoking Prevalence in Peru**. *Asian Pac J Cancer Prev* (2022.0) **23** 435-43. DOI: 10.31557/APJCP.2022.23.2.435 27. Bertuccio P, Santucci C, Carioli G, Malvezzi M, La Vecchia C, Negri E. **Mortality Trends from urologic cancers in Europe over the period 1980–2017 and a projection to 2025**. *Eur Urol Oncol* (2021.0) **4** 677-96. DOI: 10.1016/j.euo.2021.05.005 28. Greiman AK, Rosoff JS, Prasad SM. **Association of Human Development Index with global bladder, kidney, prostate and testis cancer incidence and mortality**. *BJU Int* (2017.0) **120** 799-807. DOI: 10.1111/bju.13875 29. 29.Instituto Nacional de Estadistica e Informatica. Perú: Enfermedades No Transmisibles y Transmisibles., 2021. Available in: https://www.inei.gob.pe/media/MenuRecursivo/publicaciones_digitales/Est/Lib1839/cap01.pdf. 30. Sobhanardakani S, Tayebi L, Hosseini SV. **Health risk assessment of arsenic and heavy metals (cd, Cu, Co, Pb, and Sn) through consumption of caviar of Acipenser persicus from Southern Caspian Sea**. *Environ Sci Pollut Res* (2018.0) **25** 2664-71. DOI: 10.1007/s11356-017-0705-8
--- title: Effects of OsomeFood Clean Label plant-based meals on the gut microbiome authors: - Dwiyanto Jacky - Chia Bibi - Look Melvin Chee Meng - Fong Jason - Tan Gwendoline - Lim Jeremy - Chong Chun Wie journal: BMC Microbiology year: 2023 pmcid: PMC10061721 doi: 10.1186/s12866-023-02822-z license: CC BY 4.0 --- # Effects of OsomeFood Clean Label plant-based meals on the gut microbiome ## Abstract ### Background Plant-based diets offer more beneficial microbes and can modulate gut microbiomes to improve human health. We evaluated the effects of the plant-based OsomeFood Clean Label meal range (‘AWE’ diet), on the human gut microbiome. ### Methods Over 21 days, ten healthy participants consumed OsomeFood meals for five consecutive weekday lunches and dinners and resumed their regular diets for other days/meals. On follow-up days, participants completed questionnaires to record satiety, energy and health, and provided stool samples. To document microbiome variations and identify associations, species and functional pathway annotations were analyzed by shotgun sequencing. Shannon diversity and regular diet calorie intake subsets were also assessed. ### Results Overweight participants gained more species and functional pathway diversity than normal BMI participants. Nineteen disease-associated species were suppressed in moderate-responders without gaining diversity, and in strong-responders with diversity gains along with health-associated species. All participants reported improved short-chain fatty acids production, insulin and γ-aminobutyric acid signaling. Moreover, fullness correlated positively with Bacteroides eggerthii; energetic status with B. uniformis, B. longum, Phascolarctobacterium succinatutens, and Eubacterium eligens; healthy status with Faecalibacterium prausnitzii, Prevotella CAG 5226, Roseburia hominis, and Roseburia sp. CAG 182; and overall response with E. eligens and Corprococcus eutactus. Fiber consumption was negatively associated with pathogenic species. ### Conclusion Although the AWE diet was consumed for only five days a week, all participants, especially overweight ones, experienced improved fullness, health status, energy and overall responses. The AWE diet benefits all individuals, especially those of higher BMI or low-fiber consumption. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12866-023-02822-z. ## Introduction The human gut microbiota comprises a vast and complex community of almost 100 trillion microorganisms (predominantly bacteria) and an estimated 5000 species [1] that inhabit the gastrointestinal tract. A normal gut flora, or core microbiota, consists primarily of Bacteroidetes (Bacteroides and Prevotella) and Firmicutes (Clostridium, Enterococcus, Lactobacillus, Ruminococcus, Eubacterium and Faecalibacterium). In normal, healthy adult guts, Firmicutes are the most abundant phyla, followed by Bacteroidetes [2]. However, the microbiome is dynamic, and various factors, including diet, age, lifestyle (e.g., stress), weight and the presence of disease, can affect its short-term and long-term diversity, composition and function. Changes to human health and wellbeing [3] have occurred when altering the diet with prebiotics and probiotics [4, 5] to modulate the microbiome, although evidence of this effect are mixed, and a complex relationship exists with the gut resistome [6]. While probiotics and prebiotics were not beneficial [7] in pre-diabetic adults and less beneficial than synbiotics in ulcerative colitis [8], probiotics were only marginally beneficial in diabetic patients [9], positively beneficial in cognitive impairment and mood or depressive disorders [10, 11], but negatively beneficial [12, 13] following antibiotic use. Thus, the potential for microbiome modulation through direct intervention is still actively pursued, as shown by the increase in randomized controlled trials being conducted. Human gastrointestinal microbiota ferment intestinal mucus and indigestible dietary fiber, resulting in metabolites like bacteriocins, short-chain fatty acids (SCFAs), amino acids and vitamins [14]. These metabolites have key roles in activating intestinal immune responses to invading pathogens, while SCFAs also function as signaling molecules that regulate physiological processes like metabolism and inflammation [15, 16]. Plant-based diets are often linked to lower mortality rates [17], and their increased fibre and polyphenol levels are associated with a greater diversity in beneficial or healthy-gut microbes [18]. Plant-based diets also increase SCFA levels as they increase the amounts of microbes which metabolize complex carbohydrates and polysaccharides [19]. Omnivorous, ovo-lacto vegetarian, and vegan diets provide more nutrients that support a diverse gut microbiome, with the microbiome profile of vegans and vegetarians likely to have more beneficial bacteria than that of omnivores. In fact, omnivores have a more altered gut microbiome than vegans, as they have more bile-resistant microbes which can potentially become harmful [20]. One cross-sectional study found that Firmicutes and Bacteroidetes comprised up to $97.7\%$ of the total vegan and omnivore gut microbiome, while Firmicutes comprised up to $58.6\%$ and Bacteroidetes comprised $39\%$ of the microbiome [21]. Interestingly, Prevotella species dominate in populations with plant-based diets, like those in Africa, Asia, and South America, whereas Bacteroides dominate in Western populations with diets high in animal proteins and saturated fats [22]. Individuals with diets rich in indigestible carbohydrates like whole grains and wheat bran have more Bifidobacterium and Lactobacillium while those with diets high in starches and whole grain barley may have more lactic acid bacteria (LAB; e.g., Lactobacillus sp.). How the gut microbiome composition and function might be changed by short-term dietary changes remains to be established. Diet impacts the microbiome and may produce a chronic but mild inflammation, leading to chronic diseases like type II diabetes, cardiovascular disease and cancer, or chronic conditions like obesity [23]. Unregulated changes or imbalances in microbiome composition or function [24] can also manifest clinically as rheumatic diseases [25], psychiatric disorders [26], diabetes [27], hypertension [28] and cancers [29]. Gut microbiome manipulation offers a way to improve these disease risks. One dietary intervention study found that microbiome changes caused by switching diets [30], with Prevotella-enriched vegetarians or Bacteroides-enriched Western diet individuals all experiencing altered microbiome compositions within 24 h of swapping diets [30, 31]. A study in Thai vegetarians [32] also found similar results with Prevotella-enriched microbiomes in vegetarians. However, microbiome composition requires further study as others have noted conflicting results [33]. The commercially-available AWE by OsomeFood™ [34], is a nutrition-focused, both clean and functional plant-based meals that combines its OsomeFood super ingredients and its iteration of different clean sauces and ingredients to complete a meal. All meals are made without artificial additives, extracts, fortifications, synthetic ingredients, genetically-modified organisms or preservatives and yet naturally supercharged with nutritional goodness. All ingredients undergone strict qualifications, from the source of produce, cleansing technologies, activation through dehydrating or fermentation, to encapsulation and curated pairing of nutrients to achieve optimal absorption and maximum nutrition. OsomeFood’s food is made primarily from fungi and algae (single cell protein) as well as nuts and plant protein. ## Methods The AWE study aimed to assess changes in wellbeing and the gut microbiome signature in ten study participants ($$n = 10$$) who consumed 900–2000 cal/week plant-based meals provided by OsomeFood. Participants were able to continue their regular diets for all meals except weekday lunches and dinners. For 21 days, participants had access to over 30 different types of meals. Some examples of OsomeFood meals are fish balls, fish cakes, protein noodles and collagen egg made from fungi (including mushrooms fermented into mycoproteins), *Undaria pinnatifida* seaweed, white chia seeds, burdock root and kombu kelp seaweed. [ 35]. To prepare the meals, participants are only required to thaw each OsomeFood meal pack and consume them heated up with the recommended heating methods. ## Study protocol and design This study has been approved by the AMILI Institutional Review Board, which adheres to the Declaration of Helsinki (AMILI IRB Ref: $\frac{2022}{0201}$). All participants were at least 18 years of age and have provided their written informed consent. Participants strictly adhered to OSomeFoods’ plant-based meal plans for five consecutive days (Monday through to Friday; ‘AWE’), and were allowed unrestricted meals for two consecutive days over the weekend (Saturday and Sunday; ‘non-AWE’) (Fig. 1). The study was performed in March 2022. Healthy participants were recruited from Singapore, all of whom provided written informed consents for their participation in this research study. Fig. 1Schematic of study design. Subjects underwent a 21-day non-continuous plant-based diet intervention, with stool specimen collected on day (D)0, D7, and D21. Meanwhile, wellness survey was administered on D1, D3, D7, D11, D17, and D21. Demographic data were obtained on D0 ## Participants/inclusion and exclusion criteria Included participants were those aged 21 years and older, with a Body Mass Index (BMI) between 18 and 28, able to provide informed consent, and who were meat eaters. Excluded participants were those using oral antibiotics, antifungal and/or antiviral treatments within the prior 3 months, those with existing medical issues, those on any other long-term medications, and vegetarians. ## Data and sample collection Participants were not required to complete and maintain a food frequency questionnaire (FFQ) on AWE days but did self-report their meal consumption on non-AWE days. On AWE days, subjects were followed-up on days (D) 1, 3, 7, 11, 17, and 21, to record their self-perception of three metrics: satiety, energy, and health. Insights from two prior in-house food trial pilot studies showed that changes in gut microbiome diversity and abundance occurred by 21 days (data not shown), hence this was used as our observational timepoint. Each metric was evaluated on a 3-point ordinal scale (1: worst rating, 3: best rating). For microbiome analysis, participants provided stool samples on the first day of the intervention (D1), and at the end of week 1 (D7) and week 3 (D21). ## Data and sample analysis To document microbiome variations occurring during the AWE diet period, and identify associations with the additional FFQ, we analyzed species and functional pathway annotations, Shannon diversity and cheat day calorie intake subsets. For species and functional pathway annotation, DNA was extracted from stool samples using the QIAamp® PowerFecal® Pro DNA Kit Handbook (QIAGEN GmbH, Hilden, Germany) according to the manufacturer’s protocols, and was processed for shotgun sequencing using the Illumina NovaSeq 6000 Sequencing System (RRID:SCR_016387; Illumina, San Diego, CA, USA) according to the manufacturer’s protocols. ## Species and functional pathway annotations DNA was extracted from the collected stool samples and shotgun sequencing was performed by Macrogen Asia Pacific Pte. Ltd. (Singapore). The resulting FASTQ sequences were then fed into the BioBakery 3 pipeline for reference-based taxonomic pathway annotation using MetaPhlAn 3 and functional pathway annotation using HUMAnN. ## Statistical analysis After excluding species with < $1\%$ relative abundance and prevalence in < $5\%$ of the participants, 236 species remained for further analysis. We determined whether the change in diet affected microbiome composition across the study duration through permutational multivariate analysis of variance analysis (PERMANOVA). Briefly, Shannon diversity was calculated using the R package phyloseq version 1.40.0. Beta-diversity analysis was conducted on the species and pathway composition data which was centered-log-ratio transformed to account for the compositionality of the dataset. Subsequently, features with < $1\%$ abundance and < $5\%$ prevalence were excluded. PERMANOVA was conducted to determine the significant variation across the samples using the R package vegan version 2.6–2 using the Euclidean distance with 999 permutations. Differentially-abundant features were determined using pairwise Wilcoxon Rank Sum test, with multigroup comparisons adjusted using the Benjamini–Hochberg method. Questionnaire output and feature abundance were correlated using a linear mixed model method under the R package lme4 version 1.1–29, with age, sex, and BMI accounted as fixed effect and subject adjusted as random effects. ## Participants demographics Ten participants ($50\%$ male, $50\%$ female) were recruited and completed the study, and ranged in age from 20-49 years, although the majority were aged 30-39 years. The majority ($70\%$) were also overweight [BMI: 23-24.9kg/m2], while the remaining participants were either underweight ($10\%$), normal ($10\%$) or obese ($10\%$). ## Species composition analysis A total of 369 species were detected from the compiled gut microbiome profile; 236 of which remained after excluding those with < $1\%$ relative abundance and found in < $5\%$ subjects. The Shannon diversity metric is commonly used to assess the diversity of microbial communities in human gut microbiome studies. The Shannon diversity index accounts for the number of different types of microorganisms (species richness) and their relative abundances and provides a more comprehensive measure of diversity than metrics that consider only one of these factors. In human gut microbiome research, a high Shannon diversity (a diverse gut microbiome) is generally considered to be a marker of gut health. Conversely, low microbial diversity has been associated with a variety of health conditions, including inflammatory bowel disease, obesity, and type 2 diabetes. There was no significant variation in Shannon diversity across timepoint (KW test, $p \leq 0.05$; Fig. 2a), even when the analyses were done across demographic factors (Supplementary Fig. 1). Despite this, beta-diversity analysis determined a significant variation in the species composition across timepoint (PERMANOVA stratified for subject variation, permutations = 999, R2 = 0.0222, Pseudo-$F = 0.3069$, $$p \leq 0.005$$; Fig. 2b). Differential abundance analysis identified seven species with different abundances between D0 and D21, three of which (Bacteroides thetaiotaomicron, Bacteroides xylanisolvens and Leuconostoc garlicum) were elevated at the end of the study, while four were depleted (Wilcoxon test, q < 0.1; Weissella confusa, Romboutsia ilealis, *Collinsella intestinalis* and a Bacteroides phage) (Fig. 2c). Among these, B. thetaiotaomicron was significantly higher in D7 and D21 compared to the baseline (Wilcoxon test, $p \leq 0.05$; Fig. 2d). Additionally, 19 bacteria species related to cancer, inflammation, sepsis, weight management and non-alcoholic fatty liver disease (NAFLD), were suppressed throughout and at the end (D21) of the study in participants, with a constant increase in abundance in 30 species known to confer health benefits such as cholesterol, immunity and weight management (Supplementary Fig. 2).Fig. 2a Shannon diversity index of the subjects over the intervention period; b Principal component analysis of the subjects’ gut microbiota profile ordinated based on subject (color) and timepoint (shape) using centered-log-ratio transformation on a Euclidean distance, with significant variation across timepoint (Stratified PERMANOVA R2 = 0.022, $$p \leq 0.008$$); c Species differentially abundant between baseline and day 21 (end of the intervention period), measured using Wilcoxon test and adjusted for multiple comparison using the Benjamini–Hochberg method (q < 0.1); d Abundance of B. thetaiotaomicron across timepoint ## Correlations with functional pathway data A total of 453 functional pathways were found, out of which 384 remained after excluding low-abundance (< $1\%$) and low-prevalence pathways (< $5\%$). Similar to the species composition profile, Shannon diversity analysis identified no significant difference across timepoints (KW test, $p \leq 0.05$; Fig. 3b). However, PERMANOVA also failed to identify any significant difference across timepoints based on composition profiles (PERMANOVA stratified for subject variation, permutations = 999, R2 = 0.037, $p \leq 0.05$) (Fig. 3a). Despite this, pairwise comparison across timepoints identified two functional pathways with significantly different abundance: UDP-N-acetyl-D-glucosamine biosynthesis I and chondroitin sulfate degradation I (bacterial) (Wilcoxon test, q < 0.05, Fig. 3c). Moreover, all participants reported improved SCFA production, insulin and γ-aminobutyric acid (GABA) signaling after the AWE diet, with related functional pathways continuing to increase and associated with participants’ improved health profiles (Supplementary Fig. 3). Most of the functional pathways that improved after the AWE diet were associated with vitamin K production, immunity, gut lining integrity and detoxification. Fig. 3a Principal component analysis of the subjects’ gut functional pathway composition profile ordinated based on subject (color) and timepoint (shape) using centered-log-ratio transformation on a Euclidean distance, with nonsignificant variation across timepoint (Stratified PERMANOVA R2 = 0.037, $$p \leq 0.204$$); b Shannon diversity of the functional pathway profile across timepoint; c Abundance of chondroitin sulfate degradation I and UDP-N-acetyl-glucosamine biosynthesis I across timepoint ## Microbiome correlation with wellbeing Survey Participants’ reported outcome measures of general wellbeing were evaluated through a weekly (D7, 14 and 21), 3-point survey of whether they felt they had more energy (‘energetic’), fullness (meal satiety), and perceived healthiness, throughout the study duration (at 6 evaluation timepoints). Overall, and as the study progressed, all participants’ scores of meal satisfaction, energy levels and feeling healthier, increased (linear model $p \leq 0.05$, Fig. 4a). Importantly, several species were significantly correlated with the participants’ survey metrics (Fig. 4b). Positive correlations were seen for fullness with Bacteroides eggerthii; energetic status with B. uniformis, B. longum, Phascolarctobacterium succinatutens, and Eubacterium eligens; healthy status with Faecalibacterium prausnitzii, Prevotella sp. CAG 5226, Roseburia hominis, and Roseburia sp. CAG 182; and overall response with E. eligens and Corprococcus eutactus. Negative correlations were seen for fullness with B. vulgatus; for healthy status with Bifidobacterium pseudocatenulatum; and overall response with *Dorea longicatena* and B. pseudocatenulatum. Fig. 4a Three-point self-rate survey on energy (energetic), satiety (fullness), and health (healthy), and total (Total) level of the subjects throughout the intervention period; b Species significantly associated with each of the survey variables, analysed using linear mixed model, adjusted for age, sex, and BMI, with statistical significance measured using the likelihood ratio test ($p \leq 0.05$) ## Effect of BMI on microbiome changes Participants were classified as either moderate or strong responders based on the net Shannon diversity change after the three weeks intervention period (Fig. 5a). Interestingly, strong responders all belonged to a higher weight category compared to moderate responders (Fig. 5b).Fig. 5a Distribution of responders across subject’s bodyweight class; b Shannon diversity of subjects across responder categories ## Microbiome correlation with nutritional intake Data from FFQ completed during the non-AWE phase were divided into a lower and upper quartile for convenience, and analyzed to determine if changes in participants’ microbiome diversity were nutrition-related. No nutrient consumption metric was significantly associated with changes in microbiome diversity (Wilcoxon test, $p \leq 0.05$; Supplementary Fig. 4). Despite this, subjects with low fibre consumption during the non-AWE phase seemed to exhibit a higher microbiome diversity than those with higher fibre consumption. We evaluated whether nutrient consumption was correlated with species that are also known to be beneficial or pathogenic and found 42 beneficial and 17 pathogenic species in our participants (Table 1).Table 1Number of known beneficial and pathogenic species observed in our cohortStatusPresenceNBeneficialAbsent8BeneficialPresent42PathogenicAbsent1PathogenicPresent17 Nutritional values from the FFQ data was correlated with these 59 species, filtered to include species-nutrient pairing with $p \leq 0.05$ and absolution R2 > 0.4. Nineteen unique species (pathogenic, $$n = 7$$; and beneficial, $$n = 12$$) were found, and associated with five nutrient metrics (Supplementary Fig. 5). Beneficial species were generally negatively associated with calorie, carbohydrate, fat, and protein intake, except for P. faecium which had a mixed outcome for fibre intake. In contrast, pathogenic species demonstrated the opposite trend as nutrients positively correlated with bacterial abundance, except for D. longicatena. Fibre consumption was also negatively associated with all species associated with pathogenic features. ## Overview We found 369 species and 453 functional pathways in our participants’ gut microbiomes across the study duration, with diversity remaining stable and unaffected by demographics, although overweight participants had more diversity at the end of the study (day 21) than those with normal BMI. All overweight participants responded better than other weight groups to the AWE diet. Additionally, participant-reported wellbeing scores (satiety, energetic and healthier) were unanimously higher at day 21, potentially due to diet-associated improvements in SCFA production, insulin signaling and GABA signaling. On AWE diet days, exclusion of animal fat and lower protein consumption likely created a dietary composition that suppressed 19 disease-related bacteria, as others have reported [36, 37]. ## AWE diet induced species changes in the gut microbiome In the human distal gut, B. thetaiotaomicron ferments simple carbohydrates and complex plant polysaccharides [38]. In mice, B. thetaiotaomicron BPI-5482 significantly increased total body fat and promoted fat storage [39]. B. thetaiotaomicron increases the hepcidin hormone [40] which can worsen metabolic disorders, increase weight gain and fasting glucose levels, impair glucose tolerance and increase liver accumulation of fatty acids. However, colon fermentation by B. thetaiotaomicron, B. eggerthii and B. xylanisolvens, produces beneficial prebiotic metabolites in obese individuals and obesity-related conditions [41]. B. xylanisolvens also ferments alginates [42] into SCFAs that fuel intestinal epithelial and immune cells [43], maintain gut health [44] and inhibit large intestine production of toxic metabolites [42]. Leuconostoc garlicum exists naturally in fruits, vegetables and plant roots [45], dairy products, wine and sugar [46], but are not typically part of human gut flora [47]. L. garlicum ferments sucrose into dextran [48] and is often used as a probiotic or starter [45] in plant-based fermented foods like kimchi. The increased abundance of L. garlicum is therefore expected, considering the plant-based nature of the AWE diet. However, more work is needed to understand the probiotic roles of the Leuconostoc genus. The W. confusa F213 [49]strain ferments glucose into lactic acid, ethanol and/or acetate in fermented foods [50]. However, it leads to continuous ethanol [51] production in the large bowel, thus affecting peripheral blood alcohol levels. In rats, [51] hyperlipidemia and NAFLD resulted from high-fructose intake elevating ethanol levels in faeces and peripheral blood. The suppression of W. confuse following AWE diet intervention therefore shows the potential to confer protective effect against these conditions. Romboutsia ilealis [52] is linked to protective human leukocyte antigen (HLA) haplotypes, although some HLA allele combinations and gut microbiome changes are associated with autoimmune diseases like type 1 diabetes [53]. Probiotic consumption increases R. ilealis levels [54] and decreases pro-inflammatory plasma cytokines. In primary sclerosing cholangitis with inflammatory bowel disease, gluten-free diets reduced R. ilealis [55]. Similarly, in mice studies [56] of colitis, selenium-enriched *Lactobacillus acidophilus* improved Romboutsia-promoted intestinal inflammation and significantly reduced their levels. Collinsella intestinalis ferment carbohydrates but not fiber, and flourish with low-fiber diets [57] where they may alter gut microbiome fermentation and cause harmful metabolic or inflammatory effects. High Collinsella levels are also associated with westernized [58], low-fibre and high red meat diets [59], with chronic diseases [60] and negative effects on cholesterol metabolism [61]. Collinsella levels are decreased by high-fibre diets [64]. After six weeks in one low-calorie weight-loss program, Collinsella significantly decreased by 8.4-fold [62] yet weight-loss persisted along with fecal microbiome changes. Collinsella facilitate intestinal absorption of cholesterol (thus increasing circulating cholesterols) [63], reduce liver glycogenesis and increase triglyceride production. In high-fibre macrobiotic diets, Collinsella improve metabolic responses in type II diabetes [64], although Collinsella can also be present at high levels [1]. Bacteriophages eliminate bacteria selectively [65], and can impact their host’s metabolism and immunity [66]. Dietary changes may increase stress in bacterial hosts, increase active phage numbers, and cause lysogenic phages to enter lytic stages [67]. ## Changes in functional pathways due to the AWE diet Changes in species abundance may not correlate with changes in function, as we detected few significant differences from the detected 384 functional pathways across timepoints. Nevertheless, our pairwise comparison identified UDP-N-acetyl-D-glucosamine biosynthesis I and chondroitin sulfate degradation I (bacterial) to be reduced and elevated at the end of the intervention period, respectively. UDP-N-acetyl-D-glucosamine is found in barley plant extracts and mung-bean seedlings [68], where it is part of a glucose metabolism pathway that is increased in insulin-resistant obese Chinese children and adolescents [69]. It may also play roles in the emergence of insulin resistance and diabetic vascular complications [70]. Chondroitin sulfate degradation was thought to be depleted in in vitro studies examining the effect of consuming a Korean traditional fermented soybean soup [71]. ## Wellbeing surveys correlated with various gut microbiome species Several species was positively linked with the wellbeing survey administered to the subjects across the intervention. Fullness was significantly positively correlated with B. eggerthii and negatively with B. vulgatus; energetic status was significantly positively correlated with B. uniformis, B. longum, P. succinatutens, and E. eligens; healthy status was significantly positively correlated with F. prausnitzii, Prevotella sp. CAG 5226, Roseburia hominis, and Roseburia sp. CAG 182, but negatively with B. pseudocatenulatum. The overall response was significantly positively correlated with E. eligens and C. eutactus and negatively with D. longicatena and B. pseudocatenulatum. Bifidobacterium may protect against obesity, reduce serum cholesterol, triglyceride, and glucose levels, and improve insulin resistance and glucose tolerance [72]. B. pseudocatenulatum degrades xylan-derived carbohydrates into SCFA [73] and may improve colitis by modifying the gut microbiome, blocking inflammatory cytokines and signaling, and maintaining the intestinal barrier [74]. Our study negatively correlated B. pseudocatenulatum with health status, but more work is needed to determine if this was a strain-specific observation. C. eutactus is an obligate anaerobe that and is a constituent of healthy guts [75] but is present at the lowest abundance in the irritable bowel syndrome D subtype [76]. C. eutactus leads to the production of butyrate [77], while Coprococcus species are generally associated with a better quality of life and are depleted in depression [78]. Another obligate anaerobe, D. longicatena, is negatively correlated with markers of dyslipidaemia or insulin resistance, indicating its beneficial probiotic effect [79]. However, its role is unclear as it is increased in Crohn’s remissions [80] but decreased in heart failure [81]. Supplementation with polyphenol-rich citrus fruit extracts has been found to significantly increase levels of B. eggerthii and Roseburia sp. [ 82], while red wine polyphenol and sorghum bran consumption supported Roseburia sp. growth in overweight participants. Roseburia sp. is abundant in the intestine where it produces butyrates, and may combat inflammation and obesity. Fecal sequencing detected more Prevotella and Roseburia but fewer Bacteroides in omnivores than in vegans and vegetarians [83], while abundant Roseburia sp. CAG182 was detected [84] in severe steatosis versus no-steatosis patients. Roseburia CAG182, F. prausnitzii and E. eligens, are part of a microbial signature for cardiometabolic health, and cluster with other species in healthy plant- or animal-based foods [85]. This agrees with our positive correlation of these species with energy, health and overall responses, even in our small cohort of overweight or obese individuals. Bacteroidetes associate positively with fat but negatively with Firmicutes. B. eggerthii and B. uniformis were enriched in individuals with low visceral fat [86], while B. uniformis protected against metabolic disorders and obesity [86]. Thus, some Bacteroides species could be considered as probiotics in the management of obesity. We correlated several Firmicutes species with a healthy condition. R. hominis is known to upregulate genes for chemotaxis, mobilization and motility [87], and plays roles in gut barrier function and immune modulation. Additionally, P. succinatutens degrades dietary fibre into succinate and subsequently into propionate, thus conferring anti-inflammatory and antitumor properties [88]. It is worth noting that P. succinatutens has also been reported to be enriched in patients suffering from ulcerative colitis [89], warranting further study on its role in human health. ## Association between nutritional intake and the microbiome Our participants’ nutrient consumption also correlated with seven and twelve species which have been associated with pathogenicity and beneficial health effect, respectively. Beneficial species were negatively associated with calorie, carbohydrate, fat and protein consumption, although the impact on P. faecium following fibre consumption was mixed. Pathogenic species positively correlated with bacterial abundance, and importantly, negatively correlated with fibre consumption. Commensal gut bacteria produce SCFAs by fermenting dietary fibre, which lowers postprandial insulin responses and blood glucose levels. Low-fiber diets support mucus-degrading bacteria and the growth of pathogens that compromise the colonic mucus barrier [90]. Dietary fibre also influences the immune system to produce more T cells which suppresses inflammation [3] and regulates the inflammasomes [91]. A short-term increase in dietary fiber can significantly increase F. prausnitzii [92], whereas consumption of apple pectin-derived and inulin-derived indigestible carbohydrates increase B. uniformis, B. eggerthii, B. thetaiotaomicron and B. vulgatus [93]. However, over the long-term, gut microbiome stability was similar between individuals fed high-fat/low-fiber or low-fat/high-fiber diets and those on identical, short-term diets, although Bacteroides and Prevotella were more associated with long-term consumption of proteins, carbohydrates and animal fats. Furthermore, short-term consumption of animal-based diets led to fewer Firmicutes, while plant-based diets increased bile-tolerant microbes, like Bacteroides [31]. Though limited, the existing human studies on specific foods show that microbes like Bacteroidetes are increased by fat intake, while microbes like Firmicutes decrease. Vegan and omnivorous gut microbiomes are dominated by both Firmicutes and Bacteroides, while omnivores have more Proteobacteria and Roseburia/Eubacterium rectal [94], vegans have more Verrucomicrobiota, lacto-vegetarians have more F. prausnitzii, and vegetarians and vegans both have fewer Bacteroides or Bifidobacterium [95]. F. prausnitzii may protect against obesity as it produces butyrate and anti-inflammatory metabolites [96]. As F. prausnitzii was depleted in metabolically healthy but obese individuals, its absence may promote obesity. Additionally, Prevotella are positively correlated with high-fibre diets [97], and Prevotella-rich diets are linked to weight-loss [98], less cholesterol [99] and improved glucose and succinate metabolism [100]. A Mediterranean diet interventional study found that Prevotella degrades complex polysaccharides in high-fibre diets, reducing insulin resistance in overweight participants [101]. These observations supported the beneficial effect of AWE diet which observed the positive correlation between health status and Prevotella CAG:5226. ## Benefit of AWE diet on subjects in the high BMI ranges The majority of our cohort were overweight or obese. Interestingly, obese and overweight patients were more likely to respond to the AWE intervention based on their Shannon diversity. Nevertheless, we acknowledge the skewed distribution of our data which had employed participants from a higher BMI group. Despite this, our observation suggests the potential effect of AWE diet in assisting weight management. ## Limitation Our study was limited by a limited sample size and ethnic diversity, and inability to record macronutrient and micronutrient consumption which prevented correlations between nutrient consumption and diet intervention or microbiome effects. Further study is also needed to ascertain the long-term effects of the diet on the microbiome and confirm the persistence of the diet’s benefits. ## Conclusion Our analysis of microbiome changes occurring during the consumption of the plant-based, AWE diet, highlighted the benefits of the increase in fibre intake, even though participants adhered to this meal plan for just 5 days a week and resumed their normal diets in for a subsequent 2 days. Participants, especially overweight and obese individuals, experienced positive changes associated with fullness, health status, energy and overall response. While more data is needed on the exact physiological impact exacted by the alteration of each microbe, our data suggest that the AWE diet benefits all individuals, especially those of higher BMI ranges. ## Supplementary Information Additional file 1: Supplementary Fig 1. Demographic subset of Shannon diversity index across age, sex, and BMI. Supplementary Fig 2. Species with increasing abundance over the intervention period. Supplementary Fig 3. Pathways with increasing abundance over the intervention period. Supplementary Fig 4. Shannon diversity of subjects based on levels of nutrients consumed. Supplementary Fig 5. Correlation between reported beneficial and pathogenic species with nutrient consumed. ## References 1. Rajilić-Stojanović M, de Vos WM. **The first 1000 cultured species of the human gastrointestinal microbiota**. *FEMS Microbiol Rev* (2014.0) **38** 996-1047. DOI: 10.1111/1574-6976.12075 2. Turpin W, Espin-Garcia O, Xu W, Silverberg MS, Kevans D, Smith MI. **Association of host genome with intestinal microbial composition in a large healthy cohort**. *Nat Genet* (2016.0) **48** 1413-1417. DOI: 10.1038/ng.3693 3. Singh RK, Chang HW, Yan D, Lee KM, Ucmak D, Wong K, Abrouk M, Farahnik B, Nakamura M, Zhu TH, Bhutani T, Liao W. **Influence of diet on the gut microbiome and implications for human health.**. *J Transl Med* (2017.0) **15** 73. DOI: 10.1186/s12967-017-1175-y 4. Cheng Z, Zhang L, Yang L, Chu H. **The critical role of gut microbiota in obesity**. *Front Endocrinol (Lausanne)* (2022.0) **20** 1025706. DOI: 10.3389/fendo.2022.1025706 5. Gupta A, Singh V, Mani I. **Dysbiosis of human microbiome and infectious diseases**. *Prog Mol Biol Transl Sci* (2022.0) **192** 33-51. DOI: 10.1016/bs.pmbts.2022.06.016 6. Singh S, Verma N, Taneja N. **The human gut resistome: Current concepts & future prospects**. *Indian J Med Res* (2019.0) **150** 345-358. DOI: 10.4103/ijmr.IJMR_1979_17 7. Barthow C, Hood F, Crane J, Huthwaite M, Weatherall M, Parry-Strong A. **A randomised controlled trial of a probiotic and a prebiotic examining metabolic and mental health outcomes in adults with pre-diabetes**. *BMJ Open.* (2022.0) **12** e055214. DOI: 10.1136/bmjopen-2021-055214 8. Fujimori S, Gudis K, Mitsui K, Seo T, Yonezawa M, Tanaka S. **A randomized controlled trial on the efficacy of synbiotic versus probiotic or prebiotic treatment to improve the quality of life in patients with ulcerative colitis**. *Nutrition* (2009.0) **25** 520-525. DOI: 10.1016/j.nut.2008.11.017 9. Zhang C, Jiang J, Wang C, Li S, Yu L, Tian F. **Meta-analysis of randomized controlled trials of the effects of probiotics on type 2 diabetes in adults**. *Clin Nutr* (2022.0) **41** 365-373. DOI: 10.1016/j.clnu.2021.11.037 10. Kim CS, Cha L, Sim M, Jung S, Chun WY, Baik HW. **Probiotic supplementation improves cognitive function and mood with changes in gut microbiota in community-dwelling older adults: a randomized, double-blind, placebo-controlled, multicenter trial**. *J Gerontol A Biol Sci Med Sci* (2021.0) **76** 32-40. DOI: 10.1093/gerona/glaa090 11. Karakula-Juchnowicz H, Rog J, Juchnowicz D, Łoniewski I, Skonieczna-Żydecka K, Krukow P. **The study evaluating the effect of probiotic supplementation on the mental status, inflammation, and intestinal barrier in major depressive disorder patients using gluten-free or gluten-containing diet (SANGUT study): a 12-week, randomized, double-blind, and placebo-controlled clinical study protocol**. *Nutr J* (2019.0) **18** 50. DOI: 10.1186/s12937-019-0475-x 12. Montassier E, Valdés-Mas R, Batard E, Zmora N, Dori-Bachash M, Suez J. **Probiotics impact the antibiotic resistance gene reservoir along the human GI tract in a person-specific and antibiotic-dependent manner**. *Nat Microbiol* (2021.0) **6** 1043-1054. DOI: 10.1038/s41564-021-00920-0 13. Suez J, Zmora N, Zilberman-Schapira G, Mor U, Dori-Bachash M, Bashiardes S. **Post-Antibiotic Gut Mucosal Microbiome Reconstitution Is Impaired by Probiotics and Improved by Autologous FMT**. *Cell* (2018.0) **174** 1406-1423.e16. DOI: 10.1016/j.cell.2018.08.047 14. Li Z, Quan G, Jiang X, Yang Y, Ding X, Zhang D. **Effects of Metabolites Derived From Gut Microbiota and Hosts on Pathogens**. *Front Cell Infect Microbiol* (2018.0) **14** 314. DOI: 10.3389/fcimb.2018.00314 15. Tan J, McKenzie C, Potamitis M, Thorburn AN, Mackay CR, Macia L. **The role of short-chain fatty acids in health and disease**. *Adv Immunol* (2014.0) **121** 91-119. DOI: 10.1016/B978-0-12-800100-4.00003-9 16. Valdes AM, Walter J, Segal E, Spector TD. **Role of the gut microbiota in nutrition and health**. *BMJ.* (2018.0) **13** k2179. DOI: 10.1136/bmj.k2179 17. Orlich MJ, Singh PN, Sabaté J, Jaceldo-Siegl K, Fan J, Knutsen S. **Vegetarian dietary patterns and mortality in Adventist Health Study 2**. *JAMA Intern Med* (2013.0) **173** 1230-1238. DOI: 10.1001/jamainternmed.2013.6473 18. Tomova A, Bukovsky I, Rembert E, Yonas W, Alwarith J, Barnard ND. **The Effects of Vegetarian and Vegan Diets on Gut Microbiota**. *Front Nutr* (2019.0) **17** 47. DOI: 10.3389/fnut.2019.00047 19. Holscher HD. **Dietary fiber and prebiotics and the gastrointestinal microbiota**. *Gut Microbes* (2017.0) **8** 172-184. DOI: 10.1080/19490976.2017.1290756 20. Glick-Bauer M, Yeh MC. **The health advantage of a vegan diet: exploring the gut microbiota connection**. *Nutrients* (2014.0) **6** 4822-4838. DOI: 10.3390/nu6114822 21. Cramer H, Kessler CS, Sundberg T, Leach MJ, Schumann D, Adams J. **Characteristics of Americans Choosing Vegetarian and Vegan Diets for Health Reasons**. *J Nutr Educ Behav.* (2017.0) **49** 561-567.e1. DOI: 10.1016/j.jneb.2017.04.011 22. Hjorth MF, Blædel T, Bendtsen LQ, Lorenzen JK, Holm JB, Kiilerich P. **Prevotella-to-Bacteroides ratio predicts body weight and fat loss success on 24-week diets varying in macronutrient composition and dietary fiber: results from a post-hoc analysis**. *Int J Obes (Lond)* (2019.0) **43** 149-157. DOI: 10.1038/s41366-018-0093-2 23. Beam A, Clinger E, Hao L. **Effect of diet and dietary components on the composition of the gut microbiota**. *Nutrients* (2021.0) **13** 2795. DOI: 10.3390/nu13082795 24. Hrncir T. **Gut microbiota dysbiosis: triggers, consequences, diagnostic and therapeutic options**. *Microorganisms* (2022.0) **10** 578. DOI: 10.3390/microorganisms10030578 25. Wang Y, Wei J, Zhang W, Doherty M, Zhang Y, Xie H. **Gut dysbiosis in rheumatic diseases: a systematic review and meta-analysis of 92 observational studies**. *EBioMedicine.* (2022.0) **80** 104055. DOI: 10.1016/j.ebiom.2022.104055 26. Nikolova VL, Smith MRB, Hall LJ, Cleare AJ, Stone JM, Young AH. **Perturbations in gut microbiota composition in psychiatric disorders: a review and meta-analysis**. *JAMA Psychiat* (2021.0) **78** 1343-1354. DOI: 10.1001/jamapsychiatry.2021.2573 27. Safari-Alighiarloo N, Emami Z, Rezaei-Tavirani M, Alaei-Shahmiri F, Razavi S. **Gut Microbiota and their associated metabolites in diabetes: a cross talk between host and microbes-a review**. *Metab Syndr Relat Disord* (2023.0) **21** 3-15. DOI: 10.1089/met.2022.0049 28. Fang C, Zuo K, Fu Y, Zhu X, Li J, Zhong J. **Aggravated gut microbiota and metabolomic imbalances are associated with hypertension patients comorbid with atrial fibrillation**. *Biomolecules* (2022.0) **12** 1445. DOI: 10.3390/biom12101445 29. 29.Das BK. Altered Gut Microbiota in Hepatocellular Carcinoma: Insights into the Pathogenic Mechanism and Preclinical to Clinical Findings. APMIS. 2022. 10.1111/apm.13282. Epub ahead of print. 30. Wu GD, Chen J, Hoffmann C, Bittinger K, Chen YY, Keilbaugh SA. **Linking long-term dietary patterns with gut microbial enterotypes**. *Science* (2011.0) **334** 105-108. DOI: 10.1126/science.1208344 31. David LA, Maurice CF, Carmody RN, Gootenberg DB, Button JE, Wolfe BE. **Diet rapidly and reproducibly alters the human gut microbiome**. *Nature* (2014.0) **505** 559-563. DOI: 10.1038/nature12820 32. Ruengsomwong S, La-Ongkham O, Jiang J, Wannissorn B, Nakayama J, Nitisinprasert S. **Microbial community of healthy thai vegetarians and non-vegetarians, their core gut microbiota, and pathogen risk**. *J Microbiol Biotechnol* (2016.0) **26** 1723-1735. DOI: 10.4014/jmb.1603.03057 33. Matijašić BB, Obermajer T, Lipoglavšek L, Grabnar I, Avguštin G, Rogelj I. **Association of dietary type with fecal microbiota in vegetarians and omnivores in Slovenia**. *Eur J Nutr* (2014.0) **53** 1051-1064. DOI: 10.1007/s00394-013-0607-6 34. 34.AWE by OsomeFood. https://www.awebyosomefood.com/. Accessed 11 Nov 2022. 35. 35.AWE by OsomeFood. A brand under Wholesome Savour. White Paper. [DATE]. AMILI Pte Ltd. Singapore. 36. Wan Y, Tong W, Zhou R, Li J, Yuan J, Wang F. **Habitual animal fat consumption in shaping gut microbiota and microbial metabolites**. *Food Funct* (2019.0) **10** 7973-7982. DOI: 10.1039/c9fo01490j 37. 37.Diet rich in animal foods, alcohol and sugar linked to 'inflammatory' gut microbiome. BMJ. Cited 2022 Nov 27. Available from: https://www.bmj.com/company/newsroom/diet-rich-in-animal-foods-alcohol-and-sugar-linked-to-inflammatory-gut-microbiome/. 38. Liu H, Shiver AL, Price MN, Carlson HK, Trotter VV, Chen Y. **Functional genetics of human gut commensal Bacteroides thetaiotaomicron reveals metabolic requirements for growth across environments**. *Cell Rep* (2022.0) **34** 108789. DOI: 10.1016/j.celrep.2021.108789 39. Bäckhed F, Ding H, Wang T, Hooper LV, Koh GY, Nagy A. **The gut microbiota as an environmental factor that regulates fat storage**. *Proc Natl Acad Sci U S A* (2004.0) **101** 15718-15723. DOI: 10.1073/pnas.0407076101 40. Cho SH, Cho YJ, Park JH. **The human symbiont Bacteroides thetaiotaomicron promotes diet-induced obesity by regulating host lipid metabolism**. *J Microbiol* (2022.0) **60** 118-127. DOI: 10.1007/s12275-022-1614-1 41. Broekaert WF, Courtin CM, Verbeke K, Van de Wiele T, Verstraete W, Delcour JA. **Prebiotic and other health-related effects of cereal-derived arabinoxylans, arabinoxylan-oligosaccharides, and xylooligosaccharides**. *Crit Rev Food Sci Nutr* (2011.0) **51** 178-194. DOI: 10.1080/10408390903044768 42. Shang Q, Jiang H, Cai C, Hao J, Li G, Yu G. **Gut microbiota fermentation of marine polysaccharides and its effects on intestinal ecology: An overview**. *Carbohydr Polym* (2018.0) **1** 173-185. DOI: 10.1016/j.carbpol.2017.09.059 43. Michel C, Lahaye M, Bonnet C, Mabeau S, Barry JL. **In vitro fermentation by human faecal bacteria of total and purified dietary fibres from brown seaweeds**. *Br J Nutr* (1996.0) **75** 263-280. DOI: 10.1079/bjn19960129 44. den Besten G, van Eunen K, Groen AK, Venema K, Reijngoud DJ, Bakker BM. **The role of short-chain fatty acids in the interplay between diet, gut microbiota, and host energy metabolism**. *J Lipid Res* (2013.0) **54** 2325-2340. DOI: 10.1194/jlr.R036012 45. Salvetti E, Campedelli I, Larini I, Conedera G, Torriani S. **Exploring Antibiotic Resistance Diversity in Leuconostoc spp. by a Genome-Based Approach: focus on the lsaA Gene**. *Microorganisms* (2021.0) **9** 491. DOI: 10.3390/microorganisms9030491 46. Camarasa A, Chiner E, Sancho-Chust JN. **Absceso pulmonar por Leuconostoc spp. en un paciente no inmunodeprimido [Pulmonary abscess due to Leuconostoc species in an immunocompetent patient]**. *Arch Bronconeumol* (2009.0) **45** 471-2. DOI: 10.1016/j.arbres.2009.01.004 47. Rogasa M, Sharpe ME. **Species differentiation of human vaginal Lactobacilli**. *J Gen Microbiol* (1960.0) **23** 197-201. DOI: 10.1099/00221287-23-1-197 48. Facklam R, Elliott JA. **Identification, classification, and clinical relevance of catalase-negative, gram-positive cocci, excluding streptococci and enterococci**. *Clin Microbiol Rev* (1995.0) **8** 479-495. DOI: 10.1128/CMR.8.4.479 49. Fatmawati NND, Gotoh K, Mayura IPB, Nocianitri KA, Suwardana GNR, Komalasari NLGY. **Enhancement of intestinal epithelial barrier function by Weissella confusa F213 and Lactobacillus rhamnosus FBB81 probiotic candidates in an in vitro model of hydrogen peroxide-induced inflammatory bowel disease**. *BMC Res Notes* (2020.0) **13** 489. DOI: 10.1186/s13104-020-05338-1 50. Kim HY, Bong YJ, Jeong JK, Lee S, Kim BY, Park KY. **Heterofermentative lactic acid bacteria dominate in Korean commercial kimchi**. *Food Sci Biotechnol* (2016.0) **25** 541-545. DOI: 10.1007/s10068-016-0075-x 51. Elshaghabee FMF D, Ghadimi D M, Habermann W, de Vrese HJ. **J. Effect of Oral Administration of Weissella confusa on Fecal and Plasma Ethanol Concentrations, Lipids and Glucose Metabolism in Wistar Rats Fed High Fructose and Fat Diet**. *Hepat Med* (2020.0) **12** 93-106. DOI: 10.2147/HMER.S254195 52. Gerritsen J, Hornung B, Renckens B, van Hijum SAFT, Martins Dos Santos VAP, Rijkers GT. **Genomic and functional analysis of Romboutsia ilealis CRIBT reveals adaptation to the small intestine**. *PeerJ* (2017.0) **5** e3698. DOI: 10.7717/peerj.3698 53. Russell JT, Roesch LFW, Ördberg M, Ilonen J, Atkinson MA, Schatz DA. **Genetic risk for autoimmunity is associated with distinct changes in the human gut microbiome**. *Nat Commun* (2019.0) **10** 3621. DOI: 10.1038/s41467-019-11460-x 54. Gerritsen J, Timmerman HM, Fuentes S, van Minnen LP, Panneman H, Konstantinov SR. **Correlation between protection against sepsis by probiotic therapy and stimulation of a novel bacterial phylotype**. *Appl Environ Microbiol* (2011.0) **77** 7749-7756. DOI: 10.1128/AEM.05428-11 55. Liwinski T, Hübener S, Henze L, Hübener P, Heinemann M, Tetzlaff M. **A prospective pilot study of a gluten-free diet for primary sclerosing cholangitis and associated colitis**. *Aliment Pharmacol Ther* (2022.0). DOI: 10.1111/apt.17256 56. Wu Z, Pan D, Jiang M, Sang L, Chang B. **Selenium-enriched lactobacillus acidophilus ameliorates dextran sulfate sodium-induced chronic colitis in mice by regulating inflammatory cytokines and intestinal microbiota**. *Front Med (Lausanne).* (2021.0) **8** 716816. DOI: 10.3389/fmed.2021.716816 57. Kageyama A, Benno Y, Nakase T. **Phylogenetic and phenotypic evidence for the transfer of Eubacterium aerofaciens to the genus Collinsella as Collinsella aerofaciens gen. nov., comb. nov**. *Int J Syst Bacteriol* (1999.0) **49 Pt 2** 557-65. DOI: 10.1099/00207713-49-2-557 58. Amato KR, Yeoman CJ, Cerda G, Schmitt CA, Cramer JD, Miller ME. **Variable responses of human and non-human primate gut microbiomes to a Western diet**. *Microbiome* (2015.0) **16** 53. DOI: 10.1186/s40168-015-0120-7 59. Foerster J, Maskarinec G, Reichardt N, Tett A, Narbad A, Blaut M. **The Influence of Whole Grain Products and Red Meat on Intestinal Microbiota Composition in Normal Weight Adults: A Randomized Crossover Intervention Trial**. *PLoS One.* (2014.0) **9** e109606. DOI: 10.1371/journal.pone.0109606 60. Chen J, Wright K, Davis JM, Jeraldo P, Marietta EV, Murray J. **An expansion of rare lineage intestinal microbes characterizes rheumatoid arthritis**. *Genome Med* (2016.0) **8** 43. DOI: 10.1186/s13073-016-0299-7 61. Ridlon JM, Kang DJ, Hylemon PB. **Bile salt biotransformations by human intestinal bacteria**. *J Lipid Res* (2006.0) **47** 241-259. DOI: 10.1194/jlr.R500013-JLR200 62. Frost F, Storck LJ, Kacprowski T, Gärtner S, Rühlemann M, Bang C. **A structured weight loss program increases gut microbiota phylogenetic diversity and reduces levels of Collinsella in obese type 2 diabetics: A pilot study**. *PLoS One.* (2019.0) **14** e0219489. DOI: 10.1371/journal.pone.0219489 63. Wegner K, Just S, Gau L, Mueller H, Gerard P, Lepage P. **Rapid analysis of bile acids in different biological matrices using LC-ESI-MS/MS for the investigation of bile acid transformation by mammalian gut bacteria**. *Anal Bioanal Chem* (2017.0) **409** 1231-1245. DOI: 10.1007/s00216-016-0048-1 64. Candela M, Biagi E, Soverini M, Consolandi C, Quercia S, Severgnini M. **Modulation of gut microbiota dysbioses in type 2 diabetic patients by macrobiotic Ma-Pi 2 diet**. *Br J Nutr* (2016.0) **116** 80-93. DOI: 10.1017/S0007114516001045 65. Ogilvie LA, Caplin J, Dedi C, Diston D, Cheek E, Bowler L. **Comparative (meta)genomic analysis and ecological profiling of human gut-specific bacteriophage φB124–14**. *PLoS One.* (2012.0) **7** e35053. DOI: 10.1371/journal.pone.0035053 66. G Górski A, Wazna E, Dabrowska BW, Dabrowska K, Switała-Jeleń K, Miedzybrodzki R. **Bacteriophage translocation**. *FEMS Immunol Med Microbiol* (2006.0) **46** 313-9. DOI: 10.1111/j.1574-695X.2006.00044.x 67. Gurry T, Gibbons SM, Nguyen LTT, Kearney SM, Ananthakrishnan A. **Predictability and persistence of prebiotic dietary supplementation in a healthy human cohort**. *Sci Rep.* (2018.0) **8** 12699. DOI: 10.1038/s41598-018-30783-1 68. Mayer FC, Bikel I, Hassid WZ. **Pathway of Uridine Diphosphate N-Acetyl-d Glucosamine Biosynthesis in Phaseolus aureus**. *Plant Physiol* (1968.0) **43** 1097-1107. DOI: 10.1104/pp.43.7.1097 69. Yuan X, Chen R, Zhang Y, Lin X, Yang X, McCormick KL. **Gut Microbiota of Chinese Obese Children and Adolescents With and Without Insulin Resistance**. *Front Endocrinol (Lausanne).* (2021.0) **12** 636272. DOI: 10.3389/fendo.2021.636272 70. Ohiagu FO, Chikezie PC, Chikezie CM. **Pathophysiology of diabetes mellitus complications: Metabolic events and control**. *Biomed Res Ther* (2021.0) **8** 4243-4257. DOI: 10.15419/bmrat.v8i3.663 71. Singh V, Hwang N, Ko G, Tatsuya U. **Effects of digested Cheonggukjang on human microbiota assessed by in vitro fecal fermentation**. *J Microbiol* (2021.0) **59** 217-227. DOI: 10.1007/s12275-021-0525-x 72. 72.Hussey S and Bergman M. The Gut Microbiota and Effects on MetaboLism. December 2014. In book: Pathobiology of Human Disease (508–526). 10.1016/B978-0-12-386456-7.02009-8. 73. Wang Z, Bai Y, Pi Y, Gerrits WJJ, de Vries S, Shang L. **Xylan alleviates dietary fiber deprivation-induced dysbiosis by selectively promoting Bifidobacterium pseudocatenulatum in pigs**. *Microbiome* (2021.0) **9** 227. DOI: 10.1186/s40168-021-01175-x 74. Chen Y, Yang B, Stanton C, Ross RP, Zhao J, Zhang H. *J Agric Food Chem* (2021.0) **69** 1496-1512. DOI: 10.1021/acs.jafc.0c06329 75. Kassinen A, Krogius-Kurikka L, Mäkivuokko H, Rinttilä T, Paulin L, Corander J. **The fecal microbiota of irritable bowel syndrome patients differs significantly from that of healthy subjects**. *Gastroenterology* (2007.0) **133** 24-33. DOI: 10.1053/j.gastro.2007.04.005 76. Malinen E, Krogius-Kurikka L, Lyra A, Nikkilä J, Jääskeläinen A, Rinttilä T. **Association of symptoms with gastrointestinal microbiota in irritable bowel syndrome**. *World J Gastroenterol* (2010.0) **16** 4532-4540. DOI: 10.3748/wjg.v16.i36.4532 77. Louis P, Duncan SH, McCrae SI, Millar J, Jackson MS, Flint HJ. **Restricted distribution of the butyrate kinase pathway among butyrate-producing bacteria from the human colon**. *J Bacteriol* (2004.0) **186** 2099-2106. DOI: 10.1128/JB.186.7.2099-2106.2004 78. Valles-Colomer M, Falony G, Darzi Y, Tigchelaar EF, Wang J, Tito RY. **The neuroactive potential of the human gut microbiota in quality of life and depression**. *Nat Microbiol* (2019.0) **4** 623-632. DOI: 10.1038/s41564-018-0337-x 79. Brahe LK, Le Chatelier E, Prifti E, Pons N, Kennedy S, Hansen T. **Specific gut microbiota features and metabolic markers in postmenopausal women with obesity**. *Nutr Diabetes.* (2015.0) **5** e159. DOI: 10.1038/nutd.2015.9 80. Mondot S, Lepage P, Seksik P, Allez M, Tréton X, Bouhnik Y. **Structural robustness of the gut mucosal microbiota is associated with Crohn's disease remission after surgery**. *Gut* (2016.0) **65** 954-962. DOI: 10.1136/gutjnl-2015-309184 81. Kamo T, Akazawa H, Suda W, Saga-Kamo A, Shimizu Y, Yagi H. **Dysbiosis and compositional alterations with aging in the gut microbiota of patients with heart failure**. *PLoS One.* (2017.0) **12** e0174099. DOI: 10.1371/journal.pone.0174099 82. Sost MM, Ahles S, Verhoeven J, Verbruggen S, Stevens Y, Venema K. **A citrus fruit extract high in polyphenols beneficially modulates the gut microbiota of healthy human volunteers in a validated In Vitro Model of the Colon**. *Nutrients* (2021.0) **13** 3915. DOI: 10.3390/nu13113915 83. Tarallo S, Ferrero G, De Filippis F, Francavilla A, Pasolli E, Panero V. **Stool microRNA profiles reflect different dietary and gut microbiome patterns in healthy individuals**. *Gut* (2022.0) **71** 1302-1314. DOI: 10.1136/gutjnl-2021-325168 84. Zeybel M, Arif M, Li X, Altay O, Yang H, Shi M. **Multiomics Analysis Reveals the Impact of Microbiota on Host Metabolism in Hepatic Steatosis**. *Adv Sci (Weinh).* (2022.0) **9** e2104373. DOI: 10.1002/advs.202104373 85. Asnicar F, Berry SE, Valdes AM, Nguyen LH, Piccinno G, Drew DA. **Microbiome connections with host metabolism and habitual diet from 1,098 deeply phenotyped individuals**. *Nat Med* (2021.0) **27** 321-332. DOI: 10.1038/s41591-020-01183-8 86. Yan H, Qin Q, Chen J, Yan S, Li T, Gao X. **Gut Microbiome Alterations in Patients With Visceral Obesity Based on Quantitative Computed Tomography**. *Front Cell Infect Microbiol.* (2022.0) **11** 823262. DOI: 10.3389/fcimb.2021.823262 87. Patterson AM, Mulder IE, Travis AJ, Lan A, Cerf-Bensussan N, Gaboriau-Routhiau V. **Human gut symbiont roseburia hominis promotes and regulates innate immunity**. *Front Immunol* (2017.0) **26** 1166. DOI: 10.3389/fimmu.2017.01166 88. Gontijo VS, Viegas FPD, Ortiz CJC, de Freitas SM, Damasio CM, Rosa MC. **Molecular hybridization as a tool in the design of multi-target directed drug candidates for neurodegenerative diseases**. *Curr Neuropharmacol* (2020.0) **18** 348-407. DOI: 10.2174/1385272823666191021124443 89. Gryaznova MV, Solodskikh SA, Panevina AV, Syromyatnikov MY, Dvoretskaya YD, Sviridova TN. **Study of microbiome changes in patients with ulcerative colitis in the Central European part of Russia**. *Heliyon.* (2021.0) **7** e06432. DOI: 10.1016/j.heliyon.2021.e06432 90. Desai MS, Seekatz AM, Koropatkin NM, Kamada N, Hickey CA, Wolter M. **A dietary fiber-deprived gut microbiota degrades the colonic mucus barrier and enhances pathogen susceptibility**. *Cell* (2016.0) **167** 1339-1353.e21. DOI: 10.1016/j.cell.2016.10.043 91. Macia L, Tan J, Vieira AT, Leach K, Stanley D, Luong S. **Metabolite-sensing receptors GPR43 and GPR109A facilitate dietary fibre-induced gut homeostasis through regulation of the inflammasome**. *Nat Commun* (2015.0) **1** 6734. DOI: 10.1038/ncomms7734 92. Shen Q, Zhao L, Tuohy KM. **High-level dietary fibre up-regulates colonic fermentation and relative abundance of saccharolytic bacteria within the human faecal microbiota in vitro**. *Eur J Nutr* (2012.0) **51** 693-705. DOI: 10.1007/s00394-011-0248-6 93. Chung WS, Walker AW, Louis P, Parkhill J, Vermeiren J, Bosscher D. **Modulation of the human gut microbiota by dietary fibres occurs at the species level**. *BMC Biol* (2016.0) **11** 3. DOI: 10.1186/s12915-015-0224-3 94. Kabeerdoss J, Devi RS, Mary RR, Ramakrishna BS. **Faecal microbiota composition in vegetarians: comparison with omnivores in a cohort of young women in southern India**. *Br J Nutr* (2012.0) **108** 953-957. DOI: 10.1017/S0007114511006362 95. Zimmer J, Lange B, Frick JS, Sauer H, Zimmermann K, Schwiertz A. **A vegan or vegetarian diet substantially alters the human colonic faecal microbiota**. *Eur J Clin Nutr* (2012.0) **66** 53-60. DOI: 10.1038/ejcn.2011.141 96. Maioli TU, Borras-Nogues E, Torres L, Barbosa SC, Martins VD, Langella P. **Possible Benefits of**. *Front Pharmacol.* (2021.0) **12** 740636. DOI: 10.3389/fphar.2021.740636 97. Roager HM, Vogt JK, Kristensen M, Hansen LBS, Ibrügger S, Mærkedahl RB. **Whole grain-rich diet reduces body weight and systemic low-grade inflammation without inducing major changes of the gut microbiome: a randomised cross-over trial**. *Gut* (2019.0) **68** 83-93. DOI: 10.1136/gutjnl-2017-314786 98. Ortega-Santos CP, Whisner CM. **The Key to Successful Weight Loss on a High-Fiber Diet May Be in Gut Microbiome Prevotella Abundance**. *J Nutr* (2019.0) **149** 2083-2084. DOI: 10.1093/jn/nxz248 99. Eriksen AK, Brunius C, Mazidi M, Hellström PM, Risérus U, Iversen KN. **Effects of whole-grain wheat, rye, and lignan supplementation on cardiometabolic risk factors in men with metabolic syndrome: a randomized crossover trial**. *Am J Clin Nutr* (2020.0) **111** 864-876. DOI: 10.1093/ajcn/nqaa026 100. De Vadder F, Kovatcheva-Datchary P, Zitoun C, Duchampt A, Bäckhed F, Mithieux G. **Microbiota-Produced Succinate Improves Glucose Homeostasis via Intestinal Gluconeogenesis**. *Cell Metab* (2016.0) **24** 151-157. DOI: 10.1016/j.cmet.2016.06.013 101. Meslier V, Laiola M, Roager HM, De Filippis F, Roume H, Quinquis B. **Mediterranean diet intervention in overweight and obese subjects lowers plasma cholesterol and causes changes in the gut microbiome and metabolome independently of energy intake**. *Gut* (2020.0) **69** 1258-1268. DOI: 10.1136/gutjnl-2019-320438
--- title: In silico prioritisation of microRNA-associated common variants in multiple sclerosis authors: - Ifeolutembi A. Fashina - Claire E. McCoy - Simon J. Furney journal: Human Genomics year: 2023 pmcid: PMC10061723 doi: 10.1186/s40246-023-00478-4 license: CC BY 4.0 --- # In silico prioritisation of microRNA-associated common variants in multiple sclerosis ## Abstract ### Background Genome-wide association studies (GWAS) have highlighted over 200 autosomal variants associated with multiple sclerosis (MS). However, variants in non-coding regions such as those encoding microRNAs have not been explored thoroughly, despite strong evidence of microRNA dysregulation in MS patients and model organisms. This study explores the effect of microRNA-associated variants in MS, through the largest publicly available GWAS, which involved 47,429 MS cases and 68,374 controls. ### Methods We identified SNPs within the coordinates of microRNAs, ± 5-kb microRNA flanking regions and predicted 3′UTR target-binding sites using miRBase v22, TargetScan 7.0 RNA22 v2.0 and dbSNP v151. We established the subset of microRNA-associated SNPs which were tested in the summary statistics of the largest MS GWAS by intersecting these datasets. Next, we prioritised those microRNA-associated SNPs which are among known MS susceptibility SNPs, are in strong linkage disequilibrium with the former or meet a microRNA-specific Bonferroni-corrected threshold. Finally, we predicted the effects of those prioritised SNPs on their microRNAs and 3′UTR target-binding sites using TargetScan v7.0, miRVaS and ADmiRE. ### Results We have identified 30 candidate microRNA-associated variants which meet at least one of our prioritisation criteria. Among these, we highlighted one microRNA variant rs1414273 (MIR548AC) and four 3′UTR microRNA-binding site variants within SLC2A4RG (rs6742), CD27 (rs1059501), MMEL1 (rs881640) and BCL2L13 (rs2587100). We determined changes to the predicted microRNA stability and binding site recognition of these microRNA and target sites. ### Conclusions We have systematically examined the functional, structural and regulatory effects of candidate MS variants among microRNAs and 3′UTR targets. This analysis allowed us to identify candidate microRNA-associated MS SNPs and highlights the value of prioritising non-coding RNA variation in GWAS. These candidate SNPs could influence microRNA regulation in MS patients. Our study is the first thorough investigation of both microRNA and 3′UTR target-binding site variation in multiple sclerosis using GWAS summary statistics. ### Supplementary Information The online version contains supplementary material available at 10.1186/s40246-023-00478-4. ## Background Multiple sclerosis (MS) is a complex chronic neuro-inflammatory condition that affects the central nervous system (CNS). This condition leads to periods of neurological disability in patients and is the cause of most non-traumatic neurological injury in young adults [1, 2]. MS pathogenicity is linked to environmental and genetic factors [3, 4]. Understanding the genetic contributions to MS could aid in identifying candidate biomarkers and in predicting disease aetiology and progression. Genetic studies have uncovered part of the complexity of MS. More than 13 GWAS have been carried out on MS patients and control populations since 2007 [5]. Compared to earlier study designs, GWAS have contributed the most information about MS heritability, with over 200 non-MHC variants associated with the condition since the most recent GWAS [6]. Altogether, approximately $48\%$ of MS heritability has been accounted for [6]. However, there are challenges in interpreting the causal variants within risk loci [5, 7]. In particular, non-coding variants are not often prioritised in these interpretation strategies. MicroRNAs (miRNAs) are small ~ 22nt non-coding RNAs which are well conserved across organisms. They play central roles in post-transcriptional modification by binding the 3′UTR of their targets [8] (Fig. 1A), although other interactions have been reported within the 5′UTR, coding sequences and promoter sequences of target genes [9]. Interestingly, miRNAs have been shown to be dysregulated in immune cell subsets, cerebrospinal fluid (CSF) and plasma of MS patients, as well as in the MS mouse model, experimental autoimmune encephalomyelitis (EAE) [10, 11].Fig. 1A Schematic representation of microRNA transcription and microRNA–mRNA interaction. microRNAs are transcribed from DNA sequences and processed by DROSHA from the primary structure to precursor structure and by Dicer into the mature sequence. These processed mature microRNA sequences then interact with mRNA targets, leading to mRNA degradation or translational repression. B microRNA precursor secondary structure. Altogether, we identified SNPs which are located within precursor, mature and 5-kb microRNA flanking regions. C Flowchart summarising our microRNA exploration procedure. We used summary statistics from the largest MS GWAS meta-analysis [6] and two publically available datasets to investigate microRNA-associated variation in MS. To capture variation within human microRNAs, we extracted the genomic coordinates of human microRNA precursor and mature regions from miRBase v22 and intersected these with all human variants recorded in dbSNP v151. In addition, we extended the precursor regions by 5 kb up- and downstream to incorporate SNPs within regulatory features (D). Overall, among the SNPs tested in the IMSGC’s meta-analysis, we identified 314 SNPs within microRNA precursor/mature regions and 36,841 SNPs in 5-kb flanking regions. D In our prioritisation process, we identified microRNA SNPs [1] among known MS susceptibility SNPs, [2] in strong Linkage Disequilibrium (LD) with known MS risk SNPs and [3] which meet the adjusted p value threshold for the 314 microRNA SNPs tested in the IMSGC meta-analysis. A and D adapted from “microRNA in Cancer” and “The Principle of a Genome-wide Association Study (GWAS)” in Biorender.com [2022]. Retrieved from https://app.biorender.com/biorender-templates Despite strong evidence of microRNA dysregulation in MS patient samples and model organisms, there is limited literature on the role of microRNA variation in MS [12–15]. In contrast, variants in microRNAs and their processing machinery have been implicated in complex conditions such as cardio-metabolic conditions, colorectal cancer, glaucoma, Alzheimer’s disease and Parkinson’s disease [16–22]. We hypothesised that miRNA-associated variants are implicated in MS pathology. To explore this, we generated a bioinformatics pipeline to identify candidate MS susceptibility variants in microRNA genes and in the 3′UTR binding sites of microRNA targets using summary statistics from the most recent MS GWAS meta-analysis [6]. We then characterised and evaluated the effects of these variants using in silico methods and publically available datasets. Therefore, our main objectives were collation, identification and characterisation of novel (a) microRNA gene susceptibility SNPs in MS and (b) microRNA 3′UTR binding site SNPs in MS. ## microRNA susceptibility SNPs In order to investigate microRNA susceptibility SNPs in MS, we developed a prioritisation protocol as highlighted in Fig. 1. This protocol integrates common variation from dbSNP v151 with microRNA annotations from miRBase v22 [23, 24], in order to capture variation within precursor and mature microRNAs (Fig. 1B). We identified 60,638 SNPs in precursor/mature miRNA regions. These were obtained by intersecting 4573 mature and hairpin structures from miRBase with over 500 million SNPs from dbSNP v151 (Fig. 1C). This independent collation exceeds 56,911 microRNA SNPs (miR-SNPs) obtained from two older databases of microRNA variation, miRNASNPv3 and PolymiRTS [25, 26], and highlights the need to collate microRNA SNPs using more recent data. Overall, we examined miR-SNPs which (a) are among known MS susceptibility SNPs, (b) are in linkage disequilibrium (LD) with known MS susceptibility SNPs, (c) meet a microRNA-specific adjusted Bonferroni threshold and finally (d) MS susceptibility SNPs which lie within microRNA flanking regions (Fig. 1D). ## MicroRNA variants among known MS SNPs Initially, we identified 314 SNPs within precursor/mature microRNAs that were tested within the 2019 GWAS summary statistics [6] (Additional file 1: Table S1). However, none of these microRNA variants were among the reported MS susceptibility SNPs. To investigate this further, we mapped miR-SNPs which were in linkage disequilibrium (LD) with the susceptibility SNPs, in order to capture tagging miR-SNPs within the MS susceptibility loci. 3 of the 314 miR-SNPs (rs1414273, rs7247237 and rs7247767) were in LD with the susceptibility SNPs (see Methods). Among these 3 miR-SNPs, rs1414273 is in high LD (r2 = 0.97) with known MS susceptibility SNP rs10801908 (CD58), meets the genome-wide p value threshold ($$p \leq 8.48$$ × 10–16) and lies within the 3′ end of precursor hsa-mir-548ac (Fig. 2A). Only recently has attention been drawn to rs1414273/MIR548AC in MS. Hecker et al. [ 14] found that rs1414273 (MIR548AC) decouples transcription of CD58 and MIR548AC. Their findings support our independent prioritisation process for non-coding candidate variants which are in high LD with coding MS susceptibility SNPs. Fig. 2A Regional LocusZoom plot [62] showing high Linkage Disequilibrium (r2 > 0.9) between known MS susceptibility SNP rs10801908 (CD58) and our candidate SNP rs1414273, which lies in MIR548AC. Next, we highlight the predicted RNA secondary structure of the B reference sequence of hsa-mir-548ac compared to the C alternative (risk) allele. This figure shows the MEA prediction which has the greatest net change in free energy among the 3 models predicted by miRVaS (Additional file 1: Table S2). The arrow highlights the SNP rs1414273 (in red) located in the 3′ end (arm) of precursor sequence. Lower free energy measures indicate greater RNA stability; therefore, microRNA with the alternative risk allele is more thermodynamically stable than the reference allele. Candidate SNP rs2648841 is within genomic coordinates of MIR1208. D This variant represents a different signal from IMSGC SNPs rs6990534 and rs735542 (chr8:128175696) and E is not in LD with rs11989574, the peak SNP in its genomic region or rs1861842 (not shown) and rs759648 (chr8:129158945) which were implicated in African Americans and Europeans, respectively [53]. Although this SNP is below genome-wide significance ($$p \leq 3.86$$ × 10–5), its association with MS cannot be ruled out Next, we examined the effect of rs1414273 on the structural conformation of hsa-mir-548ac using miRVaS [27]. This SNP significantly impacts the flank and arm of the microRNA in both the 5′ and 3′ directions. The fold energy changes across the centroid (CEN), minimum fold energy (MFE) and maximum estimate accuracy (MEA) models are presented in Additional file 1: Table S2. We have represented the MEA, which has a net change of − 4.4∆ in Fig. 2B and C. In Fig. 2C, the risk allele (C) is predicted to create a more thermodynamically stable RNA secondary structure compared to the T allele. This could allow for more microRNA regulatory activity. In conclusion, rs1414273 is potentially associated with MS susceptibility and changes to MIR548AC stability. ## Genome-wide microRNA variants Next, to specifically focus on miRNA-related variants, we carried out Bonferroni correction on the p value threshold, adjusting for the 314 miRNA SNPs that were tested in the summary statistics. This process yielded 6 candidate MS-associated miR-SNPs within the precursor, loop and seed regions of 4 microRNAs: MIR548AC, MIR1208, MIR3135b and MIR6891 (Table 1). We established the functional implication of rs1414273 in MIR548AC in the previous step; therefore, we focused on the other 5 SNPs here. We examined the genomic context of the miR-SNPs and compared our candidates to reported GWAS signals.rs2648841 in the 3′ end of precursor MIR1208 (Table 1), which represents a signal separate from the IMSGC GWAS signals rs735542 (hg37 chr8:128175696), rs6990534 (PVT) (Fig. 2D), the proximal peaks rs11989574 and rs759648 (chr8:129158945 in another European GWAS) (Fig. 2E). Similar to rs1414273 (MIR548AC) above, rs2648841 was not among the prioritised effects and therefore was not among suggestive, non-replicated or no data effects outlined by the IMSGC. However, we dropped this SNP due to its nominal p value compared to the lead SNPs and because no structural changes were predicted in 2 of 3 thermodynamic models (Additional file 2: Fig. S1).Table 1Annotation of the 6 miRNA SNPs that passed the Bonferroni-adjusted p value threshold ($p \leq 0.05$/314)CHR: POS (hg38)RSIDA1/A2EUR_AFDiscovery GWAS p valueORmiRNAADmiRE annotationsOR interpretationPrioritisation results1:116560027rs1414273C/T0.14028.48E−161.2333mir-548acPrecursor_3PrimeEndriskHigh LD, structural change8:128150187rs2648841G/A,T0.01793.86E−051.1322mir-1208Precursor_3PrimeEndriskNo structural change6:31355288rs17881225G/C0.09847.72E−051.2163mir-6891Precursor_LoopriskSignal in high LD HLA region6:31355243rs2276448T/C0.23667.73E−121.2517mir-68913p_SeedriskSignal in high LD HLA region6:31355235rs2854001G/A0.21175.44E−381.5888mir-68913p_MatureriskSignal in high LD HLA region6:32749925rs4285314G/A0.53187.1E−1221.5134mir-3135bPrecursor_3PrimeEndriskSignal in high LD HLA regionADmiRE [28] was used to identify the locations of these SNPs. In order of consequence, SNPs in the seed region > mature > loop > precursor ends. The association with MIR548AC was explored in the previous section. The structural consequence of MIR1208 SNP was explored, while the microRNA-binding ability of MIR6891 was examined in the context of the seed SNP. Discovery GWAS p values and ORs of these SNPs are also presented in context Also among the 6 miR-SNPs (Table 1), 3 are within hsa-miR-6891-3p, a product of MIR6891 (rs17881225, rs2276448 and rs2854001). This microRNA is encoded within intron 4 of HLA-B and is co-transcribed with the mRNA, which is itself associated with MS susceptibility. Although these 3 candidate SNPs could have effects on target regulation, the GWAS signal is likely coming from the HLA variants identified by the IMSGC (rs2308655, rs3819284, rs1050556, HLA-B*52.01, HLA-B*38:01 and HLA-B*35:03). Among these 3 MIR6891 SNPs, rs2276448 lies within the seed region of the microRNA and possibly has the most significant effect on its target regulatory function compared to the SNPs in the mature and precursor loop regions. We explored the target-binding consequences of the seed SNP rs2276448 (MIR6891) in Additional file text (Additional file 2: Fig. S2). Finally, rs4285314 lies in the precursor 3′ end of MIR3135b, but is within the same susceptibility locus as the HLA-B variants, presenting the same challenge as the MIR6891 variants. Overall, having investigated the SNP in MIR548AC, we did not further prioritise any of the 5 Bonferroni-adjusted microRNA SNPs due to a) lack of predicted structural changes or b) high linkage disequilibrium in the MHC locus. ## microRNA flanking SNPs among known MS SNPs Having explored our microRNA variants with regard to susceptibility SNPs, LD and adjusted p values, we expanded our definition of microRNA variants to include those within ± 5-kb flanking regions of the precursors, in a similar approach to that employed by Fang and colleagues [21]. By extending the miRNA precursor coordinates by ± 5 kb, we aimed to incorporate microRNA regulatory features that might be influencing their expression. We found over 4 million SNPs in ± 5-kb precursor flanking regions of miRNAs (Fig. 1C). In total, 36,841 of these were tested in the summary statistics (Fig. 1C). Among these, two variants proximal to hsa-mir-10399 and hsa-mir-4492 were among known susceptibility SNPs. rs10271373 and rs149114341 (chr11:118783424 hg37) are downstream of hsa-mir-10399 and hsa-mir-4492, respectively (Additional file 1: Table S5), and were annotated as intergenic SNPs in the 2019 GWAS meta-analysis. An enhancer sequence is reported around the coordinates of rs149114341. However, we were unable to characterise its effects on MIR4492 using summary statistics only. rs10271373 maps downstream of MIR10399 as well as to the 3′UTR binding site of ZC3HAV1, a gene that has been implicated in MS [6]. Therefore, this SNP was prioritised in our 3′UTR binding site analysis instead. Altogether, our prioritisation process highlights rs1414273 (MIR548AC) as a candidate MS SNP among the other candidates (Additional file 1: Table S10). ## 3′UTR microRNA-binding site susceptibility SNPs Variants in the 3′UTRs of mRNAs could disrupt or create microRNA-binding sites, contributing to transcriptomic dysregulation. We explored variation in 3′UTR microRNA-binding sites that could be relevant to MS, by implementing a procedure similar to our microRNA variant collation (Fig. 3A).Fig. 3A Flowchart showing our 3′ UTR microRNA-binding site exploration pipeline using 3 publically available datasets and summary statistics provided by IMSGC [2019]. 3′UTR variant collation was performed separately from microRNA variant collation. We obtained variants from dbSNP v151 and integrated these into the 3′UTR binding sites predicted by RNA22 v2.0 and TargetScan v7.0 (see Methods) (B). Schematic showing the predicted effects of rs6742 on microRNA-binding ability to 3′UTR in SLC2A4RG. The C allele is expected to bind to 6 microRNAs differently to the T allele. Overall, we expect that the C allele is under stronger regulation than the T allele. C Venn Diagram showing the overlap between GWAS independent SNPs and our collection of 3′UTR SNPs which are in 3′UTR binding sites. Independent SNPs were identified through FUMA and a list of suggestive effects provided by the IMSGC. D Among the 19 independent SNPs from C, we tested the microRNA-binding ability of the 3′UTR binding sites containing 8 SNPs. Among these, 6 SNPs were found to cause microRNA-binding site changes in their 3′UTR sites. Here, we show the number of microRNAs that bind to the alternative and reference versions of the 3′UTR sequences, as well as the microRNAs that bind differently. The source column highlights which GWAS independent list the SNP has been output from. E LocusZoom regional plot showing the 3′UTR SNP rs2587100, which is independent, weakly suggestive, causes changes in microRNA-binding ability of BCL2L13 and is an eQTL for BCL2L13 in general monocytes and MS patient monocytes. This is our only candidate SNP which has MS patient specific eQTL evidence. No other SNPs in this region were prioritised among the genome-wide IMSGC SNPs. The highlighted intronic SNP rs9618043 (CECR2) is among the non-replicated SNPs (NR) from the IMSGC’s prioritised effects within this region (IMSGC Additional file 1: Table S6), while rs9618040 is not among the prioritised effects (intron CECR2) 3′UTR microRNA-binding sites were extracted from two microRNA–target prediction tools which implement different algorithms. Predictions from both TargetScan v7.0 and RNA22 v2.0 were used to capture microRNA–target interactions within the 3′UTRs [29, 30]. Among over 14 million RNA22-predicted binding sites, 1,223,207 sites were retained as they had the most significant p values per miRNA–target pair. In addition, over 15 million TargetScan-predicted binding sites were identified. Together, we found 1,223,207 RNA22 SNPs and 4,116,698 TargetScan SNPs after intersecting dbSNP v151 with the genomic coordinates of these 3′UTR binding sites (Fig. 3A). We collated 126,074 SNPs among the predicted 3′UTR binding sites which were also tested in the summary statistics. ## 3′UTR variants among known MS SNPs Overall, we identified two [2] IMSGC susceptibility SNPs among our collated 3′UTR binding site variants. rs10271373 ($$p \leq 3.11$$ × 10–9, GWAS joint OR = 0.946) and rs6742 ($$p \leq 4.11$$ × 10–14, GWAS joint OR = 1.149) lie within the predicted 3′UTR microRNA-binding sites of ZC3HAV1 and SLC2A4RG, respectively. We used TargetScan v7.0 to analyse miRNA-binding changes in reference versus variant 3′UTR sequences for these 2 candidate mRNAs. Of the 2 susceptibility SNPs, we observed changes in miRNA-binding ability for rs6742 only. In short, rs6742 changes which miRNAs can bind to the SLC2A4RG 3′UTR of both alleles (Additional file 1: Table S6, Fig. 3B). Overall, the risk allele of rs6742 appears to be under tighter miRNA regulation than the T allele, with a net change of + 3 miRNA interactions (Fig. 3B). ## 3′UTR variants among independent SNPs We postulated that more candidate 3′UTR binding site MS SNPs exist, but were not prioritised as susceptibility SNPs in the 2019 meta-analysis. Therefore, to broaden our 3′UTR binding site candidates, we first identified independent SNPs from the IMSGC’s Additional file [6] (Additional file 1: Table S11). In total, we extracted a list of 201 independent genome-wide SNPs and 416 independent weakly and strongly suggestive (299 WS and 117 SS) SNPs. For some of these suggestive SNPs, their joint p values were greater than their discovery p values; however, they did not meet genome-wide significance, while others replicated significantly in only one dataset [6] (Additional file 1: Table S11). We then explored whether any of our 126,074 3′UTR SNPs, which were tested in the summary statistics were among these independent SNPs from IMSGC. On intersecting both datasets, we identified 13 3′UTR SNPs within the IMSGC’s independent SNPs (Additional file 1: Table S7, Fig. 3C). To expand the methodology used to identify independent SNPs within the summary statistics, we uploaded the IMSGC GWAS summary statistics into FUMA Webtools [31]. While the IMSGC applied stepwise conditional regression to their discovery and replication cohorts to identify independent effects, FUMA uses PLINK’s [32] clumping procedure to rank independent and lead SNPs from GWAS summary statistics. We identified 318 independent SNPs from the FUMA web tools. Eight of our 3′UTR SNPs were among the FUMA independent SNPs (Additional file 1: Table S7, Fig. 3C). Additionally, two [2] 3′UTR independent SNPs were shared by both FUMA and the IMSGC (Fig. 3C). Altogether, we identified 19 3′UTR SNPs among the independent SNPs (Additional file 1: Table S7). We set out to investigate these 19 3′UTR SNPs through in silico methods and publically available functional evidence (see Methods). Other studies [33–35] have proposed criteria to validate microRNA–target interactions. These can be summarised as [1] demonstration of co-expression, [2] direct interaction between miRNA and region on target, [3] gain and loss experiments to show target protein interaction and [4] predicted changes have biological functions. We incorporated these approaches into our prioritisation process (see Methods). In short, functionally relevant 3′UTR SNPs are likely to change miRNA–target interactions at the 3′UTR binding site, act as eQTLs for the targets in MS relevant tissues (e.g. PBMCs, lymphocytes) and have the relevant microRNAs expressed in the same MS relevant tissues. We investigated these using the FiveX browser for eQTL catalogue, an MS eQTL dataset from the IMSGC, RegulomeDB v2.07 and FANTOM5 [6, 36–38] (Additional file 1: Table S11). These criteria are highlighted in Additional file 2: Fig. S3. Among our 19 candidates, we excluded 11 for the following reasons. Two had been assessed in the susceptibility SNP process, 7 had been reannotated as intronic and the relevant 3′UTR sequences were unavailable for 2 SNPs. Therefore, we carried out microRNA gain/loss on 8 independent SNPs using TargetScan 7.0 (Additional file 1: Table S7). Among these 8, we found that 6 SNPs (rs1059501 CD27, rs881640 MMEL1, rs2587100 BCL2L13, rs11648656 CIITA, rs17763689 ATF7 and rs451774 GPX5) change the microRNA-binding ability of 3′UTRs (Fig. 3D, Additional file 1: Table S8). The 3′UTR of BCL2L13 has the greatest changes in microRNA binding due to rs2587100 (GWAS joint OR = 3.43 × 10–05, joint p value = 1.049). The G allele (risk allele) binds to miR-4681 while the C allele binds to miR-27-3p, miR-513a-5p and miR-6798-5p, suggesting that the 3′UTR sequence which includes the risk allele is possibly under less regulation (Fig. 3D). rs2587100 was among the weakly suggestive effects (IMSGC), but has strong functional support for its potential role. rs2587100 is also the only SNP among our 6 candidates which is an eQTL for the target gene in non-MS and an MS patient dataset (see Methods). However, the effect of the eQTL is not consistent across the non-MS and 1 MS dataset. It decreases BCL2L13 expression in non-MS PBMCs and monocytes, and increases BCL2L13 expression in MS PBMCs and monocytes, and its relevant microRNAs are also expressed in monocytes [6, 38–40] (Additional file 1: Table S9, Additional file 1: Table S11). We have presented the genomic context of this SNP in Fig. 3E. Apart from rs9618043 CECR2, which is among the IMSGC’s non-replicated SNPs, no other SNPs in this region were among the prioritised genome-wide IMSGC SNPs. Only two [2] other independent 3′UTR SNPs (rs1059501 and rs881640) met the functional validation criteria (Fig. 4A, Additional file 1: Table S9). The significant eQTL activity or microRNA expression for rs11648656, rs17763689 and rs451774 appears to not be relevant for MS tissues (Additional file 1: Table S9, Additional file 2: Fig. S4). Therefore, we prioritised rs1059501 and rs881640 and have shown their genomic context in Fig. 4B and C. rs1059501 (CD27) is independent from the IMSGC susceptibility SNPs (rs1800693, rs2364485 and rs12832171) in that region and was ranked as strongly suggestive in the IMSGC stepwise regression. Both the protective (G) and alternative allele (T) lose and gain one miRNA, respectively (Fig. 3D). The microRNAs gained/lost due to this SNP are expressed in monocytes and haematopoietic cells (Additional file 1: Table S9) [38], while the SNP has been shown to decrease CD27 expression in T-cells and LCLs, and increase CD27 in monocytes and brain tissue [41–45]. Finally, rs881640 is independent from the IMSGC genome-wide SNP [chr1:2520527(hg37); rs6670198] in that region. Its G (risk) allele binds to miR-1471, but not to miR-634 or miR-4781, which are recognised by the T allele. Therefore, we expect that 3′UTR sequence containing the G allele is likely under less regulation than the T allele. This SNP has been shown to decrease MMEL1 expression in blood, monocytes and T-cells [41, 46, 47].Fig. 4A Table showing the p values and odds ratio (OR) for the 6 independent 3′UTR independent SNPs. Among these, only 3 (highlighted in bold) meet 3 of the microRNA–target validation criteria (see Methods, Additional file 1: Table S9). Joint p values (from IMSGC’s discovery and replication processes) are available for the IMSGC independent (suggestive) SNPs, but not for those identified by FUMA, as these were not among the suggestive effects. For the latter group, we have showed the discovery p values and ORs. B LocusZoom plots showing regions around our other 2 functionally relevant SNPs (rs2587100 is in Fig. 3E). We have highlighted the 3′UTR SNPs in rs1059501 (CD27) and rs881640 (MMEL1). Our candidate SNP rs1059501 is independent from the IMSGC susceptibility/genome-wide SNPs (rs1800693, rs2364485, rs12832171) in that region and was ranked as strongly suggestive in the IMSGC stepwise regression. C Our candidate SNP rs881640 is independent from the IMSGC susceptibility/genome-wide SNP (chr1:2520527(hg37); rs6670198) in that region Overall, we highlighted that the impact of increased regulation of SLC2A4RG due to the new miRNA interactions could be significant. In addition, 3 independent SNPs in 3′UTRs of BCL2L13, CD27 and MMEL1 meet multiple miRNA:target interaction criteria. Therefore, we have presented evidence that these SNPs could be involved in MS pathogenesis and should be prioritised for future investigation. ## Discussion In this study, we presented evidence that microRNA-associated variants could be implicated in MS. Our analysis is the first systematic exploration of both microRNA and 3′UTR target-binding site variation in MS, using GWAS summary statistics. By using the most recent meta-analysis [6], we harnessed the largest MS GWAS resource available to test our hypothesis. Altogether, we identified 30 candidate microRNA-associated variants from our collation procedure. Those variants meet a microRNA-specific Bonferroni-corrected threshold, are in LD (Linkage Disequilibrium) with known susceptibility SNPs or are suggestive SNPs from the IMSGC GWAS [6], whose microRNA functions had not been evaluated previously. We prioritised 1 of 8 miR-SNPs, rs1414273 (MIR548AC), and 4 of 22 SNPs in 3′UTR microRNA-binding sites of SLC2A4RG (rs6742), CD27 (rs1059501), MMEL1 (rs881640) and BCL2L13 (rs2587100), based on structural and functional predictions. Therefore, these 5 SNPs are our top candidate microRNA-associated variants which could play a role in MS pathogenesis. Our work successfully incorporates multiple microRNA prioritisation methods used elsewhere [12, 19, 21, 22]. The most relevant comparison to our results is a study which implicated 11 microRNAs in MS susceptibility [48]. We noted one major difference in methodology. Hecker and colleagues [48] analysed all SNPs within microRNA stem loop and Drosha cleavage sites which are close (< 250 kb) to the 233 IMSGC GWAS SNPs, irrespective of their presence in the summary statistics. Therefore, association analysis-based p values were not factored in within their prioritisation process. Among of their 12 candidate SNPs, 6 had been tested in the GWAS and were not Bonferroni-corrected. However, both studies identified hsa-mir-548ac and hsa-mir-4492 as candidate MS microRNAs. Another important comparison is against a microRNA GWAS study performed on a paediatric MS cohort [12]. Rhead et al. [ 12] adjusted the p value threshold for microRNA variants within a paediatric cohort of MS patients, but did not identify any significant SNPs. To follow up, those authors used MIGWAS [49] to identify enrichment of candidate microRNA–target network signals. Alternatively, we examined our candidates individually, to characterise effects of the variants on the functions of microRNAs and targets directly. Parallel to other studies, we successfully examined the effect of risk alleles through in silico methods [22, 50–52]. Our secondary structure prediction spotlighted that the risk allele for rs1414273 is expected to yield higher MIR548AC levels. Interestingly, rs1414273 has been shown to decouple the transcription of miR-548ac from its host gene CD58, leading to increased levels of miR-548ac [14]. This is in line with our secondary stability model. Despite rs1414273 [chr1:117102649 (hg37), 0.14 EUR MAF] being significant in the discovery cohort of the IMSGC meta-analysis, it was not among the effects prioritised for replication. It also appears not to have been captured among the 46 SNPs within that haplotype between the two replication datasets [6] (Additional file 1: Table S11). Conversely, the IMSGC’s prioritised effect SNP rs10801908 alone might paint an incomplete picture, due to the presence of MIR548AC within the first intron of CD58 and the strong linkage between rs10801908 and rs1414273. However, there is limited research into the role of MIR548AC in immunological conditions. Next, although our candidate rs2648841 did not change the structural conformation of miR-1208, another MIR1208 SNP rs1861842 has been associated with MS in African Americans [53], implicating the microRNA further. In additional in silico experiments, we identified changes to MIR6891’s binding ability, which could lead to functional changes to the mRNA. However, because MIR6891 lies within an intron of HLA-B, a class I MHC molecule with protective MS SNPs [54, 55], it is challenging to segregate the MHC signal from the microRNA signal using only summary statistics. While MIR6891 seed SNP rs2276448 itself has not been assessed in MS, miR-6891-3p is linked to changes in macrophage-driven inflammation [56]. Our microRNA gain/loss analysis also showed that the risk allele of rs6742 in SLC2A4RG is likely under stronger microRNA regulation than the other allele. This is supported by the IMSGC’s 2019 study, where SNPs within the rs6742 susceptibility locus were all associated with reduced SLC2A4RG expression in CD4 + T-cells in an MS cohort [6] (Additional file 1: Table S11). SLC2A4RG functions as a transcription factor for SLC2A/GLUT4, which is among the glucose carriers that are upregulated following lymphocyte activation [57]. This highlights a possible link between SLC2A4RG dysregulation in CD4 + T-cells and T-cell activation. After broadening our search for independent SNPs through FUMA, we identified changes to the microRNA-binding ability of CD27, MMEL1 and BCL2L13 due to rs1059501, rs881640 and rs2587100, respectively. This highlights the value of using different methods to identify independent SNPs. The eQTL rs2587100 drives increased expression of BCL2L13 in MS patients [6] (Additional file 1: Table S11) and aligns with our microRNA gain/loss experiment which shows that the risk allele is under less regulation than the C allele. BCL2L13 has been linked to mitophagy [58]; therefore, investigation of this upregulation in monocytes could be important. However, surprisingly, the non-MS eQTLs for both BCL2L13 and MMEL1 reduce their expression [38–40]. Genotyping these SNPs directly in MS patients could clarify the true direction of this eQTL. Next, at least one other group has incorporated flanking regions in microRNA-specific GWAS, in order to explore regulatory features which may influence microRNA transcription [21]. Our identification of a risk SNP in an enhancer-like domain [59] flanking MIR4492 suggests the regulation of these microRNA genes by other factors. Expression of this microRNA within B-cells is proposed to be altered due to Epstein–Barr Virus (EBV) infection, which has been shown to increase MS risk significantly [4, 13]. The effect of this enhancer SNP on MIR4492 expression in MS patients should be investigated further, especially in the context of EBV infection. The main challenges with interpreting our findings are the long-range LD in the MHC region, limited microRNA annotations and the ability of microRNAs to bind to multiple targets. We identified consequences of seed SNP rs2276448 (MIR6891), but could not confirm its independence from HLA-B SNPs (rs2308655, rs3819284, rs1050556, HLA-B*52.01, HLA-B*38:01 and HLA-B*35:03) using only publically available data. Furthermore, we were unable to measure the effect of multiple candidate SNPs on microRNAs or their targets by using only summary statistics. We also could not annotate the flanking SNPs which exceeded the 2-kb region stipulated by miRVaS. This is a challenge with microRNA tools such as miRVaS, as promoter information is not often available for intergenic microRNAs [60]. This means that the microRNA mapping tools are not fully powered to identify SNPs in enhancer regions, transcription start sites, among others; therefore, this needs to be accounted for in downstream analysis. Finally, experimental validation of our predicted changes in MIR6891, SLC2A4RG, CD27, MMEL1 and BCL2L13 will be necessary in the future due to the limitations of microRNA–target prediction algorithms. Finally, this study was limited to publically available data; therefore, the eQTL data were sourced from multiple studies. ## Conclusions Altogether, we identified 30 candidate microRNA-associated variants through systematic analysis of MS GWAS summary statistics. We prioritised 1 microRNA SNP and 4 3′UTR binding site SNPs based on the effects of the MS variants on their function, structure or regulatory abilities. Our in silico work helps to bridge the gap between MS GWAS and microRNAs implicated in MS. ## Summary statistics Summary statistics from the most recent GWAS meta-analysis [6] on MS patients were requested from the IMSGC through the webpage (https://imsgc.net/). In short, over 8 million SNPs were imputed and tested for 47,429 MS cases and 68,374 control subjects by the consortium. Genomic coordinates for all summary statistics (including autosomal and non-autosomal SNPs) were provided in hg37. We lifted over to hg38 using Ensembl’s [61] Assembly Converter for downstream hg38 SNP integration. We visualised all regional associations in LocusZoom’s web platform [62]. An overview of the pipeline and tools is presented in Additional file 2: Fig. S5. ## Text mining Prior to collating microRNA SNPs, we wanted to test whether the microRNA-associated variant databases PolymiRTS [26] and miRNASNP v3 [25] were up to date. miRNASNP v3 contains SNPs in microRNA seed and precursor regions, target 3′UTR SNPs as well as predictions of miRNA gain/loss based on these 3′UTR SNPs. PolymiRTS was last updated in 2014 and contains microRNA seed regions from miRBase v20 and 3′UTR sequences for CLASH validated targets. Altogether, this resulted in the collation of 56,911 SNPs. We compared microRNA SNPs from the literature to those within the databases. Specifically, the term “microRNA” was used in PubMed’s eFetch commandline tool, to obtain abstracts for all relevant papers published between 2014 and 2021. We then extracted rsids from these abstracts and manually confirmed whether the SNPs were referring to the microRNAs. Following this manual check, we tested the presence of those text mined SNPs within PolymiRTS and miRNASNP v3. The absence of recent miR-SNPs from the databases guided our independent collation step. ## Collation of microRNA–associated variants Variants within microRNA precursor and mature regions as well as those in ± 5-kb flanking regions were collated. To achieve this, genomic coordinates of microRNA precursor and mature sequences were downloaded from miRBase v22 [63] (https://www.mirbase.org/ftp/CURRENT/genomes/hsa.gff3) and intersected with genomic coordinates from the full dbSNP v151 [23] catalogue using BEDTools [64]. Primary transcripts of intergenic microRNAs are not well characterised. However, several studies have shown that flanking regions between ~ 1 kb and 10 kb are likely to contain transcription start sites, CpG islands, expressed sequence tag (EST)- and transcription factor (TF)-binding sites [21, 60, 65]. By extending the microRNA precursor coordinates by ± 5 kb, we aimed to incorporate microRNA regulatory features that might be influencing microRNA expression. We extracted sequences marked as “microRNA_primary_transcript” from the miRBase v22 gff file. These represent precursor sequences. These coordinates of these transcripts were extended by 5 kb in both directions using the BEDTools suite. ## microRNA SNPs tested in summary statistics We intersected the collated microRNA and ± 5-kb flanking SNPs with the lifted over summary statistics. Bonferroni correction was applied on microRNA SNPs found among the summary statistics. The p value thresholds were adjusted as follows: microRNA SNPs ($\frac{0.05}{314}$) and ± 5-kb flanking SNPs ($\frac{0.05}{36}$,841). ## microRNA-associated SNPs among susceptibility SNPs In total, 200 non-MHC autosomal SNPs were significantly associated with MS in the most recent meta-analysis [6]. Those susceptibility SNPs can be obtained from Additional file tables (Additional file 1: Table S7) of that paper. We intersected the genomic coordinates of our collated microRNA, ± 5-kb flanking and 3′UTR target SNPs with these susceptibility SNPs. Nominally significant SNPs which did not meet the genome-wide threshold were extracted from the IMSGC [6] Additional files (Additional file 1: Table S14). These were merged with the susceptibility SNPs to create a dataset of independent SNPs. The file names for all datasets extracted from the IMSGC study are listed in Additional file 1: Table S11. ## microRNA-associated SNPs in LD with susceptibility SNPs We aimed to capture entire susceptibility loci by mapping variants in linkage disequilibrium with the susceptibility SNPs. For this step, both the effect SNPs and discovery SNPs provided in Additional file 1: Table S7 of the IMSGC analysis [6] were used as susceptibility SNPs. We obtained all variants in LD with these susceptibility SNPs through Ensembl’s perl API, specifically using 1000 genomes EUR subset as the reference population. LD information was available for 174 of the 201 non-MHC susceptibility SNPs. This step was carried out for both sets of microRNA variants and the 3′UTR variants within the summary statistics. ## microRNA–target gain/loss analysis TargetScan 7.0 prediction algorithm was used locally to analyse 3′UTR binding changes in variant vs reference microRNA seed sequence. The SNP position seed sequence was located within microRNA reference FASTA sequences using SeqKit [66], which we also used to swap the reference and alternative alleles. These seed sequences were then replaced within the TargetScan miR_Family_Info.txt, while the 3′UTR file was retained. The transcripts were mapped to gene names, and differences between the predictions for both microRNA sequences were analysed in R. ## microRNA variant effect prediction ADmiRE and miRVaS were used to predict the location and effects of microRNA variants, respectively. Oak et al. [ 28] provide microRNA annotation tab files in the ADmiRE repository. These were formatted into BED files and lifted over to hg38. The BED files were intersected with vcf files of microRNA variants of interest. This procedure was implemented by Tyc and colleagues [67]. miRVaS [27] runs predictions within 2000 nucleotides of microRNA coordinates using underlying tools VARNA and RNAfold [68, 69]. miRVaS is available online, or in local Windows or Linux packages. SNP coordinates were input into miRVaS using the required format, and predictions were run based on the hg38 reference file and miRBase v21. ## TargetScan variants We intersected TargetScan v7.0 [30] bedfiles containing genomic coordinates of all predicted sites with the UTR genome coordinates available on TargetScan. Coordinates in the former set of files were lifted over from hg19 to hg38 prior to this intersection. This intersection resulted in a collection of TargetScan-predicted binding sites within 3′UTRs. The binding site coordinates in the resulting bedfile were intersected with dbSNP 151 variants (Fig. 3A) for a final dataset of 3′UTR SNPs within binding sites predicted by TargetScan v7.0. All our intersection steps were carried out using combinations of VCFtools, BEDtools and SAMtools [70–72]. ## RNA22 variants There were over 83 million predicted binding sites available from RNA22 [29] v2.0. We chose the minimum prediction p values for each microRNA–target pair predicted to interact at 3′UTRs, leading to ~ 14 million pairs (p value < 0.0314). Next, a custom R script was used to convert the cDNA coordinates to genomic coordinates. These were intersected with dbSNP v151 to get 1,223,207 SNPs (Fig. 3A). Additional file 2: Fig. S6 shows the overlap between targets predicted by TargetScan and this RNA “best probability” subset. The dataset containing the union of binding site SNPs from TargetScan and RNA22 was used to test the presence of 3′UTR SNPs among the summary statistics. ## 3′UTR susceptibility SNPs After intersecting the coordinates of the collated SNPs within 3′UTR binding sites with those of the susceptibility SNPs from the IMSGC, we identified 5 3′UTR binding sites among them. Three of the transcripts relevant to the predicted microRNA-binding sites had been archived by Ensembl. Therefore, those SNPs could no longer be annotated on those transcripts. Joint p values and ORs for the two [2] candidate SNPs were obtained from Additional files (Additional file 1: Table S7) of the IMSGC 2019 meta-analysis. ## Identification of independent 3′UTR SNPs The IMSGC identified SNPs among their prioritised effects which were independent of the lead SNPs in those regions, but did not reach genome-wide significance, and were not replicated or whose joint p values were greater than the discovery p values. These SNPs are in Additional file 1: Tables S6 and S14 of the IMSGC’s paper (Additional file 1: Table S11). They identified 201 genome-wide (GW) independent effect SNPs, 117 strongly suggestive effect SNPs and 299 weakly suggestive effects. We collated a list of the weakly and strongly suggestive SNPs from these tables. To identify independent SNPs separately from IMSGC’s process, summary statistics were input into FUMA’s [31] online platform (https://fuma.ctglab.nl/). FUMA [31] uses PLINK’s [73] clumping procedures to highlight independent SNPs and lead SNPs. The intersection between both sets of independent SNPs was used for the functional prioritisation pipeline. Among the 19 independent SNPs identified, the transcripts proposed to contain 7 3′UTR SNPs had been archived, and those SNPs had been reclassified as intronic SNPs, and the relevant 3′UTR sequences were unavailable for 2 (Additional file 1: Table S7). In addition, 2 independent SNPs had been assessed in the susceptibility SNP analysis, leaving 11 for microRNA gain/loss analysis in the next step. ## microRNA–target functional pipeline A number of groups [33–35] have proposed criteria to validate microRNA–target interactions. We have summarised these as [1] demonstration of co-expression, [2] direct interaction between miRNA and region on target, [3] gain and loss experiments to show target protein interaction and [4] predicted changes have biological functions. We have adapted these to suit our bioinformatics approach. By using in silico microRNA gain/loss, we will assess the direct interaction condition (condition 2). We will also use publically available eQTL data to meet condition 4 (changed biological functions) and are using microRNA expression data in combination with the eQTL data to test condition 1. In short, relevant 3′UTR SNPs change miRNA–target interactions at the 3′UTR binding site, act as eQTLs for the targets in MS relevant tissues (e.g. PBMCs, lymphocytes) and have the lost/gained microRNAs expressed in the same MS relevant tissues. We are limited by study design and will not be doing the protein-level gain and loss experiments (condition 3). These criteria are highlighted in Additional file 2: Fig. S3. We used the FiveX browser of eQTL catalogue [36] to identify the tissues in which our 3′UTR SNPs were acting as eQTLs for the predicted targets. We also checked our candidate SNPs within a more specific MS eQTL dataset which was provided alongside the MS GWAS [6] (Additional file 1: Table S11). We also identified the probability of those SNPs lie in regulatory regions within the genome through the probability score (best probability) and the type of regulatory site (RDB Rank) from RegulomeDB v.2.03 [74]. We also used the human.mirna.cellontology dataset from FANTOM5 [38] to check which cells our miRNAs were enriched/depleted in. In addition, checked the basal expression on the webtool Zenbu miRNA atlas (comparing microRNA expression across 0.5 low/10 medium/1000 highTPM) (Additional file 2: Fig. S3). ## microRNA gain/loss analysis We used TargetScan 7.0 prediction algorithm locally to analyse microRNA-binding changes in variant vs reference 3′UTR sequences. The TargetScan miR_Family_Info.txt file was retained, while the reference and alternative 3′UTR sequences were formatted using SeqKit [66] to match and replace the 3′UTR file. We compared the predicted microRNA families compared between output files from the alternative and reference sequences in an R script. ## Supplementary Information Additional file 1: Table S1. miRNA SNPs in summary statistics. Table S2. RNAfold rs1414273 effects. Table S3. MIR6891 validated targets lost. Table S4. MIR6891-predicted targets lost. Table S5. Candidate SNPs in flanking regions of miRNA precursors. Table S6. miRNA gain/loss rs6742. Table S7. Independent SNPs. Table S8. miRNA gain/loss independent SNPs. Table S9. Functional prioritisation. Table S10. miRNA-associated SNP candidates. Table S11. List of IMSGC data files referenced. Additional file 2. Supplementary Figures 1–6. ## References 1. Oksenberg JR, Baranzini SE, Sawcer S, Hauser SL. **The genetics of multiple sclerosis: SNPs to pathways to pathogenesis**. *Nat Rev Genet* (2008) **9** 516-526. DOI: 10.1038/nrg2395 2. Brownlee WJ, Hardy TA, Fazekas F, Miller DH. **Diagnosis of multiple sclerosis: progress and challenges**. *Lancet* (2017) **389** 1336-1346. DOI: 10.1016/S0140-6736(16)30959-X 3. Hollenbach JA, Oksenberg JR. **The immunogenetics of multiple sclerosis: A comprehensive review**. *J Autoimmun* (2015) **64** 13-25. DOI: 10.1016/j.jaut.2015.06.010 4. Bjornevik K, Cortese M, Healy BC, Kuhle J, Mina MJ, Leng Y. **Longitudinal analysis reveals high prevalence of Epstein-Barr virus associated with multiple sclerosis**. *Science* (2022) **301** 296-301. DOI: 10.1126/science.abj8222 5. Cotsapas C, Mitrovic M. **Genome-wide association studies of multiple sclerosis**. *Clin Transl Immunol* (2018) **7** 1-9. DOI: 10.1002/cti2.1018 6. Patsopoulos NA, Baranzini SE, Santaniello A, Shoostari P, Cotsapas C, Wong G. **Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility**. *Science* (2019) **365** 6460 7. Mitrovič M, Patsopoulos NA, Beecham AH, Dankowski T, Goris A, Dubois B. **Low-frequency and rare-coding variation contributes to multiple sclerosis risk**. *Cell* (2018) **175** 1679-1687.e7. DOI: 10.1016/j.cell.2018.09.049 8. Kehl T, Backes C, Kern F, Fehlmann T, Ludwig N, Meese E. **About miRNAs, miRNA seeds, target genes and target pathways**. *Oncotarget* (2017) **8** 107167-107175. DOI: 10.18632/oncotarget.22363 9. O'Brien J, Hayder H, Zayed Y, Peng C. **Overview of microRNA biogenesis, mechanisms of actions, and circulation**. *Front Endocrinol* (2018). DOI: 10.3389/fendo.2018.00402 10. Juźwik CA, Drake SS, Zhang Y, Paradis-Isler N, Sylvester A, Amar-Zifkin A, Douglas C, Morquette B, Moore CS, Fournier AE. **microRNA dysregulation in neurodegenerative diseases: a systematic review**. *Progress Neurobiol* (2019) **182** 101664. DOI: 10.1016/j.pneurobio.2019.101664 11. Teuber-Hanselmann S, Meinl E, Junker A. **MicroRNAs in gray and white matter multiple sclerosis lesions: impact on pathophysiology**. *J Pathol* (2020) **250** 496-509. DOI: 10.1002/path.5399 12. Rhead B, Shao X, Graves JS, Chitnis T, Waldman AT, Lotze T. **miRNA contributions to pediatric-onset multiple sclerosis inferred from GWAS**. *Ann Clin Transl Neurol* (2019) **6** 1053-1061. DOI: 10.1002/acn3.786 13. Afrasiabi A, Fewings NL, Schibeci SD, Keane JT, Booth DR, Parnell GP. **The interaction of human and epstein–barr virus mirnas with multiple sclerosis risk loci**. *Int J Mol Sci* (2021) **22** 1-15. DOI: 10.3390/ijms22062927 14. Hecker M, Boxberger N, Illner N, Fitzner B, Schröder I, Winkelmann A, Dudesek A, Meister S, Koczan D, Lorenz P, Thiesen H-J, Zettl UK. **A genetic variant associated with multiple sclerosis inversely affects the expression of CD58 and microRNA-548ac from the same gene**. *PLOS Genet* (2019) **15** e1007961. DOI: 10.1371/journal.pgen.1007961 15. Dehghanzad R, Panahi Moghadam S, Shirvani FZ. **Prediction of single-nucleotide polymorphisms within microRNAs binding sites of neuronal genes related to multiple sclerosis: a preliminary study**. *Adv Biomed Res* (2021) **10** 8. DOI: 10.4103/abr.abr_143_20 16. Landi D, Gemignani F, Naccarati A, Pardini B, Vodicka P, Vodickova L. **Polymorphisms within micro-RNA-binding sites and risk of sporadic colorectal cancer**. *Carcinogenesis* (2008) **29** 579-584. DOI: 10.1093/carcin/bgm304 17. Esteller M. **Non-coding RNAs in human disease**. *Nat Rev Genet* (2011) **12** 861-874. DOI: 10.1038/nrg3074 18. Ghanbari M, Franco OH, de Looper HWJ, Hofman A, Erkeland SJ, Dehghan A. **Genetic variations in MicroRNA-binding sites affect MicroRNA-mediated regulation of several genes associated with cardio-metabolic phenotypes**. *Circ Cardiovasc Genet* (2015) **8** 473-486. DOI: 10.1161/CIRCGENETICS.114.000968 19. Ghanbari M, Iglesias AI, Springelkamp H, van Duijn CM, Ikram MA, Dehghan A. **A genome-wide scan for microrna-related genetic variants associated with primary open-angle glaucoma**. *Investig Ophthalmol Vis Sci* (2017) **58** 5368-5377. DOI: 10.1167/iovs.17-22410 20. Gholami M, Zoughi M, Larijani B, Amoli MM, Bastami M. **An in silico approach to identify and prioritize miRNAs target sites polymorphisms in colorectal cancer and obesity**. *Cancer Med* (2020) **9** 9511-9528. DOI: 10.1002/cam4.3546 21. Fang L, Sørensen P, Sahana G, Panitz F, Su G, Zhang S. **MicroRNA-guided prioritization of genome-wide association signals reveals the importance of microRNA-target gene networks for complex traits in cattle**. *Sci Rep* (2018) **8** 1-14. DOI: 10.1038/s41598-018-27729-y 22. Ghanbari M, Ikram MA, De Looper HWJ, Hofman A, Erkeland SJ, Franco OH. **Genome-wide identification of microRNA-related variants associated with risk of Alzheimer’s disease**. *Sci Rep* (2016) **6** 1-9. PMID: 28442746 23. Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM. **dbSNP: the NCBI database of genetic variation**. *Nucleic Acids Res* (2001) **29** 308-311. DOI: 10.1093/nar/29.1.308 24. Kozomara A, Griffiths-Jones S. **MiRBase: annotating high confidence microRNAs using deep sequencing data**. *Nucleic Acids Res* (2014) **42** D68-73. DOI: 10.1093/nar/gkt1181 25. Liu C-J, Xin F, Xia M, Zhang Q, Zhifeng G, Guo A-Y. **miRNASNP-v3: a comprehensive database for SNPs and disease-related variations in miRNAs and miRNA targets**. *Nucl Acids Res* (2021) **49** D1276-D1281. DOI: 10.1093/nar/gkaa783 26. Bhattacharya A, Ziebarth JD, Cui Y. **PolymiRTS database 3.0: linking polymorphisms in microRNAs and their target sites with human diseases and biological pathways**. *Nucleic Acids Res* (2014) **42** D86-91. DOI: 10.1093/nar/gkt1028 27. Cammaerts S, Strazisar M, Dierckx J, Del Favero J, De Rijk P. **miRVaS: a tool to predict the impact of genetic variants on miRNAs**. *Nucleic Acids Res* (2016) **44** e23-e23. DOI: 10.1093/nar/gkv921 28. Oak N, Ghosh R, Huang K. **Framework for microRNA variant annotation and prioritization using human population and disease datasets**. *Hum Mutat* (2019) **40** 73-89. DOI: 10.1002/humu.23668 29. Miranda KC, Huynh T, Tay Y, Ang Y-S, Tam W-L, Thomson AM. **A pattern-based method for the identification of MicroRNA binding sites and their corresponding heteroduplexes**. *Cell* (2006) **126** 1203-1217. DOI: 10.1016/j.cell.2006.07.031 30. Agarwal V, Bell GW, Nam JW, Bartel DP. **Predicting effective microRNA target sites in mammalian mRNAs**. *Elife* (2015) **4** e05005. DOI: 10.7554/eLife.05005 31. Watanabe K, Taskesen E, Van Bochoven A, Posthuma D. **Functional mapping and annotation of genetic associations with FUMA**. *Nat Commun* (2017) **8** 1-10. DOI: 10.1038/s41467-017-01261-5 32. Purcell S, Neale B, Todd-Brown K. **PLINK: a tool set for whole-genome association and population-based linkage analyses**. *Am J Hum Genet* (2017) **81** 559-575. DOI: 10.1086/519795 33. Riolo G, Cantara S, Marzocchi C, Ricci C. **miRNA targets: From prediction tools to experimental validation**. *Methods Protoc* (2021) **4** 1-20. DOI: 10.3390/mps4010001 34. Kuhn DE, Martin MM, Feldman DS, Terry AV, Nuovo GJ, Elton TS. **Experimental validation of miRNA targets**. *Methods* (2008) **44** 47-54. DOI: 10.1016/j.ymeth.2007.09.005 35. Elton TS, Yalowich JC. **Experimental procedures to identify and validate specific mRNA targets of miRNAs**. *EXCLI J* (2015) **14** 758-790. PMID: 27047316 36. Kerimov N, Hayhurst JD, Peikova K, Manning JR, Walter P, Kolberg L. **A compendium of uniformly processed human gene expression and splicing quantitative trait loci**. *Nat Genet* (2021) **53** 1290-1299. DOI: 10.1038/s41588-021-00924-w 37. Dunham I, Kundaje A, Aldred SF, Collins PJ, Davis CA, Doyle F. **An integrated encyclopedia of DNA elements in the human genome**. *Nature* (2012) **489** 57-74. DOI: 10.1038/nature11247 38. Forrest ARR, Kawaji H, Rehli M, Kenneth Baillie J, de Hoon MJL, Haberle V. **A promoter-level mammalian expression atlas**. *Nature* (2014) **507** 462-470. DOI: 10.1038/nature13182 39. Quach H, Rotival M, Pothlichet J. **Genetic adaptation and neandertal admixture shaped the immune system of human populations**. *Cell* (2016) **167** 643-656.e17. DOI: 10.1016/j.cell.2016.09.024 40. Chen Y, Knight ZA. **Making sense of the sensory regulation of hunger neurons**. *BioEssays* (2016) **38** 316-324. DOI: 10.1002/bies.201500167 41. Chen L, Ge B, Casale FP, Vasquez L, Kwan T, Garrido-Martín D. **Genetic drivers of epigenetic and transcriptional variation in human immune cells**. *Cell* (2016) **167** 1398-1414.e24. DOI: 10.1016/j.cell.2016.10.026 42. Kwong A, Boughton AP, Wang M, VandeHaar P, Boehnke M, Abecasis G. **FIVEx: an interactive eQTL browser across public datasets**. *Bioinformatics* (2022) **38** 559-561. DOI: 10.1093/bioinformatics/btab614 43. Schmiedel BJ, Singh D, Madrigal A, Valdovino-Gonzalez AG, White BM, Zapardiel-Gonzalo J. **Impact of genetic polymorphisms on human immune cell gene expression**. *Cell* (2018) **175** 1701-1715.e16. DOI: 10.1016/j.cell.2018.10.022 44. Jaffe AE, Straub RE, Shin JH, Tao R, Gao Y, Collado-Torres L. **Developmental and genetic regulation of the human cortex transcriptome illuminate schizophrenia pathogenesis**. *Nat Neurosci* (2018) **21** 1117-1125. DOI: 10.1038/s41593-018-0197-y 45. Lappalainen T. **Transcriptome and genome sequencing uncovers functional variation in humans**. *Nature* (2013) **501** 506. DOI: 10.1038/nature12531 46. Gutierrez-Arcelus M, Lappalainen T, Montgomery SB, Buil A, Ongen H, Yurovsky A. **Passive and active DNA methylation and the interplay with genetic variation in gene regulation**. *Elife* (2013) **2** e00523. DOI: 10.7554/eLife.00523 47. Lepik K, Annilo T, Kukuškina V, Kisand K, Kutalik Z, Peterson P, Peterson H. **C-reactive protein upregulates the whole blood expression of CD59 - an integrative analysis**. *PLOS Comput Biol* (2017) **13** e1005766. DOI: 10.1371/journal.pcbi.1005766 48. Hecker M, Fitzner B, Putscher E, Schwartz M, Winkelmann A, Meister S, Dudesek A, Koczan D, Lorenz P, Boxberger N, Zettl UK. **Implication of genetic variants in primary microRNA processing sites in the risk of multiple sclerosis**. *EBioMedicine* (2022) **80** 104052. DOI: 10.1016/j.ebiom.2022.104052 49. Sakaue S, Hirata J, Maeda Y, Kawakami E, Nii T, Kishikawa T. **Integration of genetics and miRNA-target gene network identified disease biology implicated in tissue specificity**. *Nucleic Acids Res* (2018) **46** 11898-11909. DOI: 10.1093/nar/gky1066 50. Jacinta-Fernandes A, Xavier JM, Magno R, Lage JG, Maia A-T. **Allele-specific miRNA-binding analysis identifies candidate target genes for breast cancer risk**. *npj Genom Med* (2020). DOI: 10.1038/s41525-019-0112-9 51. Shieh M, Chitnis N, Clark P, Johnson FB, Kamoun M, Monos D. **Computational assessment of miRNA binding to low and high expression HLA-DPB1 allelic sequences**. *Hum Immunol* (2019) **80** 53-61. DOI: 10.1016/j.humimm.2018.09.002 52. Hauberg ME, Holm-Nielsen MH, Mattheisen M, Askou AL, Grove J, Børglum AD. **Schizophrenia risk variants affecting microRNA function and site-specific regulation of NT5C2 by miR-206**. *Eur Neuropsychopharmacol* (2016) **26** 1522-1526. DOI: 10.1016/j.euroneuro.2016.06.014 53. Isobe N, Madireddy L, Khankhanian P, Matsushita T, Caillier SJ, Moré JM. **An ImmunoChip study of multiple sclerosis risk in African Americans**. *Brain* (2015) **138** 1518-1530. DOI: 10.1093/brain/awv078 54. Healy BC, Liguori M, Tran D, Chitnis T, Glanz B, Wolfish C, Gauthier S, Buckle G, Houtchens M, Stazzone L, Khoury S, Hartzmann R, Fernandez-Vina M, Hafler DA, Weiner HL, Guttmann CRG, De Jager PL. **HLA B*44: Protective effects in MS susceptibility and MRI outcome measures**. *Neurology* (2010) **75** 634-640. DOI: 10.1212/WNL.0b013e3181ed9c9c 55. Moutsianas L, Jostins L, Beecham AH, Dilthey AT, Xifara DK, Ban M. **Class II HLA interactions modulate genetic risk for multiple sclerosis**. *Nat Genet* (2015) **47** 1107-1113. DOI: 10.1038/ng.3395 56. Zhang P, Sun J, Liang C, Gu B, Xu Y, Lu H. **lncRNA IGHCγ1 Acts as a ceRNA to Regulate Macrophage Inflammation via the miR-6891-3p/TLR4 Axis in Osteoarthritis**. *Mediators Inflamm* (2020) **17** 9743037 57. Lang F, Singh Y, Salker MS, Ma K, Pandyra AA, Lang PA. **Glucose transport in lymphocytes**. *Pflügers Arch - Eur J Physiol* (2020) **472** 1401-1406. DOI: 10.1007/s00424-020-02416-y 58. Lou G, Palikaras K, Lautrup S, Scheibye-Knudsen M, Tavernarakis N, Fang EF. **Mitophagy and neuroprotection**. *Trends Mol Med* (2020) **26** 8-20. DOI: 10.1016/j.molmed.2019.07.002 59. Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM. **The human genome browser at UCSC**. *Genome Res* (2002) **12** 996-1006. DOI: 10.1101/gr.229102 60. Saini HK, Griffiths-Jones S, Enright AJ. **Genomic analysis of human microRNA transcripts**. *Proc Natl Acad Sci U S A* (2007) **104** 17719-17724. DOI: 10.1073/pnas.0703890104 61. Howe KL, Achuthan P, Allen J, Allen J, Alvarez-Jarreta J, Amode MR. **Ensembl 2021**. *Nucleic Acids Res* (2021) **49** D884-D891. DOI: 10.1093/nar/gkaa942 62. Boughton AP, Welch RP, Flickinger M, VandeHaar P, Taliun D, Abecasis GR, Boehnke M. **LocusZoom.js: interactive and embeddable visualization of genetic association study results**. *Bioinformatics* (2021) **37** 3017-3018. DOI: 10.1093/bioinformatics/btab186 63. Kozomara A, Birgaoanu M, Griffiths-Jones S. **MiRBase: From microRNA sequences to function**. *Nucleic Acids Res* (2019) **47** D155-D162. DOI: 10.1093/nar/gky1141 64. Quinlan AR, Hall IM. **BEDTools: a flexible suite of utilities for comparing genomic features**. *Bioinformatics* (2010) **26** 841-842. DOI: 10.1093/bioinformatics/btq033 65. Gong J, Tong Y, Zhang HM, Wang K, Hu T, Shan G. **Genome-wide identification of SNPs in MicroRNA genes and the SNP effects on MicroRNA target binding and biogenesis**. *Hum Mutat* (2012) **33** 254-263. DOI: 10.1002/humu.21641 66. Shen W, Le S, Li Y, Hu F. **SeqKit: a cross-platform and ultrafast toolkit for FASTA/Q file manipulation**. *PLoS ONE* (2016) **11** e0163962. DOI: 10.1371/journal.pone.0163962 67. Tyc KM, Wong A, Scott RT, Tao X, Schindler K, Xing J. **Analysis of DNA variants in miRNAs and miRNA 3ʼUTR binding sites in female infertility patients**. *Lab Investig* (2021) **101** 503-512. DOI: 10.1038/s41374-020-00498-x 68. Lorenz R. **ViennaRNA package 2.0**. *Algorithms Mol Biol.* (2011) **6** 26. DOI: 10.1186/1748-7188-6-26 69. Darty K, Denise A, Ponty Y. **VARNA: interactive drawing and editing of the RNA secondary structure**. *Bioinformatics* (2009) **25** 1974-1975. DOI: 10.1093/bioinformatics/btp250 70. Danecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO. **Twelve years of SAMtools and BCFtools**. *Gigascience.* (2021) **10** 2. DOI: 10.1093/gigascience/giab008 71. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N. **The sequence alignment/map format and SAMtools**. *Bioinformatics* (2009) **25** 2078-2079. DOI: 10.1093/bioinformatics/btp352 72. Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA. **The variant call format and VCFtools**. *Bioinformatics* (2011) **27** 2156-2158. DOI: 10.1093/bioinformatics/btr330 73. Chang CC, Chow CC, Tellier LCAM, Vattikuti S, Purcell SM, Lee JJ. **Second-generation PLINK: rising to the challenge of larger and richer datasets**. *GigaScience* (2015). DOI: 10.1186/s13742-015-0047-8 74. Boyle AP, Hong EL, Hariharan M, Cheng Y, Schaub MA, Kasowski M. **Annotation of functional variation in personal genomes using RegulomeDB**. *Genome Res* (2012) **22** 1790-1797. DOI: 10.1101/gr.137323.112
--- title: 'The effect of ambient ozone exposure on three types of diabetes: a meta-analysis' authors: - Sirui Yu - Mingzhi Zhang - Jiamin Zhu - Xu Yang - Francis Manyori Bigambo - Antoine M. Snijders - Xu Wang - Weiyue Hu - Wei Lv - Yankai Xia journal: Environmental Health year: 2023 pmcid: PMC10061724 doi: 10.1186/s12940-023-00981-0 license: CC BY 4.0 --- # The effect of ambient ozone exposure on three types of diabetes: a meta-analysis ## Abstract ### Background Ozone as an air pollutant is gradually becoming a threat to people's health. However, the effect of ozone exposure on risk of developing diabetes, a fast-growing global metabolic disease, remains controversial. ### Objective To evaluate the impact of ambient ozone exposure on the incidence rate of type 1, type 2 and gestational diabetes mellitus. ### Method We systematically searched PubMed, Web of Science, and Cochrane Library databases before July 9, 2022, to determine relevant literature. Data were extracted after quality evaluation according to the Newcastle Ottawa Scale (NOS) and the agency for healthcare research and quality (AHRQ) standards, and a meta-analysis was used to evaluate the correlation between ozone exposure and type 1 diabetes mellitus (T1D), type 2 diabetes mellitus (T2D), and gestational diabetes mellitus (GDM). The heterogeneity test, sensitivity analysis, and publication bias were performed using Stata 16.0. ### Results Our search identified 667 studies from three databases, 19 of which were included in our analysis after removing duplicate and ineligible studies. Among the remaining studies, three were on T1D, five were on T2D, and eleven were on GDM. The result showed that ozone exposure was positively correlated with T2D [effect size (ES) = 1.06, $95\%$ CI: 1.02, 1.11] and GDM [pooled odds ratio (OR) = 1.01, $95\%$ CI: 1.00, 1.03]. Subgroup analysis demonstrated that ozone exposure in the first trimester of pregnancy might raise the risk of GDM. However, no significant association was observed between ozone exposure and T1D. ### Conclusion Long-term exposure to ozone may increase the risk of T2D, and daily ozone exposure during pregnancy was a hazard factor for developing GDM. Decreasing ambient ozone pollution may reduce the burden of both diseases. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12940-023-00981-0. ## Introduction Ozone in the troposphere is created in the presence of solar radiation, due to the reaction of nitrogen oxides and volatile organic compounds. Growing evidence have shown that high concentration of ozone exposure could threaten people’s health and might be linked to lower life expectancy [22, 32, 63]. Ozone as a typical air pollutant can exacerbate lung injury, increase the risk of respiratory diseases [39, 66], cardiovascular disease, reproductive abnormalities, as well as neurological abnormalities [50]. It is worth noting that through neuro-endocrine regulation, ozone exposure may cause metabolic syndrome, characterized by glucose intolerance and hyperlipidemia [49, 50]. Importantly, glucose intolerance often indicates pre-existing diabetes or predisposition to diabetes. Diabetes is the most common chronic metabolic disease and its high incidence has caused a heavy medical and economic burden on society. Diabetes can be divided into the following categories: type 1 diabetes or T1D (insufficient insulin secretion), type 2 diabetes or T2D (insulin resistance with progressive insulin secretory defect), and gestational diabetes or GDM (various levels of impaired glucose tolerance which first occur or are first detected during pregnancy) [1]. T1D mainly occurs in children and adolescents. T1D is often first diagnosed from a routine blood test indicating modest hyperglycemia which then evolves into severe hyperglycemia or ketoacidosis if left untreated [12, 14]. T2D can cause devastating macrovascular complications and microvascular complications, which can cause severe sequelae, such as diabetic retinopathy, blindness, kidney failure, and neuropathy [9]. GDM as a type of metabolic disturbance during pregnancy, may cause various health risks in the mother and the child. In women, it can cause serious perinatal complications such as cardiovascular diseases and it can evolve into T2D after pregnancy. In women with GDM, the fetus has an increased risk of developing macrosomia, birth injury and cardiometabolic disease later in life [5, 24, 54, 65]. Chuang et al. demonstrated that increased ozone exposure was associated with increased fasting blood glucose and HbA1c levels, a biomarker of glucose metabolism [8]. Experimental evidence also indicated that ozone may cause damage to β cells [38], and exert insulin resistance, possibly due to oxidative stress and inflammatory responses [25]. These suggest that ozone exposure may lead to the appropriate type of diabetes in different populations. However, the epidemiological evidence of ozone exposure on three types of diabetes still remains controversial. Evidence shows that ozone exposure increases the overall prevalence of diabetes [40], however, existing epidemiological studies suggest that increased ozone exposure is associated with a decrease in diabetes prevalence, which persists after adjusting for possible confounding factors [29, 52]. Hathout et al. found the positive correlation between ozone and T1D [18], but a negative correlation was observed by Elten et al. [ 11]. A study in areas with low average ozone exposure found significant positive effects [21], but not in areas with higher levels [62]. Results of studies also varies on the association between GDM and ozone exposure [19, 41]. Overall, findings on the association between ozone exposure and the three types of diabetes are inconsistent, which may depend on study design, sample size, exposure measurement methods, and outcome assessment. At present, direct evidence on the relationship between different types of diabetes and ozone exposure is still being studied. Integration of the results of current studies on this topic is urgently needed to obtain more representative and reliable conclusions with a larger sample size and a wider study area, and stronger statistical power. Thus, a meta-analysis was conducted to explore whether ozone exposure is associated with three types of diabetes. In this study, we performed a meta-analysis to evaluate the relationship of ozone exposure to T1D, T2D, and GDM, aimed to provide evidence for the potentially harmful effects of ozone. In addition, impacts on average ozone concentration, socioeconomic status, exposure measurement methods on T2D and GDM were also investigated via subgroup analyses. ## Search methods This study was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines (Supplemental Table S2). Relevant articles were retrieved from three databases: Web of Science, PubMed and Cochrane Library up to July 9, 2022. According to the PECO (Patients, Intervention, Comparison, Outcomes), we have defined eligible studies as follows. P: People with three types of diabetes; E: Ozone exposure before illness; C: People who did not have diabetes and have a negative glucose tolerance test; O: The documented result is the development of the specific type of diabetes. The following search terms were used to screen the articles across three databases, and three types of diabetes were retrieved separately: #1: (ozone) OR (O3)#2: (Type 1 Diabetes) OR (Insulin-Dependent Diabetes Mellitus) OR (Diabetes Mellitus, Type 1) OR T1D#3: (Type 2 Diabetes) OR (Non-Insulin-Dependent Diabetes Mellitus) OR (Diabetes Mellitus, Type 2) OR T2D#4: (Pregnancy-Induced Diabetes) OR (Gestational Diabetes Mellitus) OR (GDM)#5: #1 AND #2 (#1 AND #3 or #1 AND #4) We also manually searched the list of references to ensure that there were no omissions. ## Selection criteria The criteria of inclusion and exclusion were as follows: Inclusion criteria:Epidemiological studies were based on observation and analysis such as cohort studies, case–control studies, and cross-sectional studies;Exposure factor was ozone;The outcome was the correlation between ozone exposure and the risk of diabetes mellitus;Data, such as OR, risk ratio (RR), hazard ratio (HR), and $95\%$ CI (confidence interval), were provided in the study. Exclusion Criteria:Animal studies, reviews, conference abstracts, systematic reviews, and meta-analyses;Studies that did not fit into the research topic;Incomplete articles, including lack of statistical analysis details;Studies with low quality score < 7. For example, there is a lack of key covariates or studies in which covariates differ significantly from other studies. ## Study screening and data extraction Articles were imported into Endnote for management, and duplicates were removed. We manually screened the retrieved articles by the title and abstract based on the inclusion and exclusion criteria. In addition, full texts were reviewed for further confirmation and the acquisition of data. Two researchers independently completed the literature screening process. The data extracted included: the first author's name, published year, country, study design, sample size, participants’ age, ozone exposure period, type of diabetes, effect size, and $95\%$ CI. ## Quality assessment NOS was used to evaluate the quality of the cohort and case–control studies included in this analysis, a score of 7 was considered a high-quality article. In addition, the 11-items standard recommended by the AHRQ was used to evaluate the cross-sectional studies [64]. The literature was divided into levels as follows: A score of 0–3 was considered low quality, a score of 4–7 was moderate quality, and a score of 8–11 was high quality [20].We used the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach to assess the evidence level for each outcome [4]. ## Statistical methods STATA version 16.0 was used to perform statistical analyses. Inclusive HRs, ORs and their $95\%$ CI were fed into the package for effect merging. All effect values were included in the same meta-analysis and the ES was used to estimate the overall effect [2]. During analyses, there were specific steps: [1] Standardization: Inclusive effect values were normalized to 10 μg/m3 as the unit of increase. [ 2] Unit conversation: Considering some articles used ppb as unit, we refer to WHO for the method to convert research into the same indicators, that is, the conversion coefficient from parts per billion (ppb) to μg/m3 (1 ppb = 1.96 μg/m3 ozone) [26]. The following formula was applied to recalculate the RR for the standardized increment [27]:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${RR}_{Standardized}=e^{\left(\frac{\ln\left({RR}_{Origin}\right)}{{Increment}_{Origin}}\times\;{Increment}_{Standardized}\right)}$$\end{document}RRStandardized=elnRROriginIncrementOrigin×IncrementStandardized [3] Heterogeneity test was measured by I2 statistic: If I2 > $50\%$ or $p \leq 0.05$, the value of combined effects was calculated using the random effects model (REM) to reduce the significant heterogeneity, which was visualized with a forest plot. The REM estimates confidence intervals based on sampling error within studies and variation between studies. When heterogeneity was statistically significant, REM was more conservative and robust than the fixed-effect model. The DerSimonian-Laird method was used, which encompasses the variability within and between studies [47]. [ 4] Subgroup analysis: In articles of gestational diabetes, subgroup analysis was performed based on trimesters exposure to ozone to reduce heterogeneity. The impacts of average ozone concentration, socioeconomic status, exposure measurement methods on T2D and GDM were also investigated via subgroup analyses. [ 5] Test and correction of publication bias: Publication bias was tested by Begg's Test and Egger's Test, and was visualized using funnel plots. [ 6] Sensitivity analysis: In order to assess the reliability of studies included in this meta-analysis, each article was excluded one by one for sensitivity analysis. ## Study search results The study screening process is shown in Fig. 1. Three types of diabetes were separately retrieved based on the search strategy, and a total of 667 records were initially identified (T1D 240, T2D 332, GDM 95).Fig. 1The flowchart of study screening and selection ## T1D A total of 16 records entered the next round of screening after duplicate verification and summary assessment. Through further review of the full-text and removing articles of irrelevant exposure or outcome, three studies were included in our analysis [11, 17, 18]. Since there was a deficiency of articles on exposure during pregnancy, we adopted three articles on childhood exposure (Table 1, Supplemental Table S1).Table 1Characteristics of included literature and quality evaluationAuthorYearCountry/regionStudy designSample sizeAge at diagnosis(years)Exposure periodCovariateOutcomeQuality assessment scorePMIDElten et al2020CanadaCohort study754,698 < 6trimesters, childhoodpollutant, sex, maternal age at delivery, smoking,birth weight parity, gestational ageT1D732,120,123Hathout et al2002AmericaCase–control study110 < 18childhoodage715,016,145Hathout et al2006AmericaCase–control study4027.4 ± 4childhoodage716,629,713Jerrett et al2017AmericaCohort study453,221 ≥ 30daily 8-h maximum O3smoking status, exercise, diet, parental history of diabetes, BMI, neighborhood socio-economic status (SES), educationT2D828,153,529Li et al2021TaiwanCohort study6,426,80265.17 ± 12.82daily average concentrations of O3age, sex, SES, urbanization level, temperature, humidity and baseline chronic comorbidity status833,412,098Renzi et al2018ItalyCohort study1,425,580 ≥ 35daily 8-h maximum O3sex, SES, place of birth, occupation, educationa, preexisting comorbidities, marital status829,253,730Yang et al2018ChinaCross-sectional study15,47718–74long-termage, sex, BMI, education, family income, smoking, alcohol consumption, diet, exercise, family history of diabetes, and district729,615,239Yu et al2021AmericaCohort study1,09070.5 ± 6.9daily 8-h maximum O3age, sex, education, occupation, physical activity, smoking status, and household income at baseline734,494,856Hu et al2015AmericaCase–control study410,267/trimester 1, trimester 2, entire pregnancymaternal age, race, education, marital status, season of conception and year of delivery, median household income, prenatal care began, urbanizationGDM825,794,412Jo et al2019AmericaCohort study239,57432.4 ± 5.4preconception, trimester 1, trimester 2maternal age, education, race, household income831,234,004Lin et al2020ChinaCohort study12,842/trimester 1, trimester 2, two trimestersmaternal age, race, education, marital status, conception season, occupation, temperature, humidity, pre-pregnancy BMI832,739,627Liu et al2022ChinaCohort study20,11330preconception, trimester 1, trimester 2maternal age, pre-pregnancy BMI, education, family history of diabetes, parity, season of LMP, temperature934,798,119Pan et al2017TaiwanCohort study19,60631.9 ± 4.5trimester 1, trimester 2, trimester 3maternal age, BMI, weight gain, fetal gender, parity and annual household income728,672,129Robledo et al2015AmericaCohort study219,952/preconception, trimester 1maternal age, race and study site825,601,734Shen et al2017TaiwanCase–control study13,43431.30 ± 4.54preconception, trimester 1, trimester 2season of delivery, number of births, obesity, history of polycystic ovary syndrome (PCOS), personal monthly income, disease burden, median family income, level of urbanization929,261,145Sun et al2022AmericaCohort study395,92730.3 ± 5.7preconception, trimester 1, trimester 2, entire pregnancymaternal age, race, education, family household income, pre-pregnancy BMI, smoking, insurance type, season of conception and year of delivery834,563,749Wu et al2016AmericaCohort study44,949/trimester 1, trimester 2, trimester 3maternal age, race, education, median household income829,659,239Yao et al2020ChinaCohort study5,427/preconception, trimester 1maternal age, education, season of blood collection, fruit and dessert intake frequency, pre-pregnancy BMI, parity, physical activity during pregnancy, family history of diabetes, temperature, and relative humidity732,278,159Yan et al2022ChinaCohort study3,75429.6 ± 4.3trimester 1, trimester 2, trimester 3, entire pregnancymaternal age, diabetes mellitus, pre- pregnancy BMI, pre-pregnancy hypertension and residential region, sex, season of conception83,567,971“/” represent all age groups ## T2D After the removal of the articles with irrelevant exposure or outcome and literature quality scoring, five studies were included in our analysis [21, 34, 43, 59, 62]. ## GDM A total of 79 records that did not meet the inclusion criteria were removed after duplicate verification and summary assessment. After further review of the remaining 16 full-text articles, five studies were removed because two studies had irrelevant exposure or outcome, another two studies had incomplete statistics, and one study had hierarchical data and could not be included in the analysis. The remaining 11 studies were included in our analysis [19, 23, 35, 36, 41, 44, 48, 51, 56, 58, 60]. ## Characteristics overview Table 1 shows the characteristics of 19 studies included in our analysis. In terms of the number of studies (N), America had 9 studies, followed by China ($$n = 5$$), Taiwan ($$n = 3$$), Italy ($$n = 1$$), and Canada ($$n = 1$$). In terms of the number of samples (n) and proportion of samples, most of the included samples were from Taiwan ($$n = 6$$,459,842; $62\%$), followed by America ($$n = 1$$,765,492; $17\%$), Italy ($$n = 1$$,425,580; $14\%$), Canada ($$n = 754$$,698; $7\%$), and China ($$n = 57$$,613; $0.55\%$). Among them, 14 studies were cohort studies, 4 were case-control studies, and 1 was a cross-sectional study. The quality of the 19 selected studies ranged from 7 to 9 according to the NOS standard, indicating that all studies were of moderate to high-quality. The initial certainty of evidence for observational studies was low. Based on study limitations, inconsistency, imprecision, indirectness and publication bias, we further adjusted the evidence certainty of these studies and presented them in Table S1. The downgrading was mainly due to imprecision. Of these, 16 of the outcomes were low and 12 of the outcomes were very low. For the T1D studies, the sample size ranged from 110 to 754,698. Ozone exposure time was childhood, including 0–18 years of age. For the T2D studies, the sample size ranged from 1,090 to 6,426,802. Ozone exposure time was long-term and the age ranged from 18 to 75 years of age. For the GDM studies, the sample size ranged from 3,754 to 410,267. The majority of the study population was between 20–35 years of age. The diagnosis of GDM was validated through an oral glucose tolerance test (OGTT) and uniform criteria. The exposure time included preconception and the three trimesters throughout pregnancy (the 1st trimester: 1–13 gestational weeks; the 2nd trimester: 14–27 or 14–26 gestational weeks; and the 3rd trimester: over 27 weeks of pregnancy). To reduce significant heterogeneity, subgroup analysis was performed based on the exposure time. The average value of daily 8-h maximum ozone concentration or the daily average ozone concentration was used for exposure assessment. The OR and $95\%$ CI of eligible studies were collected after adjustment for potential confounding factors including children/gestational age, BMI, smoking, education level and race. Hathout et al. used age as an adjustment factor [17, 18]. Emphasis was placed on factors such as socio-economic status, marital status, place of birth and sex in the article of Renzi et al. [ 43]. We investigated the relationship between ozone exposure during childhood and T1D, daily ozone exposure and T2D, and ozone exposure during pregnancy and GDM. ## The association between ozone exposure and T1D The effectors of three studies were pooled to analyze the association between ozone exposure and T1D. The random effect model was adopted due to significant heterogeneity among these effects (tau-squared = 0.11, I2 = $79.7\%$, $$p \leq 0.007$$) (Fig. 2). The results from the forest plot showed that the increase (10 μg/m3) in ozone exposure in childhood was correlated with T1D, but not statistically significant (ES = 1.30, $95\%$ CI: 0.86, 1.98).Fig. 2Forest plot for T1D and ozone exposure during childhood (per 10 μg/m3 increase) ## The association between ozone exposure and T2D The effect size of five studies was included in the analysis and REM was utilized to represent the relationship between ozone exposure and T2D with tau-squared = 0.00, I2 = $95.3\%$ ($p \leq 0.001$). The forest plot results showed a positive association between the increase (10 μg/m3) in ozone exposure and T2D, which was statistically significant (ES = 1.06, $95\%$ CI: 1.02, 1.11) (Fig. 3).Fig. 3Forest plot for T2D and long-time ozone exposure (per 10 μg/m3 increase) ## The association between ozone exposure and GDM Eleven studies were included to explore the association between ozone exposure and GDM. We performed four subgroup analyses according to exposure time. The overall results of subgroups showed that ozone exposure (per 10 μg/m3 increase) was associated with GDM, with the overall pooled OR = 1.01 ($95\%$ CI: 1.00, 1.03). Eleven studies were included to evaluate the association of ozone exposure in the first trimester with GDM, and the result was statistically significant (OR = 1.02, $95\%$ CI: 1.00, 1.03) (Fig. 4). The effect sizes of the five articles, which explored the association between the second trimester ozone exposure and GDM showed no significant association with OR = 1.01 ($95\%$ CI: 0.96, 1.05). Moreover, three articles that explore the association between ozone exposure and GDM in the entire pregnancy were statistically insignificant with OR = 1.05 ($95\%$ CI: 0.92, 1.21). In addition, the effect sizes of the eight articles, which were preconception ozone exposure revealed marginal significance with OR = 0.99 ($95\%$ CI: 0.97, 1.01). The relationship was shown by adopting the REM (tau-squared = 0.00, I2 = $96.8\%$, $p \leq 0.001$). Subgroup analyses of other factors were similar to the primary results and presented in the supplementary material (Supplemental Fig. S7-S9).Fig. 4Forest plot for GDM and ozone exposure (per 10 μg/m3 increase) during preconception, the first trimester, the second trimester and entire pregnancy ## Sensitivity analyses Sensitivity analysis was conducted by excluding each study one by one to ensure the reliability of each study. Due to the high heterogeneity, REM was used. The results did not show significant change in these effect sizes, indicating the robustness of the results presented in our study (Supplemental Fig. S1-S3). We further removed the only study in T2D with OR as an outcome measure and the results remain robust (Supplemental Fig. S10). ## Publication bias The Begg’s funnel-plot and Egger’s test were used to detect publication bias and the results are displayed in Supplemental Fig. S4-S6. No significant publication bias was detected in the T1D (Egger’s test, $$p \leq 0.910$$; Begg’s test, $$p \leq 1.000$$) and T2D (Egger’s test, $$p \leq 0.910$$; Begg’s test, $$p \leq 0.806$$) studies. The result from Egger’s test further suggested publication bias of GDM ($$p \leq 0.013$$), however, the Begg’s test indicated no statistical significance ($$p \leq 0.559$$). Potential causes of publication bias may include a tendency to report positive results, exaggerated publication bias due to difficult estimation of population heterogeneity, and a greater likelihood of publication bias in observational studies [37, 53]. This meta-analysis may overestimated the effect of ambient ozone exposure on diabetes due to publication bias. ## Discussion Ozone is a common air pollutant, and its potential health hazard have gradually become a key public health concern. Our study analyzed existing evidence to evaluate the effects of ozone exposure on three types of diabetes including three studies on T1D risk involving 755,210 cases, five studies on T2D risk involving 8,322,170 cases, eleven studies on GDM risk involving 1,385,845 cases. We found that exposure to ozone (per 10 μg/m3 increase) was positively correlated with the risk of GDM, especially in pregnant women exposed to ozone during the first trimester of pregnancy. Additionally, ozone exposure was positively associated with risk for the development of T2D. However, no statistically significant association was found between ozone exposure and risk for the development of T1D. In this meta-analysis, the exposure time to ozone in T1D was childhood and children’s age ranged from 0–18 years old. In T2D, the subjects were exposed to ozone for a long-term and their age distribution was widespread from 18 to over 75 years. In GDM, the research subjects’ exposure time was during or before pregnancy with 20–35 years of age. Most eligible studies were adjusted for multiple factors such as age, maternal age, race, education, birth year, and household income. Most of the included studies were conducted in America and China, others were in Canada and Italy. Our results are consistent with most studies, but there are still some discrepancies. Elten et al. reported a negative correlation between ozone and T1D [11]. However, this study did not accurately distinguish between T1D and T2D suggesting that there was a possibility of bias, although the proportion of T2D in children is expected to be small. A cohort study by Li et al. did not observe an adverse effect of ozone on T2D [33]. Through comprehensive comparisons among studies we extracted, we found that the absence of adjustments for socioeconomic status may be a major contributor to these discrepancies. A systematic review on the risk of ozone inhalation and adverse metabolic effects concluded that the current evidence is insufficient to conclude whether ozone exposure causes T1D and is insufficient or suggestive for the association with T2D [28]. As a result, more evidence is needed to explore these associations. Pan et al. surveyed the prevalence of GDM in the form of a questionnaire, which may reduce sensitivity and underestimate the role of ozone [41]. In a retrospective cohort study reported by Jo et al., ozone exposure was measured on a basis of the child's birth address rather than the residential geocoding of pregnant women, which may lead to information bias [23]. Despite the utilization of the REM model, significant heterogeneity was still found among studies during effect sizes combining. This may be attributed to the inconsistency in study design, exposure assessment, and adjustment of covariates. First, the number of studies on the effect of ozone exposure on T1D and T2D is limited. Moreover, most studies are retrospective studies, which may have introduced retrospective bias. Second, ozone concentrations vary greatly between indoor and outdoor environments [45]. However, in most studies included in our analysis, the data on ozone exposure was derived from outdoor fixed-site monitoring stations, which may not accurately reflect individual exposure levels [31, 46] especially for children with diabetes, the elderly or chronic patients, and pregnant women who may spend more time indoors. Models that relate indoor ozone concentrations to outdoor concentrations may be utilized to reduce this error [55]. Third, the various covariates in the literature included were unevenly distributed in different regions and populations, and the degree of control for potential confounding factors may be different, both can lead to bias. Although the statistics included in some studies were adjusted for similar factors, such as age, sex, ethnicity, smoking, etc., information errors still could not be completely ruled out. The mechanism of ozone -induced diabetes was explored in both animal and molecular models. T2D is usually the result of β cell dysfunction in the context of chronic insulin resistance. Evidence has shown that ozone is a strong oxidant and produces reactive oxygen species (ROS), which can impact insulin-stimulated glucose uptake through oxidative stress response [15]. Oxidative stress has been confirmed as the basic mechanism for the pro-inflammatory response induced by air pollutants [57]. And the pro-inflammatory response is believed to promote the development of T2D [7]. Ozone activates transcription factors through ROS, mediating the NF-κB activation in ozone-exposed cells, which can increase the release of inflammatory cytokines (TNF-α and IL-8) and the expression of adhesion genes [6]. Bailey et al. suggested that exposure to ozone may induce changes in gut microbiota, which may contribute to the increased risk of T2D [3, 16]. GDM shares common pathogenic mechanisms with T2D [30], but in a special physiological state of pregnancy. Studies have shown that women may suffer a higher risk of T2D after GDM [61]. The placenta secretes hormones and cytokines, which contribute to the occurrence of reactions such as oxidative stress in the neuroendocrine system, resulting in insulin resistance [42]. In addition, Snow et al. showed that ozone exposure can excite the sympathetic nerve increasing the circulation of adrenal derived stress hormones [50], which leads to damage to the pancreas, fat, muscle tissue, and liver, ultimately contributing to the development of GDM through different mechanisms [42]. T1D is an autoimmune disease caused by insufficient insulin secretion and the destruction of pancreatic β cells [10]. *Both* genetic and non-genetic factors are likely to contribute to the development of T1D. The interaction between genetics and ozone exposure on initiation and development of T1D requires further exploration. There are some strengths in this study. First, the size of the population sample contained in this study was relatively large. Second, our study covers countries at different levels of socio-economic development, and are thus a more representative sampling, avoiding unnecessary bias and improving the applicability of the results to most countries. Third, considering that the units of ozone are inconsistent, we referred to WHO to obtain standard unit conversion factors, in order to combine the effect values and we performed a logarithmic conversion to reduce heterogeneity. Fourth, since GDM is diagnosed in the middle and late trimester of pregnancy, the data obtained from the 1st and 2nd trimester of pregnancy account for a large proportion, which suggests the rationality in time sequence. This study also has several limitations. First, in this study publication bias may exists. Selective reporting is unavoidable. It remains possible that studies measured more than one air pollutant including ozone in relation to diabetes risk, but only reported on positive associations potentially leaving out negative results on the association between ozone and diabetes risk, although some studies have reported negative results. Second, the number of articles included in this study was limited, however the size of the population sample contained was relatively large. Third, we only explored the effects of a single air pollutant on diabetes. However, some studies have shown that the single pollutant model is closer to reality than the composite pollutant model due to offsetting confounding and measurement errors [13]. ## Conclusion Ozone exposure was positively associated with T2D and GDM, especially during the first trimester of pregnancy, although the current studies were of low level on evidence grade. Therefore, more effective preventive measures and prenatal care to strengthen ozone exposure control are needed to improve the health of both adults and children. Future research is needed focused on the complex ozone-environment- diabetes interactions including the effects of mixed exposure reactions. ## Supplementary Information Additional file 1. ## References 1. American DA. **(2) Classification and diagnosis of diabetes**. *Diabetes Care* (2015.0) **38** S8-S16. DOI: 10.2337/dc15-S005 2. Anderson HR, Favarato G, Atkinson RW. **Long-term exposure to air pollution and the incidence of asthma: meta-analysis of cohort studies**. *Air Qual Atmos Health* (2013.0) **6** 47-56. DOI: 10.1007/s11869-011-0144-5 3. Bailey MJ, Naik NN, Wild LE, Patterson WB, Alderete TL. **Exposure to air pollutants and the gut microbiota: a potential link between exposure, obesity, and type 2 diabetes**. *Gut Microbes* (2020.0) **11** 1188-1202. DOI: 10.1080/19490976.2020.1749754 4. Balshem H, Helfand M, Schünemann HJ, Oxman AD, Kunz R, Brozek J. **GRADE guidelines: 3. Rating the quality of evidence**. *J Clin Epidemiol* (2011.0) **64** 401-6. DOI: 10.1016/j.jclinepi.2010.07.015 5. Billionnet C, Mitanchez D, Weill A, Nizard J, Alla F, Hartemann A. **Gestational diabetes and adverse perinatal outcomes from 716,152 births in France in 2012**. *Diabetologia* (2017.0) **60** 636-644. DOI: 10.1007/s00125-017-4206-6 6. Bromberg PA. **Mechanisms of the acute effects of inhaled ozone in humans**. *Biochim Biophys Acta* (2016.0) **1860** 2771-2781. DOI: 10.1016/j.bbagen.2016.07.015 7. Calle MC, Fernandez ML. **Inflammation and type 2 diabetes**. *Diabetes Metab* (2012.0) **38** 183-191. DOI: 10.1016/j.diabet.2011.11.006 8. Chuang KJ, Yan YH, Cheng TJ. **Effect of air pollution on blood pressure, blood lipids, and blood sugar: a population-based approach**. *J Occup Environ Med* (2010.0) **52** 258-262. DOI: 10.1097/JOM.0b013e3181ceff7a 9. Cole JB, Florez JC. **Genetics of diabetes mellitus and diabetes complications**. *Nat Rev Nephrol* (2020.0) **16** 377-390. DOI: 10.1038/s41581-020-0278-5 10. Eisenbarth GS. **Type I diabetes mellitus. A chronic autoimmune disease**. *N Engl J Med* (1986.0) **314** 1360-8. DOI: 10.1056/nejm198605223142106 11. Elten M, Donelle J, Lima I, Burnett RT, Weichenthal S, Stieb DM. **Ambient air pollution and incidence of early-onset paediatric type 1 diabetes: a retrospective population-based cohort study**. *Environ Res* (2020.0) **184** 109291. DOI: 10.1016/j.envres.2020.109291 12. Erbagci AB, Tarakcioglu M, Coskun Y, Sivasli E, Sibel NE. **Mediators of inflammation in children with type I diabetes mellitus: cytokines in type I diabetic children**. *Clin Biochem* (2001.0) **34** 645-650. DOI: 10.1016/s0009-9120(01)00275-2 13. Evangelopoulos D, Katsouyanni K, Schwartz J, Walton H. **Quantifying the short-term effects of air pollution on health in the presence of exposure measurement error: a simulation study of multi-pollutant model results**. *Environ Health* (2021.0) **20** 94. DOI: 10.1186/s12940-021-00757-4 14. Galassetti P, Riddell MC. **Exercise and type 1 diabetes (T1DM)**. *Compr Physiol* (2013.0) **3** 1309-1336. DOI: 10.1002/cphy.c110040 15. Gerber PA, Rutter GA. **The role of oxidative stress and hypoxia in pancreatic beta-cell dysfunction in diabetes mellitus**. *Antioxid Redox Signal* (2017.0) **26** 501-518. DOI: 10.1089/ars.2016.6755 16. Gurung M, Li Z, You H, Rodrigues R, Jump DB, Morgun A. **Role of gut microbiota in type 2 diabetes pathophysiology**. *EBioMedicine* (2020.0) **51** 102590. DOI: 10.1016/j.ebiom.2019.11.051 17. Hathout EH, Beeson WL, Ischander M, Rao R, Mace JW. **Air pollution and type 1 diabetes in children**. *Pediatr Diabetes* (2006.0) **7** 81-87. DOI: 10.1111/j.1399-543X.2006.00150.x 18. Hathout EH, Beeson WL, Nahab F, Rabadi A, Thomas W, Mace JW. **Role of exposure to air pollutants in the development of type 1 diabetes before and after 5 yr of age**. *Pediatr Diabetes* (2002.0) **3** 184-188. DOI: 10.1034/j.1399-5448.2002.30403.x 19. Hu H, Ha S, Henderson BH, Warner TD, Roth J, Kan H. **Association of atmospheric particulate matter and ozone with gestational diabetes mellitus**. *Environ Health Perspect* (2015.0) **123** 853-859. DOI: 10.1289/ehp.1408456 20. Hu J, Dong Y, Chen X, Liu Y, Ma D, Liu X. **Prevalence of suicide attempts among Chinese adolescents: a meta-analysis of cross-sectional studies**. *Compr Psychiatry* (2015.0) **61** 78-89. DOI: 10.1016/j.comppsych.2015.05.001 21. Jerrett M, Brook R, White LF, Burnett RT, Yu J, Su J. **Ambient ozone and incident diabetes: a prospective analysis in a large cohort of African American women**. *Environ Int* (2017.0) **102** 42-47. DOI: 10.1016/j.envint.2016.12.011 22. Jerrett M, Burnett RT, Pope CA, Ito K, Thurston G, Krewski D. **Long-Term Ozone Exposure and Mortality**. *New England J Med* (2009.0) **360** 1085-1095. DOI: 10.1056/NEJMoa0803894 23. Jo H, Eckel SP, Chen JC, Cockburn M, Martinez MP, Chow T. **Associations of gestational diabetes mellitus with residential air pollution exposure in a large Southern California pregnancy cohort**. *Environ Int* (2019.0) **130** 104933. DOI: 10.1016/j.envint.2019.104933 24. Johns EC, Denison FC, Norman JE, Reynolds RM. **Gestational diabetes mellitus: mechanisms, treatment, and complications**. *Trends Endocrinol Metab* (2018.0) **29** 743-754. DOI: 10.1016/j.tem.2018.09.004 25. Kelishadi R, Mirghaffari N, Poursafa P, Gidding SS. **Lifestyle and environmental factors associated with inflammation, oxidative stress and insulin resistance in children**. *Atherosclerosis* (2009.0) **203** 311-319. DOI: 10.1016/j.atherosclerosis.2008.06.022 26. Khreis H, Kelly C, Tate J, Parslow R, Lucas K, Nieuwenhuijsen M. **Exposure to traffic-related air pollution and risk of development of childhood asthma: a systematic review and meta-analysis**. *Environ Int* (2017.0) **100** 1-31. DOI: 10.1016/j.envint.2016.11.012 27. 27.Kim HB, Shim JY, Park B, Lee YJ. Long-term exposure to air pollutants and cancer mortality: a meta-analysis of cohort studies. Int J Environ Res Public Health. 2018;15. 10.3390/ijerph15112608. 28. LaKind JS, Burns CJ, Pottenger LH, Naiman DQ, Goodman JE, Marchitti SA. **Does ozone inhalation cause adverse metabolic effects in humans? A systematic review**. *Crit Rev Toxicol* (2021.0) **51** 467-508. DOI: 10.1080/10408444.2021.1965086 29. Lanzinger S, Rosenbauer J, Sugiri D, Schikowski T, Treiber B, Klee D. **Impact of long-term air pollution exposure on metabolic control in children and adolescents with type 1 diabetes: results from the DPV registry**. *Diabetologia* (2018.0) **61** 1354-1361. DOI: 10.1007/s00125-018-4580-8 30. Lappas M, Hiden U, Desoye G, Froehlich J, Hauguel-de Mouzon S, Jawerbaum A. **The role of oxidative stress in the pathophysiology of gestational diabetes mellitus**. *Antioxid Redox Signal* (2011.0) **15** 3061-3100. DOI: 10.1089/ars.2010.3765 31. Lee K, Parkhurst WJ, Xue J, Ozkaynak AH, Neuberg D, Spengler JD. **Outdoor/Indoor/Personal ozone exposures of children in Nashville, Tennessee**. *J Air Waste Manag Assoc* (2004.0) **54** 352-359. DOI: 10.1080/10473289.2004.10470904 32. Li C, Balluz LS, Vaidyanathan A, Wen XJ, Hao Y, Qualters JR. **Long-term exposure to ozone and life expectancy in the United States, 2002 to 2008**. *Medicine (Baltimore)* (2016.0) **95** e2474. DOI: 10.1097/MD.0000000000002474 33. Li H, Duan D, Xu J, Feng X, Astell-Burt T, He T. **Ambient air pollution and risk of type 2 diabetes in the Chinese**. *Environ Sci Pollut Res Int* (2019.0) **26** 16261-16273. DOI: 10.1007/s11356-019-04971-z 34. Li YL, Chuang TW, Chang PY, Lin LY, Su CT, Chien LN. **Long-term exposure to ozone and sulfur dioxide increases the incidence of type 2 diabetes mellitus among aged 30 to 50 adult population**. *Environ Res* (2021.0) **194** 110624. DOI: 10.1016/j.envres.2020.110624 35. Lin Q, Zhang S, Liang Y, Wang C, Wang C, Wu X. **Ambient air pollution exposure associated with glucose homeostasis during pregnancy and gestational diabetes mellitus**. *Environ Res* (2020.0) **190** 109990. DOI: 10.1016/j.envres.2020.109990 36. Liu WY, Lu JH, He JR, Zhang LF, Wei DM, Wang CR. **Combined effects of air pollutants on gestational diabetes mellitus: A prospective cohort study**. *Environ Res* (2022.0) **204** 112393. DOI: 10.1016/j.envres.2021.112393 37. Mathur MB, VanderWeele TJ. **Estimating publication bias in meta-analyses of peer-reviewed studies: a meta-meta-analysis across disciplines and journal tiers**. *Res Synth Methods* (2021.0) **12** 176-191. DOI: 10.1002/jrsm.1464 38. Miller DB, Snow SJ, Henriquez A, Schladweiler MC, Ledbetter AD, Richards JE. **Systemic metabolic derangement, pulmonary effects, and insulin insufficiency following subchronic ozone exposure in rats**. *Toxicol Appl Pharmacol* (2016.0) **306** 47-57. DOI: 10.1016/j.taap.2016.06.027 39. Nuvolone D, Petri D, Voller F. **The effects of ozone on human health**. *Environ Sci Pollut Res Int* (2018.0) **25** 8074-8088. DOI: 10.1007/s11356-017-9239-3 40. Orioli R, Cremona G, Ciancarella L, Solimini AG. **Association between PM10, PM2.5, NO2, O3 and self-reported diabetes in Italy: a cross-sectional, ecological study**. *PLoS One* (2018.0) **13** e0191112. DOI: 10.1371/journal.pone.0191112 41. Pan SC, Huang CC, Lin SJ, Chen BY, Chan CC, Leon Guo YL. **Gestational diabetes mellitus was related to ambient air pollutant nitric oxide during early gestation**. *Environ Res* (2017.0) **158** 318-323. DOI: 10.1016/j.envres.2017.06.005 42. 42.Plows JF, Stanley JL, Baker PN, Reynolds CM, Vickers MH. The Pathophysiology of Gestational Diabetes Mellitus. Int J Mol Sci. 2018;19. 10.3390/ijms19113342. 43. Renzi M, Cerza F, Gariazzo C, Agabiti N, Cascini S, Di Domenicantonio R. **Air pollution and occurrence of type 2 diabetes in a large cohort study**. *Environ Int* (2018.0) **112** 68-76. DOI: 10.1016/j.envint.2017.12.007 44. Robledo CA, Mendola P, Yeung E, Mannisto T, Sundaram R, Liu D. **Preconception and early pregnancy air pollution exposures and risk of gestational diabetes mellitus**. *Environ Res* (2015.0) **137** 316-322. DOI: 10.1016/j.envres.2014.12.020 45. Romieu I, Moreno-Macias H, London SJ. **Gene by environment interaction and ambient air pollution**. *Proc Am Thorac Soc* (2010.0) **7** 116-122. DOI: 10.1513/pats.200909-097RM 46. Sarnat JA, Brown KW, Schwartz J, Coull BA, Koutrakis P. **Ambient gas concentrations and personal particulate matter exposures: implications for studying the health effects of particles**. *Epidemiology* (2005.0) **16** 385-395. DOI: 10.1097/01.ede.0000155505.04775.33 47. 47.Schwingshackl L, Schwedhelm C, Galbete C, Hoffmann G. Adherence to mediterranean diet and risk of cancer: an updated systematic review and meta-analysis. Nutrients. 2017;9. 10.3390/nu9101063. 48. 48.Shen HN, Hua SY, Chiu CT, Li CY. Maternal exposure to air pollutants and risk of gestational diabetes mellitus in Taiwan. Int J Environ Res Public Health. 2017;14. 10.3390/ijerph14121604. 49. Shore SA. **The Metabolic Response to Ozone**. *Front Immunol.* (2019.0) **10** 2890. DOI: 10.3389/fimmu.2019.02890 50. Snow SJ, Henriquez AR, Costa DL, Kodavanti UP. **Neuroendocrine regulation of air pollution health effects: emerging insights**. *Toxicol Sci* (2018.0) **164** 9-20. DOI: 10.1093/toxsci/kfy129 51. Sun Y, Li X, Benmarhnia T, Chen JC, Avila C, Sacks DA. **Exposure to air pollutant mixture and gestational diabetes mellitus in Southern California: Results from electronic health record data of a large pregnancy cohort**. *Environ Int* (2022.0) **158** 106888. DOI: 10.1016/j.envint.2021.106888 52. Tamayo T, Rathmann W, Stahl-Pehe A, Landwehr S, Sugiri D, Krämer U. **No adverse effect of outdoor air pollution on HbA1c in children and young adults with type 1 diabetes**. *Int J Hyg Environ Health* (2016.0) **219** 349-355. DOI: 10.1016/j.ijheh.2016.02.002 53. Thornton A, Lee P. **Publication bias in meta-analysis: its causes and consequences**. *J Clin Epidemiol* (2000.0) **53** 207-216. DOI: 10.1016/s0895-4356(99)00161-4 54. Wendland EM, Torloni MR, Falavigna M, Trujillo J, Dode MA, Campos MA. **Gestational diabetes and pregnancy outcomes–a systematic review of the World Health Organization (WHO) and the International Association of Diabetes in Pregnancy Study Groups (IADPSG) diagnostic criteria**. *BMC Pregnancy Childbirth* (2012.0) **12** 23. DOI: 10.1186/1471-2393-12-23 55. Weschler CJ. **Ozone's impact on public health: contributions from indoor exposures to ozone and products of ozone-initiated chemistry**. *Environ Health Perspect* (2006.0) **114** 1489-1496. DOI: 10.1289/ehp.9256 56. Wu J, Laurent O, Li L, Hu J, Kleeman M. **Adverse reproductive health outcomes and exposure to gaseous and particulate-matter air pollution in pregnant women**. *Res Rep Health Eff Inst* (2016.0) **2016** 1-58. PMID: 29659239 57. Wu W, Doreswamy V, Diaz-Sanchez D, Samet JM, Kesic M, Dailey L. **GSTM1 modulation of IL-8 expression in human bronchial epithelial cells exposed to ozone**. *Free Radic Biol Med* (2011.0) **51** 522-529. DOI: 10.1016/j.freeradbiomed.2011.05.006 58. Yan M, Liu N, Fan Y, Ma L, Guan T. **Associations of pregnancy complications with ambient air pollution in China**. *Ecotoxicol Environ Saf* (2022.0) **241** 113727. DOI: 10.1016/j.ecoenv.2022.113727 59. Yang BY, Qian ZM, Li S, Chen G, Bloom MS, Elliott M. **Ambient air pollution in relation to diabetes and glucose-homoeostasis markers in China: a cross-sectional study with findings from the 33 Communities Chinese Health Study**. *Lancet Planet Health* (2018.0) **2** e64-e73. DOI: 10.1016/S2542-5196(18)30001-9 60. Yao M, Liu Y, Jin D, Yin W, Ma S, Tao R. **Relationship betweentemporal distribution of air pollution exposure and glucose homeostasis during pregnancy**. *Environ Res* (2020.0) **185** 109456. DOI: 10.1016/j.envres.2020.109456 61. You H, Hu J, Liu Y, Luo B, Lei A. **Risk of type 2 diabetes mellitus after gestational diabetes mellitus: a systematic review & meta-analysis**. *Indian J Med Res* (2021.0) **154** 62-77. DOI: 10.4103/ijmr.IJMR_852_18 62. Yu Y, Jerrett M, Paul KC, Su J, Shih IF, Wu J. **Ozone exposure, outdoor physical activity, and incident type 2 diabetes in the SALSA cohort of older Mexican Americans**. *Environ Health Perspect* (2021.0) **129** 97004. DOI: 10.1289/EHP8620 63. Zanobetti A, Schwartz J. **Ozone and survival in four cohorts with potentially predisposing diseases**. *Am J Respir Crit Care Med* (2011.0) **184** 836-841. DOI: 10.1164/rccm.201102-0227OC 64. Zeng X, Zhang Y, Kwong JS, Zhang C, Li S, Sun F. **The methodological quality assessment tools for preclinical and clinical studies, systematic review and meta-analysis, and clinical practice guideline: a systematic review**. *J Evid Based Med* (2015.0) **8** 2-10. DOI: 10.1111/jebm.12141 65. Zhang H, Wang Q, He S, Wu K, Ren M, Dong H. **Ambient air pollution and gestational diabetes mellitus: A review of evidence from biological mechanisms to population epidemiology**. *Sci Total Environ* (2020.0) **719** 137349. DOI: 10.1016/j.scitotenv.2020.137349 66. Zhang JJ, Wei Y, Fang Z. **Ozone pollution: a major health hazard worldwide**. *Front Immunol* (2019.0) **10** 2518. DOI: 10.3389/fimmu.2019.02518
--- title: 'Different roles of protein biomarkers predicting eGFR trajectories in people with chronic kidney disease and diabetes mellitus: a nationwide retrospective cohort study' authors: - Michael Kammer - Andreas Heinzel - Karin Hu - Heike Meiselbach - Mariella Gregorich - Martin Busch - Kevin L. Duffin - Maria F. Gomez - Kai-Uwe Eckardt - Rainer Oberbauer journal: Cardiovascular Diabetology year: 2023 pmcid: PMC10061741 doi: 10.1186/s12933-023-01808-5 license: CC BY 4.0 --- # Different roles of protein biomarkers predicting eGFR trajectories in people with chronic kidney disease and diabetes mellitus: a nationwide retrospective cohort study ## Abstract ### Background Chronic kidney disease (CKD) is a common comorbidity in people with diabetes mellitus, and a key risk factor for further life-threatening conditions such as cardiovascular disease. The early prediction of progression of CKD therefore is an important clinical goal, but remains difficult due to the multifaceted nature of the condition. We validated a set of established protein biomarkers for the prediction of trajectories of estimated glomerular filtration rate (eGFR) in people with moderately advanced chronic kidney disease and diabetes mellitus. Our aim was to discern which biomarkers associate with baseline eGFR or are important for the prediction of the future eGFR trajectory. ### Methods We used Bayesian linear mixed models with weakly informative and shrinkage priors for clinical predictors ($$n = 12$$) and protein biomarkers ($$n = 19$$) to model eGFR trajectories in a retrospective cohort study of people with diabetes mellitus ($$n = 838$$) from the nationwide German Chronic Kidney Disease study. We used baseline eGFR to update the models’ predictions, thereby assessing the importance of the predictors and improving predictive accuracy computed using repeated cross-validation. ### Results The model combining clinical and protein predictors had higher predictive performance than a clinical only model, with an \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^{2}$$\end{document}R2 of 0.44 ($95\%$ credible interval 0.37–0.50) before, and 0.59 ($95\%$ credible interval 0.51–0.65) after updating by baseline eGFR, respectively. Only few predictors were sufficient to obtain comparable performance to the main model, with markers such as Tumor Necrosis Factor Receptor 1 and Receptor for Advanced Glycation Endproducts being associated with baseline eGFR, while Kidney Injury Molecule 1 and urine albumin-creatinine-ratio were predictive for future eGFR decline. ### Conclusions Protein biomarkers only modestly improve predictive accuracy compared to clinical predictors alone. The different protein markers serve different roles for the prediction of longitudinal eGFR trajectories potentially reflecting their role in the disease pathway. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12933-023-01808-5. ## Background The prevalence of metabolic syndrome and diabetes mellitus (DM) is on the rise worldwide in adults, adolescents and even in children [1–4]. Chronic kidney disease (CKD) is a common comorbidity in these people, and a key risk factor for life limiting conditions such as arterial hypertension and cardiovascular disease. In the last decade effective treatments emerged that reduce the risk of progression for CKD [5–7], making an accurate, early prediction of the highly variable individual decline of kidney function in terms of estimated glomerular filtration rate (eGFR) an important clinical goal. The combination of clinical predictors with plasma biomarkers was found to improve predictive accuracy for individual eGFR loss in early stages of the disease, but so far showed limited clinical utility [8–19]. Furthermore, few studies addressed how the biomarkers contributed to the predictions. Kidney Injury Molecule 1 (KIM1) for example has been shown in experimental models of kidney injury and human studies to be an intrinsic kidney injury marker whereas other markers such as Tumor Necrosis Factor Receptor 1 (TNFR1) represent filtration markers even in settings without intrinsic kidney damage [20, 21]. Therefore, it is likely that different biomarkers contribute differently to the prediction of longitudinal eGFR trajectories, i.e. some may be strongly associated with values close to the baseline, while others may be predictive for future eGFR decline. To better understand the roles of established plasma biomarkers the specific aims of our study were to validate and discern predictors associated with baseline eGFR and future eGFR decline, as well as to assess their predictive abilities in combination with clinical predictors. We made use of Bayesian linear mixed models to analyze data from persons with diabetes in the German Chronic Kidney Disease (GCKD) study, one of the largest prospective cohort studies of people with moderately advanced CKD [22]. ## Study design and outcome of interest We determined eGFR according to the CKD-EPI creatinine equation [23]. To validate the set of selected protein biomarkers, our first objective was to use baseline values of biomarkers and clinical predictors to prognosticate the entire longitudinal eGFR trajectory, thereby assessing the predictive capabilities of these data independently of baseline eGFR. To discern the roles of the predictors, our second objective was to elucidate the added long-term predictive capabilities of the biomarkers on top of baseline eGFR, which is most relevant to clinical practice. ## Study cohort We analyzed the subcohort of people with DM in the GCKD study, a prospective observational nationwide cohort study in Germany of people under regular nephrological care without the need for kidney replacement therapy [22]. The study did a long-term observation with yearly visits, alternating between in person visits and telephone interviews until year six. It is one of the world’s largest long-term observational CKD cohort studies with more than 5000 patients enrolled between March 2010 and March 2012. The inclusion criteria for the GCKD study were an eGFR of 30–60 ml/min/1.73m2 or an eGFR > 60 ml/min/1.73m2 with overt albuminuria (defined as albumin excretion > 300 mg/g creatinine, protein excretion > 500 mg/g creatinine, or corresponding values for 24 h urinary excretion). Exclusion criteria comprised non-Caucasian ethnicity, solid organ or bone marrow transplantation, active malignancy within 24 months prior to screening, heart failure of New York Heart Association Stage IV, and inability to provide consent. Due to limitation on sample availability, the first in-person follow-up visit two years after enrolment into the GCKD study was referred to as “baseline”, and defined time zero for all computations of observation times in the remainder of this manuscript. Additional inclusion criteria for our study cohort on top of those for the GCKD study were diagnosis of DM, an eGFR of 25–70 ml/min/1.73m2 to reflect the natural decline of eGFR between enrolment into GCKD and our analysis baseline, and at least one eGFR measurement post-baseline to contribute to our longitudinal outcome of interest. Persons were defined as diagnosed with DM if they had an HbA1C measurement of at least $6.5\%$, or if they had a prescription for at least one drug used to treat DM comprising a compound from any class starting with code “A10” (“Drugs used in diabetes”) according to the Anatomical Therapeutic Chemical Classification System [24]. At our baseline, data from 4245 people between 24. January 2012 and 25. October 2019 (data lock) were available, including 1332 with DM. We provide an overview of participant inclusion in Fig. 1.Fig. 1Flowchart of participant inclusion ## Clinical predictors We analyzed several common clinical predictors: age, sex, body mass index (BMI), smoking status (never / ever), mean arterial pressure (MAP), serum cholesterol, urine albumin-creatinine-ratio (UACR), hemoglobin A1C (HbA1c), hemoglobin, intake of blood pressure lowering medication, antidiabetic medication, and lipid lowering medication. ## Protein biomarker selection and measurement We selected 19 protein biomarkers from a pool of candidates with prior evidence for an association with renal function derived from two recently published studies by Niewczas et al. and Gerstein et al., as well as earlier analyses within the BEAt-DKD and RHAPSODY consortia [10, 12, 13, 15]. We maximized the number of biomarkers that could be measured using a single sample aliquot by optimizing the selection regarding the availability of multiplexed Luminex and ELISA assays. A Human Premixed Multi-Analyte Luminex Kit (RD-LXSAHM-13, R&D Systems, Minneapolis, USA) was used to measure 13 serum biomarkers with 1:2 sample dilution: Alpha 1-Microglobulin, Angiopoietin-2, C–C motif Chemokine 11 (CCL11), C–C motif Chemokine 15 (CCL15), Chemerin, Fas, Fas Ligand, Growth Differentiation Factor 15 (GDF15), Interleukin 1 Receptor Type 1 (IL1R1), Matrix Metallopeptidase 7 (MMP7), Receptor for Advanced Glycation Endproducts (RAGE), TNFR1, and u-Plasminogen Activator (uPA). An additional Human Premixed Multi-Analyte Luminex Kit (RD-LXSAHM-05, R&D Systems) was used to measure five serum biomarkers with 1:50 sample dilution: Angiopoietin-1, C–C motif Chemokine 5 (CCL5), C–C motif Chemokine 14 (CCL14), Galectin-3 and Myoglobin. Assays were processed following the protocol provided by the manufacturer and measured on a Luminex 200 (Luminex Corporation, Austin, USA) using the xPonent software (Luminex Corporation) with settings recommended in the protocol. Additionally, KIM1 was measured using an ELISA (RD-DSKM100, R&D Systems). A 1:2 sample dilution was applied and the assay was processed according to the manufacturer’s protocol. Optical density was determined using a TriStar2 LB 942 Modular Multimode Microplate Reader (Berthold Technologies, Bad Wildbad, Germany) with the MikroWin2010 software (v5.21, Berthold Technologies) set as instructed in the assay protocol. All samples were measured as technical replicates. A coefficient of variation (CV) ≤ $15\%$ was required for a measurement to be considered valid. Incurred sample reruns of > $10\%$ of all measured samples were performed on different plates, requiring an inter-plate CV of < $20\%$ to consider the measurements as valid. Three quality control samples (high, medium, low concentration) diluted from the supplied high standard of each assay were included on each measured plate. Concentrations from raw fluorescence signals outside of the standard range were truncated to fixed values (1/√2 times the lowest or √2 times the highest respective standard value). The measurement with smallest CV was preferred when multiple measurements were available due to reruns. ## Statistical analysis We report the cohort demographics by medians and interquartile ranges (IQR) for continuous variables, as well as by absolute and relative frequencies for discrete variables. ## General modeling strategy We analyzed the longitudinal eGFR trajectories using Bayesian multivariable linear mixed models (BLMM). Such models allow to discern the main term modeling overall eGFR levels (baseline coefficient), and an interaction term with observation time modeling the eGFR decline (slope coefficient) for each independent variable [25]. Person-specific trajectories were modelled using random intercepts and slopes. We fitted several BLMM comprising different variable sets as fixed effects. First, univariable BLMM using single protein biomarkers to assess the univariable association with eGFR. Second, the clinical BLMM using only clinical predictors to serve as a reference model in terms of prediction performance. Third, the main BLMM combining clinical predictors and biomarkers. All models also included observation time and interaction terms with time to model eGFR decline. The univariable and clinical BLMM used weakly informative Student-t distributions as coefficient prior distributions, while the main BLMM used regularized Horseshoe prior distributions to enforce sparsity and shrink the effects of unimportant variables towards zero [26, 27]. All variance parameters used weakly informative priors. We assessed the choice of hyperparameters via sensitivity analyses. Model convergence was evaluated by graphical inspection of the Markov chain traceplots, the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\widehat{R}$$\end{document}R^ statistic and other sampler diagnostics [28, 29]. We assessed model fit via the normality of residuals and calibration plots. All biomarker levels and UACR were log2-transformed during modeling to achieve more symmetric distributions. For comparability, coefficients are reported on a standardized scale corresponding to units of standard deviations, and are given as summaries of the model posteriors, i.e. the median of the distribution and a $95\%$ equal tailed Bayesian credible interval (BCI). These intervals represent a contiguous region that contains the unobserved coefficient value with $95\%$ probability, given our modeling assumptions. Model prediction performance via marginal predictions using only fixed effects was assessed in terms of the explained variation \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^{2}$$\end{document}R2 and the adjusted \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^{2}$$\end{document}R2 (computed as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1-\frac{(1-R^2)\, (n-1) }{n-p-1}$$\end{document}1-(1-R2)(n-1)n-p-1, with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n$$\end{document}n the number of observations, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p$$\end{document}p the number of fixed effects), as well as the root mean squared error (RMSE). We used 5-times repeated fivefold cross-validation to estimate the out-of-sample performance. ## Model update by baseline eGFR Each model included baseline eGFR as part of the longitudinal outcome (objective 1). However, to reflect the practical use of the models (objective 2) we incorporated baseline eGFR for predictions of future (post-baseline) eGFR for unseen individuals by updating the random coefficient posteriors, i.e. computing the best linear unbiased predictors of the random effects conditional on the observed baseline values [30]. Thereby we prevented over-optimistic model fit when using baseline eGFR as independent variable, but still gained improved prediction performance for the future eGFR trajectory. This also allowed us to elucidate the impact of baseline eGFR on the model’s predictions. ## Variable importance We assessed the importance of predictors in the main BLMM for both objectives by ordering them by the increase in cross-validated RMSE when removing a single variable from the full main model and its updated version. Subsequently, we used this ordering to obtain a sequence of nested submodels of the main model, which provide predictions that become incrementally better approximations of the main model predictions as variables are added one-by-one. In detail, we started with a model comprising only the intercept and observation time, and then added more variables (main term and interaction with time) according to the ordering by cross-validated RMSE to obtain incrementally larger models. We computed the submodel predictions using a reference model based projection approach [31–33]. Due to the impact of baseline eGFR the orderings for both objectives differed, discerning the importance of variables as a replacement of baseline eGFR, and for predicting future eGFR in addition to baseline eGFR. The variable ordering is reported in Additional file 1: Table S6 and the corresponding incremental submodel performances are shown in Fig. 4 for the cross-validated \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^{2}$$\end{document}R2 (Additional file 1: Figure S6 shows cross-validated RMSE). The results corroborated the important roles of TNFR1, RAGE and age for objective 1, while KIM1 and UACR ranked as the most relevant predictors for objective 2 when incorporating baseline eGFR for predictions. This reflected their different roles for the prediction of eGFR trajectories: markers like TNFR1 and RAGE were relevant as a replacement of baseline eGFR and predictive of values close in time to baseline, while KIM1 and UACR were predictive of the future eGFR decline. Only few predictors were sufficient to approximate the performance of the full main model for both objectives, while the remaining predictors did not improve prediction performance substantially and were largely exchangeable. This is particularly evident for the prediction of future eGFR decline, in which case only KIM1 and to a lesser extent UACR provided substantial added predictive value on top of baseline eGFR.Fig. 4Approximation of main model by incremental submodels using the top 15 predictors, defined according to the ranking of variables by increase in cross-validated RMSE. The dashed line (posterior median \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^{2}$$\end{document}R2) and the dark and light grey shaded areas ($50\%$ and $95\%$ BCI) indicate the full model performance in terms of cross-validated \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^{2}$$\end{document}R2. For submodels, the points indicate the posterior median \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^{2}$$\end{document}R2, thick and thin bars give $50\%$ and $95\%$ BCIs, respectively. The left panel depicts results when baseline eGFR is used as part of the longitudinal outcome vector, the right panel results when baseline eGFR is used to update predictions for post-baseline eGFR. The variables used in the submodels increase from left to right, starting with Intercept and time, then adding the first predictor according to the ranking (TNFR1 and KIM1, respectively), then adding the next predictor (RAGE and UACR, respectively), and so on. In particular, in the right panel the results show the added predictive performance for the predictors on top of baseline eGFR. The ordering shown is the ordering obtained across all cross-validation folds ## Missing data We used multiple imputation with 20 imputations to account for missing data. All models were fitted in each imputed dataset, and the resulting posteriors pooled to obtain a single posterior incorporating the additional uncertainty due to missing data. ## Implementation details We used the R statistical software (version 4.0.4) for all analyses, implementing the BLMM in Stan (version 2.21.0) accessed via the brms package (version 2.16), and the multiple imputation using the mice package (version 3.13) [34]. We provide additional details in the extended Statistical Methods in the Supplementary Material, and considerations regarding sample size in Additional file 1: Figure S1. ## Results In total, we measured 19 protein biomarkers at baseline in 838 people with DM (predominantly Type 2 DM). Demographics of our study cohort are presented in Table 1. For most participants two post-baseline eGFR measurements were available ($$n = 525$$, $63\%$), and the median observed follow-up after baseline was 3.9 years (IQR [3.5, 4.1]). Overall loss-to-follow-up in the GCKD cohort was low: 45 persons ($5\%$) from our subcohort died during follow-up and 9 ($1\%$) dropped out due to other reasons. The median decline in eGFR, estimated via person-specific regression models, was -0.8 ml/min/1.73m2 per year (IQR [− 3.0, 1.1]).Table 1Cohort demographics of study patients with *Diabetes mellitus* ($$n = 838$$) at study baseline. Data are median and IQR for continuous variables, or absolute and relative frequencies for categorical variablesVariableBaseline valueMissingAge (years)69 [62, 73]0 ($0\%$)Sex (female)287 ($34\%$)0 ($0\%$)Body mass index (kg/m2)32 [28, 36]1 (< $1\%$)*Smoking status* (ever)505 ($50\%$)1 (< $1\%$)Mean arterial pressure (mmHg)97 [90, 105]2 (< $1\%$)Serum cholesterol (mg/dL)192 [164, 225]0 ($0\%$)HbA1c (mmol/L)53 [48, 61]6 (< $1\%$)HbA1c (%)7.0 [6.6, 7.8]Hemoglobin (g/dL)13.7 [12.6, 14.8]6 (< $1\%$)Serum creatinine (mg/dL)1.5 [1.3, 1.8]0 ($0\%$)Urine albumin-creatinine-ratio (mg/g)37 [9, 224]18 ($2\%$)eGFR (mL/min/1.73m2)42 [35, 51]0 ($0\%$)Blood pressure medication (intake)793 ($95\%$)0 ($0\%$)Diabetes medication (intake)697 ($83\%$)0 ($0\%$)Lipid lowering medication (intake)555 ($66\%$)0 ($0\%$) Measured protein biomarker concentrations used in the analysis are depicted in Additional file 1: Figure S2. The proportion of missing biomarker measurements was low at around $3\%$. We provide an overview of biomarker availability, truncation and measurement issues in Additional file 1: Tables S1 and S2. The Spearman correlation (Additional file 1: Figure S3) between clinical variables (except creatinine and eGFR) and biomarkers was generally low (median 0.03, IQR [− 0.02, 0.07]). In contrast, the correlations between biomarkers and creatinine (0.25 [0.09, 0.35]) or eGFR (− 0.30 [− 0.38, − 0.12]) were higher in magnitude. ## Models for eGFR All BLMM reported in the following showed satisfactory convergence (Additional file 1: Table S3) and model fit (Additional file 1: Figure S4 shows the main model fit). The results reported here remained unchanged in all our sensitivity analyses (see Extended Statistical Methods in the Supplementary Material). ## Univariable protein biomarker models In terms of median posterior adjusted \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^{2}$$\end{document}R2 pooled across observation time, TNFR1 (0.30, $95\%$ BCI [0.26, 0.33]) and RAGE (0.17 [0.14, 0.21]) showed the strongest associations with eGFR in univariable BLMM. All other markers had adjusted \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^{2}$$\end{document}R2 values below 0.12, and most of them showed an association via their baseline coefficients (i.e. their $95\%$ BCI excluded zero). For KIM1 (adjusted \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^{2}$$\end{document}R2 0.12 [0.09, 0.15]) the standardized slope coefficient had the greatest magnitude of all biomarkers, while for many other markers the association with eGFR trajectory was weak and their $95\%$ BCIs included zero (Fig. 2).Fig. 2Standardized coefficients estimated by univariable Bayesian linear mixed models. The thin black bars indicate $95\%$ Bayesian credible intervals for the coefficients; the thick black bars indicate $50\%$ Bayesian credible intervals. The intersection point of the horizontal and vertical bars indicated by the point gives the values of the baseline and slope coefficients. The top-5 biomarkers in terms of posterior median adjusted \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^{2}$$\end{document}R2 pooled over all observation times are annotated in the graphic. Note the different x- and y-axis scales. Most biomarkers are concentrated around the x-axis, indicating an association with baseline eGFR, but weak association with the longitudinal eGFR trajectory ## Clinical reference model The model using clinical predictors (12 in total) showed modest predictive performance for the whole eGFR trajectory (objective 1). Its cross-validated median posterior \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^{2}$$\end{document}R2 was 0.17 ($95\%$ BCI [0.11, 0.22], RMSE 11.79 [10.83, 12.66]). Using baseline eGFR to update the model’s predictions (objective 2), the cross-validated performance for post-baseline eGFR values greatly improved with an \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^{2}$$\end{document}R2 of 0.56 ($95\%$ BCI [0.47, 0.62], RMSE 9.00 [8.09, 10.31]). See Additional file 1: Table S4 for a breakdown of performance by follow-up time. ## Main model The model combining clinical and biomarker predictors (31 in total) had improved predictive performance compared to the clinical model for objective 1. Its predictions were well calibrated, indicating adequate model fit (Fig. 3 and Additional file 1: Figure S4). The cross-validated median posterior \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^{2}$$\end{document}R2 was 0.44 ($95\%$ BCI [0.37, 0.50], RMSE 9.51, $95\%$ BCI [8.60, 10.15]). Predictive performance for post-baseline eGFR was further improved by updating with baseline eGFR for objective 2, with a cross-validated \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^{2}$$\end{document}R2 of 0.59 ($95\%$ BCI [0.51, 0.65], RMSE 8.80, $95\%$ BCI [7.80, 9.95]). See Additional file 1: Table S4 for a breakdown of performance by follow-up time. Many of the predictors’ coefficients were shrunken towards zero (Additional file 1: Table S5 and Additional file 1: Figure S5). In terms of magnitude, TNFR1 had the largest standardized baseline coefficient, followed by other protein biomarkers (RAGE, Myoglobin, CCL14, IL1R1) and age. Only few predictors showed a relevant slope coefficient, with KIM1 and UACR being by far the largest in magnitude. Fig. 3Calibration of posterior median of marginal predictions from the main model, before and after update by baseline eGFR values and stratified by time of observation. Overall, the calibration of predictions was satisfactory over the whole follow-up period. Updating by baseline eGFR led to better calibration and prediction performance, as demonstrated by a more narrow spread around the diagonal line of perfect prediction, even for later follow-up times. The evaluation is stratified by planned follow-up times, actually observed follow-up times used in the model differ slightly. Cross-validated performances by follow-up are reported in Additional file 1: Table S4 ## Discussion In this study, we used Bayesian linear mixed modeling in a cohort of people with DM and moderately reduced eGFR to validate and discern the ability of a set of established serum protein biomarkers to predict eGFR trajectories. We found that in particular TNFR1 and RAGE contributed to the estimation of baseline eGFR values, while KIM1 and the clinical marker UACR were predictive for the future eGFR decline. This is in line with the current understanding of these markers. TNFR1 constitutes a marker of filtration, RAGE of general inflammatory response. On the other hand, KIM1 reflects kidney damage and thus plays an important role in the prediction of eGFR decline. Protein biomarkers slightly improved predictive performance in addition to clinical predictors alone. Nevertheless, only few predictors were sufficient to achieve similar performance to the full set of predictors. Baseline eGFR had a strong impact on predictive performance on top of all other variables. Studies like ours, bringing together a strong set of potential predictors for eGFR and evaluating their performance in a large cohort, are important to narrow down research efforts. Future work focused on improving our understanding of the most relevant protein biomarkers and their individual contributions to the prediction of eGFR decline may help to make them more clinically relevant in the treatment of CKD in people with DM. The results from this work corroborate conclusions from our earlier studies that many biomarkers were associated with baseline eGFR, but that this association with eGFR diminished with increasing follow-up time [10, 15]. This indicated that the clinical utility of the biomarkers remained low compared to eGFR. A possible exception would be KIM1, which consistently demonstrated added value for the prediction of eGFR trajectories on top of baseline eGFR across a wide population at different CKD stages. Furthermore, as TNFR1 showed the strongest association with baseline eGFR it may be relevant to refine the accuracy with which the current disease status of an individual can be determined. Having multiple outcome related variables as opposed to a single measurement increases the reliability of an individual’s disease diagnosis and reduces issues with replicability of the results. The findings from our work are in line with other studies. The investigations by Niewczas et al. and Gerstein et al. were used to define the pool of candidate biomarkers for our study [12, 13, 16]. While these studies also used selection techniques to identify markers important for predictions, they focused on the predictive abilities of the markers. On the other hand, our study tried to disambiguate the roles of the markers found in those studies in the prediction of longitudinal eGFR trajectories, which reflect their systemic biological functions. The KidneyIntelX model was recently derived and validated as a prognostic tool for eGFR decline based on electronic health records, clinical predictors such as eGFR and the plasma biomarkers TNFR1, TNFR2 and KIM1 [17]. The investigators evaluated the predictions for a composite outcome of eGFR decline of 5 ml/min/1.73m2 per year or more, $40\%$ or more sustained decline, or kidney failure within five years in biobanked plasma samples from two cohorts. We identified similar biomarkers in this study and were able to discern how they affect predictions by using a longitudinal outcome rather than a classification outcome. Other investigators evaluated the KidneyIntelX risk score for the prediction of therapy response on longitudinal eGFR trajectories in a multinational cohort of people with diabetic kidney disease [14]. Treatment with the SGLT-2 inhibitor was found to reduce the KidneyIntelX score over time, and changes in the score from baseline to one year were associated with disease progression. The baseline status of an individual was important as people with higher baseline scores experienced more events compared to those with lower baseline scores. Therefore, an accurate diagnosis of the current disease state is relevant to predicting future disease progression. Our work similarly corroborates the importance of baseline eGFR for predictions of future eGFR decline. Recent investigations of data from the multinational CANVAS study, a randomized trial assessing the effect of the SGLT2-inhibitor Canagliflozin on cardiovascular and kidney outcomes, also focused on TNFR1, TNFR2 and KIM1 as potential biomarkers [35, 36]. The studies found associations of TNFR1 and TNFR2 with progression of albuminuria, but did not show an association of KIM1 with albuminuria. Furthermore, Canagliflozin led to a modest attenuation of serum levels of TNFR1 and a decrease of KIM1 levels over time, indicating potential as markers for treatment response. The evidence from these studies complements our work, in which we found TNFR1 and KIM1 to be most promising candidates for eGFR prediction from a broad set of established biomarkers. Other studies established \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{2}$$\end{document}β2-microglobulin as another potentially interesting filtration marker for prediction of rapid renal function decline [19, 37]. While we did not measure this marker for our analysis, it was also shown to be highly correlated to TNFR1 (another marker of filtration) in these studies, which may serve as replacement in our analysis. Our study has some limitations. The analysis cohort comprised people with mixed types of DM, but we can assume that most had type 2 DM. Our cohort baseline was the first in-person follow-up of the GCKD cohort rather than the actual enrolment visit due to sample availability. This potentially introduced bias due to people being lost to follow-up between the GCKD enrolment and our baseline. The death rate was low and the demographics of our study cohort showed largely similar characteristics as expected from the actual GCKD inclusion criteria. For these reasons, we assume that the loss-to-follow-up is largely not associated with study outcomes, and that the impact on our analysis results is low. Due to the limited sample availability, there were fewer follow-ups per person available to our analysis. We attempted to mitigate associated problems of large intra-individual variability by the use of mixed models for longitudinal eGFR values as outcome, rather than modeling surrogate endpoints. The GCKD cohort is a national study with participants from Germany, therefore representing a Caucasian population. However, since our results are in accordance and extend several other studies, we believe the findings to be generalizable to a broader population, or at least may foster further research in other settings. Strengths of our study include the almost complete follow-up of the GCKD cohort and the low amount of missing data. The serum biomarkers in our study were pre-selected via available prior evidence, thus representing a strong set of predictors for eGFR decline. The *Bayesian analysis* used shrinkage priors to identify important predictors, while incorporating uncertainty about missing data and model fit. Furthermore, by updating the predictions by baseline eGFR we were able to discern for which parts of the longitudinal trajectory the variables were predictive, without being unduly influenced by the presence of baseline eGFR as independent variable. In conclusion, we found that different serum protein biomarkers serve different roles for the prognostication of eGFR trajectories. These results may help to focus research efforts for such markers to improve understanding of their functions in the pathophysiology of CKD in people with DM and to make them more relevant to clinical applications. ## Supplementary Information Additional file 1: Table S1. Protein biomarker measurement availability for analysis. Table S2. Protein biomarker measurement issues. Table S3. Convergence of Bayesian mixed modelsused in our study. Table S4. Cross-validated model performance by follow-up. Table S5. Coefficient posteriorsof main model. Table S6. Variable rankingsestimated via cross-validation according to contribution to prediction of eGFR values. Figure S1. Power analysisvia simulation. Figure S2. Measured protein biomarker concentrationsused for analysis(log2 transformed). Figure S3. Spearman correlationsbetween variablesin the analysis(based in pairwise complete observations). Figure S4. Residualsfor main BLMM. Figure S5. Coefficient posteriorsfor main model using Horseshoe shrinkage priorsand clinical and protein biomarkersaspredictors. Figure S6. Approximation of main model by incremental submodelsusing the top 15 predictors, defined according to the ranking of variablesby increase in cross-validated RMSE. ## References 1. Menke A, Casagrande S, Geiss L, Cowie CC. **Prevalence of and trends in diabetes among adults in the United States, 1988–2012**. *JAMA* (2015.0) **314** 1021-1029. DOI: 10.1001/jama.2015.10029 2. Wang L, Peng W, Zhao Z, Zhang M, Shi Z, Song Z. **Prevalence and treatment of diabetes in China, 2013–2018**. *JAMA* (2021.0) **326** 2498-2506. DOI: 10.1001/jama.2021.22208 3. Danaei G, Finucane MM, Lu Y, Singh GM, Cowan MJ, Paciorek CJ. **National, regional, and global trends in fasting plasma glucose and diabetes prevalence since 1980: systematic analysis of health examination surveys and epidemiological studies with 370 country-years and 2·7 million participants**. *Lancet* (2011.0) **378** 31-40. DOI: 10.1016/S0140-6736(11)60679-X 4. Noubiap JJ, Nansseu JR, Lontchi-Yimagou E, Nkeck JR, Nyaga UF, Ngouo AT. **Global, regional, and country estimates of metabolic syndrome burden in children and adolescents in 2020: a systematic review and modelling**. *Lancet Child Adolesc Health* (2020.0). DOI: 10.1016/S2352-4642(21)00374-6 5. Heerspink HJL, Jongs N, Chertow GM, Langkilde AM, McMurray JJV, Correa-Rotter R. **Effect of dapagliflozin on the rate of decline in kidney function in patients with chronic kidney disease with and without type 2 diabetes: a prespecified analysis from the DAPA-CKD trial**. *Lancet Diabetes Endocrinol* (2021.0) **9** 743-754. DOI: 10.1016/S2213-8587(21)00242-4 6. Agarwal R, Filippatos G, Pitt B, Anker SD, Rossing P, Joseph A. **Cardiovascular and kidney outcomes with finerenone in patients with type 2 diabetes and chronic kidney disease: the FIDELITY pooled analysis**. *Eur Heart J* (2022.0) **43** 474-484. DOI: 10.1093/eurheartj/ehab777 7. Gerstein HC, Sattar N, Rosenstock J, Ramasundarahettige C, Pratley R, Lopes RD. **Cardiovascular and renal outcomes with Efpeglenatide in type 2 diabetes**. *N Engl J Med* (2021.0) **385** 896-907. DOI: 10.1056/NEJMoa2108269 8. Kerschbaum J, Rudnicki M, Dzien A, Dzien-Bischinger C, Winner H, Heerspink HL. **Intra-individual variability of eGFR trajectories in early diabetic kidney disease and lack of performance of prognostic biomarkers**. *Sci Rep* (2020.0) **10** 19743. DOI: 10.1038/s41598-020-76773-0 9. Dunkler D, Gao P, Lee SF, Heinze G, Clase CM, Tobe S. **Risk prediction for early CKD in type 2 diabetes**. *Clin J Am Soc Nephrol* (2015.0) **10** 1371-1379. DOI: 10.2215/CJN.10321014 10. Kammer M, Heinzel A, Willency JA, Duffin KL, Mayer G, Simons K. **Integrative analysis of prognostic biomarkers derived from multiomics panels helps discrimination of chronic kidney disease trajectories in people with type 2 diabetes**. *Kidney Int* (2019.0) **96** 1381-1388. DOI: 10.1016/j.kint.2019.07.025 11. Mayer G, Heerspink HJ, Aschauer C, Heinzel A, Heinze G, Kainz A. **Systems biology-derived biomarkers to predict progression of renal function decline in type 2 diabetes**. *Diabetes Care* (2017.0) **40** 391-397. DOI: 10.2337/dc16-2202 12. Niewczas MA, Pavkov ME, Skupien J, Smiles A, Md Dom ZI, Wilson JM. **A signature of circulating inflammatory proteins and development of end-stage renal disease in diabetes**. *Nat Med* (2019.0) **25** 805-813. DOI: 10.1038/s41591-019-0415-5 13. Gerstein HC, Pare G, Hess S, Ford RJ, Sjaarda J, Raman K. **Growth differentiation factor 15 as a novel biomarker for metformin**. *Diabetes Care* (2017.0) **40** 280-283. DOI: 10.2337/dc16-1682 14. Lam D, Nadkarni GN, Mosoyan G, Neal B, Mahaffey KW, Rosenthal N. **Clinical utility of kidneyintelx in early stages of diabetic kidney disease in the CANVAS trial**. *Am J Nephrol* (2022.0). DOI: 10.1159/000519920 15. Heinzel A, Kammer M, Mayer G, Reindl-Schwaighofer R, Hu K, Perco P. **Validation of plasma biomarker candidates for the prediction of eGFR decline in patients with type 2 diabetes**. *Diabetes Care* (2018.0) **41** 1947-1954. DOI: 10.2337/dc18-0532 16. Gerstein HC, Paré G, McQueen MJ, Lee SF, Bangdiwala SI, Kannt A. **Novel biomarkers for change in renal function in people with dysglycemia**. *Diabetes Care* (2019.0) **43** 433-439. DOI: 10.2337/dc19-1604 17. Chan L, Nadkarni GN, Fleming F, McCullough JR, Connolly P, Mosoyan G. **Derivation and validation of a machine learning risk score using biomarker and electronic patient data to predict progression of diabetic kidney disease**. *Diabetologia* (2021.0) **64** 1504-1515. DOI: 10.1007/s00125-021-05444-0 18. Colombo M, Looker HC, Farran B, Hess S, Groop L, Palmer CNA. **Serum kidney injury molecule 1 and β2-microglobulin perform as well as larger biomarker panels for prediction of rapid decline in renal function in type 2 diabetes**. *Diabetologia* (2019.0) **62** 156-168. DOI: 10.1007/s00125-018-4741-9 19. Looker HC, Colombo M, Hess S, Brosnan MJ, Farran B, Dalton RN. **Biomarkers of rapid chronic kidney disease progression in type 2 diabetes**. *Kidney Int* (2015.0) **88** 888-896. DOI: 10.1038/ki.2015.199 20. Gutiérrez OM, Shlipak MG, Katz R, Waikar SS, Greenberg JH, Schrauben SJ. **Associations of plasma biomarkers of inflammation, fibrosis, and kidney tubular injury with progression of diabetic kidney disease: a cohort study**. *Am J Kidney Dis* (2021.0). DOI: 10.1053/j.ajkd.2021.09.018 21. Saulnier PJ, Gand E, Velho G, Mohammedi K, Zaoui P, Fraty M. **Association of circulating biomarkers (adrenomedullin, TNFR1, and NT-proBNP) With renal function decline in patients with type 2 diabetes: a french prospective cohort**. *Diabetes Care* (2017.0) **40** 367-374. DOI: 10.2337/dc16-1571 22. Eckardt K-U, Bärthlein B, Baid-Agrawal S, Beck A, Busch M, Eitner F. **The German chronic kidney disease (GCKD) study: design and methods**. *Nephrol Dial Transplant* (2012.0) **27** 1454-1460. DOI: 10.1093/ndt/gfr456 23. Levey AS, Stevens LA, Schmid CH, Zhang Y, Castro AF, Feldman HI. **A new equation to estimate glomerular filtration rate**. *Ann Intern Med* (2009.0) **150** 604-612. DOI: 10.7326/0003-4819-150-9-200905050-00006 24. 24.ATC classification index with DDDsWHO Collaborating Centre for Drug Statistics Methodology2022Oslo, NorwayNorwegian Institute of Public Health2022. *WHO Collaborating Centre for Drug Statistics Methodology* (2022.0) 2022 25. Leffondre K, Boucquemont J, Tripepi G, Stel VS, Heinze G, Dunkler D. **Analysis of risk factors associated with renal function trajectory over time: a comparison of different statistical approaches**. *Nephrol Dial Transplant* (2014.0) **30** 1237-1243. DOI: 10.1093/ndt/gfu320 26. 26.Carvalho CM, Polson NG, Scott JG. 2009 Handling Sparsity via the Horseshoe. International Conference on Artificial Intelligence and Statistics. Flordia, USA: Clearwater Beach 27. Piironen J, Vehtari A. **Sparsity information and regularization in the horseshoe and other shrinkage priors**. *Electron J Stat* (2017.0) **11** 5018-5051. DOI: 10.1214/17-EJS1337SI 28. Betancourt M. **A conceptual introduction to Hamiltonian Monte Carlo**. *arXiv* (2017.0). DOI: 10.48550/arXiv.1701.02434 29. Gelman A, Carlin JB, Stern HS, Dunson DB, Vehtari A, Rubin DB. *Bayesian Data Analysis* (2014.0) 30. Verbeke G, Molenberghs G. *Linear Mixed Models for Longitudinal Data* (2000.0) 31. Piironen J, Paasiniemi M, Vehtari A. **Projective inference in high-dimensional problems: Prediction and feature selection**. *Electron J Stat* (2020.0) **14** 2155-97. DOI: 10.1214/20-EJS1711 32. Piironen J, Vehtari A. **Projection predictive variable selection using Stan+ R**. *arXiv* (2015.0). DOI: 10.48550/arXiv.1508.02502 33. Catalina A, Bürkner P-C, Vehtari A. **Projection predictive inference for generalized linear and additive multilevel models**. *ArXiv* (2020.0). DOI: 10.48550/arXiv.2010.06994 34. van Buuren S, Groothuis-Oudshoorn K. **mice: multivariate imputation by chained equations in R**. *J Stat Softw* (2011.0) **45** 1-67. DOI: 10.18637/jss.v045.i03 35. Sen T, Li J, Neuen BL, Neal B, Arnott C, Parikh CR. **Effects of the SGLT2 inhibitor canagliflozin on plasma biomarkers TNFR-1, TNFR-2 and KIM-1 in the CANVAS trial**. *Diabetologia* (2021.0) **64** 2147-2158. DOI: 10.1007/s00125-021-05512-5 36. Waijer SW, Sen T, Arnott C, Neal B, Kosterink JGW, Mahaffey KW. **Association between TNF receptors and KIM-1 with kidney outcomes in early-stage diabetic kidney disease**. *Clin J Am Soc Nephrol* (2022.0) **17** 251-259. DOI: 10.2215/CJN.08780621 37. Colombo M, McGurnaghan SJ, Blackbourn LA, Dalton RN, Dunger D, Bell S. **Comparison of serum and urinary biomarker panels with albumin/creatinine ratio in the prediction of renal function decline in type 1 diabetes**. *Diabetologia* (2020.0) **63** 788-798. DOI: 10.1007/s00125-019-05081-8
--- title: 'Engagement with body image health promotion videos in adult men and women: differences between narrative, informational, and persuasive appeal approaches' authors: - Jo R Doley - Siân A McLean journal: BMC Psychology year: 2023 pmcid: PMC10061748 doi: 10.1186/s40359-023-01120-7 license: CC BY 4.0 --- # Engagement with body image health promotion videos in adult men and women: differences between narrative, informational, and persuasive appeal approaches ## Abstract ### Background Body dissatisfaction is a public health issue, however, low awareness of its seriousness, and stigma, may inhibit treatment seeking. The current study evaluated engagement with videos promoting awareness of body dissatisfaction using a persuasive communication approach. ### Method Men ($$n = 283$$) and women ($$n = 290$$) were randomly allocated to view one of five videos; [1] Narrative, [2] *Narrative plus* persuasive appeal, [3] Informational, [4] *Informational plus* persuasive appeal and [5] Persuasive appeal only. Engagement (relevance, interest, and compassion) was examined post-viewing. ### Results Among both men and women, superior engagement ratings (in compassion for women, and relevance and compassion for men) were demonstrated for the persuasive appeal and informational videos relative to narrative approaches. ### Conclusion Videos using clear and factual approaches may promote engagement in body image health promotion videos. Further work should be done to examine interest in such videos specific to men. ### Supplementary Information The online version contains supplementary material available at 10.1186/s40359-023-01120-7. ## Introduction Body dissatisfaction, negative evaluation of one’s appearance, shape, or weight, poses a serious public health issue [1]. It is associated with depressive mood [2] and eating disorders [3], and leads to poor health outcomes in multiple domains [4–6]. Little research has examined the public’s awareness of body dissatisfaction as a serious mental health issue, but the research that exists finds that mental health literacy around the problem is very low. For example, one study demonstrated that $65.2\%$ of women and $34.8\%$ of men indicated that there was nothing wrong with the behaviour (e.g., social comparison, avoiding social events) exhibited by a character in a vignette with body dissatisfaction and only $18.4\%$ of participants in the same study correctly identified that the character’s mental health problem was body dissatisfaction [7]. Additionally, a qualitative study identified that participants at an exercise facility had poor understanding of the construct of body image, and sometimes conflated a thinner appearance or weight loss with positive body image [8]. Similarly, poor understanding of the consequences of body dissatisfaction for men were identified among male participants [9]. As body dissatisfaction mental health literacy is generally low in the population, it is important that evidence-based, wide-reaching campaigns about this problem are available to facilitate appropriate treatment-seeking [10]. This study aimed to evaluate the effects of viewing short video messages for social marketing campaigns – specifically, examining engagement with those messages – about body dissatisfaction through a lens of persuasive communication. Social marketing campaigns are becoming increasingly common within mental health [11–13], including body dissatisfaction [14, 15]. A common technique in such campaigns is persuasive communication [16–18]. Persuasive communication is communication that is designed to change, produce, or influence attitudes and behaviours [19, 20]. Previous research has demonstrated that persuasive communication techniques have produced positive changes in body image [21], and attitudes towards eating disorders [22] and disordered eating behaviours [23]. While several studies have evaluated the effectiveness on individual attitudes or knowledge, research evaluating broader applications of such campaigns [10], such as whether viewers find the campaigns engaging, or whether the campaigns motivate them to share or learn more about the issue, is lacking. ## Social marketing engagement One essential component of social marketing campaigns is that they are engaging. Social marketing engagement is a relatively recent area of research, and as such the literature is not yet extensive. Definitions of social media or social marketing engagement have typically come from research around brands. It is important to note that, within the context of this research and its applications, the term “brand” is applied broadly, and inclusive of non-government organisations and academic institutions, rather than solely focusing on capitalist consumer brands. While there is no one definition of engagement, there are commonalities in theoretical models of the construct [24–27]. Models of engagement define engagement as having cognitive, affective, and behavioural [28] components; with some including social components [27]. Affective components refer to the consumer’s emotional response to the content (e.g., enthusiasm, anger), behavioural components refer to the consumer’s behavioural response to the content (e.g., sharing, liking) and cognitive components refer to the thoughts the consumer has in response to the content (e.g., processing the content in regards to one’s own life experiences; [24, 25]. The less commonly examined construct, the social component of engagement, refers to co-creation of content (e.g., photographs, memes) and interaction with a brand [26]. As such, it is agreed that engagement is a multidimensional construct [24], and high engagement contributes beneficial outcomes for social marketing in that it promotes dissemination and discussion of information, rather than simply viewing information [29, 30]. Generally, engagement may be active (e.g., sharing content, liking content, co-creating content) or more passive (e.g., consuming content; [31, 32]. As the social component of co-creation and interaction would not necessarily be possible with a one-off video in an experimental study, engagement is agreed to include affective, behavioural, and cognitive components [24, 25], the current study focused on these three domains. ## Persuasive communication The type of communication that may result in greater engagement in a message has not been investigated within the context of body image. Previous research has identified two broad types of persuasive communication; narrative and informational [33, 34]. Informational communication conveys statistics, facts, or scientific information [33, 35]; for instance, an informational campaign about body dissatisfaction may include information about the characteristics of body dissatisfaction and the demographics most affected. An informational approach relies on the assumption that people generally process information using logic and reasoning. Narrative communication uses a storytelling approach with defined characters and may be fictional or non-fictional – as highlighted by Hinyard and Kreuter [33] there is great variation in the definition of narrative communication in the literature. As such, they proposed that “A narrative is any cohesive and coherent story with an identifiable beginning, middle, and end that provides information about scene, characters, and conflict; raises unanswered questions or unresolved conflict; and provides resolution.” ( p.778). For instance, a narrative story about body dissatisfaction may portray a person experiencing body dissatisfaction who discusses their negative thoughts and behaviours (e.g., being distressed about their weight or shape), include questioning the usefulness of these thoughts and behaviours, and resolve with the person being more self-compassionate about their body. The effectiveness of both informational and narrative approaches has been found elsewhere throughout the literature with small effects on behavioural and attitudinal outcomes in meta-analyses [35, 36]. It has been identified that narrative communication may be less resistant to counterarguing – whereby viewers raise arguments or objections to the premise of a persuasive message – than informational communication [37], as the focus is on experiences and feelings of characters. Neither persuasive style has been evaluated for engagement per se, however one study identified that non-narrative information was more likely to result in elaboration of the arguments presented [38]. Persuasive health messages may also contain persuasive appeals, which are intended to clarify the content and meaning of narrative communication [11, 18, 39]. For instance, an episode of a television program about drink driving contained a message explicitly explaining the danger of binge drinking and drink driving to serve as a clarification of a narrative that depicted an incident of drink driving [18]. Inclusion of such appeals is intended to further reduce counterarguing or misinterpretation of the message and have shown to be effective when combined with narrative information [17, 40, 41]. As informational approaches may be less open to individual interpretation compared with narrative approaches, it is unsurprising that adding persuasive appeals to informational communication has not yet been investigated. However, the inclusion of a direct persuasive appeal may increase engagement with informational messages, which could otherwise be perceived as dry and bland. Additionally, to the authors’ knowledge, the effect of a persuasive appeal alone has not been investigated relative to informational or narrative approaches. A persuasive appeal alone may be an effective approach for a social media or general media audience, to attempt to capture attention in a social media environment with a great deal of competing content. ## Impact of persuasive approaches on domains of engagement While no literature exists examining the impacts of persuasive communication approaches on engagement, previous work suggests that narrative and informational approaches persuade using different mechanisms [17, 33, 42]. As such, they may impact different domains of engagement. The narrative approach, with its focus on experiences and feelings of characters [33, 35, 41], theoretically may influence the affective domain more strongly than the cognitive or behavioural; whereas the informational approach, with its focus on statistics and reasoning [33, 35], may promote more central or systematic processing of messages and thus may be more likely to impact the cognitive domain over others. Hinyard and Kreuter [33] elaborate on the manner through which narrative approaches are thought to persuade people, describing that typically they are approached through hedonic processing (i.e. entertainment). Whether the addition of a persuasive appeal may influence a particular outcome is unknown. Some empirical work on attitude change suggests different persuasive messages may have differing outcomes on attitude change [43, 44], also suggesting different mechanisms of action. For instance, previous research on the effect of attitude bases (i.e., whether attitudes are thought to be driven more by cognitive or affective components) suggest that cognitive (which is primarily informational) and affective (an important component of narratives) messages may impact attitude change differently [45]. However, there are mixed findings on whether a match or a mismatch between attitude basis and message type is best for persuasive impact [43–45]. Additionally, this research has focused on attitude change rather than engagement. Identifying whether particular domains of engagement may be particularly useful for targeted campaigns; for instance, campaigns aiming to promote socio-political change, may benefit from changing behavioural engagement, while campaigns aiming to reduce stigma may wish to impact cognitive and affective domains. As such, this study also sought to understand whether the type of persuasive communication may impact different engagement domains. ## Current study Little research in body dissatisfaction and eating disorders has identified the specific approach taken in persuasive communications as either informational or narrative [22]. In our earlier analysis of a separate element of the current study, it was found that informational communication had stronger effects for improving perceptions of the seriousness of body dissatisfaction than narrative messages, whereas narrative and informational approaches, with or without a persuasive appeal, were equivalent in producing positive effects for reducing body dissatisfaction in viewers [46]. The level of engagement in such messages has not yet been investigated. Outside the body image sphere, both informational [47] and emotionally engaging (e.g., which may come from a narrative approach) content from brands result in users sharing posts [48, 49], but these approaches have not been compared directly. Additionally, we sought to compare these approaches separately for men and women, as attitudes around body dissatisfaction tend to differ between genders. For instance, men tend to attribute greater blame for illness towards those with eating disorders than do women and have poorer recognition of symptoms of eating disorders [50] and body dissatisfaction [7] than women. As social media (and increasingly, traditional media with a social media presence) relies heavily on sharing of information (e.g., sharing a health message campaign to one’s own social media account, or through email) and engagement (e.g., comments, likes, emotional investment in the topic), the current study sought to understand the effect of different types of persuasive communication on engagement in a body image social marketing video. Thus, the current study aimed to examine the extent to which such a campaign can generate cognitive, affective, and behavioural engagement and to compare levels of engagement across informational, narrative, and persuasive appeal communication approaches. As engagement has not previously been investigated within this context, no specific hypotheses were made. However, the following research questions were explored: RQ1: Of the persuasive approaches (informational, narrative, persuasive appeal, informational and persuasive, narrative and persuasive), which results in superior engagement in the topic of body dissatisfaction mental health literacy? RQ2: Do particular persuasive communication approaches impact the cognitive, affective, and behavioural domains of engagement differently? ## Participants Participants were recruited through Prolific, an online participant platform. Men and women aged 18–45 from Australia, Canada, or the United Kingdom were eligible to take part in the study. Participation was limited to persons from these countries as they are culturally similar to Australia, in which the stimulus materials were filmed. Initially, 633 participants responded to the study. After removing participants who chose to withdraw their data following debriefing where they learned the true aims of the study ($$n = 24$$), failed the attention check ($$n = 3$$), were ineligible (had seen the stimulus video previously, $$n = 8$$), or did not provide responses after watching the video ($$n = 25$$) that would exclude them from analyses, a final sample of $$n = 573$$ ($$n = 116$$ narrative only, $$n = 109$$ narrative + persuasive appeal, $$n = 118$$ persuasive appeal only, $$n = 118$$ informational only, $$n = 112$$ informational + persuasive appeal) of approximately equal numbers of men ($$n = 283$$; $49.7\%$) and women ($$n = 290$$, $50.3\%$) remained. Participants ranged in age from 18 to 44 ($M = 32.42$, SD = 6.15), and $$n = 7$$ not indicating their age. The majority of participants had one or more children ($61.0\%$). The majority of participants resided in the United Kingdom ($90.1\%$), followed by Canada ($8.6\%$), and Australia ($1.2\%$). ## Materials Three videos were professionally produced in Australia by a media company for use in a social marketing campaign (independent from and prior to the research) to raise awareness of the seriousness of body dissatisfaction; one reflected a narrative approach to communication, one an informational approach, and the other was an explicit persuasive appeal. Experts in the field of body image and eating disorders gave feedback on the scripting and information prior to the production of the videos. Permission to use the materials in the research was granted by the media company. ## Narrative approach The narrative video (2.48 min) featured a woman in her early thirties engaging in an internal monologue of negative body talk (e.g., “If Mia’s mum needs to lose 5 kilos to get rid of her non-existent cellulite, how many kilos do I need to lose to be part of the short-shorts sports carnival day mothers’ club?!”) while driving in the car with her child. The woman eventually realises the harshness of her self-talk and acknowledges the functionality of her body over its appearance. The woman is white and thin, and was shown sitting. The actor was pregnant at the time of filming but this was not visible within the video as a bag was covering her torso. The narrative video can be viewed at https://vimeo.com/entertainthinkinspire/tmp1. ## Informational approach The informational video (2.54 min) was comprised of interviews with five body image experts (an academic, a dietician, a medical practitioner, and chief executive officer and education manager of two eating disorders support services) who discussed factual information about body image (e.g., “Body dissatisfaction is a problem across our society; young, old, males, females… body image problems just don’t go away with age, it’s not that you get to some level of enlightenment and say “Ok I’m fine and I’m not worried about these issues anymore”, actually they hang around for a really long time”. The information included topics such as contributing factors, sociocultural appearance pressures, appearance comparison, help-seeking for body dissatisfaction, and the potential usefulness of challenging appearance ideals. All presenters in the video were white and cisgender, and all but one were both a) thin and b) women. One presenter was a larger bodied man. Only the upper bodies, not full-body view of presenters were shown. The video can be viewed at https://vimeo.com/entertainthinkinspire/tmp5. ## Persuasive appeal The persuasive appeal video (1.20 min) featured the female actor from the narrative approach video presenting a direct appeal to the viewer to increase their awareness and understanding of body dissatisfaction, and question and challenge appearance ideals (e.g., “What can we do about it? I think the first thing is to be aware of it…” “Starting to have conversations within ourselves, within our social circles, within our families, within our community and hopefully then globally”. The actor briefly mentioned her own difficulties with body dissatisfaction. The actor, as previously described, was white and thin, although she was pregnant at the time. This video can be viewed at https://vimeo.com/156214950. ## Narrative approach + persuasive appeal The narrative approach video was shown followed by the persuasive appeal video (4.08 min), as described above. ## Informational approach + persuasive appeal The informational approach video was shown with footage edited in from the persuasive appeal video (3.45 min). ## Demographic questions Demographic questions included age, gender (from the following: male, female, other1), country of residence, height, weight, and number of children. ## Body satisfaction measures A series of visual analogue scales (VAS) were used to assess weight satisfaction, shape satisfaction, and muscularity satisfaction before and after viewing the stimulus. Only pre-video exposure data around weight satisfaction, shape satisfaction, and muscularity satisfaction were used in the current analysis solely for the purposes of examining confounding effects, as the focus of the current study is on engagement outcomes. Participants were asked to indicate how satisfied they feel right now, from 0 (not at all) to 100 (very much so). Previous research has demonstrated that scores from VAS are valid and reliable for assessing body satisfaction [51, 52]. Higher scores indicated higher body satisfaction. Items were used separately in analyses. Post-video weight satisfaction, shape satisfaction, and muscularity satisfaction are reported elsewhere. Other measures, including mental health literacy, and behavioural intentions, were assessed both pre- and post-exposure but are reported elsewhere [46]. ## Engagement A set of 18 items measured on VAS was developed specifically for the current study to measure general engagement with the videos and topic. Items were informed by previous research examining responses to health campaigns [14, 53–55]. To investigate the factor structure of the engagement assessment items, an exploratory factor analysis was conducted. An initial Principal Components Analysis was conducted to check the suitability of the data for EFA. The results of KMO tests (0.891) suggested the sample size of 576 was sufficient and Bartlett’s test of sphericity, χ2[153] = 4056.95, $p \leq .001$, suggested that there were sufficiently high correlations among items to perform EFA. Four components were identified with an eigenvalue of > 1, which explained $57.96\%$ of the total variance. Parallel analysis using Vivek et al. ’s [56] web based engine indicated that three factors should be retained, as indicated by their eigenvalues being greater than the mean eigenvalue. An exploratory factor analysis was conducted to identify item loadings for three components. Oblique rotation demonstrated that the component correlations between the four components were relatively low (ranging from $r = .01$ to $r = .36$), and as such an orthogonal rotation was used. The rotated component matrix is displayed in the supplementary material (Table S1). Internal consistency analyses demonstrated excellent internal consistency for Factor 1 (α = 0.90), acceptable internal consistency for factor 2 (α = 0.62), and factor 3 (α = 0.69). Item deletion was deemed not to result in improvement for Cronbach’s alpha for any of the scales. The three factors explained $52.93\%$ of the variance (see Table S2). These corresponded with affective (compassion), behavioural (interest), and cognitive (relevance) domains of engagement. ## Interest Eight VAS items identified from the factor analysis were used to measure participant interest (behavioural engagement) in the video and topic. Participants were asked to indicate their interest, from 0 (not at all) to 100 (very much so). A mean score from items responses was used for the scale score. Higher scores indicated greater interest. Cronbach’s alpha demonstrated excellent internal consistency, with α = 0.90. As measures of pre-test muscularity, weight, and shape dissatisfaction were unrelated to interest in women, a univariate ANOVA was conducted to examine the differences between video conditions on interest. There was no significant effect of video condition on interest, F[4, 285] = 1.22, $$p \leq .30$$, partial η2 = 0.02. As measures of pre-test muscularity, weight, and shape dissatisfaction were unrelated to interest in men, a univariate ANOVA was conducted to examine the differences between video conditions on interest. There was no significant effect of video condition on interest, F [4, 277] = 0.66, $$p \leq .62$$, η2 = 0.01. ## Compassion Five VAS items identified from the factor analysis were used to measure participant compassion towards people with body dissatisfaction, as well as self-compassion. These VAS items measured affective engagement. Participants were asked to indicate the degree to which the video was respectful, increased other-directed compassion, oversimplified body image issues (reverse scored), increased blame (reverse scored), and was perceived to make other people feel more concerned about their appearance (reverse scored), from 0 (not at all) to 100 (very much so). A mean score from items responses was used for the scale score. Cronbach’s alpha demonstrated acceptable internal consistency, with α = 0.62. Higher scores indicated greater compassion. As measures of pre-test muscularity, weight, and shape dissatisfaction were unrelated to compassion in women, univariate ANOVA was conducted to examine the differences between video conditions on compassion. There was a significant effect of video condition on compassion, F [4, 284] = 4.67, $$p \leq .001$$, η2 = 0.06. Holm corrections revealed that the persuasive appeal video ($M = 71.05$, SE = 1.82) resulted in significantly greater compassion than the narrative only video ($M = 60.82$, SE = 1.87), adjusted $$p \leq .002.$$ The information only video ($M = 70.03$, SE = 1.81) resulted in significantly greater compassion than the narrative only video ($M = 60.82$, SE = 1.87), adjusted $$p \leq .005.$$ No other significant differences were found. As measures of pre-test muscularity, weight, and shape dissatisfaction were unrelated to compassion in men, a univariate ANOVA was conducted to examine the differences between video conditions on compassion. There was a significant effect of video condition on compassion, F [4, 278] = 3.57, $$p \leq .007$$, partial η2 = 0.05. Holm corrections revealed that the information and persuasive appeal ($M = 67.99$, SE = 1.83, adjusted $$p \leq .003$$), information only ($M = 66.82$, SE = 1.80, adjusted $$p \leq .006$$), and persuasive appeals ($M = 65.19$, SE = 1.78, adjusted $$p \leq .022$$) all resulted in significantly greater compassion than the narrative only video ($M = 59.36$, SE = 1.78). No other significant differences were found. ## Relevance Three items identified from the factor analysis were used to measure relevance of the video and topic, which reflected cognitive engagement. Participants were asked to indicate the degree to which the video was relevant to their own lives, covered an important topic, and they could recognise their own experiences in the video, from 0 (not at all) to 100 (very much so). A mean score from items responses was used for the scale score. Cronbach’s alpha demonstrated acceptable internal consistency, with α = 0.69. Higher scores indicated greater relevance. As measures of pre-test weight satisfaction, shape satisfaction, and muscularity satisfaction were related to relevance in women, an ANCOVA controlling for these variables was conducted to examine the differences between video conditions on relevance. Pre-test weight satisfaction (F [1, 279] = 42.38, $p \leq .001$, partial η2 = 0.13), and shape satisfaction (F [1, 279] = 0.9.34, $$p \leq .002$$, partial η2 = 0.03) were significantly related to relevance; but muscularity satisfaction (F [1, 279] = 2.39, $$p \leq .12$$, partial η2 = 0.01) was not. There was no significant effect of video condition on relevance, F [4, 287] = 1.61, $$p \leq .17$$, partial η2 = 0.02. As measures of pre-test weight satisfaction, shape satisfaction, and muscularity satisfaction were related to relevance in men, an ANCOVA controlling for these variables was conducted to examine the differences between video conditions on relevance. Pre-test shape satisfaction was significantly related to relevance; F [1, 273] = 7.44, $$p \leq .007$$, partial η2 = 0.03, but weight satisfaction (F [1, 273] = 0.34, $$p \leq .56$$, partial η2 < 0.01) and muscularity satisfaction (F [1, 273] = 1.96, $$p \leq .16$$, partial η2 = 0.01) were not. There was a significant effect of video condition on relevance, F [4, 273] = 4.45, $$p \leq .002$$, partial η2 = 0.06. Holm corrections revealed that the information only video ($M = 70.61$, SE = 2.18) resulted in significantly greater relevance than the narrative only video ($M = 59.93$, SE = 2.18; adjusted $$p \leq .005$$) and the narrative + persuasive appeal video ($M = 62.50$, SD = 2.14; adjusted $$p \leq .027$$). The persuasive appeal ($M = 69.57$, SE = 2.16) resulted in significantly greater relevance than the narrative only ($M = 59.93$, SE = 2.18; adjusted $$p \leq .008$$), and the narrative + persuasive appeal video ($M = 62.50$, SE = 2.14; adjusted $$p \leq .042$$). The information + persuasive appeal video ($M = 66.93$, SE = 2.21) resulted in significantly greater relevance than the narrative only video ($M = 59.93$, SE = 2.18; adjusted $$p \leq .025$$). No other significant differences were found. ## Ethics approval was granted by the La Trobe University Human Ethics Committee, approval number HEC15-116. To reduce the likelihood of a biased sample with high interest in the topic of body dissatisfaction, the true purpose of the study was partially concealed. Participants were invited to take part in a study on health promotion videos and were informed that they would view either a video on body image or self-esteem. The study took place on Qualtrics online survey software, and participants completed the study in an environment of their choosing. Participants provided their consent, then completed pre-exposure measures of body satisfaction. Participants were then randomly assigned to one of the five video conditions previously described. Simple randomisation was performed automatically through the Qualtrics software on an even basis across all five conditions. After watching the video, participants completed measures of interest, compassion, relevance, and demographic variables. Following completion of the study measures, participants were then debriefed on the true aims of the study and given the option to withdraw their data. Participation took, on average, 16.96 min. ## Manipulation check To ensure that the quality of the videos was reasonably similar across groups, participants were asked five questions, measured on a Likert-type scale from 1 (Strongly disagree) to 5 (Strongly agree) about whether the video they watched was engaging2, humorous, factual, had high production values, and was visually appealing. Items were analysed separately. ## Data Analysis Analyses were separated by gender as women tend to have higher body dissatisfaction than men, which was verified using an independent samples t-test. Scores on all body satisfaction items were higher in men than in women; all $p \leq .01.$ Missing value analysis revealed no patterns of missing data, with only two variables for each gender group missing $1\%$ or less. As such, participants with missing data on a relevant variable were excluded from that analysis. Pearson correlations were used to inspect whether pre-existing body satisfaction was related to engagement as a confound. For women, weight, muscularity, and shape satisfaction were significantly related to relevance (all $p \leq .001$); as such, due to the potential for body satisfaction to confound the relationship between videos and engagement (e.g., greater personal relevance for those with low body satisfaction) this was controlled for in the analysis of relevance for women [57]. Thus, to test whether videos produced differences in relevance for women, an ANCOVA was conducted, controlling for pre-existing muscularity, weight, and shape satisfaction. All assumptions of ANCOVA were met. Pre-existing body image scores were not related to compassion or interest for women (all $p \leq .05$), and as such two univariate ANOVAs were conducted to test whether videos produced differences in compassion and interest. All assumptions of ANOVA were met. For men, pre-existing muscularity, weight, and shape satisfaction were related to relevance. Thus, to test whether videos produced differences in relevance for men, an ANCOVA was conducted, controlling for pre-existing muscularity ($p \leq .05$), weight, and shape satisfaction (both $p \leq .001$) as potential confounds. Assumptions of ANCOVA were met. Body image variables were unrelated to interest or compassion for men (all $p \leq .05$), so two univariate ANOVAs were conducted to test whether videos produced differences in interest. All assumptions of ANOVA were met. Post-hoc power analyses for ANOVA and ANCOVA omnibus tests were conducted using G*Power [58], which revealed that all analyses were sufficiently powered at between 1-β = 0.93 and 0.94 to detect a medium effect of $f = 0.25$ with α = 0.05. All post-hoc comparisons were examined using Holm corrections to control for Type 1 error. ## Manipulation checks To ensure that the quality of the videos was reasonably similar, we examined participants’ responses to the manipulation check questions. A series of one-way ANOVAs examining the differences between the five groups on the manipulation check questions were conducted. Holm corrections were used to account for multiple comparisons. The information and information + persuasive appeal videos were rated more factual than both the narrative and narrative + persuasive appeal videos (ps < 0.001). The narrative and narrative + persuasive appeal videos were significantly more humorous than all other conditions (ps < 0.001). These findings suggest the manipulation was successful. The production quality was rated significantly higher in the narrative condition, the information condition, and information + persuasive appeal than the persuasive appeal condition ($p \leq .001$). Additionally, the visual appeal was rated as higher in the narrative only video, the information + persuasive appeal and the narrative + persuasive appeal than the persuasive appeal video (all ps < 0.001). ## Descriptive statistics The means and standard deviations for compassion, relevance, and interest in each video are presented in Table 1. Overall, compassion appeared to be moderate to high among both men and women, across all conditions. Relevance appeared to be moderate to high across all conditions. Interest was moderate to high across all video conditions. Upon inspection of the descriptive statistics, it also appears that scores for men were generally lower than scores for women. Overall, videos appeared to be engaging for the audience. Table 1 Means and Standard Deviations for Engagement Variables by Condition and Gender Information OnlyInformation + Persuasive AppealNarrative OnlyNarrative + Persuasive AppealPersuasive Appeal Men Women Men Women Men Women Men Women Men Women Compassion (Range: 0-100)66.83 (13.38)70.03 (13.41)67.99 (16.09)66.63 (14.41)59.36 (12.79)60.83 (14.83)63.25 (12.84)67.12 (15.31)65.19 (12.04)71.05 (13.23)Relevance(Range: 13.33–100)69.35 (18.22)79.63 (15.72)66.57 (17.43)78.22 (14.08)60.28 (15.51)73.65 (16.69)63.19 (17.96)79.88 (17.49)70.09 (15.49)76.04 (18.43)Interest (Range: 33–100)59.28 (19.14)67.83 (16.87)60.60 (21.60)68.61 (13.85)56.35 (18.29)66.94 (18.73)60.73 (18.33)71.84 (16.33)57.78 (18.65)64.49 (20.12) ## Engagement in videos among women All descriptive statistics for ANOVAs and ANCOVA analyses for women examining the differences in engagement by video condition are displayed in Table 2. Table 2 Means and Standard Errors from ANOVAs and ANCOVA for Differences between Video Condition Engagement in Women Variable Information Only $$n = 62$$ Information + Persuasive Appeal $$n = 58$$ Narrative Only $$n = 58$$ Narrative + Persuasive Appeal $$n = 50$$ Persuasive Appeal $$n = 61$$ Pairwise comparisons Compassion70.03 (1.81)66.63 (1.87)60.82 (1.87)67.12 (2.01)71.05 (1.82)PA, IN > NARelevancea. b4.28 (0.21)4.46 (0.22)4.87 (0.21)4.15 (0.23)4.58 (0.21)nsInterestb5.55 (0.20)5.56 (0.21)5.60 (0.21)5.12 (0.23)5.77 (0.21)nsNote: a Shape satisfaction, muscularity satisfaction, and weight satisfaction were the covariates, and means are estimated marginal means. bTransformed using a square root and reflect transformation. IN = Information only, IN + PA – information + persuasive appeal, NA – narrative-only, NA + PA – narrative + persuasive appeal, ns – non-significant, PA – Persuasive appeal ## Engagement in videos among men All descriptive statistics for ANOVAs and ANCOVA analyses for men examining the differences in engagement by video condition are displayed in Table 3. Table 3 Means and Standard Errors from ANCOVAs and ANOVA Examining Differences by Video Condition on Engagement in Men Variable Information Only $$n = 56$$ Information + Persuasive Appeal $$n = 54$$ Narrative Only $$n = 56$$ Narrative + Persuasive Appeal $$n = 58$$ Persuasive Appeal $$n = 57$$ Pairwise comparisons Compassion66.82 (1.80)67.99 (1.83)59.36 (1.78)63.25 (1.75)65.19 (1.78)IN + PA, IN, PA > NARelevancea70.61 (2.18)66.93 (2.21)59.93 (2.18)62.50 (2.14)69.57 (2.16)IN > NA, NA + PA; PA > NA, NA + PA. IN + PA > NA.Interestb6.10 (1.58)5.94 (1.75)6.36 (1.51)6.00 (1.54)6.24 (1.50)nsNote: a Shape satisfaction, weight satisfaction, and muscularity satisfaction were the covariates, and means are estimated marginal means reported with standard errors. bVariable was transformed using the square root procedure IN = Information only, IN + PA – information + persuasive appeal, NA – narrative-only, NA + PA – narrative + persuasive appeal, ns – non-significant, PA – Persuasive appeal ## Discussion The aim of this study was to compare participants’ reported engagement with different forms of persuasive communication about body dissatisfaction, focusing on informational, narrative, and direct persuasive appeal. We proposed two research questions. The first was whether a particular persuasive approach resulted in superior engagement over other approaches in the context of body dissatisfaction mental health literacy. It was found that there was some advantage for both persuasive appeals and information-only approaches over a narrative approach for both gender groups, and some advantage for the information approach with the addition of a persuasive appeal for men. The second was to understand whether different persuasive communication approaches impacted cognitive, affective, and behavioural domains of engagement differently. It was found that there were some domains impacted differently depending on the communication approach, in particular for cognitive and affective domains. Overall, while all communication types were engaging, there appeared to be some advantage for persuasive appeals and informational videos across both men and women. This is only partially consistent with the previous literature around persuasive communication, which has found benefits overall for both informational and narrative approaches [35, 36]. Although ratings of compassion and relevance were reasonably high in all conditions, the current study’s findings clarify that narrative approaches were slightly less likely to promote engagement relative to other approaches within the context of body image social marketing. One explanation for this finding is that, while it is quite common to have knowledge of a person who struggles with body dissatisfaction (as in the narrative video), it may be less common for people to receive evidence-based and statistical information about body dissatisfaction or hear a persuasive appeal around body dissatisfaction, as indicated by poor levels of mental health literacy [7]. As such, the novelty of these video approaches may have resulted in higher ratings for engagement. An alternative explanation is that the use of humour in the narrative context was ineffective for promoting engagement, or potentially clouded the message, relative to the other videos. Non-humorous approaches should be investigated in future studies to examine whether this effect can be replicated. It should be noted that all approaches were equally effective for body satisfaction outcomes in our previous work [46]; and narrative approaches were not associated with poorer outcomes for interest and relevance for women, or interest for men, relative to other videos in the current study. The benefits observed from viewing a persuasive appeal (relative to the narrative videos) are a novel finding, as to our knowledge the effects of viewing a standalone persuasive appeal were previously unexamined in the literature. This finding may indicate that the nature of a persuasive appeal is appropriate for use on social media or in other forms of media. The message is short, clear, and action-based, giving viewers a specific call to action to learn more about body dissatisfaction as an important mental health issue. This appeal bears resemblance to very successful strategies commonly used in social media (e.g. call to action) which are intended to generate engagement [59]. It is interesting that the addition of a persuasive appeal did not appear to boost the engagement response to the narrative-only video, despite apparent benefits for adding a persuasive appeal to a narrative in previous literature [18]. One explanation for this is continuity between the first and second video message (e.g., the transition between the tone and other characteristics of the narrative video and the persuasive appeal video) – perhaps the videos seemed too disjointed and participants could not see consistency in the messages. This may be a characteristic to examine in future studies. The persuasive appeal did not appear to boost engagement in the informational video, although for men, it was associated with equally high compassion and relevance as the information only video. In line with the suggestion for examining the impact of persuasive appeals on narrative videos, research may further knowledge in this area by examining the effect of a persuasive appeal with greater continuity. Different impacts on domains of engagement were found, which were partially consistent with literature on processing communication [43, 44]. Interestingly, for men, the findings for informational videos affecting cognitive engagement were consistent with Willoughby and Liu [38], who found that informational approaches to communication may result in greater elaboration. This may reflect central or systematic processing [38, 60]. While this advantage for the cognitive domain of engagement was not observed for women, elaboration of the content may have impacted affective responses. Our previous analyses [46] for these videos indicated that informational approaches were more successful in producing perceptions of body dissatisfaction as serious relative to narrative approaches. This finding may be aligned with this study’s results of greater compassion in the informational and persuasive appeal condition compared with the narrative condition, as empathy for someone with body dissatisfaction is related to recommending help-seeking – i.e., that the condition is serious [7]. As the compassion factor also measured self-compassion, informational videos may help people to recognise the seriousness of their own body dissatisfaction. Inconsistent with theories of processing and attitude bases [33, 41, 44, 60], narrative approaches did not appear to impact compassion to a greater extent than other approaches for either men or women. This may be explained by the humorous content of the narrative video, and as such future research should examine effects of narrative persuasion that do not use humour. It is notable, however, that persuasive appeals and informational videos (whether or not they contained a persuasive appeal) improved compassion in men relative to narrative videos. Considering the high levels of stigmatization towards eating disorders and body image difficulties observed among men in previous literature [61], persuasive appeals and informational videos are strategies that researchers may like to assess for their effectiveness in increasing compassion. Considering that the narrative video depicted a woman’s experience, this may have been less effective in men than other video types, as men may have been less able to relate to the experience depicted in the video. Future research may wish to examine the effects of narrative videos featuring men’s stories. Notably, although short videos showed differences for compassion and relevance (for men) and compassion (for women), no videos appeared to produce superior results in all domains of engagement across genders. This finding is not especially concerning in itself, particularly when examining the findings for relevance and interest in women (i.e., both relevance and interest were fairly high among women regardless of video condition). It should be of note, however, that interest (i.e., behavioural engagement) was moderate among men, which may indicate less intent to engage with body image content more generally as body dissatisfaction and eating disorders are perceived to be feminine issues [61]. Reasons for this belief previously identified in research include lack of representation of men’s body dissatisfaction in media narratives [9], and masculine norms preventing men from discussing their dissatisfaction [9, 62, 63]. Future research could examine whether videos designed specifically with men in mind, using the same approaches used in the current study, may impact engagement differently. The findings of this study have implications for social marketing campaigns within the body image field, and research into their efficacy. First, it is clear that further research exploring the impact of persuasive appeals should be conducted, as they were more engaging than narrative approaches within some contexts. Second, contextualising the current study’s findings with our previous work, social marketing campaigns that aim to educate and promote awareness through engagement may take a different approach from those aimed at improving body satisfaction. There may be some advantages to informational and persuasive appeals relative to narrative approaches within the context of engagement. Campaigns may wish to use expert information, or direct appeals to the audience, particularly to generate interest online. Third, research should further examine the impact of particular persuasive communication approaches on domains of engagement. For instance, a call to action may best be used to impact affective engagement. Further examination of persuasive communication approaches on specific domains of engagement would be beneficial for social marketers and researchers. ## Strengths and Limitations A strength of this study was its generalisability; a mixed-age, community sample of men and women was used, which is not common in evaluating social marketing or anti-stigma campaigns for body image and eating disorders [64]. The measurement of multiple domains of engagement is also a strength; previous research on social marketing engagement has tended to focus on sharing and ‘liking’ content. It should be noted, however, that our behavioural measures were intended behaviour rather than actual behaviour, and it would be beneficial to evaluate such messages including a measure of actual behaviour – for example, whether participants click on a link to visit a website about body image. Further limitations were that engagement was examined at one point in time, which precluded examination of change in engagement resulting from video viewing, and that the use for the cover story likely did not fully conceal the purpose of the study. In addition, the standalone persuasive appeal video was rated as less visually appealing and with lower production quality than the other videos, which may have impacted interest and attention to the videos. Another limitation is the varying length of the videos, in particular the difference in viewing time between the persuasive appeal only video, and the conditions to which the persuasive appeal video was added. As such, the effects of video length from video content may be difficult to separate. Although all videos were short in duration, future research should consider using equivalent length videos in all conditions. An additional limitation is around cultural relevance; although we collected country of residence, we did not collect information on other demographic characteristics; which may be of relevance considering academics and actors appearing in the videos were white. Future research examining engagement in such videos may wish to collect information such as cultural background, to better assess generalisability of the research, and aim to ensure the cast is not all or majority white to better reflect a community sample’s characteristics. Finally, a limitation is that most people in the video were thin; this may have unintentionally reinforced the idea that body dissatisfaction is only a problem when the person’s body is smaller; further research should examine the impact and potential benefits of educating the public that body dissatisfaction has a negative impact at all sizes, the issue of weight stigma, and attempt to include more size diversity. ## Conclusion The current study demonstrated that attempts to raise awareness of body dissatisfaction through persuasive communication were highly engaging in relation to behavioural (interest), affective (compassion) and cognitive (relevance) domains. Although all videos were rated as highly engaging, persuasive appeals and informational approaches were rated more highly on some engagement domains by men and by women. These findings suggest that video messages that are demonstrated to be effective in increasing perceptions of the seriousness of body dissatisfaction, as our previous research demonstrated [46], may also lead to greater dissemination of information and education about the topic. ## Electronic supplementary material Below is the link to the electronic supplementary material. Supplementary Table S1 and S2 ## References 1. Bucchianeri MM, Neumark-Sztainer D. **Body dissatisfaction: an overlooked public health concern**. *J Public Mental Health* (2014.0) **13** 64-9. DOI: 10.1108/JPMH-11-2013-0071 2. Paxton SJ, Neumark-Sztainer D, Hannan PJ, Eisenberg ME. **Body dissatisfaction prospectively predicts depressive mood and low self-esteem in adolescent girls and boys**. *J Clin Child Adolesc Psychol* (2006.0) **35** 539-49. DOI: 10.1207/s15374424jccp3504_5 3. Stice E, Shaw HE. **Role of body dissatisfaction in the onset and maintenance of eating pathology: a synthesis of research findings**. *J Psychosom Res* (2002.0) **53** 985-93. DOI: 10.1016/S0022-3999(02)00488-9 4. Černelič-Bizjak M, Jenko-Pražnikar Z. **Impact of negative cognitions about body image on inflammatory status in relation to health**. *Psychol Health* (2014.0) **29** 264-78. DOI: 10.1080/08870446.2013.844807 5. Ganem PA, Heer Hd, Morera OF. **Does body dissatisfaction predict mental health outcomes in a sample of predominantly hispanic college students?**. *Pers Indiv Differ* (2009.0) **46** 557-61. DOI: 10.1016/j.paid.2008.12.014 6. Mond JM, Mitchison D, Latner J, Hay P, Owen C, Rodgers B. **Quality of life impairment associated with body dissatisfaction in a general population sample of women**. *BMC Public Health* (2013.0) **13** 920. DOI: 10.1186/1471-2458-13-920 7. Swami V, Knowles V. **Mental health literacy of negative body image: symptom recognition and beliefs about body image in a british community sample**. *Int J Cult Mental Health* (2014.0) **7** 199-215. DOI: 10.1080/17542863.2013.769611 8. Bailey KA, Gammage KL, van Ingen C. **How do you define body image? Exploring conceptual gaps in understandings of body image at an exercise facility**. *Body Image* (2017.0) **23** 69-79. DOI: 10.1016/j.bodyim.2017.08.003 9. O’Gorman B, Sheffield J, Clarke R, Griffiths S. **Guys don’t talk about their bodies”: a qualitative investigation of male body dissatisfaction and sociocultural influences in a sample of 40 australian males**. *Clin Psychol* (2020.0) **24** 123-32. DOI: 10.1111/cp.12198 10. 10.Austin SB. A public health approach to eating disorders prevention: It’s time for public health professionals to take a seat at the table.BMC Public Health. 2012;12. 11. 11.Cohen EL, Alward D, Zajicek D, Edwards S, Hutson R. Ending as intended: The educational effects of an epilogue to a TV show episode about bipolar disorder.Health Communication. 2017:Advance online publication. 12. Corrigan PW. **Strategic Stigma Change (SSC): five principles for social marketing campaigns to reduce stigma**. *Psychiatric Serv* (2011.0) **62** 824-6. DOI: 10.1176/ps.62.8.pss6208_0824 13. Jorm AF, Christensen H, Griffiths KM. **The impact of beyondblue: the national depression initiative on the australian public’s recognition of depression and beliefs about treatments**. *Aust N Z J Psychiatry* (2005.0) **39** 248-54. DOI: 10.1080/j.1440-1614.2005.01561.x 14. Garnett BR, Buelow R, Franko DL, Becker C, Rodgers RF, Austin SB. **The importance of campaign saliency as a predictor of attitude and behavior change: a pilot evaluation of social marketing campaign Fat Talk Free Week**. *Health Commun* (2014.0) **29** 984-95. DOI: 10.1080/10410236.2013.827613 15. Meng J, Bissell KL, Pan P-L. **YouTube video as health literacy tool: a test of body image campaign effectiveness**. *Health Mark Q* (2015.0) **32** 350-66. DOI: 10.1080/07359683.2015.1093883 16. Alhabash S, McAlister AR, Lou C, Hagerstrom A. **From clicks to Behaviors: the Mediating Effect of Intentions to like, share, and comment on the relationship between message evaluations and offline behavioral intentions**. *J Interact Advertising* (2015.0) **15** 82-96. DOI: 10.1080/15252019.2015.1071677 17. Moyer-Gusé E. **Toward a theory of entertainment persuasion: explaining the persuasive effects of entertainment-education messages**. *Communication Theory* (2008.0) **18** 407-25. DOI: 10.1111/j.1468-2885.2008.00328.x 18. Moyer-Gusé E, Jain P, Chung AH. **Reinforcement or reactance? Examining the effect of an explicit persuasive appeal following an entertainment-education narrative**. *J Communication* (2012.0) **62** 1010-27. DOI: 10.1111/j.1460-2466.2012.01680.x 19. 19.Miller GR. The SAGE Handbook of Persuasion: Developments in Theory and Practice. 2012 2019/12/10. Thousand Oaks Thousand Oaks, California: SAGE Publications, Inc. 2. Available from: http://sk.sagepub.com/reference/hdbk_persuasion2ed. 20. Stiff JB, Mongeau PA. *Persuasive communication* (2016.0) 21. Halliwell E, Easun A, Harcourt D. **Body dissatisfaction: can a short media literacy message reduce negative media exposure effects amongst adolescent girls?**. *Br J Health Psychol* (2011.0) **16** 396-403. DOI: 10.1348/135910710X515714 22. McLean SA, Paxton SJ, Massey R, Hay PJ, Mond JM, Rodgers B. **Identifying persuasive public health messages to change community knowledge and attitudes about bulimia nervosa**. *J Health Communication* (2016.0) **21** 178-87. DOI: 10.1080/10810730.2015.1049309 23. Park S-Y, McSweeney JH, Yun GW. **Intervention of eating disorder symptomatology using educational communication messages**. *Communication Res* (2009.0) **36** 677-97. DOI: 10.1177/0093650209338910 24. Dessart L, Veloutsou C, Morgan-Thomas A. **Consumer engagement in online brand communities: a social media perspective**. *J Prod Brand Manage* (2015.0) **24** 28-42. DOI: 10.1108/JPBM-06-2014-0635 25. Hollebeek L. **Exploring customer brand engagement: definition and themes**. *J Strategic Mark* (2011.0) **19** 555-73. DOI: 10.1080/0965254X.2011.599493 26. Tarute A, Nikou S, Gatautis R. **Mobile application driven consumer engagement**. *Telematics Inform* (2017.0) **34** 145-56. DOI: 10.1016/j.tele.2017.01.006 27. Vivek SD, Beatty SE, Morgan RM. **Customer Engagement: exploring customer Relationships beyond Purchase**. *J Mark Theory Pract* (2012.0) **20** 122-46. DOI: 10.2753/MTP1069-6679200201 28. 28.Gatautis R, Banyte J, Piligrimiene Z, Vitkauskaite E, Tarute A. The impact of gamification on consumer brand engagement.Transformations in Business & Economics. 2016;15(1). 29. 29.Oh J, Bellur S, Sundar S, editors., editors. A conceptual model of user engagement with media. mass communication division at the 60th annual conference of the International Communication Association, Singapore; 2010. 30. Smith BG, Gallicano TD. **Terms of engagement: analyzing public engagement with organizations through social media**. *Comput Hum Behav* (2015.0) **53** 82-90. DOI: 10.1016/j.chb.2015.05.060 31. Heldman AB, Schindelar J, Weaver JB. **Social Media Engagement and Public Health communication: implications for Public Health Organizations being truly “Social**. *Public Health Rev* (2013.0) **35** 13. DOI: 10.1007/BF03391698 32. Khan ML. **Social media engagement: what motivates user participation and consumption on YouTube?**. *Comput Hum Behav* (2017.0) **66** 236-47. DOI: 10.1016/j.chb.2016.09.024 33. Hinyard LJ, Kreuter MW. **Using narrative communication as a tool for health behavior change: a conceptual, theoretical, and empirical overview**. *Health Educ Behav* (2007.0) **34** 777-92. DOI: 10.1177/1090198106291963 34. Kreuter MW, Green MC, Cappella JN, Slater MD, Wise ME, Storey D. **Narrative communication in cancer prevention and control: a framework to guide research and application**. *Ann Behav Med* (2007.0) **33** 221-35. DOI: 10.1007/BF02879904 35. Braddock K, Dillard JP. **Meta-analytic evidence for the persuasive effect of narratives on beliefs, attitudes, intentions, and behaviors**. *Communication Monogr* (2016.0) **83** 446-67. DOI: 10.1080/03637751.2015.1128555 36. Snyder LB, Hamilton MA, Mitchell EW, Kiwanuka-Tondo J, Fleming-Milici F, Proctor D. **A meta-analysis of the effect of mediated health communication campaigns on behavior change in the United States**. *J Health Communication* (2004.0) **9** 71-96. DOI: 10.1080/10810730490271548 37. Kreuter MW, Holmes K, Alcaraz K, Kalesan B, Rath S, Richert M. **Comparing narrative and informational videos to increase mammography in low-income african american women**. *Patient Educ Couns* (2010.0) **81** 6-S14. DOI: 10.1016/j.pec.2010.09.008 38. Willoughby JF, Liu S. **Do pictures help tell the story? An experimental test of narrative and emojis in a health text message intervention**. *Comput Hum Behav* (2018.0) **79** 75-82. DOI: 10.1016/j.chb.2017.10.031 39. Cohen EL. **Exploring Subtext Processing in Narrative Persuasion: the role of Eudaimonic Entertainment-Use Motivation and a supplemental conclusion scene**. *Communication Q* (2016.0) **64** 273-97. DOI: 10.1080/01463373.2015.1103287 40. Lane R, Miller AN, Brown C, Vilar N. **An examination of the Narrative Persuasion with Epilogue through the Lens of the Elaboration Likelihood Model**. *Communication Q* (2013.0) **61** 431-45. DOI: 10.1080/01463373.2013.799510 41. Slater MD, Rouner D. **Entertainment-education and elaboration likelihood: understanding the processing of narrative persuasion**. *Communication Theory* (2002.0) **12** 173-91 42. 42.Bruner JS, Austin GA. A study of thinking:Transaction publishers; 1986. 43. Mayer ND, Tormala ZL. **Think” Versus “Feel” framing Effects in Persuasion**. *Pers Soc Psychol Bull* (2010.0) **36** 443-54. DOI: 10.1177/0146167210362981 44. See YHM, Petty RE, Fabrigar LR. **Affective and cognitive meta-bases of attitudes: unique effects on information interest and persuasion**. *J Personal Soc Psychol* (2008.0) **94** 938-55. DOI: 10.1037/0022-3514.94.6.938 45. Keer M, van den Putte B, Neijens P, de Wit J. **The influence of affective and cognitive arguments on message judgement and attitude change: the moderating effects of meta-bases and structural bases**. *Psychol Health* (2013.0) **28** 895-908. DOI: 10.1080/08870446.2013.764428 46. McLean SA. **Impact of viewing body image health promotion videos in adult men and women: comparison of narrative and informational approaches**. *Body Image* (2020.0) **33** 222-31. DOI: 10.1016/j.bodyim.2020.04.001 47. Araujo T, Neijens P, Vliegenthart R. **Getting the word out on Twitter: the role of influentials, information brokers and strong ties in building word-of-mouth for brands**. *Int J Advertising* (2017.0) **36** 496-513. DOI: 10.1080/02650487.2016.1173765 48. Dobele A, Lindgreen A, Beverland M, Vanhamme J, van Wijk R. **Why pass on viral messages? Because they connect emotionally**. *Bus Horiz* (2007.0) **50** 291-304. DOI: 10.1016/j.bushor.2007.01.004 49. Eckler P, Bolls P. **Spreading the Virus**. *J Interact Advertising* (2011.0) **11** 1-11. DOI: 10.1080/15252019.2011.10722180 50. Bullivant B, Rhydderch S, Griffiths S, Mitchison D, Mond JM. **Eating disorders “mental health literacy”: a scoping review**. *J Mental Health* (2020.0) **29** 336-49. DOI: 10.1080/09638237.2020.1713996 51. Durkin SJ, Paxton SJ. **Predictors of vulnerability to reduced body image satisfaction and psychological wellbeing in response to exposure to idealized female media images in adolescent girls**. *J Psychosom Res* (2002.0) **53** 995-1005. DOI: 10.1016/S0022-3999(02)00489-0 52. Heinberg LJ, Thompson J. **Body image and televised images of thinness and attractiveness: a controlled laboratory investigation**. *J Soc Clin Psychol* (1995.0) **14** 325. DOI: 10.1521/jscp.1995.14.4.325 53. Jensen JD, King AJ, Carcioppolo N, Davis L. **Why are tailored messages more effective? A multiple mediation analysis of a breast Cancer screening intervention**. *J Commun* (2012.0) **62** 851-68. DOI: 10.1111/j.1460-2466.2012.01668.x 54. Puhl R, Luedicke J, Lee Peterson J. **Public reactions to obesity-related health campaigns: a randomized controlled trial**. *Am J Prev Med* (2013.0) **45** 36-48. DOI: 10.1016/j.amepre.2013.02.010 55. Puhl R, Peterson JL, Luedicke J. **Motivating or stigmatizing? Public perceptions of weight-related language used by health providers**. *Int J Obes* (2013.0) **37** 612-9. DOI: 10.1038/ijo.2012.110 56. 56.Vivek H, Singh SN, Mishra Sanjay, Donavan DT. Parallel Analysis Engine to Aid in Determining Number of Factors to Retain using R [Computer Software] 2017 [Available from: https://analytics.gonzaga.edu/parallelengine/. 57. Field A. *Discovering statistics using IBM* (2013.0) 58. Faul F, Erdfelder E, Lang A-G, Buchner A. **G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences**. *Behav Res Methods* (2007.0) **39** 175-91. DOI: 10.3758/BF03193146 59. Westberg K, Stavros C, Smith ACT, Munro G, Argus K. **An examination of how alcohol brands use sport to engage consumers on social media**. *Drug Alcohol Rev* (2018.0) **37** 28-35. DOI: 10.1111/dar.12493 60. Petty RE, Cacioppo JT. *The elaboration likelihood model of persuasion* (1986.0) 1-24 61. Griffiths S, Mond JM, Murray SB, Touyz S. **The prevalence and adverse associations of stigmatization in people with eating disorders**. *Int J Eat Disord* (2015.0) **48** 767-74. DOI: 10.1002/eat.22353 62. Jankowski GS, Gough B, Fawkner H, Halliwell E, Diedrichs PC. **Young men’s minimisation of their body dissatisfaction**. *Psychol Health* (2018.0) **33** 1343-63. DOI: 10.1080/08870446.2018.1496251 63. Whitaker C, Gough B, Fawkner H, Deighton-Smith N. **Young men’s body dissatisfaction: a qualitative analysis of anonymous online accounts**. *J Health Psychol* (2021.0) **26** 636-49. DOI: 10.1177/1359105319832352 64. Doley JR, Hart LM, Stukas AA, Petrovic K, Bouguettaya A, Paxton SJ. **Interventions to reduce the stigma of eating disorders: a systematic review and meta-analysis**. *Int J Eat Disord* (2017.0) **50** 210-30. DOI: 10.1002/eat.22691
--- title: 'Ethnic differences in the lifestyle behaviors and premature coronary artery disease: a multi-center study' authors: - Media Babahajiani - Ehsan Zarepur - Alireza Khosravi - Noushin Mohammadifard - Feridoun Noohi - Hasan Alikhasi - Shima Nasirian - Seyed Ali Moezi Bady - Parisa Janjani - Kamal Solati - Masoud Lotfizadeh - Samad Ghaffari - Elmira Javanmardi - Arsalan Salari - Mahboobeh Gholipour - Mostafa Dehghani - Mostafa Cheraghi - Ahmadreza Assareh - Habib Haybar - Seyedeh Mahdieh Namayandeh - Reza Madadi - Javad Kojuri - Marjan Mansourian - Nizal Sarrafzadegan journal: BMC Cardiovascular Disorders year: 2023 pmcid: PMC10061766 doi: 10.1186/s12872-023-03192-0 license: CC BY 4.0 --- # Ethnic differences in the lifestyle behaviors and premature coronary artery disease: a multi-center study ## Abstract ### Background Diverse ethnic groups that exist in Iran may differ regarding the risk factors such as hypertension, hyperlipidemia, dyslipidemia, diabetes mellitus, and family history of non-communicable disease. Premature Coronary Artery Disease (PCAD) is more endemic in Iran than before. This study sought to assess the association between ethnicity and lifestyle behaviors in eight major Iranian ethnic groups with PCAD. ### Methods In this study, 2863 patients aged ≤ 70 for women and ≤ 60 for men who underwent coronary angiography were recruited in a multi-center framework. All the patients’ demographic, laboratory, clinical, and risk factor data were retrieved. Eight large ethnicities in Iran, including the Farses, the Kurds, the Turks, the Gilaks, the Arabs, the Lors, the Qashqai, and the Bakhtiari were evaluated for PCAD. Different lifestyle components and having PCAD were compared among the ethnical groups using multivariable modeling. ### Results The mean age of the 2863 patients participated was 55.66 ± 7.70 years. The Fars ethnicity with 1654 people, was the most subject in this study. Family history of more than three chronic diseases (1279 ($44.7\%$) was the most common risk factor. The Turk ethnic group had the highest prevalence of ≥ 3 simultaneous lifestyle-related risk factors ($24.3\%$), and the Bakhtiari ethnic group had the highest prevalence of no lifestyle-related risk factors ($20.9\%$). Adjusted models showed that having all three abnormal lifestyle components increased the risk of PCAD (OR = 2.28, $95\%$ CI: 1.04–1.06). The Arabs had the most chance of getting PCAD among other ethnicities (OR = 2.26, $95\%$CI: 1.40–3.65). While, the Kurds with a healthy lifestyle showed the lowest chance of getting PCAD (OR = 1.96, $95\%$CI: 1.05–3.67)). ### Conclusions This study found there was heterogeneity in having PACD and a diverse distribution in its well-known traditional lifestyle-related risk factors among major Iranian ethnic groups. ## Background Coronary Artery Disease (CAD) is the most common heart-related non-communicable disease in industrialized countries. It is responsible for increasing deaths due to Cardiovascular Disease (CVD) worldwide [1, 2], affecting older people of all ethnicities and races [3–5]. Furthermore, the morbidity and mortality from CAD in patients with Premature Coronary Artery Disease (PCAD) (males < 55 years and females < 65 years) may have a devastating impact on the families of these patients [6, 7]. It is estimated that about 4–$10\%$ of individuals with documented CAD are premature [2, 8]. CAD-related deaths in Iran account for approximately $39.3\%$ of the total deaths per year [9]. According to the GBD report in 2015, Iran was one of the countries with the most CVD rate in the world (9000 cases of CVD per 100 000 persons) [10, 11]; moreover, the prevalence of CAD and its risk factors is higher than in western countries [12]. Ardabil, North West province of Iran, with $50.1\%$ has the most prevalent CVD in Iran [13]; on the contrary, the prevalence of CVD in the south of Iran reported was $10.4\%$ [14]. In 2020, a study predicted mean 10-year CVD development as $16.4\%$ [14], which makes it vital to conduct more studies. The majority of the burden entailed by CAD is related to modifiable risk factors. Various studies have proved that modifiable lifestyle factors such as smoking cessation, exercise, and a healthy diet can help reduce the risk of CAD [15–18]. Moreover, previous studies have shown that adherence to a healthy lifestyle, including a combination of the factors above, has reduced the incidence of CAD by approximately 40 − $45\%$ [19, 20]. Different ethnicities may experience different CAD severities due to lifestyle factors [3–5, 21–24]. Iran, a multiethnic Middle Eastern country with diverse cultures, traditions, habits, and diets [25], is susceptible to CAD risk factors. The Farses, the Kurds, the Turks, the Gilaks, the Arabs, the Lors, the Qashqaei, and the Bakhtiari are Iran’s main ethnic groupings. However, no information has been published on the potential relationship of life-related risk factor diversity with the risk of premature CAD in different ethnicities. Thus, the present study aimed to analyze, for the first time, whether the lifestyle is associated with premature CAD in the Iranian ethnic groups. ## Design and subjects This was a case-control study named Iran-premature coronary artery disease (I-PAD) study started in 2020, and is still ongoing on Iranian patients. Patients underwent coronary angiography with different ethnicities in Iran; including the Farses, the Kurds, the Turks, the Gilaks, the Arabs, the Lors, the Qashqaei, and the Bakhtiari. The patients were registered from hospitals with catheterization laboratories in different cities. The angiography databank at a multi-center framework was utilized for the current study. The demographic, laboratory, clinical, and risk factor data of all patients, who underwent coronary angiography were collected by trained physicians. Patients completed questionnaires that included information such as demographic, type of ethnicity, metabolic variables, lifestyle behaviors, and family history of illness [26]. ## Inclusion and exclusion criteria Inclusion criteria included patients who underwent coronary angiography, age ≤ 65 and ≤ 55 years for male and female, respectively. Patients were labeled as having CAD if they had at least $75\%$ or more of a single coronary artery obstruction or $50\%$ or more of the left main coronary artery. Patients labeled as not having PCAD if had normal arteries. Patients with a registered history of coronary artery diseases such as balloon angioplasty, Coronary Artery Bypass Graft (CABG), or Percutaneous Coronary artery Intervention (PCI) were excluded from the study. Hence, 3033 patients were eligible to enter the study, out of which 170 patients were excluded from the study due to incorrect information. ## Measures Lifestyle-related factors, which are a combination of three variables; smoking, physical activity, and diet, were collected through the questionnaires. Eating habits were measured using the validated semi-qualitative Food Frequency Questionnaire (FFQ). For dietary scoring purposes, 12 food categories were predetermined, and the consumption frequency for each food category in each patient was measured. Healthy food groups included fruits, vegetables, dairy products, non-hydrogenated vegetable oils, legumes, nuts, and white meat; unhealthy food groups included hydrogenated vegetable oils, red meat, processed meat, all grains, Pizza, and sweets. Patients with the highest healthy food consumption were scored one, and those with the highest unhealthy food consumption scored zero for that category. Diet in patients was scored from zero to 12, those with a score of eight or higher being classified as having a healthy diet and receiving a score of one. On the other hand, those with a score of less than eight were classified as having an unhealthy diet receiving a score of zero [27]. For the smoking variable, those who quit smoking six months ago or never smoked at all were defined as low-risk and received a score of one. Moreover, for the physical activity variable, those who had exercised for an average of at least 30 min of daily exercise with moderate or high intensity were classified as low-risk and received a minimum score [27]. The lifestyle variable combined three variables (smoking, physical activity, and diet), and it was scored as a value between zero to three for each patient (zero for having no abnormal status of these three variables, one for patients who had one abnormal lifestyle component and two for patients with two abnormal components). Patients with all abnormal components scored as three. Individuals with a score of one or higher were classified as having an unhealthy lifestyle. The data on family history gathered were the history of first-degree relatives with definite chronic disease (family history of CVD, hyperlipidemia, diabetes mellitus, and hypertension), hypertension (defined by current use of antihypertensive medication or history of blood pressure > 140⁄95 mm Hg), and diabetes mellitus (Self-reported). Body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters (kg ⁄m2). Obesity was defined as BMI ≥ 30 kg⁄m2, hyperlipidemia (defined by total cholesterol ≥ 240, LDL cholesterol ≥ 130, Non-HDL cholesterol triglycerides ≥ 150, and HDL cholesterol < 40) [28]. Further details on the study method are described in depth in the I-PAD methodology Sect. [ 26]. ## Statistical analysis Patients were categorized into eight groups according to different ethnicities. The continuous variables were described with means and standard deviations (SDs), and the categorical variables were expressed as frequencies with percentages among the ethnic groups. Chi-square tests were used to compare the distribution of different categorical characteristics and ethnicities. One-way Analysis of Variance (ANOVA) or Kruskal-Wallis test was used to compare the level of continuous factors in different ethnicities. All clinically important variables were considered using stepwise logistic regression analyses. Adjusted logistic regression was performed to evaluate the relationship between PCAD as the dependent variable, and the risk factors such as sex, age, BMI, hyperlipidemia, dyslipidemia, diabetes mellitus, and family history of chronic disease, and dummy variables for the ethnic groups as the independent variables. The Fars was determined as the reference group among all ethnicities, due to its dominant lifestyle in Iran. Next, we applied four separate multivariable models for each ethnic group. Akaike information criterion (AIC) was used to compare different models. P-Value ≤ 0.05 was considered statistically significant. All the data analyses were conducted by SPSS 22.0 (IBM Corp, Armonk, NY, USA). ## Results In this study, among 2863 patients (1556 male ($54.3\%$)) with a mean age of 55.66 ± 7.70 who underwent coronary angiography, 1756 ($61.3\%$) had a positive result for PCAD. The distribution of study subjects from different ethnicities were the Farses (1654 ($57.8\%$)), the Turks (103 ($3.6\%$)), the Gilaks (238 ($8.3\%$)), the Kurds (364 ($12.7\%$)), the Arabs (90 ($3.1\%$)), the Lors (71 ($2.5\%$)), the Qashqaei (127 ($4.4\%$)), and the Bakhtiari (196 ($6.8\%$)). Table 1 shows the patient characteristics of the study sample. Among all patients, according to their angiography results, the most common risk factor was family history of more than three chronic diseases (1279 ($44.7\%$), followed by hyperlipidemia (1148 ($40.1\%$)), hypertension (1013 ($35.4\%$)), and diabetes mellitus (722 ($25.2\%$)). Out of these risk factors, the prevalence of hyperlipidemia and family history of chronic disease was significantly different among different ethnic groups (p-value < 0.05). The highest prevalence of diabetes mellitus and hyperlipidemia were presented in the Gilak ethnicity, with values of 80 ($33.6\%$) and 116 ($48.7\%$), respectively. In addition, the Lor had the highest prevalence of family history of chronic diseases ($51.2\%$) (Table 1). Table 1 Characteristics of Study Patients between Different Ethnicity Groups TotalN = 2863FarsN = 1654TurkN = 103GilakN = 238KurdN = 364ArabN = 90LorN = 71GhashghaeiN = 127BakhtiariN = 196P-value Positive Angiography results (abnormal) n (%) 1756($61.3\%$)1008($60.9\%$)72($69.9\%$)172($72.3\%$)192($52.7\%$)62($68.9\%$)48($67.6\%$)71($55.9\%$)124($63.3\%$)< 0.001 Age (yr.) 55.66 ± 7.7055.83 ± 7.3754.35 ± 8.9857.21 ± 7.8355.29 ± 7.9953.67 ± 8.0554.25 ± 10.7656.00 ± 7.0655.08 ± 7.700.003 Sex (Male) n (%) 1556($54.3\%$)951 ($57.5\%$)71($68.9\%$)109($45.8\%$)157($43.1\%$)36 ($40.0\%$)41 ($57.7\%$)66 ($52.0\%$)66 ($52.0\%$)< 0.001 BMI ≥ 30n (%) 891 ($31.1\%$)503 ($30.4\%$)33 ($32.0\%$)65 ($27.3\%$)131($36.0\%$)32 ($35.6\%$)28 ($39.4\%$)40 ($31.5\%$)55 ($28.1\%$)0.191 Diabetes n (%) 722 ($25.2\%$)422 ($25.5\%$)33 ($32.0\%$)80 ($33.6\%$)72 ($19.8\%$)29 ($32.2\%$)14 ($19.7\%$)18 ($14.2\%$)47 ($24.0\%$)< 0.001 Hypertension n (%) 1013($35.4\%$)567 ($34.3\%$)42 ($41.2\%$)100 ($42.2\%$)137($37.6\%$)40 ($44.4\%$)23 ($32.4\%$)40 ($31.5\%$)58 ($29.6\%$)0.051 Hyperlipidemia n (%) 1148($40.1\%$)677 ($40.9\%$)50 ($48.5\%$)116 ($48.7\%$)135($37.1\%$)34 ($37.8\%$)20 ($28.2\%$)39 ($30.7\%$)68 ($34.7\%$)0.002 Family History of chronic disease ≥ 3 n (%) 1279($44.7\%$)772($46.7\%$)51($49.5\%$)65($27.3\%$)175($48.1\%$)35($38.9\%$)37($52.1\%$)61($48.0\%$)76($38.8\%$)< 0.001 BMI = body mass index Figure 1 shows that among different ethnicities, the Kurd, the Gilak, and the Turk ethnic groups had the highest prevalence of one, two, and three lifestyle risk factors (unhealthy dietary intake, smoking habit, and low physical activity), respectively. Fig. 1 Prevalence of Different Numbers of Unhealthy Lifestyle Components across Different Ethnic Groups Multiple logistic regression was conducted to test the relationship between PCAD and ethnicity, and the relationship between PCAD and lifestyle. The odds ratios were calculated in the adjusted and unadjusted models (Table 2). Table 2The Association of, Lifestyle Component and Ethnicity with PCAD according to both Adjusted and Un-Adjusted modelAdjusted Model*Un-Adjusted ModelLifestylep-ValueOdds Ratio($95\%$ CI)Lifestylep-ValueOdds Ratio($95\%$ CI)No risk factorReferenceNo risk factorReferenceHaving one abnormal component0.1021.27(0.95- 1.71)Having one abnormal component0.1171.25(0.95- 1.66)Having two abnormal component0.3511.14(0.86- 1.52)Having two abnormal component0.3881.29(0.85- 1.48)All three component were abnormal< 0.0012.28(1.04–1.06)All three component were abnormal< 0.0012.023(1.41–2.86) Adjusted Model * Un-Adjusted Model Ethnicity p-Value Odds Ratio ($95\%$ CI) Ethnicity p-Value Odds Ratio ($95\%$ CI) FarsReferenceFarsReferenceTurk0.011.81(1.15–2.83)Turk0.0711.48(0.966- 2.294)Gilak0.0051.57(0.94- 1.58)Gilak0.0011.67(1.23–2.25)Kurd0.0130.74(0.94- 1.15)Kurd0.0040.715(0.56- 0.90)Arab0.0012.26(1.40–3.65)Arab0.1331.41(0.89- 0.2.241)Lor0.0721.62(0.95- 2.76)Lor0.2611.33(0.806 − 2.220)Ghashghaei0.130.74(0.51- 1.09)Ghashghaei0.2640.813(0.565 − 1.16)Bakhtiari0.821.04(1.75–1.43)Bakhtiari0.5281.104(0.812 − 1.5)*Adjusted for sex, age, BMI, hyperlipidemia, dyslipidemia, diabetes mellitus, and family history of chronic disease Having all three abnormal lifestyle components significantly increased the risk of PACD by 128 and $102\%$ in both adjusted and unadjusted logistic regression, respectively. Furthermore, the results of the adjusted model for the relationship between PCAD and ethnicities showed that the highest chance of getting PCAD was seen in the Arab (OR = 2.26, $95\%$ CI: 1.40–3.65) followed by the Turks (OR = 1.81, $95\%$CI: 1.15–2.83) and the Gilaks (OR = 1.57, $95\%$ CI: 0.94- 1.58); in contrast, the chance of getting PCAD was significantly lower in the Kurds rather than the Farses (OR = 0.74, $95\%$ CI: 0.94-0.1.15). Table 3 demonstrates more details on the relationship between unhealthy lifestyle behaviors (having at least one abnormal component) and PCAD among different ethnicities. In the Lors and the Farses ethnicities, an unhealthy lifestyle increased the risk of PCAD significantly in all models. The unhealthy lifestyle in the Bakhtiari and the Kurds showed a significant relationship with getting PCAD in model one, two, and three (p-Value < 0.05). Table 3Association between PCAD and Unhealthy Lifestyle Behaviors (at least one abnormal component of smoking, low physical activity, and unhealthy diet) in Different EthnicitiesEthnicitiesCrude Model aModel One bModel Two cModel three dOR ($95\%$ CI)p-ValueAICOR ($95\%$ CI)p-ValueAICOR ($95\%$ CI)p-ValueAICOR ($95\%$ CI)p-ValueAICFars1.98(1.40–2.79)< 0.0012216.82.25(1.58–3.19)< 0.0012188.82.11(1.48–3.02)< 0.0012130.12.09(1.46–2.99)< 0.0012128.5Turk2.00(0.67-5.93)0.211126.431.10(0.312 − 3.89)0.881111.261.28(0.33-4.91)0.723119.181.24(0.320 − 4.81)0.754120.87Gilak1.23(0.55-2.77)0.615283.911.61(0.70-3.72)0.265274.261.51(0.62 − 3.700)0.346260.462.13(1.02–4.44)0.044258.06Kurd1.39(0.78-2.48)0.257505.761.81(0.100-3.297)0.0514451.92(1.03–3.56)0.039437.341.96(1.05–3.67)0.035433.2Arab1.69(0.64-4.43)0.287115.231.629(0.57-4.61)0.358111.451.78(0.566 − 5.62)0.3231181.78(0.563 − 5.61)0.327119.45Lor3.20(0.1.11–9.21)0.03190.183.11(1.06–9.11)0.03890.594.28(1.24–14.73)0.02193.484.16(1.19–14.46)0.02594.68Ghashghaei1.58(0.74-3.39)0.241126.432.04(0.86-4.87)0.106126.822.05(0.81-5.19)0.127131.381.91(0.741 − 4.90)0.181133.19Bakhtiari1.31(0.719 − 2.40)0.375258.592.15(1.02–4.56)0.045250.532.27(1.04–4.92)0.039254.192.23(1.03–4.86)0.043256.18 CI = Confidence Interval, OR = Odds Ratio “ a ” = Unhealthy Lifestyle “ b ” = Model “a” ” and additionally adjusted Sex and Age “ c ” = Model “b” and additionally adjusted for body mass index, hypertension, hyperlipidemia, dyslipidemia, diabetes mellitus “ d ” = Model “c” and additionally adjusted for family history of cardiovascular disease, family history of hyperlipidemia, family history of diabetes mellitus, family history of hypertension Risk factors = unhealthy dietary intake, smoking habits, and low physical activity Based on the full adjusted model results, the most significant increase in odds ratio was for the Lors ethnicity (OR = 4.156, $95\%$ CI: 1.990–14.460), and the least significant increase was related to the Kurds (OR = 1.964, $95\%$ CI: 1.050–3.671). ## Discussion The current study examined the relationship between PCAD and lifestyle risk factors expressed as diet, physical activity, and smoking while considering ethnicity. The prevalence of PCAD among the Gilaks and the Turks ethnicities was significantly higher than in other ethnic groups and considerably lower in the Kurd ethnic group. Results showed that the prevalence of three and two simultaneous modifiable risk factors was significantly higher in the Turks and the Gilaks, respectively, compared with other ethnicities. At the same time, the Kurds had the lowest modifiable lifestyle risk factors. We designed a logistic regression model to adjust the risk factors according to the observed differences between ethnicities. Patients with all three lifestyle risk factors had two times more chance of developing PCAD than others with no lifestyle risk factors. Furthermore, the full-adjusted model showed that the Lors and the Kurds ethnic groups had a higher and lower increase in the odds ratio of PCAD, respectively. As it is visible, the differences are still considerable. Various studies have confirmed the relationship between ethnicity and cardiovascular diseases. They have also considered the importance of knowing the characteristics of different ethnicities to reduce racial and ethnic differences in heart disease [22, 29]. In a study in Kazakhstan, Zea-Vera et al. investigated this relationship. After adjusting for the traditional risk factors of cardiovascular diseases and the quality of life index, they found that the prevalence of this disease is higher in Russians than in Kazakhs [30]. In Spain, a cohort study from the University of Navarre called the Seguimiento Universidad de Navarra (SUN) cohort found that participants with better HLS scores (healthy lifestyle scores) had a significant and inverse relationship with the risk of primary cardiovascular events [31]. Moreover, Tran et al. showed that CVD rates were higher in Norwegians than in other ethnic minorities [32]. The present study, which examined most ethnicities in Iran with acceptable sample size, also provides results confirming the relationship between ethnicity and PCAD. Our results also revealed that the chance of PCAD in the Arabs, followed by the Turks and the Gilaks, was significantly higher than in the Farses ethnicity. In contrast, the chance of PCAD was significantly lower in Kurds than in the Fars ethnicity, highlighting the characteristics of the different lifestyles associated with these ethnicities. The Kurds are the third most populous ethnic group in Iran. The major Kurd ethnic groups in Iran live in the Zagros Mountains near the Turkey and Iraq borders. They are mostly residents of the western provinces of Iran, such as Kurdistan, Kermanshah, and Ilam. The mountainous habitat, along with dietary habits, has been able to influence the lifestyles of the people of this region and reduce the risk of PCAD compared with the Farses ethnicity, who are the common ethnicity of Iran [25, 33] and mostly live in the central regions of Iran. In contrast, the Arab ethnicity in Iran, who mostly live in southern provinces such as Khuzestan, Bandar Abbas, and cities along the coasts of the Persian Gulf, have a hot climate, with temperatures reaching 50 °C in dry seasons [34]. The sedentary lifestyle of the Arabs ethnicity, its hot climate, and unique cultural barriers to physical activity are essential indicators of the rapidly increasing prevalence of obesity in this population, as people prefer to spend more time at home and have less physical activity [35]. Some studies have shown that physical activity and diabetes mellitus are inversely related, and this relationship is much stronger in people with high genetic susceptibility. The fact that consanguineous marriages are common in Arab culture becomes as important as it concerns [36]. Therefore, due to the climatic, cultural, environmental, and regional factors, high-fat dietary habits and a sedentary lifestyle have led to a higher chance of developing PCAD than the Fars ethnicity. Nevertheless, the results of the study by Najafi et al. in the Persian cohort study of Guilan showed an inverse causality between (BMI) and physical activity. With increasing weight, the participants tended to have less physical activity [35]. Considering the lifestyle, high-calorie dietary habits, and high-carbohydrate foods could be the main reasons for the increase in obesity and overweight in the Gilak population in the north of the country. The evidence in the literature can highlight the high risk of PCAD in the Gilak men and women. On the one hand, studies in this ethnic group have shown that the migration of the Gilak population from rural to urban areas due to economic problems has led to lifestyle changes and reduced physical activity. The average age of marriage for the Gilak women is about 20 years [37] and according to the results of Persian cohort studies in Guilan, $85.10\%$ of women are married [38]. Since sex hormone-related factors may also play a role in weight gain [24], low marriage age can also contribute to the high PCAD risk in this ethnic group. The study results regarding the Gilak and Turk ethnicity were in line with the results of Abbasi et al. They pointed out that the reasons for the high risk of PCAD in this ethnic group compared to the Fars group are a sedentary lifestyle, consumption of high-fat foods, and genetic factors [33]. Various studies performed in Azerbaijan have suggested that poor eating habits, diets high in carbohydrates and sodium, and inadequate consumption of healthy foods, and dairy products have increased the risk of cardiovascular disease in Turks. Moreover, the overall prevalence of smoking in these regions is higher than in Iran and the neighboring countries of Azerbaijan [39, 40]. Since lifestyle is a modifiable risk factor for PCAD, improving its components can reduce the risk of PCAD in the Fars, the Gilak, and the Bakhtiari populations. ## Strengths and Limitations The present study is the first report to investigate the relationship between the lifestyle of different Iranian ethnic groups and PCAD. One of the study’s strengths is the large number of recruited patients representing most Iranian ethnicities. Another strength of the study is the recruitment of study patients, which excludes people of mixed ethnicities (ethnicities resulting from intermarriage). However, there were some measurement errors in this study, particularly in evaluating diet and physical activity due to self-reporting, which may have weakened the observed correlations. Second, there are other ethnicities in Iran, but their community is not large enough to be considered. ## Conclusion This study found there was heterogeneity in having PACD and diverse distribution of its well-known traditional lifestyle-related risk factors among major Iranian ethnic groups. The findings of this study add to our understanding of the role of lifestyle in different ethnic groups and may help health policymakers implement prevention programs in vulnerable ethnic groups. ## References 1. Barth J, Schumacher M, Herrmann-Lingen C. **Depression as a risk factor for mortality in patients with coronary heart disease: a meta-analysis**. *Psychosom Med* (2004.0) **66** 802-13. DOI: 10.1097/01.psy.0000146332.53619.b2 2. Mohammad AM, Jehangeer HI, Shaikhow SK. **Prevalence and risk factors of premature coronary artery disease in patients undergoing coronary angiography in Kurdistan, Iraq**. *BMC Cardiovasc Disord* (2015.0) **15** 155. DOI: 10.1186/s12872-015-0145-7 3. Dalton AR, Bottle A, Soljak M, Majeed A, Millett C. **Ethnic group differences in cardiovascular risk assessment scores: national cross-sectional study**. *Ethn Health* (2014.0) **19** 367-84. DOI: 10.1080/13557858.2013.797568 4. Francis DK, Bennett NR, Ferguson TS, Hennis AJM, Wilks RJ, Harris EN. **Disparities in cardiovascular disease among caribbean populations: a systematic literature review**. *BMC Public Health* (2015.0) **15** 828. DOI: 10.1186/s12889-015-2166-7 5. Kurian AK, Cardarelli KM. **Racial and ethnic differences in cardiovascular disease risk factors: a systematic review**. *Ethn Dis* (2007.0) **17** 143-52. PMID: 17274224 6. Khot UN, Khot MB, Bajzer CT, Sapp SK, Ohman EM, Brener SJ. **Prevalence of conventional risk factors in patients with coronary heart disease**. *JAMA* (2003.0) **290** 898-904. DOI: 10.1001/jama.290.7.898 7. Panwar RB, Gupta R, Gupta BK, Raja S, Vaishnav J, Khatri M. **Atherothrombotic risk factors & premature coronary heart disease in India: a case-control study**. *Indian J Med Res* (2011.0) **134** 26-32. PMID: 21808131 8. Achari V, Thakur AK. **Association of major modifiable risk factors among patients with coronary artery disease–a retrospective analysis**. *J Assoc Phys India* (2004.0) **52** 103-8 9. Karimi-Moonaghi H, Mojalli M, Khosravan S. **Psychosocial complications of coronary artery disease**. *Iran Red Crescent Med J* (2014.0) **16** e18162. DOI: 10.5812/ircmj.18162 10. Sarrafzadegan N, Mohammmadifard N. **Cardiovascular Disease in Iran in the last 40 years: Prevalence, Mortality, Morbidity, Challenges and Strategies for Cardiovascular Prevention**. *Arch Iran Med* (2019.0) **22** 204-10. PMID: 31126179 11. Shams-Beyranvand M, Farzadfar F, Naderimagham S, Tirani M, Maracy MR. **Estimation of burden of ischemic heart diseases in Isfahan, Iran, 2014: using incompleteness and misclassification adjustment models**. *J Diabetes Metab Disord* (2017.0) **16** 12. DOI: 10.1186/s40200-017-0294-6 12. Ebrahimi M, Kazemi-Bajestani SM, Ghayour-Mobarhan M, Ferns GA. **Coronary artery disease and its risk factors status in iran: a review**. *Iran Red Crescent Med J* (2011.0) **13** 610-23. DOI: 10.5812/kowsar.20741804.2286 13. Alipour V, Zandian H, Yazdi-Feyzabadi V, Avesta L, Moghadam TZ. **Economic burden of cardiovascular diseases before and after Iran’s health transformation plan: evidence from a referral hospital of Iran**. *Cost Eff Resour Alloc* (2021.0) **19** 1. DOI: 10.1186/s12962-020-00250-8 14. Baeradeh N, Ghoddusi Johari M, Moftakhar L, Rezaeianzadeh R, Hosseini SV, Rezaianzadeh A. **The prevalence and predictors of cardiovascular diseases in Kherameh cohort study: a population-based study on 10,663 people in southern Iran**. *BMC Cardiovasc Disord* (2022.0) **22** 244. DOI: 10.1186/s12872-022-02683-w 15. 15.Winzer EB, Woitek F, Linke AJJotAHA. Physical activity in the prevention and treatment of coronary artery disease. 2018;7(4):e007725. 16. 16.Hajar RJHvtojotGHA. Risk factors for coronary artery disease: historical perspectives. 2017;18(3):109. 17. 17.Poorzand H, Tsarouhas K, Hozhabrossadati SA, Khorrampazhouh N, Bondarsahebi Y, Bacopoulou F et al. Risk factors of premature coronary artery disease in Iran: A systematic review and meta-analysis. 2019;49(7):e13124. 18. 18.Wang M. Coronary Artery Disease: Therapeutics and Drug Discovery. 2020. 19. Dimovski K, Orho-Melander M, Drake I. **A favorable lifestyle lowers the risk of coronary artery disease consistently across strata of non-modifiable risk factors in a population-based cohort**. *BMC Public Health* (2019.0) **19** 1575. DOI: 10.1186/s12889-019-7948-x 20. Khera AV, Emdin CA, Drake I, Natarajan P, Bick AG, Cook NR. **Genetic risk, adherence to a healthy lifestyle, and Coronary Disease**. *N Engl J Med* (2016.0) **375** 2349-58. DOI: 10.1056/NEJMoa1605086 21. Babusik P, Duris I. **Comparison of obesity and its relationship to some metabolic risk factors of atherosclerosis in Arabs and South Asians in Kuwait**. *Med principles practice: Int J Kuwait Univ Health Sci Centre* (2010.0) **19** 275-80. DOI: 10.1159/000312713 22. Budoff MJ, Nasir K, Mao S, Tseng PH, Chau A, Liu ST. **Ethnic differences of the presence and severity of coronary atherosclerosis**. *Atherosclerosis* (2006.0) **187** 343-50. DOI: 10.1016/j.atherosclerosis.2005.09.013 23. Misra A, Khurana L. **The metabolic syndrome in South Asians: epidemiology, determinants, and prevention**. *Metab Syndr Relat Disord* (2009.0) **7** 497-514. DOI: 10.1089/met.2009.0024 24. Yoo KY, Kim H, Shin HR, Kang D, Ha M, Park SK. **Female sex hormones and body mass in adolescent and postmenopausal korean women**. *J Korean Med Sci* (1998.0) **13** 241-6. DOI: 10.3346/jkms.1998.13.3.241 25. 25.Shaffer B. Iran Is More Than Persia Ethnic Politics in the Islamic Republic. 2021:364–400. 26. Zarepur E, Mohammadifard N, Mansourian M, Roohafza H, Sadeghi M, Khosravi A. **Rationale, design, and preliminary results of the Iran-premature coronary artery disease study (I-PAD): a multi-center case-control study of different iranian ethnicities**. *ARYA atherosclerosis* (2020.0) **16** 295-300. PMID: 34122584 27. Sarrafzadegan N, Kelishadi R, Esmaillzadeh A, Mohammadifard N, Rabiei K, Roohafza H. **Do lifestyle interventions work in developing countries? Findings from the Isfahan Healthy Heart Program in the Islamic Republic of Iran**. *Bull World Health Organ* (2009.0) **87** 39-50. DOI: 10.2471/BLT.07.049841 28. Nelson RH. **Hyperlipidemia as a risk factor for cardiovascular disease**. *Prim Care* (2013.0) **40** 195-211. DOI: 10.1016/j.pop.2012.11.003 29. Graham G. **Population-based approaches to understanding disparities in cardiovascular disease risk in the United States**. *Int J Gen Med* (2014.0) **7** 393-400. DOI: 10.2147/IJGM.S65528 30. 30.Zea-Vera R, Asokan S, Shah RM, Ryan CT, Chatterjee S, Wall MJ Jr et al. Racial/ethnic differences persist in treatment choice and outcomes in isolated intervention for coronary artery disease. 2022. 31. 31.Díaz-Gutiérrez J, Ruiz-Canela M, Gea A, Fernández-Montero A, Martínez-González M. Association Between a Healthy Lifestyle Score and the Risk of Cardiovascular Disease in the SUN Cohort. Revista espanola de cardiologia (English ed). 2018;71(12):1001–9. 32. Tran AT, Straand J, Diep LM, Meyer HE, Birkeland KI, Jenum AK. **Cardiovascular disease by diabetes status in five ethnic minority groups compared to ethnic Norwegians**. *BMC Public Health* (2011.0) **11** 554. DOI: 10.1186/1471-2458-11-554 33. Abbasi SH, Sundin Ö, Jalali A, Soares J, Macassa G. **Ethnic differences in the risk factors and severity of coronary artery disease: a patient-based study in Iran**. *J racial ethnic health disparities* (2018.0) **5** 623-31. DOI: 10.1007/s40615-017-0408-3 34. Masoudi M, Elhaeesahar M. **Trend assessment of climate changes in Khuzestan Province**. *Iran %J Nat Environ Change* (2016.0) **2** 143-52 35. Najafi F, Soltani S, Karami Matin B, Kazemi Karyani A, Rezaei S, Soofi M. **Socioeconomic - related inequalities in overweight and obesity: findings from the PERSIAN cohort study**. *BMC Public Health* (2020.0) **20** 214. DOI: 10.1186/s12889-020-8322-8 36. 36.Abuyassin B, Laher I. Obesity-linked diabetes in the Arab world: a review. Eastern Mediterranean health journal = La revue de sante de la Mediterranee orientale = al-Majallah al-sihhiyah li-sharq al-mutawassit. 2015;21(6):420–39. 37. Hajian-Tilaki KO, Heidari B. **Prevalence of obesity, central obesity and the associated factors in urban population aged 20–70 years, in the north of Iran: a population-based study and regression approach**. *Obes reviews: official J Int Association Study Obes* (2007.0) **8** 3-10. DOI: 10.1111/j.1467-789X.2006.00235.x 38. Moslemi M, Mahdavi-Roshan M, Joukar F, Naghipour M, Mansour-Ghanaei F. **Food Behaviors and its Association with Hypertension and Cardiovascular Diseases in Sowme’eh Sara (North of Iran): the PERSIAN Guilan Cohort Study (PGCS)**. *Prev Nutr Food Sci* (2021.0) **26** 262-8. DOI: 10.3746/pnf.2021.26.3.262 39. 39.Fatemeh E, Roshanak R, Marjan K, Haleh A, Sakineh NS, Alireza A et al. Household milk consumption and its socio-economic associates in West Azarbayejan province, North-west Iran. 2015. 40. Sadeghi-Bazargani H, Jafarzadeh H, Fallah M, Hekmat S, Bashiri J, Hosseingolizadeh G. **Risk factor investigation for cardiovascular health through WHO STEPS approach in Ardabil**. *Iran* (2011.0) **7** 417
--- title: Birth weight concerning obesity and diabetes gene expression in healthy infants; a case-control study authors: - Sahar Cheshmeh - Shima Moradi - Seyyed Mostafa Nachvak - Arman Mohammadi - Nastaran Najafi - Azadeh Erfanifar - Arezoo Bajelani journal: BMC Pregnancy and Childbirth year: 2023 pmcid: PMC10061768 doi: 10.1186/s12884-023-05538-0 license: CC BY 4.0 --- # Birth weight concerning obesity and diabetes gene expression in healthy infants; a case-control study ## Abstract ### Background Since obesity and diabetes are prevalent worldwide, identifying the factors affecting these two conditions can effectively alter them. We decided to investigate the expression of obesity and diabetes genes in infants with birth weights lower than 2500 g in comparison with infants with normal birth weights. ### Methods 215 healthy infants between the ages of 5–6 months were used in the current case-control research, which was conducted at health and treatment facilities in Kermanshah. Infants who were healthy were chosen for the research after their weight and height were measured and compared to the WHO diagram to ensure that they were well-grown and in good health. There were 137 infants in the control group and 78 infants in the case group. All newborns had 5 cc of blood drawn intravenously. To assess the expression of the genes MC4R, MTNR1B, PTEN, ACACB, PPAR-γ, PPAR-α, NRXN3, NTRK2, PCSK1, A2BP1, TMEM18, LXR, BDNF, TCF7L2, FTO and CPT1A, blood samples were gathered in EDTA-coated vials. Chi-square, Mann-Whitney U, and Spearman analyses were used to examine the data. ### Results A significant inverse correlation between birth weight and obesity and diabetes genes, including MTNR1B, NTRK2, PCSK1, and PTEN genes (r= -0.221, -0.235, -0.246, and − 0.418, respectively). In addition, the LBW infant’s expression level was significantly up-regulated than the normal-weight infants ($$P \leq 0.001$$, 0.007, 0.001, and < 0.001, respectively). The expression level of the PPAR-a gene had a significantly positive correlation with birth weight ($r = 0.19$, $$P \leq 0.005$$). The expression level of the PPAR-a gene in the normal-weight infants was significantly up-regulated than the LBW infants ($$P \leq 0.049$$). ### Conclusion The expression levels of MTNR1B, NTRK2, PCSK1, and PTEN genes were up-regulated in the LBW infants; however, the expression level of PPAR-a gene was significantly down-regulated in the LBW infants compared to the infants with normal birth weight. ## Introduction According to the data published by the Centers for Disease Control and Prevention (CDC), the prevalence of obesity in the American population is estimated to be $40\%$ in 2022 [1]. The increasing prevalence of obesity in recent years in the worldwide has turned it into a major challenge for health systems and especially in children [2]. Moreover, type 2 diabetes has been developing resulting in childhood obesity which may contribute to increasing in non-communicable diseases [3]. Low birth weight (LBW) is enhanced short-term and long-term consequences such as infant mortality, cognitive disorders, and growth failure, and is associated with obesity and diabetes in adulthood [4, 5]. According to the definition by World Health Organization, LBW infants refer to infants whose birth weight is less than 2500 g [6]. Based on the UNICEF-WHO reports, approximately 15–$20\%$ of births are related to LBW infants which annually account for about 20 million births in worldwide, and about half of them are in South Asia [7]. Some studies have found a U-shaped or J-shaped association between birth weight and obesity in adulthood, although these findings have been contradictory [8, 9]. On the other hand, it has been suggested that LBW can increase the risk of developing diabetes in adulthood [8]. Since obesity and diabetes are prevalent worldwide; identifying the factors affecting these two conditions can effectively modify them [10, 11]. Both environmental and genetic factors are involved in the pathogenesis of obesity and diabetes [11–13]. The relationship between obesity and the human genome map and biological pathways involved in pathogenesis is limited [12]. Studies on twins and families have reported the heritability of obesity to be between $40\%$ and $70\%$ [12, 14]. As a result, the knowledge of genetic approaches effective in obesity can be used to define the basic physiological and molecular mechanisms that control body weight [14]. Furthermore, genetic factors play an important role in determining a person’s tendency to gain weight [15]. Among the genes involved in the etiology of obesity and diabetes can be named Melanocortin 4 receptor (MC4R), Melatonin Receptor 1B (MTNR1B), Peroxisome proliferator-activated receptor gamma (PPAR-γ), Phosphatase and tensin homolog (PTEN), Acetyl-CoA Carboxylase Beta (ACACB), and peroxisome proliferator-activated receptor-α (PPAR-α), MC4R gene encodes melanocortin receptor, MC4 protein, a G-protein receptor that high it’s expression is related to body fat distribution and energy intake in children [16]. MTNR1B gene is located on chromosome 11q21 and synthesis melatonin receptor 2 in which this gene is related to all diabetes types including diabetes mellitus, type 1 diabetes, and gestational diabetes [17, 18]. In addition, down- regulated of PTEN and ACACB genes can decrease blood sugar and subsequently prevent diabetes and obesity PTEN is a phosphatase which plays role in signaling pathway and suppression of tumor [17, 19]. ACACB is a biotin dependent enzyme which catalyzes irreversible carboxylation of acetyl CoA to manolyl CoA and is effective in obesity and diabetes by reducing fatty acids oxidation and increasing of insulin resistance [20, 21]. Peroxisome proliferative activating receptors (PPARs) are part of the nuclear hormone receptors [22]. The up-regulated expression level of PPAR-γ regulates the secretion of adipose tissue hormones and reduces insulin resistance [23]. In addition, PPAR-α is expressed mostly in tissues with a high level of fatty acid catabolism. Thus, up-regulated PPAR-α reduces obesity and body fat [24]. According to the role of genetic factors on obesity and diabetes etiology, this study aimed to investigate the expression of obesity and diabetes genes in infants with birth weights lower than 2500 g in comparison with in infants with normal birth weight. ## Methods and materials The current case-control study was performed on healthy infants aged 5–6 months referred to health and treatment centers in Kermanshah. The sample size was calculated based on the weighted mean of children in the study by Zarrati et al. [ 25] using the sample size formula for case studies with $90\%$ power and $95\%$ confidence in each group of 45 infants. Inclusion criteria were healthy infants, lack of metabolic diseases, with a weight range of 800–4000 g, not using medications last month, breastfeeding or using infant milk (formula), not initiating supplementary nutrition, healthy parent, and absence of metabolic illness of their parents (such as gestational diabetes), and did not have any drug addiction. Based on birth weight, we considered infants with a birth weight of less than 2500 g in the case group and weight between 2500 and 4000 g in the control group [6]. For more reassurance, we entered 80 infants in the case group and twice as many in the control group. Since some samples’ information was incomplete, therefore, 5 infants were excluded. Overall, 78 infants remained in the case group and 137 infants in the control group. ## Ethical consideration Initially, the parents were given a thorough explanation of the study’s procedures and were asked for their written permission after being fully informed. This research from Kermanshah University of Medical Science was approved by the ethics committee (ethical number: IR.KUMS.RES.1397.081). ## Anthropometry Firstly the health information of infant including term or preterm, birth height, weight, and head circumference, and the food kind was recorded in the questionnaire. We considered birth weight lower than 2500 g as LBW and birth weight between 2500 and 4000 g as normal weight. After that, the infants’ height was measured by tape and in supine position and the infants weight was measured with the least cloth and without diaper. After measuring weight and height of the infant, the values were compared with the diagram of WHO to assure being healthy and well-grown and infants who were healthy, were selected to the study. According to the WHO growth diagram, children whose height and weight growth percentiles were between 3 and 85 were selected as normal. ## Sampling and expression of obesity and diabetes genes 5 cc of intravenous blood was collected from all infants. The blood sample was then centrifuged at 500 rpm for 15 min. The serum isolated from the blood sample was frozen at minus − 80 °C. Blood samples were collected in Ethylenediaminetetraacetic acid (EDTA) coated vials to evaluate the expression of MC4R, MTNR1B, PTEN, ACACB, PPAR-γ, PPAR-α, Neurexin-3-alpha (NRXN3), Neurotrophic Receptor Tyrosine Kinase 2 (NTRK2), Proprotein convertase 1(PCSK1), Ataxin-2 binding protein 1 (A2BP1), Transmembrane 18 gene (TMEM18), Liver X receptor (LXR), Brain-derived neurotrophic factor (BDNF), transcription factor 7-like 2 (TCF7L2), fat mass and obesity-associated (FTO), and carnitine palmitoyltransferase IA (CPT1A) genes. The Ficoll-Histopaque solution gradient isolated peripheral blood mononuclear cells (PBMC) during density gradient centrifugation (Ficoll-paque, Miltenyi Biotec GmbH, and Germany). The Trisor Regaent kit extracted total RNA from PBMC cells (Iranian pure YTzol RNA). According to the manufacturer’s instructions, one microgram of extracted RNA was applied for complementary DNA synthesis (cDNA) by Prime Script-RT Reagent kits (Takara Bio Ink. Tokyo, Japan). Dedicated primers were purchased and designated from Metabion (Metabion, Germany) (Table 1). Data were normalized as housekeeping genes by 2-tCt expressing 18s-rRnan. All samples were done in three versions. Table 1Primers sequences for RT-PCR amplificationGene name and symbol Sequence (5'→3')Gene name and symbol Sequence (5'→3') MC4R F: 5'-CTG ATG GAG GGT GCT ACG AG-3' BDNF F:5-GGCTTGACATCATTGGCTGAC-3'R:5'-TGG GTG AAT GCA GAT TCT TGT T-3'R:5-TGTGCAGTGTGAGAAAGGCTT-3' MTNR1B F: 5'-GCA TGG CCT ACC ACC GAA TC-3' ACACB F: 5-CAAGCCGATCACCAAGAGTAAA-3'R: 5'-AAT AGA TGC GTG GGT CGT ACT-3'R: 5-CCCTGAGTTATCAGAGGCTGG-3' TMEM18 F: 5'-TGT TAA AGT CGA TGG TGT AGC TC-3' PTEN F: 5-CAAGATGATGTTTGAAACTATTCCAATG-3'R: 5'-GTC CTT GTC CGG TTG TGA ACT-3'R: 5-CCTTTAGCTGGCAGACCACAA-3' TCF7L2 F: 5'- CGGCGAGTCTATGCCACTAT-3' LXR-α F:5-CCTTCAGAACCCACAGAGATCC-3'R: 5'-ACACAGGGACCGAGTAATGC-3'R:5-ACGCTGCATAGCTCGTTCC-3' NRXN3 F: 5'-AGG GGA AAA TTG GAG TTG TCT TC-3' PPAR-γ F:5-GATGCCAGCGACTTTGACTC-3'R: 5'-CCG TCA TTT ACA GGG GTT CTC T-3'R:5-ACCCACGTCATCTTCAGGGA-3' NTRK2 F: 5'-ACC CGA AAC AAA CTG ACG AGT − 3' FTO F:5-ACTTGGCTCCCTTATCTGACC-3'R: 5'-AGC ATG TAA ATG GAT TGC CCA-3'R:5-TGTGCAGTGTGAGAAAGGCTT-3' PCSK1 F: 5'-ACC CGA AAC AAA CTG ACG AGT − 3' CPT1A F:5-TCCAGTTGGCTTATCGTGGTG-3'R: 5'-AGC ATG TAA ATG GAT TGC CCA-3'R:5-TCCAGAGTCCGATTGATTTTTGC-3' A2BP1 F: 5'-ATTCAAACTACTGCCACC-3'-3' PPAR-a F:5-ATGGTGGACACGGAAAGCC-3'R: 5'-TGTCTAACACCATCTGCTT-3'R:5-CGATGGATTGCGAAATCTCTTGG-3' 18s rRNA F:5'-ACCCGTTGAACCCCATTCGTG A-3'F, forwardR:5'-GCCTCACTAAACCATCCAATCGG-3'R, reverse ## Statistical analysis All variables in the current research were examined using SPSS (SPSS Inc. Chicago, IL, USA version 19) and STATA (Stata Corp, College Station, TX, version 14). The data normality was checked using Kolmogorov-Smirnov test. Basic characteristics of infants are described by mean, frequency, and per cent frequency. The Chi-square test was used to compare qualitative variables. Mann-Whitney U, Spearman correlation, and linear regression tests were used to evaluate the expression of obesity and diabetes genes in peripheral blood cells and their relationship with birth weight. A significance threshold of less than 0.05 was taken into consideration for all tests. ## Results In this study, 137 infants and 78 infants were enrolled in the control and case study groups, respectively. In terms of the gender of the infants, the food kind of the infants, their mother’s age, their mother’s weight before pregnancy, and their mother and father’s BMI were no differences between the two studied groups. The mean weight in the control and case groups were 3.18 ± 0.27 and 1.79 ± 0.48 kg, respectively ($P \leq 0.001$). All characteristics of infants are presented in Table 2. Table 2Basic characteristics of infants based on the birth weightVariablesNormal weightLow birth weight P1($$n = 137$$)($$n = 78$$)Gender, boy %5451.30.403Preterm, %083.3< 0.001Food kind, % Breast feeding34.330.80.09 Formula3524.4 Mixed30.744.9Birth weight, kg3.18 ± 0.27*1.79 ± 0.48< 0.001Birth height, cm51.87 ± 3.7142.92 ± 5.30< 0.001Head circumference, cm33.69 ± 2.4928.54 ± 2.85< 0.001Current weight, kg7.87 ± 0.836.43 ± 1.4< 0.001Mother age, years27.29 ± 5.8628.41 ± 6.320.186Mother weight before pregnancy, kg69.7 ± 10.0975.26 ± 13.130.359Pregnancy weight gain, kg14.18 ± 4.059.47 ± 8.63< 0.001Mother BMI, kg/m226.62 ± 4.0925.74 ± 4.010.746Father BMI, kg/m226.17 ± 3.1426.12 ± 3.040.739Mother smoking, %2.200.257*Mean ± SDP1 was obtained Chi square and Mann-Whitney U Tests The current study found a significant inverse correlation between birth weight and obesity and diabetes genes, including MTNR1B, NTRK2, PCSK1, and PTEN (r= -0.221, -0.235, -0.246, and − 0.418, respectively). In addition, the LBW infant’s expression level was significantly up-regulated than the normal weight infants ($$P \leq 0.001$$, 0.007, 0.001, and < 0.001, respectively) (Table 3). Table 3The expression level of obesity and diabetes genes in peripheral blood cells based on the birth weightObesity and diabetes genesSpearman’s rho of birth weightNormal weight($$n = 137$$)Low birth weight($$n = 78$$)P2B(Unstandardized Coefficients)Beta(Standardized Coefficients)P3rP1Mean ± SDMean ± SD MC4R 0.0790.28813.27 ± 11.37*16.54 ± 23.680.195-0.001-0.0290.838 MTNR1B -0.2210.00111.72 ± 11.9022.83 ± 26.200.0010.0020.0640.6 NRXN3 -0.0110.8736.93 ± 7.4612.51 ± 18.710.4-0.005-0.0920.534 NTRK2 -0.2350.0018.02 ± 10.5215.48 ± 21.080.007-0.006-0.1450.294 PCSK1 -0.2460.0016.23 ± 6.3918.52 ± 24.030.0010.0040.0770.578 A2BP1 -0.0960.22211.32 ± 8.3420.58 ± 22.930.094-0.004-0.0920.533 TMEM18 -0.0270.71710.19 ± 11.129.04 ± 9.220.760.0220.2780.033 PPAR-γ -0.0670.3327.29 ± 10.7911.90 ± 18.090.651-0.005-0.1160.333 LXR 0.110.1146.88 ± 8.924.19 ± 5.540.0970.0160.1420.272 PTEN -0.418< 0.0012.32 ± 2.8249.16 ± 48.18< 0.001-0.009-0.553< 0.001 ACACB -0.0380.60618.04 ± 20.0822.37 ± 26.640.76-0.013-0.3790.011 BDNF -0.0010.9882.64 ± 2.922.56 ± 2.380.6090.030.1010.453 TCFL2 0.0810.2992.40 ± 2.742.42 ± 2.580.955-0.049-0.1730.191 FTO -0.060.39612.81 ± 16.1418.72 ± 23.110.142-0.009-0.1770.223 PPAR-a 0.190.00540.04 ± 17.6221.51 ± 22.71< 0.00100.1680.143 CPT1A -0.1040.14222.19 ± 26.2525.59 ± 25.450.1610.0110.3470.017P1 was obtained Spearman’s correlationP 2 was obtained Mann-Whitney UP3 was obtained Liner regression Furthermore, in Table 3, we observed that the expression level of the PPAR-a gene had a significantly positive correlation with birth weight ($r = 0.19$, $$P \leq 0.005$$). The expression level of the PPAR-a gene in the normal weight infants was significantly up-regulated than the LBW infants ($$P \leq 0.049$$). Moreover, linear regression and graphs were applied to better showing these association the birth weight and these genes including MTNR1B ($$P \leq 0.6$$), NTRK2 ($$P \leq 0.294$$), PCSK1 ($$P \leq 0.578$$), PTEN ($P \leq 0.001$), and PPAR-a ($$P \leq 0.143$$) (Table 3; Fig. 1). Fig. 1Linear regression between birth weight and obesity and diabetes genes including (a) MTNR1B, (b) NTRK2, (c) PCSK1, (d) PTEN, (e) PPAR a ## Discussion This present case-control study demonstrated overexpression of MTNR1B, NTRK2, PCSK1, and PTEN genes and down-regulation of PPAR-a gene in LBW. Birth weight is considered a suitable indicator for the quality of fetal growth and a predictor of health throughout life [26, 27]. Previous studies have indicated that LBW is associated with increased development of obesity, diabetes, neurodevelopment failure, cardiovascular disease, and other metabolic disorders [26]. Why does this happen? it is not clear. However, it seems that the decrease in birth weight is associated with a decrease in body hormone levels and changes in body composition, which is effective in the pathogenesis of obesity and diabetes in adulthood. Among these changes is the increase in leptin levels in the LBW infants, which seems to show a kind of resistance to leptin hormone in these infants [8, 28]. It has also been observed in animal studies that increased growth in these infants is mostly associated with fat cell hypertrophy and fat tissue dysfunction [29, 30]. Therefore, LBW increases fat absorption in childhood and subsequently adulthood [31]. Another possible effective mechanism was the decrease in the level of adiponectin hormone in some studies in the umbilical cord blood of LBW infants [32, 33]. However, since studies on the role of LBW on obesity and diabetes gene expression were limited, to the best of our knowledge, this current study evaluated the association between birth weight and obesity and diabetes gene expression in healthy infants. Our findings indicated that birth weight was associated with the up-regulated expression level of MTNR1B. Holzapfel et al. [ 34] observed that MTNR1B was associated with diabetes in children and adolescents. Another study by Liang et al. [ 35] showed that maternal MTNR1B genotype is involved in the etiology of childhood obesity. Recently, MTNR1B has increased the risk of obesity and type 2 diabetes [34, 36]. It is also highly expressed in retinal cells, pancreas, and pancreatic islet cells. Melatonin is a neuro-hormone secreted by the pineal gland which can adjust the circadian rhythm and regulate the insulin level. However, melatonin secretion is impaired in diabetics [36]. We also indicated the up-regulated expression level of the NTRK2 with increasing birth weight. In a study by Metrustry et al., variants in the NTRK2 gene and birth weight were examined. This study showed this gene was highly expressed in LBW twins [37]. NTRK2 is located on 9q21.33. NTRK2 encodes a member of the neurotrophic tyrosine receptor kinase (NTRK) family, a membrane-bound receptor for BDNF and regulates energy balance downstream of MC4R. Also, it is involved (involves) in the MAPK pathway and cell differentiation [38]. Mutations of NTRK2 have been associated with obesity and eating behavior [37, 39]. In the current study, the expression level of the PCSK1 was up-regulated in LBW infants. Ruiz-Narváez et al. showed that LBW plays a role in the expression level of the PCSK1 by disrupting central nervous system mechanisms and increasing obesity in adulthood [40]. PCSK1, located on 5q15 encodes a prohormone convertase $\frac{1}{3}$ (PC $\frac{1}{3}$) involved in pro-insulin processing under the influence of TCF7L2 [41]. PCSK1 is also engaged in processing pro-opiomelanocortin, proglucagon, proGnRH and proper. In addition, PCSK1 variants are associated with extreme obesity, impaired glucose tolerance, and polycystic ovarian syndrome. Rare mutations in PCSK1 cause childhood obesity, impaired pro-hormone processing and abnormal glucose homeostasis with increasing pro- insulin concentrations [40, 42]. In the present study, another up-regulated gene expression level was related to the PTEN gene. Li et al. showed that [43] the high expression level of gene PTEN was associated with increased insulin resistance. Although the gene was first identified as a tumor suppressor, it has recently been shown to be important with its antagonistic function in the insulin signaling cascade and is involved in glucose metabolism [44]. PTEN is a phosphatase that plays a role in the signaling pathway and tumor suppression in which it can suppress phosphatidylinositol 3-kinase (PI3K) signaling [17, 19, 45]. Since activation of PI3K is essential for insulin performance, PTEN is known to be effective in developing insulin resistance by reducing PI3K [46]. On the other hand, we observed that the expression level of the PPAR-a gene was significantly down-regulated in the LBW infants. Laleh et al. [ 47] demonstrated that the high expression level of PPAR-a suppressed appetite in obesity. Priego et al. [ 48] showed that the higher expression level of the PPAR-α gene in infants is associated with a lower risk of being overweight. PPAR-a is a group of legend activated nuclear receptors that are mainly expressed in tissues that are vital for fatty acid metabolisms, such as the liver, kidney, and heart, where they play an important role in regulating transcription of fatty acid metabolism, lipid homeostasis, and regulation of obesity [49]. During starvation and energy depletion, the PPAR-α increases fatty acid beta-oxidation. An animal study showed that PPAR-a deficiency was related to obesity and dyslipidemia [24]. ## Limitations This is the first case-control investigation into healthy infants’ birth weight, obesity, and diabetes gene expression. It was, however, constrained in some ways. The sample number was small because of financial limitations. The second, difference between the two groups was that many of the infants in the case group were preterm in terms of birth weight, pregnancy weight gain, and current weight infants, despite the fact that we controlled many infants and maternal variables between the two groups. ## Conclusions In conclusion, this present study reflected that the expression level of MTNR1B, NTRK2, PCSK1, and PTEN genes were up-regulated in the LBW infants; however, the expression level of PPAR-a gene was significantly down-regulated in the LBW infants compared to the infants with normal birth weight. ## References 1. Cohen CW, Fontaine KR, Arend RC, Gower BA. **A ketogenic Diet is acceptable in women with ovarian and endometrial Cancer and has no adverse Effects on blood lipids: a Randomized, Controlled Trial**. *Nutr Cancer* (2020.0) **72** 584-94. DOI: 10.1080/01635581.2019.1645864 2. Blüher M. **Obesity: global epidemiology and pathogenesis**. *Nat Reviews Endocrinol* (2019.0) **15** 288-98. DOI: 10.1038/s41574-019-0176-8 3. Cioana M, Deng J, Nadarajah A, Hou M, Qiu Y, Chen SSJ. **The prevalence of obesity among children with type 2 diabetes: a systematic review and Meta-analysis**. *JAMA Netw Open* (2022.0) **5** e2247186-e. DOI: 10.1001/jamanetworkopen.2022.47186 4. Giapros V, Vavva E, Siomou E, Kolios G, Tsabouri S, Cholevas V. **Low-birth-weight, but not catch-up growth, correlates with insulin resistance and resistin level in SGA infants at 12 months**. *J Matern Fetal Neonatal Med* (2017.0) **30** 1771-6. DOI: 10.1080/14767058.2016.1224838 5. Goldenberg RL, Culhane JF. **Low birth weight in the United States**. *Am J Clin Nutr* (2007.0) **85** 584S-90S. DOI: 10.1093/ajcn/85.2.584S 6. 6.World Health Organization. Optimal feeding of low-birth-weight infants: technical review. / Karen Edmond, Rajiv Bahl. World Health Organization. 2006. 7. 7.United Nations Children’s Fund (UNICEF), World Health Organization (WHO). UNICEF-WHO Low birthweight estimates: Levels and trends 2000–2015. Geneva: World Health Organization; 2019 Licence: CC BY-NC-SA 3.0 IGO 8. Jornayvaz FR, Vollenweider P, Bochud M, Mooser V, Waeber G, Marques-Vidal P. **Low birth weight leads to obesity, diabetes and increased leptin levels in adults: the CoLaus study**. *Cardiovasc Diabetol* (2016.0) **15** 1-10. DOI: 10.1186/s12933-016-0389-2 9. Zhao Y, Wang S-F, Mu M, Sheng J. **Birth weight and overweight/obesity in adults: a meta-analysis**. *Eur J Pediatr* (2012.0) **171** 1737-46. DOI: 10.1007/s00431-012-1701-0 10. Khan MAB, Hashim MJ, King JK, Govender RD, Mustafa H, Al Kaabi J. **Epidemiology of type 2 diabetes–global burden of disease and forecasted trends**. *Epidemiol Glob Health* (2020.0) **10** 107. DOI: 10.2991/jegh.k.191028.001 11. 11.Lim HJ, Xue H, Wang Y. Global trends in obesity. In: Meiselman HL, editor. Handbook of eating and drinking: interdisciplinary perspectives. Cham: Springer International Publishing; 2020. p. 1217–35 12. Mahmoud R, Kimonis V, Butler MG. **Genetics of obesity in humans: a clinical review**. *Int J Mol Sci* (2022.0) **23** 11005. DOI: 10.3390/ijms231911005 13. Schnurr TM, Jakupović H, Carrasquilla GD, Ängquist L, Grarup N, Sørensen TI. **Obesity, unfavourable lifestyle and genetic risk of type 2 diabetes: a case-cohort study**. *Diabetologia* (2020.0) **63** 1324-32. DOI: 10.1007/s00125-020-05140-5 14. Loos RJF, Yeo GSH. **The genetics of obesity: from discovery to biology**. *Nat Rev Genet* (2022.0) **23** 120-33. DOI: 10.1038/s41576-021-00414-z 15. Lin X, Li H. **Obesity: epidemiology, pathophysiology, and therapeutics**. *Front Endocrinol* (2021.0) **12** 706978. DOI: 10.3389/fendo.2021.706978 16. Aykut A, Özen S, Gökşen D, Ata A, Onay H, Atik T. **Melanocortin 4 receptor (MC4R) gene variants in children and adolescents having familial early-onset obesity: genetic and clinical characteristics**. *Eur J Pediatrics* (2020.0) **179** 1445-52. DOI: 10.1007/s00431-020-03630-7 17. James D, Lessen R. **Position of the american Dietetic Association: promoting and supporting breastfeeding**. *J Am Diet Assoc* (2009.0) **109** 1926-42. DOI: 10.1016/j.jada.2009.09.018 18. Riancho JA, Vazquez L, Garcia-Perez MA, Sainz J, Olmos JM, Hernandez JL. **Association of ACACB polymorphisms with obesity and diabetes**. *Mol Genet Metab* (2011.0) **104** 670-6. DOI: 10.1016/j.ymgme.2011.08.013 19. Beckman JA, Creager MA, Libby P. **Diabetes and atherosclerosis: epidemiology, pathophysiology, and management**. *JAMA* (2002.0) **287** 2570-81. DOI: 10.1001/jama.287.19.2570 20. Abu-Elheiga L, Matzuk MM, Abo-Hashema KA, Wakil SJ. **Continuous fatty acid oxidation and reduced fat storage in mice lacking acetyl-CoA carboxylase 2**. *Science* (2001.0) **291** 2613-6. DOI: 10.1126/science.1056843 21. Oh W, Abu-Elheiga L, Kordari P, Gu Z, Shaikenov T, Chirala SS. **Glucose and fat metabolism in adipose tissue of acetyl-CoA carboxylase 2 knockout mice**. *Proc Natl Acad Sci USA* (2005.0) **102** 1384-9. DOI: 10.1073/pnas.0409451102 22. Xu Y, Wu Y, Huang Q. **Comparison of the effect between pioglitazone and metformin in treating patients with PCOS: a meta-analysis**. *Arch Gynecol Obstet* (2017.0) **296** 661-77. DOI: 10.1007/s00404-017-4480-z 23. Rangwala SM, Lazar MA. **Peroxisome proliferator-activated receptor γ in diabetes and metabolism**. *Trends Pharmacol Sci* (2004.0) **25** 331-6. DOI: 10.1016/j.tips.2004.03.012 24. Kersten S. **Integrated physiology and systems biology of PPARalpha**. *Mol metabolism* (2014.0) **3** 354-71. DOI: 10.1016/j.molmet.2014.02.002 25. Zarrati M, Shidfar F, Razmpoosh E, Nezhad FN, Keivani H, Hemami MR. **Does low birth weight predict hypertension and obesity in schoolchildren?**. *Ann Nutr Metabolism* (2013.0) **63** 69-76. DOI: 10.1159/000351869 26. Erasun D, Alonso-Molero J, Gómez-Acebo I, Dierssen-Sotos T, Llorca J. **Schneider JLow birth weight trends in Organisation for Economic Co-operation and Development countries, 2000–2015: economic, health system and demographic conditionings**. *BMC Pregnanc Childbirth* (2021.0) **21** 1-8. DOI: 10.1186/s12884-020-03484-9 27. Jornayvaz FR, Vollenweider P, Bochud M, Mooser V, Waeber G, Marques-Vidal P. **Low birth weight leads to obesity, diabetes and increased leptin levels in adults: the CoLaus study**. *Cardiovasc Diabetol* (2016.0) **15** 73. DOI: 10.1186/s12933-016-0389-2 28. Chiesa C, Osborn JF, Haass C, Natale F, Spinelli M, Scapillati E. **Ghrelin, leptin, IGF-1, IGFBP-3, and insulin concentrations at birth: is there a relationship with fetal growth and neonatal anthropometry?**. *Clin Chem* (2008.0) **54** 550-8. DOI: 10.1373/clinchem.2007.095299 29. Guan H, Arany E, van Beek JP, Chamson-Reig A, Thyssen S, Hill DJ. **Adipose tissue gene expression profiling reveals distinct molecular pathways that define visceral adiposity in offspring of maternal protein-restricted rats**. *Am J Physiology-Endocrinology Metabolism* (2005.0) **288** E663-E73. DOI: 10.1152/ajpendo.00461.2004 30. Bieswal F, Ahn MT, Reusens B, Holvoet P, Raes M, Rees WD. **The importance of catch-up growth after early malnutrition for the programming of obesity in male rat**. *Obesity* (2006.0) **14** 1330-43. DOI: 10.1038/oby.2006.151 31. Eriksson JG, Kajantie E, Lampl M, Osmond C. **Trajectories of body mass index amongst children who develop type 2 diabetes as adults**. *J Intern Med* (2015.0) **278** 219-26. DOI: 10.1111/joim.12354 32. Meyer DM, Brei C, Stecher L, Much D, Brunner S, Hauner H. **Cord blood and child plasma adiponectin levels in relation to childhood obesity risk and fat distribution up to 5 y**. *Pediatr Res* (2017.0) **81** 745-51. DOI: 10.1038/pr.2016.275 33. Cekmez F, Canpolat F, Pirgon O, Aydemir G, Tanju I, Genc F. **Adiponectin and visfatin levels in extremely low birth weight infants; they are also at risk for insulin resistance**. *Eur Rev Med Pharmacol Sci* (2013.0) **17** 501-6. PMID: 23467949 34. Holzapfel C, Siegrist M, Rank M, Langhof H, Grallert H, Baumert J. **Association of a MTNR1B gene variant with fasting glucose and HOMA-B in children and adolescents with high BMI-SDS**. *Euro J Endocrinol* (2011.0) **164** 205. DOI: 10.1530/EJE-10-0588 35. Liang Z, Liu H, Wang L, Chen Y, Zhou T, Heianza Y. **Maternal MTNR1B genotype, maternal gestational weight gain, and childhood obesity**. *Am J Clin Nutr* (2020.0) **111** 360-8. DOI: 10.1093/ajcn/nqz296 36. Reinehr T, Scherag A, Wang HJ, Roth CL, Kleber M, Scherag S. **Relationship between MTNR1B (melatonin receptor 1B gene) polymorphism rs10830963 and glucose levels in overweight children and adolescents**. *Pediatr Diabetes* (2011.0) **12** 435-41. DOI: 10.1111/j.1399-5448.2010.00738.x 37. Metrustry SJ, Edwards MH, Medland SE, Holloway JW, Montgomery GW, Martin NG. **Variants close to NTRK2 gene are associated with birth weight in female twins**. *Twin Res Hum Genet* (2014.0) **17** 254-61. DOI: 10.1017/thg.2014.34 38. Gray J, Yeo G, Hung C, Keogh J, Clayton P, Banerjee K. **Functional characterization of human NTRK2 mutations identified in patients with severe early-onset obesity**. *Inter J Obesity* (2007.0) **31** 359-64. DOI: 10.1038/sj.ijo.0803390 39. Rask-Andersen M, Almén MS, Olausen HR, Olszewski PK, Eriksson J, Chavan RA. **Functional coupling analysis suggests link between the obesity gene FTO and the BDNF-NTRK2 signaling pathway**. *BMC Neurosci* (2011.0) **12** 117. DOI: 10.1186/1471-2202-12-117 40. Ruiz-Narváez EA, Haddad SA, Rosenberg L, Palmer JR. **Birth weight modifies the association between central nervous system gene variation and adult body mass index**. *J Hum Genet* (2016.0) **61** 193-8. DOI: 10.1038/jhg.2015.139 41. Pépin L, Colin E, Tessarech M, Rouleau S, Bouhours-Nouet N, Bonneau D. **A new case of PCSK1 pathogenic variant with congenital proprotein convertase 1/3 deficiency and literature review**. *J Clin Endocrinol Metab* (2019.0) **104** 985-93. DOI: 10.1210/jc.2018-01854 42. Stijnen P, Tuand K, Varga TV, Franks PW, Aertgeerts B, Creemers JW. **The association of common variants in PCSK1 with obesity: a HuGE review and meta-analysis**. *Am J Epidemiol* (2014.0) **180** 1051-65. DOI: 10.1093/aje/kwu237 43. Li Y-y, Xiao R, Li C-p, Huangfu J, Mao J-f. **Increased plasma levels of FABP4 and PTEN are Associated with more severe insulin resistance in women with gestational diabetes Mellitus**. *Med Sci Mon Int Med J Exp Clin Res* (2015.0) **21** 426 44. 44.Yin L, Cai W-J, Chang X-Y, Li J, Zhu L-Y, Su X-H et al. Analysis of PTEN expression and promoter methylation in Uyghur patients with mild type 2 diabetes mellitus. Medicine. 2018;97:49(e13513) 45. Temelkova-Kurktschiev T, Stefanov T. **Lifestyle and genetics in obesity and type 2 diabetes**. *Exp Clin Endocrinol Diabetes* (2012.0) **120** 1-6. DOI: 10.1055/s-0031-1285832 46. Maehama T, Dixon JE. **The tumor suppressor, PTEN/MMAC1, dephosphorylates the lipid second messenger, phosphatidylinositol 3, 4, 5-trisphosphate**. *J Biol Chem* (1998.0) **273** 13375-8. DOI: 10.1074/jbc.273.22.13375 47. Laleh P, Yaser K, Abolfazl B, Shahriar A, Mohammad AJ, Nazila F. **Oleoylethanolamide increases the expression of PPAR-Α and reduces appetite and body weight in obese people: a clinical trial**. *Appetite* (2018.0) **128** 44-9. DOI: 10.1016/j.appet.2018.05.129 48. Priego T, Sanchez J, Pico C, Ahrens W, Bammann K, De Henauw S. **Influence of breastfeeding on blood-cell transcript-based biomarkers of health in children**. *Pediatr Obes* (2014.0) **9** 463-70. DOI: 10.1111/j.2047-6310.2013.00204.x 49. Tang Y, Vanlandingham MM, Wu Y, Beland FA, Olson GR, Fang J-L. **Role of peroxisome proliferator-activated receptor alpha (PPARα) and PPARα-mediated species differences in triclosan-induced liver toxicity**. *Arch Toxicol* (2018.0) **92** 3391-402. DOI: 10.1007/s00204-018-2308-7
--- title: The magnitude and determinants of delayed initiation of antenatal care among pregnant women in Gambia; evidence from Gambia demographic and health survey data authors: - Solomon Gedlu Nigatu - Tilahun Yemanu Birhan journal: BMC Public Health year: 2023 pmcid: PMC10061770 doi: 10.1186/s12889-023-15506-0 license: CC BY 4.0 --- # The magnitude and determinants of delayed initiation of antenatal care among pregnant women in Gambia; evidence from Gambia demographic and health survey data ## Abstract ### Background Despite gains throughout the 20th century, maternal health remains a major public health concern. Despite global efforts to enhance access to maternal and child healthcare services, women in low- and middle-income countries still have a high risk of dying during pregnancy and after birth. This study aimed to determine the magnitude and determinants of late antenatal care initiation among reproductive age women in Gambia. ### Method Secondary data analysis was conducted using the 2019-20 Gambian demographic and health survey data. All reproductive age women who gave birth in the five years preceding the survey and who had an antenatal care visit for the last child were included in this study. The total weighted sample size analyzed was 5310. Due to the hierarchical nature of demographic and health survey data, a multi-level logistic regression model was performed to identify the individual and community level factors associated with delayed first antenatal care initiation. ### Result In this study, the prevalence of delayed initiation of initial antenatal care was $56\%$ ranged from 56 to $59\%$. Women with age 25–34 [Adjusted Odds Ratio = 0.77; $95\%$ CI 0.67–0.89], 35–49 [Adjusted Odds Ratio = 0.77; $95\%$ CI 0.65–0.90] and women reside in urban area [Adjusted Odds Ratio = 0.59; $95\%$ CI 0.47–0.75] respectively had lower odds of delayed first antenatal care initiation. While women with unplanned pregnancy [Adjusted Odds Ratio = 1.60; $95\%$ CI 1.37–1.84], no health insurance [Adjusted Odds Ratio = 1.78; $95\%$ CI 1.14–2.76] and previous history of cesarean delivery [Adjusted Odds Ratio = 1.50; $95\%$ CI 1.10–2.07] had higher odds of delayed initiation of antenatal care. ### Conclusion Despite the established advantages of early antenatal care initiation, this study revealed that late antenatal care initiation is still common in Gambia. Unplanned pregnancy, residence, health insurance, history of caesarian delivery, and age were significantly associated with delayed first antenatal care presentation. Therefore, focusing extra attention on these high-risk individuals could reduce delayed first antenatal care visit and this further minimizes maternal and fetal health concerns by recognizing and acting early. ## Introduction Despite gains throughout the 20th century, maternal health remains a major public health concern [1, 2]. Globally, there were 152 maternal deaths for every 100,000 live births in 2020 [3, 4]. Despite the fact that the majority of maternal deaths are preventable, the majority ($94\%$) found within limited resource settings [5]. Two-thirds of all maternal deaths worldwide occur in Sub-Saharan Africa (SSA) [6, 7]. While there have been international efforts to improve maternal and child health services, such as offering health education, developing health infrastructure, and screening sexually transmitted diseases for early detection and prevention of unfavorable pregnancy outcomes, women in low- and middle-income countries still face a high risk of death during pregnancy and after delivery [2, 8]. The use of antenatal care (ANC) services is a crucial indicator of how well the Sustainable Development Goals 3 (SDGs 3) are being achieved [9]. Reducing adverse pregnancy outcomes, such as maternal death and stillbirth, requires the early implementation and adequate use of ANC services [10, 11]. In addition to identifying risk factors, ANC exposes women to health education about danger signs and birth readiness and encourages them to give birth in a medical facility or with a skilled attendant [1, 10, 12–14]. Attending ANC during pregnancy allows pregnant women to gain knowledge that they can use during pregnancy and after giving birth [15–17]. The World Health Organization (WHO) and the Gambian Ministry of Health recommend eight antenatal care (ANC) visits for healthy pregnancies, with the first examination starting before 12 weeks of gestation [8, 18]. But studies show that the vast majority of women in sub-Saharan Africa begin antenatal care much later than recommended [11, 19–23]. Many pregnant women in West Africa, particularly teenage girls, start their antenatal care later than necessary, depriving them of the opportunity to receive preventive and curative services [2, 6, 22, 24–26]. Pregnancy outcomes and the number of prenatal visits or gestational age at ANC initiation have been related in epidemiological studies [17, 27]. Initiating ANC later than recommended may result in worse outcomes, such as low birth weight and preterm birth, and raise the overall cost of prenatal care [28–30]. According to earlier studies, ANC late onset initiation may have a greater impact on outcomes than visit frequency [16, 29, 31–33]. According to earlier research, women who initiate ANC later tend to be younger, more gravid or parous, single, of lower socioeconomic status, less educated, and have less access to healthcare services [24, 26, 34–36]. Women who experience unplanned pregnancies, find out they are pregnant later than average, and have had healthy pregnancies in the past are also more likely to start ANC later in their pregnancy [20, 30, 37–39]. Additionally, if women have had bad experiences in the past or have a negative perception of the quality of the services, they will not attend ANC [30, 40, 41]. The use of ANC has been reported to be influenced by cultural conceptions of pregnancy and beliefs, which may cause mothers to delay or skip ANC visits [41]. According to other studies, the reasons for the delayed initiation of ANC include younger ages, unplanned pregnancies, and ignorance of the value of early ANC [11, 30, 36, 42–44]. No study has been conducted at national level in Gambia to investigate the magnitude and its predictors of late ANC contact. Therefore, the current study analyzed a data from Gambia demographic and health survey (GDHS) to determine magnitude of late ANC initiation and to determine its predictors among women attending antenatal care service in Gambia. ## Study design, area, and period The data set was accessed from the demographic and Health Survey (DHS) website (http://www.dhsprogram.com) after registering and stating the purpose of the study. The *Gambia is* the home of 1,692,865 people according to the 2013 national census done by the Gambia Bureau of Statistics [45]. It has a dense and multi-ethnic population. The administration is divided into eight Local Government Area (LGA). Those eight LGA are Banjul, Kanifing, Brikama, Mansakonko, Kerewan, Kuntaur, Janjanbureh and Basse. The health system of the country is a three-tier (primary, secondary, and tertiary level) [46]. The study period was from 21st November 2019 to 30th March 2020. ## Sample and study population All women in the reproductive age group in the Gambia were the source population. The Source population was all women with a birth in the last 5 years before the survey. The birth should be the most recent birth if the woman had multiple births. Women who had birth history within the last 5 years before the survey but had no ANC recorded were excluded. ## Sample size and sampling procedure Sample weighted was done to avoid over or under representative of each LGA then the final sample was 5310 women who had a birth history in the last 5 years before the survey and had recorded ANC visits. Sample selection is done using a multistage sample method. At the initial, the country was divided into eight LGA and this LGA was stratified into urban and rural. Using probability proportional 281 enumeration areas (EA) were selected from both urban and rural residencies. The next stage was selecting a household. A systematic random sampling method was employed to select a household and 7,025 households were selected. The detailed sampling procedure is also available on GDHS 2019-20 report [47]. ## Dependent variable The dependent variable was late ANC visit initiation of pregnant women. If Women’s first ANC visit happened after 16 weeks of gestational age, it was considered as a lately initiated ANC visit. GDHS reported the first ANC visit in months so that it was recorded as “1” when the gestational age was greater than four months. Otherwise, it was early initiation when it was booked within four months and recoded as “0”. ## Independent variables The predictor variables for this study were individual and communality level variables. Among the individual variables some of the were socio-demographic variable: age of women, marital status, residency, ethnicity, women education, husband education, religion, women occupation, wealth index, media exposure, had health insurance, sex of household, household size, and distance from health facilities. The other individual-level variables were Obstetric-related variables such as ANC, parity, stillbirth, abortion history, planned pregnancy, and cesarean section History [27, 46, 48–50]. ## Data collection procedure The study was conducted based on GDHS data by accessing from the DHS program official database [51] after permission was granted through an online request by explaining the objective of our study. The raw data was collected from all parts of the country on childbearing aged women using a structured and pre-tested questionnaire. We used the Individual Record (IR file) data set and extracted the outcome and independent variables. ## Data management and analysis The data were weighted using sampling weight, primary sampling unit, and strata before any statistical analysis to restore the representativeness of the survey and to tell the STATA to undertake in to account the sampling design when calculating standard errors to obtain reliable parameter estimates. Cross tabulation and summery statistics were conducted to describe the study population by STATA 16. ## Multi-level analysis The outcome variable was a binary (late initiated ANC or early initiated ANC) and DHS used a multistage sampling method, women within the same cluster exhibited the same characteristics, to address this issue multilevel mixed-effect logistic regression model was the best. First, bivariable multilevel mixed-effects logistic regression analysis was done separately on both the individual-level and community-level variables to identify candidate variables for multivariable analysis and P-value < 0.2 was used as a cut point. In multivariable multilevel mixed-effects logistic regression analysis was done and a variable with a p-value ≤ 0.05 was declared as significant predictors of late initiated ANC visit. ## Model building For the current study generally 4 models were fitted. The first was the null model (Model 0) (without predictor variable) used to check variation in community regarding late initiation of ANC and provide evidence to assess random effects at the community level. The second model (Model I) was the multivariable model adjustment for individual-level variables. The third model (Model II) was adjusted for community-level variables. The last model (Model II) all candidate variables from both individual and community-level variables were fitted with the outcome variable. ## Parameter estimation methods The fixed effects were used to estimate the association between the likelihood of late ANC visit initiation and explanatory variables at both community and individual level and were expressed as odds ratio with $95\%$ confidence interval. Regarding the measures of variation (random-effects) intra cluster correlation coefficient (ICC), Proportional Change in Community Variance (PCV) and median odds ratio (MOR) were used. The MOR helps to translate the area level variance in the widely used odds ratio (OR) scale. The MOR is defined as the median value of the odds ratio between the area at the highest risk and the area at the lowest risk when randomly picking out two areas. The MOR can be understood as the increased risk that (in median) would have if moving to another area with a higher risk. It is computed by; MOR = exp[√(2×Va)×0.6745] [52]. Where; VA is the area level variance, and 0.6745 is the 75th centile of the cumulative distribution function of the normal distribution with mean 0 and variance 1. Whereas the proportional change in variance is calculated as PCV = [(VA-VB)/ VA]*100 [53]. Where; where VA = variance of the initial model, and VB = variance of the model with more terms. ## Socio-demographic characteristics Of the total, almost half of the study participants ($$n = 2$$,643; $49.8\%$), were aged between 35 and 34. Four thousand eight hundred seventy-seven ($91.9\%$) of the studied women were married. Nearly two-thirds of the study subjects ($$n = 3$$,549; $66.8\%$) were urban by their residency. Almost all study participants ($$n = 5$$,176; $97.5\%$) were not had health insurance. Among the study participants ($$n = 3$$, 941; $74.2\%$) were had no problem accessing health facilities (Table 1). Table 1Background characteristics of study participantVariableCategoryFrequency ($$n = 5310$$)percentAge15–241,21222.825–342,64349.835–491,45527.4Marital statusSingle2574.8Married4,87791.9Widowed581.1Divorced1182.2ResidencyUrban3,54966.8Rural1,76133.2EthnicityMandinka/Jahanka1,70032.0Wollof67312.7Fula/TUkulur/Lorobo103019.4Sarahule4157.8Non-Gambia68512.9Others80715.2Local Government AreaBanjul561.1Kanifing96918.2Brikama2,18541.1Mansakonko2264.3Kerewan60911.5Kuntaur3105.8Janjanbureh3356.3Basse62011.7Women EducationNo education2,42645.7Primary93317.6Secondary1,69932.0Higher2524.7Husband EducationNo education2,40049.4primary73015.0Secondary1,33427.4Higher3998.2ReligionMuslim5,16397.2Christianity1472.8Women occupationHad work3,53766.6Not working1,77333.4Wealth indexPoor2,25342.4Middle1,11521.0Rich1,94236.6Media exposureYes4,36785.9No71814.1Had health insuranceNo5,17697.5Yes1342.5Sex of householdFemale88216.6Male4,42883.4Household size1–44127.85–904,89892.2Distance from health facilitiesNot a big problem3,94174.2Big problem1,36925.8 ## Obstetric-related characteristics A high proportion of women ($95.8\%$) had fewer than eight ANC visits during their last pregnancy time. Regarding stillbirth, 4767 ($89.9\%$) of the participants had a history of stillbirth in their life. About four-fifths ($79.7\%$) of the women who gave responses were planned to become pregnant (Table 2). Table 2Reproductive characteristics of pregnant women in GambiaVariableCategoryFrequency ($$n = 5310$$)percentANC< 85,08695.8>=82244.2ParityPrim-pareous1,06620.1Multi- pareous4,24479.9Still BirthNo54310.2Yes476789.8Abortion HistoryNo4,76789.8Yes54310.2Planned pregnancyYes4,23179.7No1,07920.3CS History [5306]Yes2214.2No5,08595.8 ## The magnitude of late ANC initiation in Gambia In this study, we found that the magnitude of late ANC initiation was 57.08 ($95\%$ CI: 55.75, 58.42). Of those who had delayed ANC initiation, the majority ($49.64\%$) of study participants had their first ANC initiation at second trimester (Fig. 1).The highest prevalence of late ANC initiation recorded in Brikama local government area of Gambia. It consisted half of the total women who is lately initiated their ANC visit (Fig. 2). Fig. 1Prevalence of first ANC intiation with gestational age of reproductive women in Gambia Fig. 2Prevalence of first ANC initiation at different local government area in Gambia ## Random effect analysis and model fitness Table 3 revealed that in the null model, about $12\%$ of the total variation on delayed initiation of first ANC visit was occurred the clustered level and it is attributable to the community level factors. In addition, the null model had the highest MOR value (2.17) indicating when randomly select an individual from one cluster with a higher risk of delayed initiation of first ANC booking and the other cluster at lower risk, Individuals at cluster with a higher risk of delayed initiation of first ANC presentation had 2.17 times higher odds of having a delayed first ANC initiation as compared with their counter parts. Furthermore, the highest ($62\%$) PCV in the full model (Model III), indicates that $62\%$ of the community-level variation on delayed first ANC initiation was explained by the combined factors at both the individual and community levels. The model fitness was done using deviance in which the final model (Model III) was best fitted model since it had the lowest deviance [7168] (Table 3). Table 3Multilevel multivariable Analysis of Factors Associated with delayed initiation of ANC in GambiaIndividual and community-level variablesModelsNull modelModel IModel IIModel IIIAOR ($95\%$CI)AOR($95\%$CI)AOR($95\%$CI)AOR($95\%$CI) Age 15–241125–340.79 (0.68,0.91)0.77(0.67, 0.89) **35–490.79 (0.67,0.93)0.77(0.65, 0.90) ** Ethnicity Mandinka/Jahanka11Wollof1.23(1.01,1.59)1.40(1.12, 1.75) **Fula/TUkulur/Lorobo0.92(0.76,1.11)0.92 (0.77, 1.11)Sarahule0.75(0.55,1.01)0.88 (0.66, 1.17)Non-Gambia1.22(0.99,1.52)1.12 (0.90, 1.39)Others1.14(0.90,1.44)1.00 (0.79, 1.26) Women Education No education11Primary0.97(0.83,1.14)0.95 (0.81, 1.11)Secondary1.13(0.96,1.32)1.07 (0.91, 1.25)Higher0.89(0.62,1.28)0.81 (0.57, 1.16) Wealth index Poor11Middle1.31(1.10,1.56)1.00 (0.83, 1.20)Rich1.36(1.13,1.64)0.82 (0.67, 1.02) Had health insurance Yes11No1.73(1.11,2.71)1.78 (1.14, 2.76) * Still Birth No11Yes1.41 (0.92,2.15)1.40 (0.91, 2.14) Planned pregnancy Yes11No1.64 (1.41,1.90)1.59 (1.37, 1.84) ** CS History Yes11No1.40(1.02, 1.94)1.50 (1.09, 2.07) *Community level factors Residency Rural11Urban0.65(0.52,0.81)0.59(0.47, 0.75) ** Local Government Area Banjul11Kanifing1.07(0.76,1.49)1.17 (0.82, 1.67)Brikama1.60(1.16,2.21)1.44 (1.03, 2.03) *Mansakonko0.82(0.55,1.22)0.77(0.51, 1.18)Kerewan0.53(0.36,0.77)0.43(0.29, 0.64) **Kuntaur0.65(0.44,0.97)0.52 (0.34, 0.79) **Janjanbureh0.84(0.57,1.24)0.75(0.50, 1.13)Basse0.63(0.44,0.91)0.58(0.39, 0.85) ** ## Determinants of late ANC initiation among reproductive age women in Gambia, GDHS 2019-20 On models, II and III bivariable analysis was done to identify candidate variables for multivariable analysis using p-value < 0.20. Therefore, women’s age, ethnicity, education, had health insurance, ANC, history of stillbirth, planned pregnancy, and CS history from the individual level variables and also residency and LGA from the community level variable were significant. In Model IV multivariable multilevel logistic regression model was done by incorporating both individual and community-level variables. The following variables were insignificant at a p-value < 0.05: women age, residency, LGA, ethnicity, had health insurance, ANC, planned pregnancy, and CS history. The odds of late ANC initiation among women age 25–34 and 35–49 decreased by $23\%$ as compared to age group 15–24 (AOR = 0.77;$95\%$CI: 0.67,0.89), and (AOR = 0.77;$95\%$CI: 0.65,0.90) respectively. The odds of late ANC initiation among women from Wollof ethnicity increased by $40\%$ as compared to women from Mandinka/Jahanka ethnicity (AOR = 1.40; $95\%$ CI: 1.12, 1.75). The odds of late ANC initiation among women who had no health insurance coverage increased by $78\%$ as compared to women who had health insurance coverage (AOR = 1.78; $95\%$ CI: 1.14,1.76). The odds of late ANC initiation among women who had less than eight ANC visits were 18.26 times (AOR = 18.26, $95\%$ CI 10.97,30.39) higher than women who had eight and more ANC visits. The odds of late ANC initiation among women who did not plan their last pregnancy increased by $59\%$ as compared to women who planned their pregnancy (AOR = 1.59; $95\%$ CI: 1.37,1.84). The odds of late ANC initiation among women who had no history of CS increased by $50\%$ as compared to women who had had no history of CS (AOR = 1.50; $95\%$ CI: 1.09,2.07). The odds of late ANC initiation among women rural residents decreased by $41\%$ compared to women urban residents (AOR = 0.59; $95\%$CI: 0.47, 0.75). The odds of late ANC initiation among women residing in Brikama LGA increased by $44\%$ as compared to women residing in Banjul LGA (AOR = 1.44; $95\%$ CI:1.03, 2.03). The odds of late ANC initiation among women residing in Kerewan LGA decreased by $57\%$ as compared to women residing in Banjul LGA (AOR = 0.43; $95\%$ CI:0.29, 0.64). The odds of late ANC initiation among women residing in Kuntaur LGA decreased by $48\%$ as compared to women residing in Banjul LGA (AOR = 0.52; $95\%$ CI:0.34, 0.79). The odds of late ANC initiation among women residing in Basse LGA decreased by $42\%$ as compared to women residing in Banjul LGA (AOR = 0.58; $95\%$ CI:0.39, 0.85) (Table 4). Table 4Multilevel random effect analysis and model fitness on delayed ANC initiation in Gambia Random effects Community variance (SE) 0.444(0.062)0.375 (0.060)0.179(0.035)0.168(0.035) ICC% $11.90\%$$10.24\%$$5.17\%$$4.86\%$ PCV% $115.54\%$$59.68\%$$62.16\%$ MOR $2.172.041.631.61\%$ Model comparison LR test vs. logistic model 267.97**168.35**78.11**62.88** LLR -3830.10-3652.40-3763.31-3584.04 Deviance 7660.207304.807526.627168.08 AIC 7664.217342.797546.617222.08 BIC 7677.517469.177613.137401.66 ## Discussion Timely initiation of ANC was essential in a low-income country like Gambia because the outcomes for maternal and child health can be improved by timely introduction and sustained attendance at ANC programs. The objective of this study was estimating the magnitude and its predictors of delayed initiation of ANC among pregnant women in Gambia. The finding of this study revealed that the magnitude of delayed initiation ANC was $57\%$ with a $95\%$ Confidence Interval (CI) ($56\%$; $59\%$) which is lower than studies conducted in South Africa [44], West Africa [54] and Ethiopia [55]. This implies that a significant proportion of expectant women begin their first ANC booking at the recommended gestational age. Early first ANC initiation offers a chance to collect baseline information on the mother’s general health and the fetus [37, 38]. The pregnant woman and fetus benefit from supplementing with iron and folic acid early in the first gestational age [11, 34, 56, 57]. Our finding indicated that women without health insurance have greater odds of delayed initiation of ANC compared with insured women consistent with previous studies reported elsewhere [58–60]. Since health insurance offers adequate protection from catastrophic costs and is linked to other socioeconomic factors like wealth and schooling, which are known to influence the onset of ANC early in life. Also, the finding of our study indicated that unplanned pregnancy have the higher odds of delayed initiation of ANC as compared to planed pregnancy consistent with other findings published in South Africa [44] and Ethiopia [31, 32, 50, 56]. Since unplanned pregnancies may cause people to consider abortion or deny they are pregnant, both of which may postpone the initiation of first ANC services [61, 62]. Since unplanned pregnancies are also associated with societal and cultural factors that affect health-seeking behaviours [61–63]. In addition, the finding of this study revealed that pregnant women reside in rural areas have higher odds of late initiation of ANC service consistent with previous studies conducted in Ethiopia [32, 35, 64] and Bhutan [65]. This is because women who live in rural areas frequently have poor economic status, limited media exposure, and limited access to healthcare, all of which limit how effectively ANC services are presented [66, 67]. While women who live in urban areas, experienced family members and friends, access to media, internet, and healthcare workers all played crucial role in providing information and guidance about the necessities of ANC. Additionally, pregnant women with older age have higher odds of late ANC presentation in line with earlier studies [66–69]. Since this is the fact that old women have multiple tasks and able to deny the necessity of early ANC visit in their as well as child health care service. Moreover, the finding of this study indicated that women were experienced previous caesarean delivery have higher odds of late ANC visit as compared to women delivered in vaginally. ## Strength and limitation of the study The main strength of this study was that it used a nationally representative data with large sample size. The other strength was that we employed an advanced and appropriate statistical approach (multilevel analysis) to accommodate the hierarchical nature of the data. However, this study had limitations in that the GDHS survey is relied on respondents’ self-report and might have the possibility of recall bias because respondents/mothers were asked to remember things happened in the past. Again, this study only generates associations between delayed first ANC booking and some important individual-level and community-level factors that is limited in its design to establish causality between the outcome of interest and these important independent variables. ## Conclusion Despite known benefits of early antenatal care initiation, the finding of this study revealed that the rate of late ANC booking is still high in Gambia. Unplanned pregnancy, residence, Health insurance, history of CS delivery, and age were significantly associated with delayed first ANC initiation. ## Recommendation Health promotion initiatives that inform women about the necessity of early ANC booking, particularly those who lack or have little formal education, live in rural areas, or are older, should help to raise their level of knowledge about the advantages of doing so. The government and minister of Gambia should also inform the population about the value of health insurance in order to promote the use of maternal health services. ## References 1. Mlandu C, Matsena-Zingoni Z, Musenge E. **Trends and determinants of late antenatal care initiation in three east african countries, 2007–2016: a population based cross-sectional analysis**. *PLOS Global Public Health* (2022.0) **2** e0000534. DOI: 10.1371/journal.pgph.0000534 2. Mugo NS, Mya KS, Raynes-Greenow C. **Country compliance with WHO-recommended antenatal care guidelines: equity analysis of the 2015–2016 demography and Health Survey in Myanmar**. *BMJ global health* (2020.0) **5** e002169. DOI: 10.1136/bmjgh-2019-002169 3. Sajedinejad S. **Maternal mortality: a cross-sectional study in global health**. *Globalization and health* (2015.0) **11** 1-13. DOI: 10.1186/s12992-015-0087-y 4. 4.Kanagat N et al.Gates Open Research.2021. 5. 5.Organization WH. Trends in maternal mortality 2000 to 2017: estimates by WHO, UNICEF, UNFPA, World Bank Group and the United Nations Population Division 2019. 6. Warri D, George A. **Perceptions of pregnant women of reasons for late initiation of antenatal care: a qualitative interview study**. *BMC Pregnancy Childbirth* (2020.0) **20** 1-12. DOI: 10.1186/s12884-020-2746-0 7. 7.Organization WH. Trends in maternal mortality: 1990–2015: estimates from WHO, UNICEF, UNFPA, World Bank Group and the United Nations Population Division. World Health Organization; 2015. 8. Paulson KR. **Global, regional, and national progress towards sustainable development goal 3.2 for neonatal and child health: all-cause and cause-specific mortality findings from the global burden of Disease Study 2019**. *The Lancet* (2021.0) **398** 870-905. DOI: 10.1016/S0140-6736(21)01207-1 9. Raynes-Greenow C. **Gaps and challenges underpinning the first analysis of global coverage of early antenatal care**. *The Lancet Global Health* (2017.0) **5** e949-50. DOI: 10.1016/S2214-109X(17)30346-7 10. 10.Jiwani SS et al. Timing and number of antenatal care contacts in low and middle-income countries: analysis in the countdown to 2030 priority countries. Journal of global health, 2020. 10(1). 11. 11.Jihad M, Woldemichael K, Gezehagn Y. Determinants of late initiation for Antenatal Care follow up among pregnant mothers attending Public Health Centers at Jimma Town, South West Ethiopia, 2021: unmatched case–control study. medRxiv; 2022. 12. Jiee SFA. **Late antenatal booking and its predictors in Lundu district of Sarawak, Malaysia**. *Int J Public Health Res* (2018.0) **8** 956-64 13. Mulondo SA. **Factors associated with underutilisation of antenatal care services in Limpopo, South Africa**. *Br J Midwifery* (2020.0) **28** 788-95. DOI: 10.12968/bjom.2020.28.11.788 14. Riang’a RM, Nangulu AK, Broerse JEW. **I should have started earlier, but I was not feeling ill!“ perceptions of Kalenjin women on antenatal care and its implications on initial access and differentials in patterns of antenatal care utilization in rural Uasin Gishu County Kenya**. *PLoS ONE* (2018.0) **13** e0202895. DOI: 10.1371/journal.pone.0202895 15. Stacey T. **Antenatal care, identification of suboptimal fetal growth and risk of late stillbirth: findings from the Auckland Stillbirth Study**. *Aust N Z J Obstet Gynaecol* (2012.0) **52** 242-7. DOI: 10.1111/j.1479-828X.2011.01406.x 16. Weldearegawi GG. **Determinants of late antenatal care follow up among pregnant women in Easter zone Tigray, Northern Ethiopia, 2018: unmatched case–control study**. *BMC Res Notes* (2019.0) **12** 1-9. DOI: 10.1186/s13104-019-4789-8 17. Turyasiima M. **Determinants of first antenatal care visit by pregnant women at community based education, research and service sites in Northern Uganda**. *East Afr Med J* (2014.0) **91** 317-22. PMID: 26640281 18. 18.Organization WH. Maternal mortality. Fact sheet No. 348. Geneva: World Health Organization; 2012. 2015. 19. Abate KH. **Gender disparity in prevalence of depression among patient population: a systematic review**. *Ethiop J health Sci* (2013.0) **23** 283-8. PMID: 24307828 20. Al-Wutayd O. **Inadequate and late antenatal contacts among saudi mothers: a hospital-based cross-sectional study**. *Int J Womens Health* (2020.0) **12** 731-8. DOI: 10.2147/IJWH.S265941 21. Choté AA. **Explaining ethnic differences in late antenatal care entry by predisposing, enabling and need factors in the Netherlands. The Generation R Study**. *Matern Child Health J* (2011.0) **15** 689-99. DOI: 10.1007/s10995-010-0619-2 22. Manda-Taylor L, Sealy D, Roberts J. **Factors associated with delayed antenatal care attendance in Malawi: results from a qualitative study**. *Med J Zambia* (2017.0) **44** 17-25. DOI: 10.55320/mjz.44.1.62 23. Ramotsababa M, Setlhare V. **Late registration for antenatal care by pregnant women with previous history of caesarean section**. *Afr J Prim Health Care Fam Med* (2021.0) **13** e1-e9. DOI: 10.4102/phcfm.v13i1.2776 24. Kluckow H. **Socio-demographic predictors of unintended pregnancy and late antenatal booking in Honiara, Solomon Islands**. *Aust N Z J Obstet Gynaecol* (2018.0) **58** 349-57. DOI: 10.1111/ajo.12782 25. Mahomed O, Kader ZA. **Prevalence and risk factors associated with diabetes retinopathy amongst type II diabetes mellitus at a primary care vision clinic in the eThekwini District, KwaZulu-Natal in 2017**. *Afr Vis Eye Health* (2020.0) **79** 1-6 26. Manzi A. **Assessing predictors of delayed antenatal care visits in Rwanda: a secondary analysis of Rwanda demographic and health survey 2010**. *BMC Pregnancy Childbirth* (2014.0) **14** 1-8. DOI: 10.1186/1471-2393-14-290 27. Tola W. **Late initiation of antenatal care and associated factors among pregnant women attending antenatal clinic of Ilu Ababor Zone, southwest Ethiopia: a cross-sectional study**. *PLoS ONE* (2021.0) **16** e0246230. DOI: 10.1371/journal.pone.0246230 28. Tolefac PN. **Why do pregnant women present late for their first antenatal care consultation in Cameroon?**. *Matern Health Neonatol Perinatol* (2017.0) **3** 29. DOI: 10.1186/s40748-017-0067-8 29. Some A. **Prevalence and factors Associated with Late First Antenatal Care visit in Kaya Health District, Burkina Faso**. *Afr J Reprod Health* (2020.0) **24** 19-26. PMID: 34077088 30. Allen J. **Does the way maternity care is provided affect maternal and neonatal outcomes for young women? A review of the research literature**. *Women Birth* (2012.0) **25** 54-63. DOI: 10.1016/j.wombi.2011.03.002 31. Tadele F. **Late initiation of antenatal care and associated factors among pregnant women in Jimma Zone Public Hospitals, Southwest Ethiopia, 2020**. *BMC Health Serv Res* (2022.0) **22** 1-8. DOI: 10.1186/s12913-022-08055-6 32. Teshale AB, Tesema GA. **Prevalence and associated factors of delayed first antenatal care booking among reproductive age women in Ethiopia; a multilevel analysis of EDHS 2016 data**. *PLoS ONE* (2020.0) **15** e0235538. DOI: 10.1371/journal.pone.0235538 33. Schmidt CN. **Towards stronger antenatal care: understanding predictors of late presentation to antenatal services and implications for obstetric risk management in Rwanda**. *PLoS ONE* (2021.0) **16** e0256415. DOI: 10.1371/journal.pone.0256415 34. 34.Gidey G et al. Timing of first focused antenatal care booking and associated factors among pregnant mothers who attend antenatal care in Central Zone, Tigray, Ethiopia BMC research notes, 2017. 10(1): p. 1–6. 35. Gebrekidan K, Worku A. **Factors associated with late ANC initiation among pregnant women in select public health centers of Addis Ababa, Ethiopia: unmatched case-control study design**. *Pragmat Obs Res* (2017.0) **8** 223-30. PMID: 29138615 36. 36.Farooqi A, et al. A systematic review and meta-analysis to compare the prevalence of depression between people with and without type 1 and type 2 diabetes. Primary Care Diabetes; 2021. 37. Barrow A, Jobe A. **Predictors of postnatal Care Service utilization among women of Childbearing Age in the Gambia: analysis of multiple indicators Cluster Survey**. *Int J Womens Health* (2020.0) **12** 709-18. DOI: 10.2147/IJWH.S268824 38. Appiah F. **Individual and community-level factors associated with early initiation of antenatal care: multilevel modelling of 2018 Cameroon demographic and Health Survey**. *PLoS ONE* (2022.0) **17** e0266594. DOI: 10.1371/journal.pone.0266594 39. Tolossa T. **Association between pregnancy intention and late initiation of antenatal care among pregnant women in Ethiopia: a systematic review and meta-analysis**. *Syst reviews* (2020.0) **9** 1-10. DOI: 10.1186/s13643-020-01449-9 40. Aduloju OP. **Gestational age at initiation of antenatal care in a tertiary hospital, Southwestern Nigeria**. *Niger J Clin Pract* (2016.0) **19** 772-7. DOI: 10.4103/1119-3077.181398 41. Ada CN. **Determinants of late booking for antenatal care among pregnant women in selected hospitals in South East Nigeria**. *Int J Nurs Midwifery* (2018.0) **10** 74-80. DOI: 10.5897/IJNM2018.0308 42. 42.Adere A, Tilahun S. Magnitude of late initiation of antenatal care and its associated factors among pregnant women attending antenatal care in Woldia Public Health Institution. North Wollo, Ethiopia; 2020. 43. Fagbamigbe AF, Idemudia ES. **Barriers to antenatal care use in Nigeria: evidences from non-users and implications for maternal health programming**. *BMC Pregnancy Childbirth* (2015.0) **15** 1-10. DOI: 10.1186/s12884-015-0527-y 44. Ebonwu J. **Determinants of late antenatal care presentation in rural and peri-urban communities in South Africa: a cross-sectional study**. *PLoS ONE* (2018.0) **13** e0191903. DOI: 10.1371/journal.pone.0191903 45. 45.Office Gs. The Gambia 2013 Population and Housing Census Preliminary Results 2013. 46. Weldemariam S. **Late antenatal care initiation: the case of public health centers in Ethiopia**. *BMC Res Notes* (2018.0) **11** 562. DOI: 10.1186/s13104-018-3653-6 47. 47.Gambia Bureau of Statistics (GBoS) and ICF. 2021. The Gambia Demographic and Health Survey 2019-20. Banjul, The Gambia and Rockville, Maryland, USA: GBoS and ICF 48. Venyuy MA. **Determinants to late antenatal clinic start among pregnant women: the case of Saint Elizabeth General Hospital, Shisong, Cameroon**. *Pan Afr Med J* (2020.0) **35** 112. DOI: 10.11604/pamj.2020.35.112.18712 49. Warri D, George A. **Perceptions of pregnant women of reasons for late initiation of antenatal care: a qualitative interview study**. *BMC Pregnancy Childbirth* (2020.0) **20** 70. DOI: 10.1186/s12884-020-2746-0 50. Woldeamanuel BT, Belachew TA. **Timing of first antenatal care visits and number of items of antenatal care contents received and associated factors in Ethiopia: multilevel mixed effects analysis**. *Reproductive health* (2021.0) **18** 1-16. DOI: 10.1186/s12978-021-01275-9 51. 51.Oh J et al. Factors associated with the continuum of care for maternal, newborn and child health in The Gambia: a cross-sectional study using Demographic and Health Survey 2013 BMJ open, 2020. 10(11): p. e036516. 52. Patel PB, Rupani MP, Patel SS. **Antenatal care registration and predicting factors of late registration among pregnant women**. *Trop Doct* (2013.0) **43** 9-12. DOI: 10.1177/0049475513480772 53. Yang M, Lynch J. **Råstam L. A brief conceptual tutorial on multilevel analysis in social epidemiology: interpreting neighbourhood differences and the effect of neighbourhood characteristics on individual health**. *J Epidemiol Community Health* (2005.0) **59** 1022-8. DOI: 10.1136/jech.2004.028035 54. Dadjo J, Ahinkorah BO, Yaya S. **Health insurance coverage and antenatal care services utilization in West Africa**. *BMC Health Serv Res* (2022.0) **22** 311. DOI: 10.1186/s12913-022-07698-9 55. Grum T, Brhane E. **Magnitude and factors associated with late antenatal care booking on first visit among pregnant women in public health centers in central zone of Tigray Region, Ethiopia: a cross sectional study**. *PLoS ONE* (2018.0) **13** e0207922. DOI: 10.1371/journal.pone.0207922 56. 56.Geta MB, Yallew WW. Early initiation of antenatal care and factors associated with early antenatal care initiation at health facilities in southern Ethiopia Advances in Public Health, 2017. 2017. 57. Kumbeni MT. **The relationship between time spent during the first ANC contact, home visits and adherence to ANC contacts in Ghana**. *Glob Health Action* (2021.0) **14** 1956754. DOI: 10.1080/16549716.2021.1956754 58. Dadjo J, Ahinkorah BO, Yaya S. **Health insurance coverage and antenatal care services utilization in West Africa**. *BMC Health Serv Res* (2022.0) **22** 1-9. DOI: 10.1186/s12913-022-07698-9 59. 59.Yaya S. Wealth status, health insurance, and maternal health care utilization in Africa: evidence from Gabon BioMed research international, 2020. 2020. 60. Frimpong JA. **The complex association of health insurance and maternal health services in the context of a premium exemption for pregnant women: a case study in Northern Ghana**. *Health Policy Plann* (2014.0) **29** 1043-53. DOI: 10.1093/heapol/czt086 61. Abame DE. **Relationship between unintended pregnancy and antenatal care use during pregnancy in Hadiya Zone, Southern Ethiopia**. *J Reprod infertility* (2019.0) **20** 42 62. Exavery A. **How mistimed and unwanted pregnancies affect timing of antenatal care initiation in three districts in Tanzania**. *BMC Pregnancy Childbirth* (2013.0) **13** 1-11. DOI: 10.1186/1471-2393-13-35 63. Amo-Adjei J, Anamaale D. **Effects of planned, mistimed and unwanted pregnancies on the use of prenatal health services in sub‐Saharan Africa: a multicountry analysis of demographic and health survey data**. *Tropical Med Int Health* (2016.0) **21** 1552-61. DOI: 10.1111/tmi.12788 64. Mulindahabi N, Walker D, Dorji T. **If we miss this chance, it’s futile later on” - late antenatal booking and its determinants in Bhutan: a mixed-methods study**. *PLoS ONE* (2019.0) **19** 158 65. Dorji T. **If we miss this chance, it’s futile later on”–late antenatal booking and its determinants in Bhutan: a mixed-methods study**. *BMC Pregnancy Childbirth* (2019.0) **19** 1-13. DOI: 10.1186/s12884-019-2308-5 66. Amoako BK, Anto F. **Late ANC initiation and factors associated with sub-optimal uptake of sulphadoxine-pyrimethamine in pregnancy: a preliminary study in Cape Coast Metropolis, Ghana**. *BMC Pregnancy Childbirth* (2021.0) **21** 1-10. DOI: 10.1186/s12884-021-03582-2 67. Anaba EA, Afaya A. **Correlates of late initiation and underutilisation of the recommended eight or more antenatal care visits among women of reproductive age: insights from the 2019 Ghana Malaria Indicator Survey**. *BMJ open* (2022.0) **12** e058693. DOI: 10.1136/bmjopen-2021-058693 68. Haddrill R. **Understanding delayed access to antenatal care: a qualitative interview study**. *BMC Pregnancy Childbirth* (2014.0) **14** 207. DOI: 10.1186/1471-2393-14-207 69. Jinga N. **Reasons for late presentation for antenatal care, healthcare providers’ perspective**. *BMC Health Serv Res* (2019.0) **19** 1016. DOI: 10.1186/s12913-019-4855-x
--- title: AVPR2 is a potential prognostic biomarker and correlated with immune infiltration in head and neck squamous cell carcinoma authors: - Linwei Mao - Zhiyong Pan - Wenzhi Chen - Weiqun Hu - Xiufen Chen - Huiting Dai journal: BMC Medical Genomics year: 2023 pmcid: PMC10061778 doi: 10.1186/s12920-023-01500-3 license: CC BY 4.0 --- # AVPR2 is a potential prognostic biomarker and correlated with immune infiltration in head and neck squamous cell carcinoma ## Abstract ### Purpose To explore the potential of AVPR2 in the immunotherapy of head and neck squamous cell carcinoma (HNSCC), thus providing insights into a novel antitumour strategy. ### Methods In this study, we performed a comprehensive analysis of the AVPR2 gene in HNSCC using public datasets from The Cancer Genome Atlas and Gene Expression Omnibus. We explored the potential molecular mechanism of HNSCC in clinical prognosis and tumour immunity from the aspects of gene expression, prognosis, immune subtypes, and immune infiltration. ### Results AVPR2 expression was significantly downregulated in primary HNSCC tissue compared with normal tissue. HNSCC patients with high AVPR2 expression had a better prognosis. Moreover, the results of GSEA showed that immune subtype surface AVPR2 is involved in immune modulation. Furthermore, significant strong correlations between AVPR2 expression and infiltrating immune cells existed in HNSCC, and marker genes of infiltrating immune cells were also significantly related to AVPR2 expression in HNSCC. These results suggest that AVPR2 expression can influence the infiltration of tumour immune cells. Finally, we found that only high levels of B-cell infiltration, rather than those of other immune cells, can predict a longer overall survival in patients with HNSCC. Future studies are needed to explore the role of AVPR2 and tumour-infiltrating B cells in HNSCC. ### Conclusions The AVPR2 gene may be a prognostic biomarker of HNSCC. Moreover, AVPR2 may play a role in HNSCC immune modulation, and the regulation of tumour-infiltrating B cells by AVPR2 may be a key link. ## Introduction According to the World Health Organization's most recent data, head and neck squamous cell carcinoma (HNSCC) is the eighth most common cancer globally. This disease is also the ninth leading cause of cancer-related mortality [1]. For patients with early HNSCC, either surgery or radiotherapy alone can achieve satisfactory results. However, the majority of patients in the locoregionally advanced stage when diagnosed need strategies to improve the efficacy of combination therapy. Despite the major advances in imaging techniques, surgery, radiotherapy, and chemotherapy in recent decades, the outcome in terms of survival remains unsatisfactory [2]. Therefore, further exploration of the molecular mechanism involved in the pathogenesis of HNSCC is essential for the development of innovative therapeutic approaches to improve survival. Arginine vasopressin receptor 2 (AVPR2) belongs to the seven-transmembrane domain G protein-coupled receptor (GPCR) superfamily. This molecule is expressed primarily in the distal convoluted tubules and collecting ducts. Under physiological conditions, its main function is to concentrate urine and maintain water balance in the body by stimulating mechanisms in response to the pituitary hormone arginine pressor (AVP). Mutations in this gene are the most important cause of congenital nephrogenic diabetes insipidus [3]. The expression of AVPR2 has been reported in a variety of cancers, including osteosarcoma, renal cell carcinoma, breast cancer, pancreatic cancer, colorectal cancer, and small cell lung cancer [4–7]. Activation of AVPR2 promotes proliferation of clear cell renal carcinoma cell lines and is associated with tumour grade [8, 9]. However, in some other studies, AVPR2 may inhibit tumour proliferation by activating the canonical adenylate cyclase/cAMP/PKA axis in tumour cells [6, 10, 11]. In addition to its classical functions, AVPR2 may have immunomodulatory functions. Immune checkpoint inhibitors (ICISs) have made unprecedented progress in the treatment of cancer. Studies have shown that AVPR2 is significantly downregulated in patients with immune-related adverse events (Irae) after immunotherapy [12]. AVPR2 is associated with immune cell infiltration in renal cell carcinoma [5]. However, the role of AVPR2 in HNSCC is unclear. In this study, we extensively investigated the prognostic and immunological role of AVPR2 in HNSCC. We also studied the potential link between AVPR2 expression and immune subtypes, promising immune biomarkers, and tumour-infiltrating immune cells in the tumour microenvironment. The purpose of this study was to explore the potential of AVPR2 in the immunotherapy of HNSCC, thus providing insights into a novel antitumour strategy. ## Expression and gene alteration of AVPR2 in HNSCC cBioportal (http://www.cbioportal.org/) is a powerful genomic analysis tool for the TCGA database [13]. The OncoPrint module was used to analyse the gene alteration and mRNA expression of AVPR2 in HNSCC. UALCAN (http://ualcan.path.uab.edu), a comprehensive web resource for analysing cancer omics data [14], was used to compare AVPR2 expression between HNSCC and normal tissues. In addition, the microarray data of the GSE59102 dataset, which was obtained from the Gene Expression Omnibus (GEO) database, were used for validation. GSE59102 included the whole human genome microarray analysis data of 29 tumour tissue samples and 13 marginal tissue samples of head and neck squamous cell carcinoma treated by surgical ablation. The t test was used to estimate the significance of differences in gene expression levels between groups, and $P \leq 0.05$ was considered statistically significant. ## Immunohistochemical staining We collected clinical tissue specimens from 30 cases of squamous cell carcinoma of the head and neck (30 tumour tissues and 27 paracarcinoma tissues). The study was approved by the Ethics Committee of the Affiliated Hospital of Putian University. The expression of AVPR2 and CD40LG protein in these tissues was detected by immunohistochemistry. The primary antibodies used in the current studies were as follows: AVPR2 (YaJi Biological, Inc., Shanghai, China), and CD40LG (YaJi Biological, Inc., Shanghai, China). Tumour specimens were fixed in $4\%$ paraformaldehyde, paraffin-embedded, cut into 4-μm sections, and then dewaxed. Antigen recovery was accomplished by pressure steaming at 95 °C for 3 min. Samples were incubated with diluted primary antibody at 37 °C for 1 h. The rest of the immunohistochemical procedures were performed according to the manufacturer's instructions, and the final results were estimated independently by two pathologists. For the semiquantitative assessment of AVPR2 staining, the immunoreactivity score (IRS) was used to investigate regional differences in staining. Ten typical high-power fields were randomly observed under light microscopy. The staining intensity and percentage of stained cells were evaluated for each sample, and the final IRS was calculated. Staining intensity (SI) was scored from 0 (no staining) to 3 (strong), and the percentage of positive cells (PP) was as follows: 0 when PP < $1\%$, 1 when PP = 1–$10\%$, 2 when PP = 11–$29\%$, 3 when PP = 30–$60\%$, and 4 when PP ≥ $60\%$. The above two scores were multiplied together, and the IRS ranged from 0 to 12. For semiquantitative evaluation of CD40LG staining, the proportion of CD40LG cells was assessed by estimating the percentage of cells with strong intensity of membrane staining in the stroma cells. Statistical analysis and figure exhibition were performed using GraphPad Prism 7.0. Unpaired t tests were performed to measure the difference in continuous variables between groups. All tests were two-sided, and a P value < 0.05 was considered statistically significant. ## Analysis of the prognostic value of AVPR2 in HNSCC The GEPIA database (http://gepia.cancer-pku.cn) and PrognoScan database (http://dna00.bio.kyutech.ac.jp/PrognoScan/index.html) were used to explore the prognostic value of AVPR2 expression in human cancers [15, 16]. The GEPIA database is an online website, and its analysed tumour and normal tissue data were from the TCGA database. We used the GEPIA database to explore the correlation between AVPR2 expression and overall survival (OS) and disease-free survival (DFS) in HNSCC. In the GEPIA database, the median AVPR2 expression was used as a cutoff value to classify groups, and hazard ratios (HRs) with corresponding $95\%$ confidence intervals (CIs) and log-rank P values were calculated. Dataset GSE2837 on the PrognoScan website obtained from the GEO database was used to further verify the prognostic value of AVPR2. The prognostic value was considered statistically significant when the P value was < 0.05. ## Database applied to analyse AVPR2 expression in immune subtypes of HNSCC The TISIDB database (http://cis.hku.hk/TISIDB) is an online integrated repository portal that collects human cancer data from the TCGA database [17]. The correlation of AVPR2 expression with immune subtypes of HNSCC was explored through the TISIDB database. When the P value was < 0.05, the difference was considered to be statistically significant. ## Analysis of the correlation Between AVPR2 expression and immune infiltrating cells and their marker genes TIMER is a comprehensive resource for systematically analysing immune infiltration in different cancer types (https://cistrome.shinyapps.io/timer/) [18] that contains 10,897 samples across 32 cancer types from TCGA. *Using* gene modules, we analysed AVPR2 expression and the correlation of AVPR2 expression with the abundance of immune infiltrates, including B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and dendritic cells. The impact of infiltration of 6 types of immune cells on the overall survival of HNSCC patients was also analysed using TIMER. The SCNA module was utilized to compare the tumour infiltration levels with different somatic copy number alterations (SCNAs) for AVPR2. Correlations between AVPR2 expression and gene markers of tumour-infiltrating immune cells were explored via correlation modules in TIMER. *The* gene markers of tumour-infiltrating immune cells included markers of neutrophils, monocytes, TAMs, M1 macrophages, M2 macrophages, natural killer (NK) cells, dendritic cells (DCs), B cells, CD8+ T cells, T cells (general), T-helper 1 (Th1) cells, T-helper 2 (Th2) cells, follicular helper T (Tfh) cells, T-helper 17 (Th17) cells, Tregs, and exhausted T cells. The GEPIA2 database (http://gepia2.cancer-pku.cn/) was used to verify the above results. The correlation analysis module can analyse the immune cell signature gene lists of interest, and the normal tissue was compared. Differences with a P value < 0.05 were considered statistically significant. ## Analysis of the correlation between AVPR2 expression and immune-related genes Consecutively, TIMER was used to explore the relationship between AVPR2 expression and immune-related genes. We comprehensively examined the relationship between several series of immune checkpoint molecules, including antigen presentation, cell surface receptors, ligands, cell adhesion, co-stimulators and co-inhibitors molecules, related to AVPR2. Differences with a P value < 0.05 were considered statistically significant. ## Database used to explore AVPR2 co-expression networks LinkedOmics (http://www.linkedomics.org) is a publicly available portal that includes multiomics data from all 32 TCGA cancer types [19]. Pearson correlation was used to statistically analyse the co-expression of AVPR2 in HNSCC patients with HiSeq RNA sequencing from the TCGA database. The function module of LinkedOmics performs analysis of Gene Ontology and Panther/Reactome pathway enrichment by gene set enrichment analysis (GSEA). Gene Ontology (GO) was used to categorize the genes according to biological processes, cellular components, and molecular functions. The rank criterion was the false discovery rate, and 500 simulations were performed; enriched gene sets were postprocessed by both affinity propagation and weighted set cover methods to reduce redundancy. We imported the top 50 significant genes that were positively correlated with AVPR2 expression and the top 50 significant genes that were negatively correlated into STRING (https://string-db.org/) and performed protein–protein interaction (PPI) analysis with the minimum required interaction score set to 0.4. The nodes that did not interact with other proteins were hidden, and TSV files were output. The files were imported into Cytoscape 3.9.1 to obtain core modules from the PPI network via the MCODE plugin to help us discover genes in the PPI network that are more closely associated with AVPR2. ## The mRNA expression levels and gene alterations of AVPR2 in HNSCC The cBioPortal website was used to investigate AVPR2 in HNSCC. The results showed that genomic alterations of AVPR2 occurred in $3.25\%$ of patients with HNSCC. Among them, amplification was the most common type of mutation. ( Fig. 1a). In addition, the overall oncoprint of AVPR2 gene expression in the HNSCC cohort (TCGA PanCancer) showed that mRNA expression was downregulated by $11.26\%$ in all patients (Fig. 1b). Analysis of RNA-seq data from TCGA by UALCAN showed that AVPR2 expression was significantly downregulated in primary HNSCC tissue ($$n = 520$$) compared with normal tissue ($$n = 44$$) ($$P \leq 1.73$$E-02) (Fig. 1c). In addition, compared with that in the normal tissue ($$n = 44$$), the HPV (−) group ($$n = 434$$) expression was significantly decreased ($$P \leq 1.15$$E−02), while the HPV (+) group ($$n = 80$$) expression was not significantly different ($$P \leq 1.37$$E−01). There was also a significant difference between the HPV (+) group and the HPV [-] group ($$P \leq 9.48$$E−03) (Fig. 1e). AVPR2 expression was lower in tissues from both males ($$P \leq 1.94$$E−02) and females ($$P \leq 1.42$$E−02) than in normal tissues; however, there was no significant difference between male and female patients ($$P \leq 6.00$$E−01) (Fig. 1d). GEO data further showed that AVPR2 expression was downregulated in HNSCC tissues ($$P \leq 1.63$$E−02) (Fig. 1f). Therefore, our data further support that AVPR2 was decreased in HNSCC, which might mainly originate from the alteration of DNA copy number. Fig. 1Expression and gene alteration of AVPR2 in HNSCC. a AVPR2 gene alteration types in HNSCC analysed by the cBioPortal database. b Oncoprint displays the mRNA expression levels of AVPR2 genes involved in HNSCC. c AVPR2 expression levels in normal and HNSCC tissues from the TCGA database analysed by UALCAN. Two factors, d sex and e HPV, were considered. f Differential expression data of AVPR2 between normal and HNSCC tissues from the GEO database To validate the AVPR2 expression results from public databases, we evaluated the expression of AVPR2 in head and neck squamous cell carcinoma tissues and paracarcinoma tissues by IHC (Fig. 2a). The results showed that the AVPR2 protein expression in HNSCC tissues was significantly lower than that in paracarcinoma tissues ($$P \leq 2.5$$E−03) (Fig. 2b).Fig. 2Validation of AVPR2 expression in HNSCC. a Representative microphotographs showing low and high AVPR2 staining intensity in HNSCC tissues and normal tissues. Bar = 100 μm. b Expression levels of AVPR2 in tumour and paracarcinoma tissues in HNSCC ## The role of AVPR2 in the survival of HNSCC patients Subsequently, the online tool GEPIA was used to divide HNSCC samples into a high expression group and a low expression group according to the expression level of AVPR2 to study the correlation between AVPR2 and the prognosis of HNSCC patients. As shown in Fig. 3a, b, we observed that low AVPR2 expression was associated with poor prognosis in HNSCC patients. Overall survival (OS) was significantly poorer in patients with low AVPR2 expression than in those with high AVPR2 expression ($$P \leq 9.5$$E−05). A similar result was observed in the disease-free survival (DFS) analysis ($$P \leq 1.8$$E−02). Since GEPIA is a web server for analysing the RNA sequencing data from the TCGA database, to confirm the relationship between AVPR2 expression and HNSCC prognosis, we also used the GSE2837 dataset in the GEO database for verification. Although there was no significant difference due to the small sample size, the trend of relapse-free survival (RFS) in patients with high AVPR2 expression was still better than that of patients with low AVPR2 expression (Fig. 3c).Fig. 3Kaplan‒Meier survival curves comparing high and low expression of AVPR2 in HNSCC. Kaplan‒Meier survival curve of a overall survival and b disease-free survival analysed by the GEPIA database. c Based on the GSE2837 dataset, an RFS rate analysis of HNSCC patients was performed A Cox proportional hazard model was constructed to assess the prognostic value of AVPR2 expression in the overall survival of HNSCC patients using TIMER. After excluding the potential effects of age, sex, tumour stage, and tumour purity in 428 HNSCC patients, we found that compared with HNSCC patients with low AVPR2 expression, HNSCC patients with high AVPR2 expression had a lower risk of death (HR 0.484, $95\%$ CI 0.293–0.798, $$P \leq 0.004$$) (Table 1).Table 1Cox proportional hazard model of HNSCC among 428 patientsParametercoefHR$95\%$CI_lower$95\%$CI_upperP valueAge0.0241.0241.0101.0390.001Gender (male)− 0.1400.8700.6271.2060.402Stage II0.5981.8190.6355.2090.265Stage III0.7532.1220.7426.0740.161Stage IV1.2253.4051.2549.2460.016Purity− 0.1880.8290.4091.6780.601AVPR2− 0.7260.4840.2930.7980.004coef, coefficient; CI, confidence interval; HR, hazard ratio ## AVPR2 expression is related to immune subtypes in HNSCC Subsequently, the Tisidb website was used to explore the role of AVPR2 expression in HNSCC immune subtypes. Immune subtypes were classified into 6 types: C1 (wound healing), C2 (IFN-gamma dominant), C3 (inflammatory), C4 (lymphocyte depleted), C5 (immunologically quiet), and C6 (TGF-b dominant) [20]. The results showed that there were 128 cases of C1, 379 cases of C2, 2 cases of C3, 2 cases of C4, 3 cases of C6 and no cases of C5, and AVPR2 expression was related to different immune subtypes in HNSCC ($$P \leq 8.37$$E-03) (Fig. 4).Fig. 4The relationship between AVPR2 expression and HNSCC immune subtypes ## AVPR2 expression is associated with HNSCC immune infiltration and signature genes Immune cells in the tumour microenvironment have an important impact on tumour progression. Therefore, using TIMER, we evaluated the relationship between AVPR2 expression and the immune infiltration level of HNSCC. The results showed that AVPR2 expression was negatively correlated with purity in HNSCC and positively correlated with the infiltration level of B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells (Fig. 5a).Fig. 5AVPR2 expression is associated with immune cell infiltration in HNSCC. a The correlation between AVPR2 expression and immune cell infiltration in HNSCC samples. b The correlation between somatic copy number alterations and immune cell infiltration. c Correlation between the infiltration of immune cells and HNSCC patient prognosis Subsequently, we analysed the correlation between different SCNAs of AVPR2 and immune cell infiltration in HNSCC samples. The immune infiltration level for each category was compared with the diploid/normal level. As shown in Fig. 5b, our data showed that SCNAs were significantly correlated with the infiltration of all 6 immune cells: B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells. The level of immune cell infiltration appears to be highest in diploid/normal samples. We also assessed the association between immune-infiltrating cells and outcomes in HNSCC patients. As shown in Fig. 5c, a high level of B-cell infiltration ($$P \leq 0.045$$) was significantly indicative of better clinical results, while macrophages, macrophages, neutrophils, and dendritic cells showed no significant differences. Next, we used the TIMER database to study the correlation between the expression of AVPR2 and different signature gene subsets of immune cells in HNSCC. The immune cells in HNSCC were analysed, including T cells (general), CD8+ T cells, B cells, natural killer (NK) cells, dendritic cells, monocytes, tumour-associated macrophages (TAMs), M1 and M2 macrophages, and neutrophils. In addition, we analysed the T-cell subsets T helper cell 1 (Th1), T helper cell 2 (Th2), follicular helper T (TFH), T helper cell 17 (Th17), regulatory T (Treg), and exhausted T cells. Since the tumour purity of the sample impacts the immuno-infiltration analysis, the correlation analysis was adjusted accordingly. The analysis showed that the expression of AVPR2 in HNSCC was significantly correlated with the expression of signature genes in most ($\frac{52}{57}$) various immune cells and different T-cell subsets. Among the 35 signature genes of immune cells, 31 were significantly correlated with AVPR2 expression, and some of them showed moderate correlation, such as the B-cell markers CD19 ($R = 0.484$, $$P \leq 1.06$$E−27) and CD79A ($R = 0.479$, $$P \leq 2.27$$E−27), the neutrophil markers CCR7 ($R = 0.502$, $$P \leq 3.34$$E-30) and the dendritic cell markers CD1C ($R = 0.403$, $$P \leq 1.45$$E−18). Among the signature genes of 17T-cell subsets, 16 showed a correlation with the expression of AVPR2, among which STAT5A ($R = 0.418$, $$P \leq 6.06$$e−20) in Th2 cells showed a moderate correlation. In addition, the expression of AVPR2 showed a significant correlation with all 5 T-cell exhaustion types (Table 2).Table 2Correlation between AVPR2 and relate genes and markers of immune cells analyzed by TIMERDescriptionGene markersNonePurityCorP valueCorP valueNeutropholsCCR70.523***0.502***ITGAM0.362***0.327***CEACAM80.1090.0460.1050.082Natural killer cellKIR3DL30.1090.0460.0790.261KIR2DS40.0850.1380.0540.495KIR3DL20.291***0.261***KIR3DL10.213***0.204***KIR2DL40.171**0.149*KIR2DL30.176**0.1440.011KIR2DL10.143*0.1240.038MonocyteCSF1R0.390***0.345***CD860.220***0.165**M1 MacrophageNOS20.322***0.344***PTGS2− 0.0970.057− 0.0620.333IRF50.237***0.224***M2 MacrophageCD1630.292***0.253***MS4A4A0.284***0.232***VSIG40.269***0.223***TAMCCL20.424***0.379***IL100.326***0.284***CD680.1070.0340.0610.338Dendritic cellCD1C0.450***0.403***HLA-DPB10.406***0.361***HLA-DPA10.362***0.314***HLA-DRA0.347***0.297***ITGAX0.328***0.289***HLA-DQB10.308***0.275***NRP10.220***0.178**B cellCD190.504***0.484***CD79A0.495***0.479***T cell (general)CD3E0.429***0.390***CD20.424***0.387***CD3D0.396***0.353***CD8+ T cellCD8B0.365***0.331***CD8A0.314***0.271***Th1TBX210.358***0.322***STAT40.254***0.213***STAT10.0610.2430.0170.828IFNG0.182**0.135*TNF0.144*0.1280.014Th2STAT5A0.437***0.418***GATA30.273***0.245***IL130.237***0.210***STAT60.164**0.180**Th17STAT30.257***0.240***IL17A0.245***0.220***TregFOXP30.396***0.361***STAT5B0.336***0.326***CCR80.338***0.304***TGFB1− 0.1030.043− 0.1190.026TfhBCL60.278***0.325***IL210.296***0.260***T cell exhaustionPDCD10.350***0.310***CTLA40.329***0.295***HAVCR20.286***0.239***LAG30.232***0.200***GZMB0.212***0.170**TAM, tumor-associated macrophage; Th, T helper cell; Tfh, Follicular helper T cell; Treg, regulatory T cell; Cor, R value of Spearman’s correlation; None, correlation without adjustment. Purity, correlation adjusted by purity*$P \leq 0.01$; **$P \leq 0.001$; ***$P \leq 0.0001$ We verified the above relationship with the GEPIA2 database, and the results were essentially the same for both databases. The results of GEPIA2 showed that AVPR2 expression was correlated with the expression of all immune cell and T-cell subset signature gene lists except M1 macrophages. In normal tissues, only the M2 macrophage and TAM signature gene list showed a significant correlation. These results strongly suggest that AVPR2 expression is associated with immune cell infiltration in the HNSCC tumour microenvironment (Table 3).Table 3Correlation between AVPR2 and the list of signature genes of all immune cells analyzed by GEPIA2DescriptionSignature genes listTumorNormalCorP valueCorP valueNeutropholsCCR7ITGAMCEACAM80.32***0.230.13Natural killer cellKIR3DL3KIR2DS4KIR3DL2KIR3DL1KIR2DL4KIR2DL3KIR2DL10.0960.029− 0.120.44MonocyteCSF1RCD860.21***0.140.36M1 MacrophageNOS2PTGS2IRF50.070.11− 0.110.48M2 MacrophageCD163MS4A4AVSIG40.2***0.52**TAMCCL2IL10CD680.24***0.330.027Dendritic cellCD1CHLA-DPB1HLA-DPA1HLA-DRAITGAXHLA-DQB1NRP10.26***0.120.44B cellCD19CD79A0.3***− 0.120.42T cell (general)CD3ECD2CD3D0.26***− 0.240.12CD8+ T cellCD8BCD8A0.2***− 0.210.17Th1TBX21STAT4STAT1IFNGTNF0.15**− 0.0990.52Th2Th2Th2Th2Th20.23***0.0550.72Th17STAT3IL17A0.15**− 0.180.23TregFOXP3STAT5BCCR8TGFB10.22***0.0510.74TfhBCL6IL210.19***0.110.47T cell exhaustionPDCD1CTLA4HAVCR2LAG3GZMB0.16**− 0.0970.53Tumor, correlation analysis in tumor tissue of TCGA. Normal, correlation analysis in normal tissue of TCGA*$P \leq 0.01$; **$P \leq 0.001$; ***$P \leq 0.0001$ ## AVPR2 expression is associated with HNSCC immune-related genes In view of the crucial role of immune checkpoint molecules in the regulation of tumour immunity [21], we thoroughly examined the correlation between these immune-related genes and AVPR2. The expression of AVPR2 in HNSCC was shown to be generally correlated with the levels of several series of immune checkpoint molecules related to antigen presentation, cell surface receptors, ligands, cell adhesion, co-stimulators and co-inhibitor molecules (Table 4).Table 4Correlation between AVPR2 and immune-related genes analyzed by TIMERGene SymbolsFunctionImmune CheckpointNonePurityCorP valueCorP valueHLA-DPB1Antigen presentationN/A0.406***0.361***HLA-DPA1Antigen presentationN/A0.362***0.314***HLA-DQA1Antigen presentationN/A0.350***0.312***HLA-DRAAntigen presentationN/A0.347***0.297***HLA-DQB2Antigen presentationN/A0.319***0.285***HLA-DRB1Antigen presentationN/A0.327***0.281***HLA-DRB5Antigen presentationN/A0.314***0.276***HLA-DQB1Antigen presentationN/A0.308***0.275***HLA-DQA2Antigen presentationN/A0.317***0.270***MICBAntigen presentationN/A0.0990.0240.0910.043HLA-BAntigen presentationN/A0.0850.0520.0450.321HLA-AAntigen presentationN/A0.0680.1200.0430.344MICAAntigen presentationN/A0.0160.7100.0400.375HLA-CAntigen presentationN/A0.0740.0920.0320.479SELPCell adhesionStimulatory0.715***0.700***ITGB2Cell adhesionStimulatory0.427***0.382***ICAM1Cell adhesionStimulatory0.199***0.164**VTCN1Co-inhibitorInhibitory0.264***0.296***SLAMF7Co-inhibitorInhibitory0.301***0.245***BTN3A1Co-inhibitorStimulatory0.263***0.230***BTN3A2Co-inhibitorStimulatory0.262***0.224***C10orf54Co-inhibitorInhibitory0.247***0.202***CD276Co-inhibitorInhibitory− 0.0400.365− 0.0420.357PDCD1LG2Co-inhibitorN/A0.0860.0500.0290.517CD274Co-inhibitorInhibitory0.0430.322− 0.0040.922CD28Co-stimulatorStimulatory0.455***0.420***ICOSLGCo-stimulatorStimulatory0.324***0.318***CD80Co-stimulatorStimulatory0.156**0.1110.014CD40LGLigandStimulatory0.493***0.461***IL1ALigandStimulatory− 0.319***− 0.353***TNFSF4LigandStimulatory0.326***0.315***CX3CL1LigandStimulatory0.309***0.309***IL10LigandInhibitory0.326***0.284***IL12ALigandStimulatory0.240***0.246***IL2LigandStimulatory0.280***0.242***IL13LigandInhibitory0.237***0.210***IFNA1LigandStimulatory− 0.155***− 0.171**VEGFBLigandInhibitory0.138*0.168**IL1BLigandStimulatory− 0.135*− 0.157**cxcL9LigandStimulatory0.198***0.147*VEGFALigandInhibitory− 0.187***− 0.146*CCL5LigandStimulatory0.191***0.143*IFNGLigandStimulatory0.182***0.135*TNFLigandStimulatory0.144*0.128*IL4LigandInhibitory0.125*0.126*TGFB1LigandInhibitory− 0.1030.018− 0.119*CD70LigandStimulatory0.0820.0600.0610.175TNFSF9LigandStimulatory− 0.0480.277− 0.0520.253CXCL10LigandStimulatory0.0630.1490.0180.696IFNA2LigandStimulatory0.0010.9900.0140.758ENTPD1OtherStimulatory0.515***0.495***PRF1OtherStimulatory0.266***0.221***GZMAOtherStimulatory0.241***0.193***IDO1OtherInhibitory0.182***0.155**HMGB1OtherStimulatory0.0430.3310.0620.167ARG1OtherInhibitory0.0040.927− 0.0180.687ADORA2AReceptorInhibitory0.549***0.524***EDNRBReceptorInhibitory0.505***0.501***BTLAReceptorInhibitory0.512***0.490***CD27ReceptorStimulatory0.505***0.481***TNFRSF4ReceptorStimulatory0.444***0.437***TLR4ReceptorStimulatory0.450***0.410***TNFRSF14ReceptorStimulatory0.418***0.391***CD4ReceptorStimulatory0.417***0.374***TIGITReceptorInhibitory0.380***0.342***PDCD1ReceptorInhibitory0.350***0.310***CTLA4ReceptorInhibitory0.329***0.295***TNFRSF18ReceptorStimulatory0.256***0.292***IL2RAReceptorStimulatory0.326***0.288***IcosReceptorStimulatory0.307***0.264***TNFRSF9ReceptorStimulatory0.299***0.263***HAVCR2ReceptorInhibitory0.286***0.239***LAG3ReceptorInhibitory0.232***0.200***KIR2DL3ReceptorInhibitory0.176***0.144*KIR2DL1ReceptorInhibitory0.143*0.124*Cor, R value of Spearman’s correlation; None, correlation without adjustment. Purity, correlation adjusted by purity*$P \leq 0.01$; **$P \leq 0.001$; ***$P \leq 0.0001$ CD40LG is an important co-stimulatory molecule that plays a key role in B-cell activation [22], and high CD40LG expression in HNSCC predicts a better prognosis [23]. Since we found a moderate correlation between CD40LG mRNA expression and AVPR2 in the public database (Cor = 0.461, $P \leq 0.0001$), we examined the expression level of CD40LG protein in HNSCC tissues using IHC staining for further validation (Fig. 6a). Next, the current HNSCC cohort was divided into low expression (IRS < 3) and high expression (IRS ≥ 3) groups based on the approximate median level of AVPR2 expression. Notably, the expression of CD40LG was higher in the high AVPR2 group than in the low AVPR2 group ($$P \leq 2.5$$E-03) (Fig. 6b). This result further suggests an immunological role for AVPR2 in HNSCC, suggesting that targeting AVPR2 may improve immunotherapy of HNSCC.Fig. 6Validation of the immunological role of AVPR2 in HNSCC. a Representative microphotographs representing low and high AVPR2 and CD40LG staining intensities in HNSCC tissues and paracarcinoma tissues. Bar = 100 μm. b Expression of CD40LG in the low and high AVPR2 groups in HNSCC ## The co-expression network of AVPR2 is associated with the immune response To further explore the molecular mechanism of the AVPR2 gene in tumorigenesis, we attempted to identify its co-expression with AVPR2 by using the LinkedOmics method. Of the 20,032 genes, 2,891 were significantly positively correlated with CAPG, while 3,691 were significantly negatively correlated. The heatmap in Fig. 7a shows the top 50 significant genes with a positive correlation with AVPR2 expression (such as TNXA, GRRP1, CLEC3B, etc.) and 50 significant genes with a negative correlation (such as EGNB1, FOSL1, and ADM). The Gene Ontology (GO) slim summary is based upon the 17,551 unique Entrez Gene IDs; each biological process, cellular component, and molecular function category is represented by a red, blue, and green bar, respectively (Fig. 7b).Fig. 7AVPR2 co-expression genes in HNSCC analysed by the LinkedOmics database. a Heatmaps showing the top 50 genes positively and negatively correlated with AVPR2 in HNSCC. b The Gene Ontology slim summary Subsequently, we further performed gene set enrichment analysis (GSEA) on the AVPR2 co-expression dataset to determine the differentially activated Gene Ontology and signalling pathways in HNSCC. As shown in Fig. 8, the top 5 significantly enriched biological processes were T-cell activation, cellular response to vascular endothelial growth factor stimulus, protein kinase A signalling, secretion by tissue, actin filament-based movement, and divalent inorganic cation transport. The top 5 significantly enriched cellular components were platelet dense granule, MHC protein complex, immunological synapse, sarcoplasm, and side of membrane. The top 5 significantly enriched molecular functions were immunoglobulin binding, sphingolipid binding, cyclase activity, glycolipid binding, and coreceptor activity. Fig. 8Functional enrichment analysis of AVPR2, including a GO biological process, b GO cellular components, c GO molecular function, d Panther pathways, and e Reactome pathways Furthermore, Panther pathway analysis showed the top 5 significantly enriched pathways: the endothelin signalling pathway, angiotensin II-stimulated signalling through G proteins and beta-arrestin, axon guidance mediated by semaphorins, B-cell activation, and the thyrotropin-releasing hormone receptor signalling pathway. Reactome pathway analysis showed significantly enriched pathways: PD-1 signalling, generation of second messenger molecules, generation of second messenger molecules, vasopressin regulation of renal water homeostasis via aquaporins, and translocation of ZAP-70 to immunological synapses. The above results show that AVPR2 plays an important role in the immune regulation and cell metabolism of HNSCC. ## Interaction between co-expressed genes and AVPR2 We constructed a PPI network using the STRING database and obtained core modules from the PPI network via the MCODE plugin to better understand the interactions between these co-expressed genes and AVPR2 (Fig. 9a). GNG7 and ADCY4 were more closely linked to AVPR2 and had a closer interrelationship (Fig. 9b). This finding suggests that they may be potential upstream or downstream genes of AVPR2, and the specific mechanism involved in their interaction with AVPR2 should be further investigated. Fig. 9PPI network of AVPR2-related genes in HNSCC. a PPI network based on co-expressed genes. b PPI network based on co-expressed genes via the MCODE plugin ## Discussion The expression of AVPR2 has been reported in a variety of cancers. In some studies, AVPR2 can promote tumour cell proliferation [8], but in other studies, it plays a protective role [5, 6, 11]. In addition, recent reports have shown that the AVPR2 gene may play an important role in tumour immunity [5, 12, 24]. To date, the role of AVPR2 in the occurrence and development of HNSCC has not been reported. Therefore, we comprehensively analysed the AVPR2 gene in HNSCC based on information extracted from various databases, including data related to gene expression, prognostic value, and the immune microenvironment. The expression level of AVPR2 in renal cell carcinoma was significantly lower than that in normal tissues, and a high expression level of AVPR2 was associated with a better prognosis [5]. Furthermore, in AVPR2-expressing tumours, agonists that selectively act on AVPR2 exhibit robust antitumour activity [6, 11]. In this study, we used a variety of online tools to analyse HNSCC data based on high-throughput RNA sequencing data from the TCGA database and found that AVPR2 was significantly downregulated in HNSCC tissues compared with normal tissues and verified by immunohistochemistry. Moreover, survival analysis showed that AVPR2 expression was related to the prognosis of HNSCC patients. Low AVPR2 expression predicted poor prognosis in HNSCC patients, which was consistent with previous studies. Cox regression analysis further showed that AVPR2 was an independent prognostic factor of HNSCC. These results strongly suggest that AVPR2 is a potential prognostic marker for HNSCC. Liao et al. showed that in addition to its classical functions, AVPR2 exhibits encouraging immunomodulatory functions in renal cell carcinoma [5]. AVP receptors (AVPR1a, AVPR1b and AVPR2) activated by AVP are involved in regulating CD4+ T-cell differentiation and mediating immune responses in target organs [24]. To determine the role of AVPR2 in HNSCC, we performed GSEA of AVPR2 co-expressed genes in HNSCC and revealed that genes related to AVPR2 expression were significantly enriched in multiple immune-related pathways, including the T-cell receptor signalling pathway, B-cell receptor signalling pathway, antigen processing and presentation, immune checkpoint, and other immune-related pathways. Then, to further elucidate the role of AVPR2 in tumour immunity, we explored the relationship between AVPR2 expression and different immune subtypes of HNSCC. The analysis shows that most of the immune subtypes in HNSCC are concentrated in C1 (wound healing) and C2 (IFN-γ dominant), which share several common characteristics, including a low Th1/Th2 cell ratio, high proliferation rate, and high intratumoral heterogeneity [20]. The expression of AVPR2 is significantly correlated with HNSCC immune subtypes, suggesting that AVPR2 may play a role in HNSCC immune modulation. The tumour microenvironment is the "soil" for tumour survival, which has a strong influence on tumour growth and metastasis. Tumour-infiltrating immune cells have been shown to be an independent predictor of prognosis and immunotherapy efficacy in HNSCC patients [25]. We found that AVPR2 was positively correlated with all immune cells, including B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells, using the TIMER database. By analysing the relationship between SCNAs and immune cell infiltration, we found that the immune infiltration level of samples with somatic copy number alterations seemed to be lower, suggesting that alterations in somatic copy number may be involved in the regulation of tumour immune cell infiltration. We also observed that AVPR2 was associated with the majority of tumour-infiltrating immune cell markers and immune-related genes, and that the expression of CD40LG, which plays a key role in B-cell activation, was positively correlated with AVPR2. Finally, we determined that only high infiltration of B-cells, rather than other immune cells, can predict a longer overall survival in patients with HNSCC. Therefore, it is important to further explore the role of AVPR2 and tumour-infiltrating B cells in HNSCC. Multidimensional interactions between B cells and other immune cells exist in the immune microenvironment [26]. Studies have indicated that increased levels of CD8+ and CD4+ TILs colocalizing with B-cell infiltration are related to long-term survival in NSCLC [27, 28]. The colocalization of CD8+ T cells and CD20+ B cells in melanoma predicts higher patient survival, and tertiary lymphoid structures play a key role in the immune microenvironment of melanoma [29]. In addition, the study found that there are tertiary lymphatic structures in HNSCC, which have definite T-cell zones, B-cell-rich follicles, and dendritic cells. Follicular DCs promote the development, class switching, and maturation of naive B cells in TLSs. This spatial organization of immune cells and tumour-infiltrating B cells are both related to the better prognosis of HNSCC [30, 31]. These results possibly explain the protective role of AVPR2 in HNSCC. Hence, our results suggest that AVPR2 may influence HNSCC tumour immunity primarily by modulating the tumour immune microenvironment, with AVPR2 regulation of tumour-infiltrating B cells possibly being a key link. Although we conducted a systematic analysis of AVPR2 and cross-validated it using multiple databases, this study had some limitations. First, there are differences in microarray and sequencing data from different databases, which may lead to systemic bias. Second, based on bioinformatics methods and immunohistochemical assays, we concluded that AVPR2 expression was closely related to HNSCC immune cell infiltration and prognosis. However, there was no direct evidence that AVPR2 played a role in immune infiltration and thus affected prognosis. For confirmation of the biological function of AVPR2 in HNSCC, more in vivo/in vitro experiments are needed. ## Conclusion In conclusion, we found that the AVPR2 gene is an independent prognostic factor for HNSCC, with patients who have high AVPR2 expression showing a better prognosis. In addition, AVPR2 may play a role in HNSCC immune modulation, and the regulation of tumour-infiltrating B cells by AVPR2 may be a key link. ## References 1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A. **Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries**. *CA A Cancer J Clin* (2021) **71** 209-249. DOI: 10.3322/caac.21660 2. Gourd E. **Concurrent chemotherapy improves outcomes in HNSCC**. *Lancet Oncol* (2018) **19** e343. DOI: 10.1016/S1470-2045(18)30452-2 3. Li Q, Tian D, Cen J, Duan L, Xia W. **Novel AVPR2 mutations and clinical characteristics in 28 Chinese families with congenital nephrogenic diabetes insipidus**. *J Endocrinol Invest* (2021) **44** 2777-2783. DOI: 10.1007/s40618-021-01607-3 4. Juan G, Marina P, Ulises D. **The novel desmopressin analogueV4Q5]dDAVP inhibits angiogenesis, tumour growth and metastases in vasopressin type 2 receptor-expressing breast cancer models**. *Int J Oncol* (2015) **46** 2335-2345. DOI: 10.3892/ijo.2015.2952 5. Liao S, Huang H, Zhang F, Lu D, Wu Y. **Differential expression of epithelial sodium channels in human RCC associated with the prognosis and tumor stage: Evidence from integrate analysis**. *J Cancer* (2020) **11** 7348-7356. DOI: 10.7150/jca.48970 6. Sobol N, Solernó L, Beltrán B, Vásquez L, Ripoll G, Garona J. **Anticancer activity of repurposed hemostatic agent desmopressin on AVPR2-expressing human osteosarcoma**. *Exp Ther Med* (2021) **21** 566. DOI: 10.3892/etm.2021.9998 7. Weinberg RS, Grecco MO, Ferro GS, Seigelshifer DJ, Perroni NV, Terrier FJ. **A phase II dose-escalation trial of perioperative desmopressin (1-desamino-8-d-arginine vasopressin) in breast cancer patients**. *Springerplus* (2015) **4** 428. DOI: 10.1186/s40064-015-1217-y 8. Sinha S, Dwivedi N, Tao S, Jamadar A, Kakade VR, Neil MO. **Targeting the vasopressin type-2 receptor for renal cell carcinoma therapy**. *Oncogene* (2020) **39** 1231-1245. DOI: 10.1038/s41388-019-1059-0 9. Bolignano D, Medici MA, Coppolino G, Sciortino MT, Merlo FM, Campo S. **Aquaretic inhibits renal cancer proliferation: Role of vasopressin receptor-2 (V2-R)**. *Urol Oncol* (2010) **28** 642-647. DOI: 10.1016/j.urolonc.2008.12.014 10. Ripoll GV, Pifano M, Garona J, Alonso DF. **Commentary: Arginine vasopressin receptor 1a is a therapeutic target for castration-resistant prostate cancer**. *Front Oncol* (2020) **9** 1490. DOI: 10.3389/fonc.2019.01490 11. Garona J, Sobol NT, Pifano M, Segatori VI, Alonso DF. **Preclinical efficacy of [V4Q5]dDAVP, a second generation vasopressin analog, on metastatic spread and tumor-associated angiogenesis in colorectal cancer**. *Cancer Res Treat* (2018) **51** 438-450. DOI: 10.4143/crt.2018.040 12. Abdel-Wahab N, Diab A, Katayama H, Sang K, Suarez-Almazor M. **638 Plasma proteome analysis in patients with immune checkpoint inhibitors related arthritis and pneumonitis**. *J Immunother Cancer* (2020) **8** A674 13. Cerami E, Gao J, Dogrusoz U, Gross BE, Schultz N. **The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data**. *Cancer Discov* (2012) **2** 401-404. DOI: 10.1158/2159-8290.CD-12-0095 14. Chandrashekar DS, Bashel B, Balasubramanya S, Creighton CJ, Ponce-Rodriguez I, Chakravarthi B. **UALCAN: a portal for facilitating tumor subgroup gene expression and survival analyses**. *Neoplasia* (2017) **19** 649-658. DOI: 10.1016/j.neo.2017.05.002 15. Tang Z, Li C, Kang B, Gao G, Li C, Zhang Z. **GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses**. *Nucleic Acids Res* (2017) **45** W98-W102. DOI: 10.1093/nar/gkx247 16. Mizuno H, Kitada K, Nakai K, Sarai A. **PrognoScan: a new database for meta-analysis of the prognostic value of genes**. *BMC Med Genomics* (2009) **2** 18. DOI: 10.1186/1755-8794-2-18 17. Ru B, Ngar WC, Tong Y, Zhong JY, Zhong S, Wu WC. **TISIDB: an integrated repository portal for tumor–immune system interactions**. *Bioinformatics* (2019) **20** 4200-4202. DOI: 10.1093/bioinformatics/btz210 18. Li T, Fan J, Wang B, Traugh N, Chen Q, Liu JS. **TIMER: A Web server for comprehensive analysis of tumor-infiltrating immune cells**. *Can Res* (2017) **77** e108. DOI: 10.1158/0008-5472.CAN-17-0307 19. Vasaikar SV, Peter S, Wang J, Zhang B. **LinkedOmics: analyzing multi-omics data within and across 32 cancer types**. *Nucleic Acids Res* (2017) **D1** D956-D963 20. Thorsson V, Gibbs DL, Brown SD, Wolf D, Bortone DS, Yang T. **The immune landscape of cancer**. *Immunity* (2019) **51** 411-412. DOI: 10.1016/j.immuni.2019.08.004 21. Thorsson V, Gibbs DL, Brown SD, Wolf D, Bortone DS, Ou YT. **The immune landscape of cancer**. *Immunity* (2019) **51** 411-412. DOI: 10.1016/j.immuni.2019.08.004 22. Gardell JL, Parker DC. **CD40L is transferred to antigen-presenting B cells during delivery of T-cell help**. *Eur J Immunol* (2017) **47** 41-50. DOI: 10.1002/eji.201646504 23. Li YJ, Li HY, Zhang Q, Wei SL. **The prognostic value and immune landscape of a cuproptosis-related lncRNA signature in head and neck squamous cell carcinoma**. *Front Genet* (2022) **13** 942785. DOI: 10.3389/fgene.2022.942785 24. Scroggins SM, Santillan DA, Peterson JM, Huberkeener KJ, Sandgren JA, Perschbacher KJ. **Vasopressin antagonists regulate immune responses in preeclampsia**. *FASEB J* (2017) **64** 874-875 25. Nguyen N, Bellile E, Thomas D, Mchugh J, Rozek L, Virani S. **Tumor infiltrating lymphocytes and survival in patients with head and neck squamous cell carcinoma**. *Head Neck* (2016) **38** 1074-1084. DOI: 10.1002/hed.24406 26. Wei Y, Huang CX, Xiao X, Chen DP, Shan H, He H. **B cell heterogeneity, plasticity, and functional diversity in cancer microenvironments**. *Oncogene* (2021) **40** 4737-4745. DOI: 10.1038/s41388-021-01918-y 27. Kinoshita T, Muramatsu R, Fujita T, Nagumo H, Sakurai T, Noji S. **Prognostic value of tumor-infiltrating lymphocytes differs depending on histological type and smoking habit in completely resected non-small-cell lung cancer**. *Ann Oncol* (2016) **27** w319. DOI: 10.1093/annonc/mdw319 28. Schalper KA, Brown J, Carvajal-Hausdorf D, McLaughlin J, Velcheti V, Syrigos KN. **Objective measurement and clinical significance of TILs in non–small cell lung cancer**. *JNCI J Natl Cancer Inst* (2015) **107** dju435. DOI: 10.1093/jnci/dju435 29. Helmink BA, Reddy SM, Gao J, Zhang S, Basar R, Thakur R. **B cells and tertiary lymphoid structures promote immunotherapy response**. *Nature* (2020) **577** 549-555. DOI: 10.1038/s41586-019-1922-8 30. Ruffin AT, Cillo AR, Tabib T, Liu A, Onkar S, Kunning SR. **B cell signatures and tertiary lymphoid structures contribute to outcome in head and neck squamous cell carcinoma**. *Nat Commun* (2021) **12** 3349. DOI: 10.1038/s41467-021-23355-x 31. Dieu-Nosjean MC, Giraldo NA, Kaplon H, Germain C, Fridman WH, Sautès-Fridman C. **Tertiary lymphoid structures, drivers of the anti-tumor responses in human cancers**. *Immunol Rev* (2016) **271** 260-275. DOI: 10.1111/imr.12405
--- title: Untargeted metabolomic analysis of ischemic injury in human umbilical vein endothelial cells reveals the involvement of arginine metabolism authors: - Ruihao Wu - Jiayin Zhong - Lei Song - Min Zhang - Lulu Chen - Li Zhang - Zhaohui Qiu journal: Nutrition & Metabolism year: 2023 pmcid: PMC10061785 doi: 10.1186/s12986-023-00737-0 license: CC BY 4.0 --- # Untargeted metabolomic analysis of ischemic injury in human umbilical vein endothelial cells reveals the involvement of arginine metabolism ## Abstract ### Objective In this study, differentially expressed metabolites of vascular endothelial cells were examined to further understand the metabolic regulation of ischemic injury by untargeted metabolomics. ### Method Human umbilical vein endothelial cells (HUVECs) were selected to construct an ischemia model using oxygen–glucose deprivation (OGD) and 0, 3, 6, and 9 h of treatment. After that, cell survival levels were determined by CCK8 detection. Flow cytometry, ROS detection, JC-1 detection, and western blotting were used to measure apoptosis and oxidative stress in cells. Then, combined with UPLC Orbitrap/MS, we verified the impacted metabolism pathways by western blotting and RT‒PCR. ### Results CCK8 assays showed that the survival of HUVECs was decreased with OGD treatment. Flow cytometry and the expression of cleaved caspase 3 showed that the apoptosis levels of HUVECs increased following OGD treatment. The ROS and JC-1 results further suggested that oxidative stress injury was aggravated. Then, combined with the heatmap, KEGG and IPA, we found that arginine metabolism was differentially altered during different periods of OGD treatment. Furthermore, the expression of four arginine metabolism-related proteins, ASS1, ARG2, ODC1 and SAT1, was found to change during treatment. ### Conclusion Arginine metabolism pathway-related proteins were significantly altered by OGD treatment, which suggests that they may have a potential role in ischemic injury. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12986-023-00737-0. ## Introduction Acute myocardial infarction (AMI) is myocardial necrosis caused by acute and continuous ischemia and hypoxia of coronary arteries, which can be complicated by shock, arrhythmia, or heart failure. According to the research data of the Atherosclerosis Risk In Communities (ARIC) of National Heart Lung and Blood Institute (NHLBI) from 2005 to 2014, there are 605,000 new cases of myocardial infarction and 200,000 recurrent cases every year [1], and the proportion of young patients hospitalized for AMI has increased, especially among women [2]. From the perspective of etiology, AMI is usually due to the rupture or erosion of unstable plaques with high lipid contents and easy breakage after coronary atherosclerosis, resulting in thrombosis and lumen blockage, which lead to certain degrees of ischemia and irreversible myocardial injury [3]. Cardiomyocytes have always been the focus of AMI research. However, as an important gateway for material exchange between cardiomyocytes and blood, the injury state and functionality of vascular cells after blockage have always been ignored. After ischemia, vascular endothelial cells first develop edema, which involves the production of reactive oxygen species (ROS) and stimulates a series of oxidative stress and apoptosis processes, followed by vasomotor dysfunction, microcirculation disorder and vascular rupture [4–6]. Previous studies have mainly focused on the effects of drug interventions on oxidative stress in endothelial cells caused by hypoxia [7], but little attention has been given to metabolism. With the increase in metabolism-related research, using a new method to explore the metabolic changes in vessels, especially vascular endothelial cells, will reach a new stage for the study of myocardial infarction. Untargeted metabolomics is a discipline that was newly developed after genomics and proteomics. It is a new technology that specializes in systematic and high-throughput analyses of changes in metabolite components. Metabolomics is usually defined as a complete set of metabolites or small molecular chemicals found and analyzed in a given organelle, cell, organ, biological fluid or organism [8, 9]. Among many metabolic analysis techniques, high-resolution mass spectrometry (HRMS) technology, including Orbitrap or time-of-flight systems, has developed rapidly. Through these systems, researchers can analyze the changes in more extensive biological metabolites without limiting the types of internal and external samples and obtain sufficient metabolic data with the minimum sample size; however, the complexity of this method imposes higher requirements on operators and analysts [10]. Therefore, many technologies, including HRMS, have promoted the progress of cardiovascular disease metabolism-related research. For instance, phenylalanine metabolism, sphingolipid metabolism, and glycolipid metabolism were reported to be seriously disturbed after AMI [11]. Some of these metabolites have been proven to be related to the pathological changes of AMI, such as upregulation of eicosatrienoic acid and eicostetraenoic acid, suggesting an inflammatory response [12]. Under normal circumstances, due to the low content of mitochondria in vascular endothelial cells, $85\%$ of ATP is still produced by glycolysis [13]. The production capacity of glutamine oxidation and fatty acid oxidation is used to compensate for the tricarboxylic acid cycle [14, 15]. However, as the first threshold of the vascular wall, the existing research on the metabolic changes in vascular endothelial cells in acute myocardial infarction is still insufficient. It is necessary to explore the cellular or molecular changes in vascular endothelial cells under the action of different injury-stimulating factors, which is also a new concept for understanding a complex disease. Previous related studies have mostly discussed the effects of exogenous factors, such as some chemicals, on the growth state, structure, and function of endothelial cells induced by injury [16, 17] and have paid little attention to the adaptive and self-regulation processes of endothelial cells in response to injury. In this study, we used an oxygen–glucose deprivation (OGD) cell model to simulate the ischemic and hypoxic environment under myocardial infarction in vitro, and human umbilical vein endothelial cells (HUVECs) were selected as the research subjects to represent the vascular endothelium. Combined with UPLC-Orbitrap/MS, we focused on the changes in endothelial cell metabolic activity after different periods of OGD treatment (0, 3, 6 and 9 h). To better carry out this study, we attempted to elucidate the self-adaptation and injury regulation processes of vascular endothelial cells under stimulation by ischemia and hypoxia in terms of metabolism by using untargeted metabolomics and further explored the expression of key regulatory proteins by western blotting and RT‒PCR. The significance of this project will not only be in determining the metabolic pathways that play an essential role in the responses of vascular endothelial cells to ischemic environments but also in trying to link and understand the differential metabolism reflected by metabonomics results and functional proteins related to metabolism; these studies are conducive to determining innovative targets and directions for future research. ## Cell culture and OGD treatment HUVECs were acquired from ScienCell Research Laboratories (San Diego, USA). The cells were cultured in endothelial cell medium (ECM) (ScienCell Research Laboratories) containing $5\%$ (v/v) fetal bovine serum (FBS) and $1\%$ penicillin/streptomycin (P/S) at 37 °C in an atmosphere with $5\%$ (v/v) CO2 and $95\%$ humidity. The cells were randomly divided into the following four groups: 0, 3, 6, and 9 h OGD-treated groups. The 0 h OGD treatment group was also called the normal control group (NC group). Before cell passage, the culture media was replaced every 3 days, and the cells were split at 70–$80\%$ confluence using $0.05\%$ trypsin–EDTA. For the cells in the OGD treatment groups, glucose-free ECM (ScienCell Research Laboratories) containing $1\%$ FBS was used to replace the previous ECM complete culture medium, and the cells were transferred to a three gas incubator and cultured for different periods with gas parameter settings of $1\%$ O2, $5\%$ CO2 and $94\%$ N2. ## Cell viability assay The viability of HUVECs after different OGD treatment periods was detected with CCK8 reagent (Beyotime, Shanghai). Cells were inoculated on 96-well plates for culture, and six secondary wells were used in each group. One hour before each OGD treatment group reached the predetermined treatment times (3, 6 and 9 h), the orifice plates were removed from the incubator, 10 µl of CCK8 reagent was added to each well and mixed evenly, and the cells in the group were returned to the three air incubator for incubation for 1 h. After reaching the set hypoxia time, the cells were removed from the instrument to measure and record the OD values. Each OGD treatment group was examined with a parallel NC group. ## Detection of cellular ROS HUVECs were inoculated in 6-well plates for culture and divided into four groups (NC group and 3-, 6- and 9-h OGD treatment groups). A ROS detection reagent (Beyotime, Shanghai) was prepared in advance; DCFH-DA was diluted with serum-free culture solution at a ratio of 1:1000 so that the final concentration was 10 μmol/L. After each group reached the predetermined treatment time, the culture medium in each well was discarded, and the cells were washed twice with PBS. Then, an appropriate volume of diluted DCFH-DA was added to each well and left to stand at 37 °C for 20 min. After reaching the predefined incubation time, the ROS fluorescence signal intensities of the cells were measured under a fluorescence microscope (× 200 field), and photographs were taken. This process used a 525 nm emission fluorescence wavelength and 488 nm excitation fluorescence wavelength (green light). ## Flow cytometry HUVECs were seeded onto 6-well plates at a density of 80,000 cells per well. After 24 h, cells were subjected to the corresponding ODG treatments associated with the above groups and cultured in a conventional 37℃ incubator or three gas incubator for a predetermined time. Then, the cells were harvested using trypsin (Gibco, USA), and subsequently incubated with Annexin V-FITC and propidium iodide (PI) (Beyotime, China) for 20 min in the dark; then, cells were transferred to an ice bath. The apoptosis rates of the HUVECs were determined by a FACSCanto II (BD Biosciences, USA). Flow Jo 10.0 was used to analyze the flow cytometry results. ## Mitochondrial membrane potential (MMP) detection The MMP values were detected with a JC-1 probe (Beyotime, Shanghai) based on the manufacturer’s instructions. In brief, HUVECs were evenly seeded on six-well plates, and when the cell density reached $70\%$, they were grouped and treated accordingly. Following treatment, the cells were incubated with JC-1 solution for 20 min at 37 °C. The JC-1 solution is a working solution that was prepared in advance by diluting JC-1 (200X) into ultrapure water and JC-1 buffer. After incubation, the cells were washed twice with JC-1 buffer (1X); then, the samples were observed and images were captured under a fluorescence microscope (× 100 field). ## Western blotting HUVECs treated with OGD for different times (0, 3, 6 and 9 h) were used as samples for western blotting. The samples were homogenized and lysed with RIPA buffer (Epizyme, Shanghai) containing $1\%$ sodium deoxycholate, $1\%$ Triton X-100, and $0.1\%$ SDS with protease and phosphatase inhibitor mixtures for 20 min on ice. The lysates were centrifuged at 12,000 × rpm at 4 °C for 25 min, and the supernatants were separated into cell extract mixtures. Next, the total protein concentrations were measured using a bicinchoninic acid assay (Beyotime, Shanghai). For western blotting analyses, the extracted protein samples were separated on 7.5–$12.5\%$ SDS‒PAGE gels (Epizyme, Shanghai) and transferred onto nitrocellulose membranes (Immobilon, USA). The membranes were incubated overnight at 4 °C with primary antibodies, followed by horseradish peroxidase-conjugated secondary antibodies (Cell Signaling Technology). Then, ECL developer (Epizyme, Shanghai) was used to soak the membranes in the dark. Immunoreactive signals were detected using a Tanon-5200 analyzer (Shanghai, China). The following antibodies were used for western blotting: rabbit monoclonal anti-cleaved caspase 3 (Asp175) antibody (#9664S, Cell Signaling Technology, 1:1000); mouse monoclonal anti-β-actin antibody (#3700S, Cell Signaling Technology, 1:1000); rabbit monoclonal anti-GAPDH antibody (#5174S, Cell Signaling Technology, 1:1000); rabbit monoclonal anti-ASS1 (#70720S, Cell Signaling Technology, 1:1000); rabbit monoclonal anti-SAT1 (#61586S, Cell Signaling Technology, 1:1000); rabbit monoclonal anti-ODC1 antibody (ab270268, Abcam, 1:1000); rabbit polyclonal anti-ARG2 antibody (ab264066, Abcam, 1:1000); goat anti-rabbit IgG antibody (ab6721, Abcam, 1:5000); and goat anti-mouse IgG antibody (ab6789, Abcam, 1:5000). ## Metabolite sample extraction Cells were inoculated in 6-cm culture dishes, and 12 culture dishes were used for each group. After inoculation, cells were cultured in an incubator at 37 °C for 24 h, and the corresponding OGD treatments were administered according to the group division. Then, the cells were harvested and quenched by the liquid nitrogen contact method. One milliliter of a precooled mixture of methanol and water (4:1, V/V) was added to each dish. The cells were fully scraped with a cell scraper and transferred to 1.5-ml centrifuge tubes, which were sealed with sealing film and stored at -80 °C for metabolomic analysis. Before all samples were officially tested on the instrument, equal volumes (approximately 20 µL) of each sample were taken as quality control (QC) samples for mixing. ## UPLC-Orbitrap/MS The UPLC system was combined with an Orbitrap/MS ion trap mass analyzer (Waters Corp, USA) equipped with an electrospray ionization source. It was operated in either positive or negative ionization mode using 70,000 mass resolution at 200 m/z. Additionally, we used data-dependent (dd-MS2, TopN = 10) MS/MS mode with a full scan mass resolution of 17,000 at 200 m/z. The chromatographic column we used was a Waters HSS T3 C18 column (2.1 × 100 mm, 1.7 µm). The main chromatographic conditions were defined as follows: 2 µL injection volume, 24 °C column temperature, 0.30 ml/min flow rate and mobile phase containing liquid aqueous solution ($0.1\%$ formic acid) and liquid b-acetonitrile ($0.1\%$ formic acid). The scan range was 150–1,500. After optimization, the chromatographic gradient was as follows: $5\%$ in liquid B in 0–2 min, 5–$95\%$ in liquid B in 2–10 min, $95\%$ in liquid B in 10–15 min, and $5\%$ in liquid B in 15–18 min. We acquired data in centroid mode by using Thermo Xcalibur 2.2 software (Thermo Fisher Scientific, USA). ## Metabolomic data analysis Peak alignment and extraction were performed with Compound Discoverer software (Thermo Fisher Scientific, USA). Then, a data table was constructed containing information about the retention times, m/z values, and peak areas. Next, we imported the data into SIMCA-P software version 13.0 (Umetrics, Umea, Sweden) for principal component analysis (PCA) and partial least squares discrimination analysis (PLSDA). PCA was used to assess the overall segregation trend between samples. A supervised PLSDA analysis model was used to screen for significantly differentially expressed metabolites among the OGD treatment groups and the NC group. According to the PLSDA model, we selected the parameters with variable importance in projection (VIP) values > 1.0, and two-tailed Student’s t tests were used to determine the p values; the statistical tests were performed by SPSS Statistics 18.0 and $p \leq 0.05$ was considered statistically significant. To identify the differentially expressed metabolites, accurate ion masses were input into the human metabolome database (HMDB) to match the exact molecular weights, and MS1/MS2 fragment ions were systematically searched. Furthermore, to confirm the metabolite details, we used our internal standard metabolite library for quantification. In addition, pathway enrichment analysis was conducted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database and MetaboAnalyst 5.0 (https://www.metaboanalyst.ca/). Heatmapping was used to better show the trend changes and internal differences of different metabolites. Finally, metabolite interaction network analysis was conducted by using the ingenuity pathway analysis (IPA) online database. ## Real-time polymerase chain reaction (RT‒PCR) We extracted 100 µL of total RNA by precipitation using TRIzol (Beyotime, Shanghai), and the RNA was reverse transcribed to complementary DNA (cDNA) in a specific reverse transcription system (TOYOBO, Japan). Ten microliters of cDNA product from each sample was used as a template to conduct quantitative PCR analysis in an Applied Biosystems QuantStudio™ 6 and 7 Pro (Thermo Fisher Scientific) using SYBR Green PCR master mix (Vazyme, Nanjing) under the following conditions: initial denaturation at 95℃ for 30 s, 40 cycles at 95℃ for 10 s, 60℃ for 30 s, and a dissociation curve at 95℃ for 15 s, 60℃ for 60 s and 95℃ for 15 s. The relative expression levels were calculated using ACTB as an internal control. The primer sequences used are listed in Additional file 1: Table S1. ## Statistical analysis GraphPad Prism 5.0 (GraphPad Software) was used for statistical analysis. One-way ANOVA was used for comparisons of more than two groups, with the Newman‒Keuls test used for multiple comparisons. All error bars represent the SEMs. Significance was defined as *$p \leq 0.05$, **$p \leq 0.01$, and ***$p \leq 0.001.$ In some results of this study, the superscripts ‘#’, ‘$’ and ‘*’ of individual groups have the same meaning and are only used for differentiation. ## Different OGD treatment times had different effects on the survival of HUVECs First, low magnification brightfield (× 40 field) observations showed that with an extension of treatment time, the number of cells decreased, the morphologies of cells were abnormal and the distributions were scattered (Fig. 1A). The growth state of HUVECs was affected by OGD treatment time. Then, the CCK8 experiment was conducted to measure the changes in cell viability of HUVECs following different OGD treatment times (0, 3, 6 and 9 h). The results are displayed as OD values (Fig. 1B). The longer the OGD treatment time, the more significant the effect on cell viability, suggesting that time is a key factor in OGD-induced HUVEC injury. Flow cytometry was used to detect apoptosis of HUVECs in this study. By comparing the proportions of the sums of early apoptosis and late apoptosis (Q2 + Q3) of the four groups of cells (Fig. 1C), the proportions of cells showing apoptosis under glucose deficiency and hypoxia increased with time. Then, the western blotting results showed that the expression of cleaved Caspase 3 significantly increased after OGD treatment, which further verified that OGD could induce apoptosis to a certain degree (Fig. 1D). Fig. 1HUVECs treat with OGD over different time periods (0, 3, 6 and 9 h) showing that the degree of apoptosis increases with time. A Brightfield (40 × field) observations showed the growth and distribution of HUVECs after different OGD treatment times, $$n = 3$$/group. B The absorbances (OD values) of each group after different OGD treatment times were measured by CCK8 assays, $$n = 5$$/group. C Annexin V-FITC/PI reagent was used to detect apoptosis of HUVECs after different OGD treatment times by flow cytometry, $$n = 3$$/group. D The level of the apoptotic marker protein cleaved Caspase3 was measured by western blotting, $$n = 3$$/group. One-way ANOVA with multiple comparisons was utilized to determine the statistical significance as follows: *p value < 0.05, **p value < 0.01, and ***p value < 0.001 ## Different OGD treatment times affected HUVEC apoptosis and oxidative stress to varying degrees A decreased mitochondrial membrane potential (Δψm) is also characteristic of the early stage of apoptosis and oxidative stress. JC-1 detection in the four groups of cells showed that the mitochondrial membrane potentials of cells decreased gradually with prolonged OGD treatment (Fig. 2A, B), which revealed the effect of OGD on HUVEC injury. Detection of cellular ROS can effectively indicate the production of ROS in mitochondria due to stimulation by injury and reflect the oxidative stress of cells to a certain extent (Fig. 2C, D). Immunofluorescence photos taken at high magnification (× 200 field) showed ROS production in HUVECs in the NC group and OGD treatment groups at three time periods. The cellular ROS contents increased with an extension of injury time. Fig. 2HUVECs showed different levels of oxidative stress and early apoptosis after different times of OGD treatment. A The mitochondrial membrane potentials of the four groups of cells were observed by JC-1 detection under 585 nm emission and 514 nm excitation with a fluorescence microscope (× 100 field), $$n = 3$$/group. B Graphical representation of the green/red fluorescence intensity ratios in JC-1 detection. C ROS immunofluorescence assays were used to observe the production of ROS in cells (× 200 field), $$n = 3$$/group. D Fluorescence intensity value of DCF compound reflecting ROS content. One-way ANOVA with multiple comparisons: *p value < 0.05, **p value < 0.01, and ***p value < 0.001 ## There were significant differences in the composition of intracellular metabolites of HUVECs among the OGD treatment groups at different times The principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) modeling methods were performed on results to verify the degree of aggregation between samples (Fig. 3A, B). The PCA results indicated a total of 2 principal components in positive mode with R½[1] X1 = 0.178 and R½[2] X2 = 0.108 and 2 principal components in negative mode with R½[1] X1 = 0.175 and R½[2] X2 = 0.0932. The result of QC also showed reasonable data (Fig. 3A). As a supervised model analysis method, PLS-DA can effectively reflect group differences between samples (Fig. 3B). The results showed 2 principal components in positive mode with R½[1] X1 = 0.171 and R½[2] X2 = 0.106 and 2 principal components in negative mode with R½[1] X1 = 0.174 and R½[2] X2 = 0.0922. Therefore, we can conclude that there are obvious differences in metabolite components among the four groups of samples; that is, after OGD treatments, the metabolic activities of HUVECs may be altered to varying degrees with treatment time. However, this result needs further verification. Fig. 3Multivariate statistical analyses of HUVECs treated with OGD at different times based on UHPLC-QTPF/MS data. A PCA of each treatment group and NC group. R½[1] X1 = 0.178 and R½[2] X2 = 0.108 in [1] and R½[1] X1 = 0.175 and R½[2] X2 = 0.0932 in [2]. B PLS-DA of each treatment group and NC group. R½[1] X1 = 0.171 and R½[2] X2 = 0.106 in [1] and R½[1] X1 = 0.174 and R½[2] X2 = 0.0922 in [2]. PLS-DA: partial least squares discriminant analysis and PCA: principal component analysis; [1] and [2] represent positive ions (left) and negative ions (right) A heatmap of the pairwise comparisons can intuitively reflect the changes in metabolites among groups. For the comparison of each group, we selected the top 50 differentially increased and decreased metabolites for the heatmap display to analyze the trend of changes in metabolite levels (Fig. 4). By analyzing these 50 differentially expressed metabolites, we determined the production stages of some key metabolic molecules in the OGD treatment process. Most phospholipids, especially phosphatidyl ethanolamine, showed upward trends with the extension of injury time throughout the OGD treatment process (Fig. 4A–C). The production level of lysophosphatide ethanolamine also showed an increasing trend (Fig. 4A). In the early stage of OGD treatment (< 6 h), the amount of tyrosine increased significantly, and the production of L-aspartic acid increased first and then decreased; meanwhile, L-glutamine showed a downward trend early in injury, and this change lasted until the late stage of damage treatment (Fig. 4C). The production levels of some small molecules related to energy metabolism, such as adenosine, hypoxanthine and adenine, maintained downward trends before 6 h of injury and exhibited slight increases thereafter; meanwhile, creatine decreased throughout the whole process (Fig. 4A–C). However, further data are needed to determine which key differential metabolic molecules show significant differences from the NC group throughout the entire OGD treatment process; this result will be of great significance because it will identify the metabolic pathways that are closely related to OGD injury. Fig. 4Significant changes in various types of metabolites were identified by comparing the differentially expressed metabolites between the glucose and oxygen deprivation treatment groups at different times in heatmaps. A The NC group and the 3-h OGD treatment group. B The 3-h OGD treatment group and the 6-h OGD treatment group. C The 6-h OGD treatment group and the 9-h OGD treatment group. D The 3-h OGD treatment group and the 9-h OGD treatment group. E The NC group and the 6-h OGD treatment group. F The NC group and the 9-h OGD treatment group. In the pairwise comparison, only the top 25 differentially up- and down-regulated metabolites were selected for heatmaps to observe the distribution of metabolite types ## The contents of 52 intracellular metabolites changed significantly after OGD treatment of HUVECs By using a Venn diagram, the data for the NC group and the injury treatment groups in each time period that were obtained by the untargeted metabolomics can be collectively analyzed, and 52 metabolites were identified by determining the intersections of differentially expressed metabolites (Fig. 5A). These metabolites showed significant differences among the NC group and OGD treatment groups for the three time periods (Table 1), and these substances exhibited apparent changes in the early to late stages of OGD treatment in the experiment and did not return to levels close to normal. The specific metabolites and corresponding HMDB-related information are listed in Table 1. The heatmap results for the 52 metabolites from the four groups also better exhibited the changes in their overall trends with extension of OGD processing time (Fig. 5B). Next, lipids and amino acids were sorted separately by using the categories defined in the HMDB (Fig. 6). A total of 22 lipids and 13 amino acids were identified, and most of them showed trends that increased with time from injury. However, among the lipids, phosphatidylserines such as PS (15:$\frac{0}{22}$:0) and phosphatidyl acids such as PA (22:0/a-25:0) decreased significantly, and most phosphatidylethanolamines showed upward trends as described above, except for a few such as PE (22:4 (7z, 10z, 13z, 16Z)/p-18:0). Lipid-related products, including endogenous cannabinoids such as anandamide (20:2, n-6), increased before OGD treatment for 6 h and then decreased, and another similar substance, oleamide, decreased significantly after injury (Fig. 6A). Although amino acids account for a small proportion of the 52 metabolites, the amount of many functionally important amino acids changed significantly during the injury process. L-glutamine decreased significantly after injury, while arginine, L-tyrosine and L-aspartic acid increased in the early stage of injury and decreased slightly in the late stage (Fig. 6B). Among other small metabolic molecules, L-(+)-lactic acid and pantothenic acid remained at low levels after OGD treatment, and creatine showed a steady downward trend with extension of treatment time; meanwhile, hypoxanthine showed a slight upward trend after 6 h compared with a low level found in the previous OGD treatment period (Fig. 6C).Fig. 5Fifty-two different metabolites were obtained by data screening, and the trends in their levels were further observed. A The metabolites with significantly different expression among the OGD treatment groups and the NC group were analyzed with a Venn diagram. B Heatmap of 52 significantly changed endogenous metabolites in the NC group and the OGD treatment group at different timesTable 1Specific information of 52 different metabolites and statistical data compared among groupsHMDB IDMetabolitesChemical FormulaOGD 3 h (campared to NC)OGD 6 h (campared to NC)OGD 9 h (campared to NC)VIPP valueVIPP valueVIPP valueHMDB0113077PE-NMe(16:1(9Z)/22:2(13Z,16Z))C44H82NO8P1.702891.92592E−051.667452.28916E−072.300934.6024E−15HMDB00269C17 SphinganineC18H39NO21.462332.352E−041.332583.065E−041.218920.014HMDB0000567Cinnamic acidC9H8O21.864021.52399E−071.735182.1238E−082.041076.44925E−08HMDB0000190L-(+)-Lactic acidC3H6O32.115092.50911E−111.900611.22435E−111.885081.83479E−12HMDB15573Niflumic acidC13H9F3N2O22.001487.47579E−111.836364.87347E−112.211573.30513E−11HMDB0009610PE(22:4(7Z,10Z,13Z,16Z)/P-18:0)C45H82NO7P1.045980.0231.567119.03504E−061.272687.476E−04HMDB0002117OleamideC18H35NO1.341760.0011.411061.044E−041.489379.962E−04HMDB0000267L-Pyroglutamic acidC5H7NO32.042813.49203E−121.852081.460E−112.226921.42635E−11HMDB0112334PS(15:$\frac{0}{22}$:0)C43H84NO10P1.104910.0181.54161.64053E−051.419248.97162E−05HMDB0002823Docosatrienoic acidC22H38O22.042492.65503E−121.740531.537E−081.589283.396E−04HMDB00269sphinganineC18H39NO22.0303.50456E−121.879218.040E−131.435120.002HMDB00596113-Thiomorpholinecarboxylic acidC5H9NO2S1.988063.120E−101.776052.80179E−092.166664.06858E−10HMDB0000158L-TyrosineC9H11NO31.62612.06472E−051.77312.57191E−092.135751.94702E−09HMDB0004080Anandamide (20:2, n-6)C22H37NO22.08252.07568E−141.9005.860E−141.73365.00426E−05HMDB0010569PE-NMe(16:$\frac{0}{18}$:1(9Z))[U]C40H78NO8P1.207260.0051.071890.0082.235745.44715E−12HMDB0000210Pantothenic acidC9H17NO51.996421.1488E−101.854321.0905E−112.252733.8706E−12HMDB02572713-Deoxyvitamin D3C27H38O31.758641.22095E−061.59581.958E−061.201330.013HMDB0008090PC(18:1(11Z)/22:6(4Z,7Z,10Z,13Z,16Z,19Z))C48H82NO8P1.515453.410E−041.866381.550E−101.871256.82929E−12HMDB147472-Ethylisonicotinic acidC8H10N2S1.502661.659E−041.364051.837E−041.724776.447E−05HMDB0000157HypoxanthineC5H4N4O1.474172.384E−041.479482.51303E−051.158490.018HMDB03047934-OxoprolineC5H6NO32.118051.96164E−111.864631.75681E−101.837141.08157E−10HMDB0259164Tributyl phosphateC12H27O4P1.716152.520E−061.723782.56868E−081.731846.6567E−05HMDB0015488AntrafenineC30H26F6N4O21.264980.0051.442799.860E−051.100690.005HMDB0000734trans-3-Indoleacrylic acidC11H9NO21.997014.86916E−101.855111.47929E−112.2103.000E−11HMDB0028910IsoleucineC12H24N2O31.27613.382E−031.591952.35788E−061.675131.282E−04HMDB0001138N-Acetyl-L-glutamic acidC7H11NO51.795973.7027E−061.273940.0011.84529.45557E−06HMDB00295861-OctadecanamineC18H39N1.7102.80175E−061.729332.15626E−081.296330.006HMDB0000191L-Aspartic acidC4H7NO42.152641.549E−121.925641.36645E−121.646396.14935E−07HMDB0011493LysoPE(0:$\frac{0}{22}$:4(7Z,10Z,13Z,16Z))C27H48NO7P1.784064.391E−071.9008.5147E−142.1401.53227E−09HMDB0009243PE(O-20:$\frac{0}{22}$:6(4Z,7Z,10Z,13Z,16Z,19Z))C47H82NO8P1.9201.76082E−071.712931.90797E−071.586942.91321E−06HMDB0004866Lactosylceramide (d18:$\frac{1}{12}$:0)C42H79NO131.570921.715E−041.578177.55103E−061.690561.398E−07/Docosylphosphonic acid/1.568574.535E−051.601141.5028E−061.936591.50443E−06HMDB0000064CreatineC4H9N3O21.401585.148E−041.761625.00067E−092.2408.62628E−12HMDB0253127(2E)-hexadecenoylcarnitineC23H43NO41.661868.090E−061.578452.720E−061.028790.035HMDB0251511ArginineC6H14N4O21.535863.530E−041.681931.62749E−071.7404.43055E−05HMDB0010662PG(18:3(6Z,9Z,12Z)/0:0)C42H77O10P1.4007.086E−041.427836.591E−051.253168.898E−03HMDB0011496LysoPE(0:$\frac{0}{22}$:6(4Z,7Z,10Z,13Z,16Z,19Z))C27H44NO7P1.76151.06164E−061.865984.13293E−121.891272.91103E−06HMDB03033206-Oxohexanoic acidC6H11NO31.4300.0011.7401.02059E−071.746171.61092E−08HMDB0304946Indole-3-acetic acidC12H13NO22.00129.2389E−111.828859.10133E−112.195439.14134E−11HMDB0112341PS(15:$\frac{0}{24}$:1(15Z))C45H86NO10P1.266416.263E−031.483425.397E−051.497562.162E−05HMDB0008949PE(16:$\frac{0}{17}$:0)[U]C37H74NO7P1.944262.57888E−081.571793.26879E−062.164044.68133E−10HMDB0000191L-(+)-Aspartic acidC4H7NO42.038921.34506E−111.8054.24244E−101.73398.90807E−05HMDB0000156Malic acidC4H6O52.03712.77918E−091.780531.85329E−081.865921.38296E−11/PC(O-16:1(11Z)/2:0)/2.108279.564E−171.684831.371E−071.254840.008HMDB0115667PA(22:0/a-25:0)C50H99O8P1.877765.210E−071.798386.83898E−091.725283.85648E−08HMDB0000097CholineC5H14NO1.259320.0041.408499.15523E−051.223270.016HMDB0000641L-GlutamineC5H10N2O31.898531.30733E−081.7605.97735E−092.1203.5229E−09HMDB0059795Gluten exorphin B5C30H38N6O111.016340.0281.033180.0121.174450.002HMDB0011487LysoPE(0:$\frac{0}{20}$:4(5Z,8Z,11Z,14Z))C25H44NO7P1.7804.515E−071.867955.18856E−121.951367.51815E−07HMDB0011494LysoPE(0:$\frac{0}{22}$:5(4Z,7Z,10Z,13Z,16Z))C27H46NO7P1.569034.46706E−051.732881.7063E−081.526296.760E−04HMDB0250301CitioloneC6H9NO2S1.967336.38225E−101.798826.35378E−102.162236.37351E−10HMDB0000159L-PhenylalanineC9H11NO21.866841.38922E−071.735172.121E−082.043835.90069E−08OGD, Oxygen–glucose deprivation; VIP, Variable important in projection; NC, normal control groupFig. 6The thermogram distinguished the 52 metabolites and quantified the relative differences between the groups. A Trends of lipid metabolites. B Trends of amino acid metabolites. C Trends of other small molecule metabolites. “ 100” of the NC group is the standardized value ## Many metabolites, including L-arginine, have potential effects on the dysfunction and regulation of HUVECs in OGD According to the bar chart showing the KEGG enrichment analysis results, there were significant differences in metabolic pathways among the three OGD treatment groups and the NC group; furthermore, most of the top changes were amino acid metabolism, especially arginine biosynthesis metabolism (Fig. 7A–C). The IPA method was utilized to further explore the correlations among the 52 metabolites, HUVEC damage, and the self-regulation pathway after OGD treatment to identify the metabolites that played key roles (Fig. 8). From the IPA results, various selected metabolites, mainly amino acids, had more intuitive direct or indirect relationships with apoptosis, mitochondrial dysfunction, Nrf2-mediated oxidative stress, and inducible nitric oxide synthase (iNOS) activation. Among these metabolites, arginine, glutamine, N-acetyl-L-glutamic acid, phenylalanine, tyrosine, isoleucine, and aspartic acid are likely to play predictable roles by affecting ERK, Akt, P70S6K and other key signal transduction pathways to some extent. In addition, important small molecule metabolites, such as creatine, lactic acid, hypoxanthine, and choline, also participate in these regulatory processes. Fig. 7KEGG enrichment analysis bar charts of enriched metabolism pathways among the OGD groups and NC group. A Comparison between the OGD 3 h group and the NC group. B Comparison between the OGD 6 h group and the NC group. C Comparison between the OGD 9 h group and the NC groupFig. 8IPA of the correlations among the 52 differentially expressed metabolites in the cell death mechanism. CP: canonical pathway; full line: direct relation; dotted line: indirect relation The KEGG enrichment analysis showed that the arginine metabolism pathway had an essential effect on the entire OGD injury process, and there were significant differences among the three treatment period groups (3, 6 and 9 h) and the NC group (Fig. 9). In this pathway, arginine is an important node in the ornithine cycle, also known as the urea cycle. *Arginine* generates ornithine and urea under the action of arginase. Ornithine reacts with carbamyl phosphate to generate citrulline, which promotes the nitrogen excretion process of the body. At the same time, various amino acids can also transfer amino groups to oxobutanedioic acid through transamination and participate in the urea cycle in the form of aspartic acid; α-ketoglutarate is also a key component of the citric acid cycle. Therefore, arginine metabolism is involved in various metabolic pathways of cells, and changes in arginine metabolism are of great value to the study of cell injury models. Fig. 9KEGG analysis results for the arginine metabolism pathway ## OGD treatment affected the expression of some key proteins in the arginine metabolic pathway and polyamine synthesis in HUVECs RT‒PCR can directly detect the mRNA levels of related proteins in cells, thus indicating changes in protein synthesis. RT‒PCR was used to detect the mRNA synthesis of ASS1 (argininosuccinate synthetase 1), ARG2 (arginase 2), SAT1 (spermidine-spermine acetyltransferase 1) and ODC1 (ornithine decarboxylase 1) in HUVECs after OGD treatment (Fig. 10) at different times. The expression of ASS1 and ODC1 significantly decreased after hypoxia and glucose deficiency (Fig. 10A, B), while the expression of ARG2 and SAT1 increased (Fig. 10C, D). The content changes of four intracellular proteins related to arginine metabolism and polyamine synthesis, ASS1, ARG2, ODC1 and SAT1, were verified by western blotting (Fig. 10E, F). After OGD treatment of cells, the protein content of ASS1 decreased, and ODC1 showed a transient increase at 3 h after injury treatment and then showed a decreasing trend. The SAT1 contents significantly increased in the later stage of treatment, while ARG2 increased in the early stage and decreased in the later stage. These results suggest that arginine-related metabolic pathways, especially the polyamine synthesis pathway, may play an important potential role in the effects of OGD-mediated glucose oxygen deprivation on cells. Fig. 10Arginine metabolism pathway-related proteins changed significantly in HUVECs after different OGD treatment periods (0, 3, 6 and 9 h). A–D Intracellular mRNA levels of ASS1, ARG2, ODC1 and SAT1 were measured by RT‒PCR, $$n = 3$$/group. E Western blotting results of four metabolism-related proteins (ASS1, ARG2, ODC1 and SAT1), $$n = 3$$/group. F *Quantitative analysis* results of the four proteins. One-way ANOVA with multiple comparisons was utilized to determine the statistical significance as follows: *p value < 0.05, **p value < 0.01, and ***p value < 0.001 ## Discussion Acute myocardial infarction (AMI) is an ischemic disease caused by acute blockage of coronary arteries. In fact, it not only causes necrosis of myocardial cells but also damages cardiac vascular cells to varying degrees under the influence of ischemic factors [4, 18]. However, vascular endothelial cells have received little attention in the study of cardiac ischemic injury. In addition to the basic functions of barrier and permeability [19], vascular endothelial cells also secrete factors that regulate vasomotor and functional homeostasis, such as nitric oxide (NO) and endothelin-1 [20–23]. In recent years, the direction of metabolic correlation has become a new research focus in the cardiovascular field [24, 25]. Lee et al. found that IFN-γ regulates tryptophan catabolism to disrupt glucose metabolism in endothelial cells and leads to an increased transfer of metabolism to fatty acid oxidation [26]. Earlier, Kerstin et al. found that the transcription factor FoxO1 can regulate cell proliferation and vascular expansion by regulating glucose metabolism and energy metabolism in endothelial cells [27]. However, the current research on metabolic and functional changes of vascular endothelial cells is still insufficient. Since HUVECs are equivalent and consistent with arterial endothelial cells [28] and using these materials is more feasible, they are often used in the cardiovascular field to study many characteristics of coronary endothelial cells and are widely recognized. Therefore, in this study, HUVECs were chosen as the research object to simulate the ischemic conditions of the vascular endothelium through the glucose and oxygen deprivation model (OGD), and the successful establishment of the model was verified through CCK8, ROS detection, flow cytometry, JC-1 detection and the detection of the apoptosis-related protein cleaved caspase 3 concentration; this study provides a basis for future metabolism-related research. Metabolism has been proven to affect cell proliferation, apoptosis, oxidative stress, inflammation and autophagy to a large extent [29, 30]. Before this, Chouchani et al. found that the selective accumulation of succinic acid in the tricarboxylic acid cycle (TCA) during ischemia‒reperfusion (I/R) is a key link leading to ROS production, which may be a new mechanism of oxidative stress damage caused by I/R [31]. Another metabolite, butyrate, can pass through the PPARδ/Mir-181b pathway to reduce NOX2 expression and ROS production in vascular endothelial cells, thereby preventing endothelial dysfunction in atherosclerosis [32]. Due to the high energy consumption of the heart [33], explorations of the mechanisms related to cardiovascular metabolism often focus on energy metabolism and glucose and lipid metabolism [34]. In recent years, researchers have begun to realize that amino acid metabolism plays an important role in the cardiovascular system [35]. One study demonstrated that endogenous glutamate drives local calcium release (LCR) in sinoatrial node pacing cells, adding a potentially important factor to the coupled clock theory that explains the origin of spontaneous firing [36]. Li's research team found that accumulations of branched chain amino acids (BCAAs) inhibited glucose metabolism by inhibiting the activity of the pyruvate dehydrogenase complex (PDH) and made the heart sensitive to ischemic injury [37]. In terms of blood vessels, L-arginine can improve endothelial cell dysfunction and vasomotor dysfunction through nitric oxide synthase (NOS)-mediated production of NO [38]. In addition, one study pointed out that the mechanism of vascular endothelial injury in coronary heart disease (CHD) with type 2 diabetes and chromosome 1q25 variations may be related to γ-glutamyl cycle impairment [39]. However, further exploration of cardiovascular metabolism needs to be combined with more advanced and newer technologies. Metabolomics enables us to measure thousands of metabolites in biological tissues, cells or body fluids, which can greatly promote the development of metabolic research and the understanding and diagnosis of complex diseases [40]. Andreas et al. found that the activities of polyamine metabolites were increased in patients with impaired left ventricular ejection fraction by using untargeted metabolomics technology, which allowed the effective detection of heart failure in patients with a reduced ejection fraction [41]. In addition, a targeted LC‒MS-based metabolomic study of plasma found that changes in several metabolites, including certain amino acids, pyrimidine metabolites and pentose phosphate pathway metabolites, were observed as early as 10 min after the planned infarction [42]. Yang's team also found through LC/MS metabolomics that serum BCAA levels were significantly higher in CAD patients than in healthy individuals and were independent of other traditional risk factors, which also added a reliable basis for conducting extensive cardiovascular research on BCAAs [43]. In this study, UPLC Orbitrap/MS was used to analyze the changes in intracellular metabolic components of HUVECs after different OGD treatment periods and identify significant changes and different types of metabolic substances through Venn diagram analysis and heatmap trend analysis. We then combined the KEGG and IPA analysis methods and found that arginine metabolism and its related pathways may play a potential role in the endothelial cell ischemia model. The core of arginine metabolism is the urea cycle (also known as the ornithine cycle), which is the key link of nitrogen metabolism. In endothelial cells, arginine synthesizes nitric oxide through endothelial nitric oxide synthase (eNOS) and plays a central role in regulating blood flow and maintaining the integrity of endothelial cells through the NO-sGC (soluble guanylate cyclase)-cGMP pathway [44, 45]. Studies have shown that eNOS deficiency is associated with severe left ventricular dysfunction [46]. However, in ischemic heart failure, leukocyte iNOS (inducible type) activation promotes local inflammation and cardiac remodeling [47]. Arginine produces NO through the classic NOS pathway to exert physiological effects, and some studies have also demonstrated that arginine-related functional regulation may have other important pathways. According to the study of Wang et al., arginine can reinternalize CD36 into the nuclear endosome through the mTOR-vATPase axis to prevent lipid accumulation [48]. In addition, L-arginine supplementation for 10 weeks was found to significantly improve the cardiac recovery of patients with heart failure and improve their quality of life [49]. In addition, polyamines, downstream products of the arginine metabolic pathway, have been shown to affect cell survival and proliferation. For example, spermidine can increase carnosine phosphorylation, prevent myocardial hypertrophy and decrease diastolic function, thus delaying the progression of heart failure [50]. In this study, a change in the arginine pathway was found in an ischemic environment. The results were not limited to a specific metabolite but focused on the proteins in the pathway to find a new research target. ASS1, ARG2, ODC1 and SAT1 are indispensable regulatory proteins related to arginine metabolism. ASS1 is a key protein that promotes the conversion of citrulline to arginine succinate in the urea cycle. A recent study indicated that ASS1 plays a central role in antimicrobial defense by controlling inflammatory macrophage activation through cellular citrulline depletion [51]. In this study, ASS1 expression was downregulated, which suggested that ASS1 might have a potential role in endothelial cell injury caused by ischemia. Another protein observed to be downregulated was ODC1, which regulates polyamine synthesis in cells and is downstream of arginine metabolism. In our results, ODC1 levels did not change significantly in the early stage of glucose oxygen deprivation and even slightly increased but there was a significant decrease after 6 h of injury. It is speculated that the changes in this protein in the early stage may be regulated by some ‘buffering’ mechanism, which still needs further exploration. In oncology, it is thought that inhibiting the activity of ODC1 can regulate the synthesis of polyamines and thus inhibit the proliferation of tumor cells [52]. This regulatory mechanism of cell proliferation has great reference value in the cardiovascular field. Compared with ARG1, ARG2 is mainly expressed in extrahepatic tissues [53], so it has become the focus of this research. The results showed that ARG2 expression transiently increased in the early stage of injury and gradually decreased in the later stage. This trend is slightly similar to that of ODC1. We speculate that this may be caused by the acute stress response in the early stage which induces the synthesis of polyamines with protective effects. In addition, it has been reported that ARG2 participates in the downregulation of IL-10-mediated inflammatory mediators such as succinic acid, hypoxia inducible Factor 1α (HIF-1α), and IL-1-β in vitro [54]; these mediators can also be a good starting point for future research. Finally, SAT1 was upregulated in the later stage of glucose oxygen deprivation-treated endothelial cells. This suggests that SAT1 may damage vascular endothelial cells under ischemia to some extent. Therefore, in this study, we confirmed abnormal changes in the arginine metabolism pathway and the polyamine synthesis pathway of vascular endothelial cells under glucose oxygen deprivation. The above results provide a new direction for future research and will help transition from the study of metabolism to exploring the mechanism of action of metabolism-related proteins. Of course, this part of the work also benefited from the implementation of metabolomics and successful screening and analysis, from which we can identified the direction of cardiovascular metabolism research that needs to be addressed next. Although untargeted metabolomics still cannot provide information regarding the changes in spatial distributions of intracellular metabolites, we still want to know the metabolic pathway changes at the subcellular level; studies on spatial distributions will be a next step. ## Conclusion In this research, a vascular endothelial cell ischemia model was established under glucose oxygen deprivation. By using UPLC Orbitrap/MS, differentially expressed metabolites were identified. Focusing on arginine-related pathways, the study found that some key proteins in this metabolism pathway changed significantly with the extension of injury time, thus providing a new understanding of the arginine metabolism pathway; specifically, the important role of metabolism-related proteins in cell injury was identified. This study will provide a good research basis to further explore the regulatory mechanism of vascular endothelial cells and other cardiovascular diseases at the molecular level. ## Supplementary Information Additional file 1: Table S1. Primer sequences used for RT-PCR; Figure S1. ( A) TIC (pos). ( B) TIC (neg), TIC: total ion flow chromatogram; pos: positive; neg: negative. ## References 1. Tsao CW, Aday AW, Almarzooq ZI, Alonso A, Beaton AZ, Bittencourt MS. **Heart Disease and Stroke Statistics-2022 update: a report from the American Heart Association**. *Circulation* (2022.0) **145** e153-639. PMID: 35078371 2. Arora S, Stouffer GA, Kucharska-Newton AM, Qamar A, Vaduganathan M, Pandey A. **Twenty year trends and sex differences in young adults hospitalized with acute myocardial infarction**. *Circulation* (2019.0) **139** 1047-1056. PMID: 30586725 3. Libby P. **Mechanisms of acute coronary syndromes and their implications for therapy**. *New Engl J Med* (2013.0) **368** 2004-2013. PMID: 23697515 4. Heusch G. **The coronary circulation as a target of cardioprotection**. *Circ Res* (2016.0) **118** 1643-1658. PMID: 27174955 5. Jiang C, Belfield EJ, Mithani A, Visscher A, Ragoussis J, Mott R. **ROS-mediated vascular homeostatic control of root-to-shoot soil Na delivery in Arabidopsis**. *EMBO J* (2012.0) **31** 4359-4370. PMID: 23064146 6. Baluchamy S, Ravichandran P, Ramesh V, He Z, Zhang Y, Hall JC. **Reactive oxygen species mediated tissue damage in high energy proton irradiated mouse brain**. *Mol Cell Biochem* (2012.0) **360** 189-195. PMID: 21948272 7. Cheng F, Lan J, Xia W, Tu C, Chen B, Li S. **Folic acid attenuates vascular endothelial cell injury caused by hypoxia via the inhibition of ERK1/2/NOX4/ROS pathway**. *Cell Biochem Biophys* (2016.0) **74** 205-211. PMID: 26906511 8. Newgard CB. **Metabolomics and metabolic diseases: where do we stand?**. *Cell Metab* (2017.0) **25** 43-56. PMID: 28094011 9. Wishart DS, Tzur D, Knox C, Eisner R, Guo AC, Young N. **HMDB: the human metabolome database**. *Nucleic Acids Res* (2007.0) **35** D521-D526. PMID: 17202168 10. Pang Z, Zhou G, Ewald J, Chang L, Hacariz O, Basu N. **Using MetaboAnalyst 5.0 for LC-HRMS spectra processing, multi-omics integration and covariate adjustment of global metabolomics data**. *Nat Protoc* (2022.0) **17** 1735-1761. PMID: 35715522 11. Fan Y, Li Y, Chen Y, Zhao YJ, Liu LW, Li J. **Comprehensive metabolomic characterization of coronary artery diseases**. *J Am Coll Cardiol* (2016.0) **68** 1281-1293. PMID: 27634119 12. 12.Akasaka H RK. Identification of the twophase mechanism of endogenour omega-6 fatty acid, arachidonic acid, regulating vascular inflammation by targeting cyclooxygenase-2 and microsomal prostaglandin E2 synthase-1. In: Editor, editor Poster presented at: 65th Annual American College of Cardiology Scientific Sessions; 2016; Chicago, Illinois. Pub Place; 2016. 13. De Bock K, Georgiadou M, Schoors S, Kuchnio A, Wong BW, Cantelmo AR. **Role of PFKFB3-driven glycolysis in vessel sprouting**. *Cell* (2013.0) **154** 651-663. PMID: 23911327 14. Wu G, Haynes TE, Li H, Yan W, Meininger CJ. **Glutamine metabolism to glucosamine is necessary for glutamine inhibition of endothelial nitric oxide synthesis**. *Biochem J* (2001.0) **353** 245-252. PMID: 11139387 15. Kalucka J, Bierhansl L, Conchinha NV, Missiaen R, Elia I, Bruning U. **Quiescent endothelial cells upregulate fatty acid beta-oxidation for vasculoprotection via redox homeostasis**. *Cell Metab* (2018.0) **28** 881-894. PMID: 30146488 16. Xu L, Willumeit-Romer R, Luthringer-Feyerabend B. **Effect of magnesium-degradation products and hypoxia on the angiogenesis of human umbilical vein endothelial cells**. *Acta Biomater* (2019.0) **98** 269-283. PMID: 30794987 17. Zhang X, Zhang Y, Jia Y, Qin T, Zhang C, Li Y. **Bevacizumab promotes active biological behaviors of human umbilical vein endothelial cells by activating TGFbeta1 pathways via off-VEGF signaling**. *Cancer Biol Med* (2020.0) **17** 418-432. PMID: 32587778 18. Gutierrez E, Flammer AJ, Lerman LO, Elizaga J, Lerman A, Fernandez-Aviles F. **Endothelial dysfunction over the course of coronary artery disease**. *Eur Heart J* (2013.0) **34** 3175-3181. PMID: 24014385 19. Wettschureck N, Strilic B, Offermanns S. **Passing the vascular barrier: endothelial signaling processes controlling extravasation**. *Physiol Rev* (2019.0) **99** 1467-1525. PMID: 31140373 20. Urbich C, Dimmeler S. **Endothelial progenitor cells: characterization and role in vascular biology**. *Circ Res* (2004.0) **95** 343-353. PMID: 15321944 21. Lillie EO, Mahata M, Khandrika S, Rao F, Bundey RA, Wen G. **Heredity of endothelin secretion: human twin studies reveal the influence of polymorphism at the chromogranin A locus, a novel determinant of endothelial function**. *Circulation* (2007.0) **115** 2282-2291. PMID: 17438153 22. Tousoulis D, Simopoulou C, Papageorgiou N, Oikonomou E, Hatzis G, Siasos G. **Endothelial dysfunction in conduit arteries and in microcirculation. Novel therapeutic approaches**. *Pharmacol Therapeut* (2014.0) **144** 253-267 23. de Agostini AI, Watkins SC, Slayter HS, Youssoufian H, Rosenberg RD. **Localization of anticoagulantly active heparan sulfate proteoglycans in vascular endothelium: antithrombin binding on cultured endothelial cells and perfused rat aorta**. *J Cell Biol* (1990.0) **111** 1293-1304. PMID: 2144002 24. Kerr M, Dodd MS, Heather LC. **The 'Goldilocks zone' of fatty acid metabolism; to ensure that the relationship with cardiac function is just right**. *Clin Sci* (2017.0) **131** 2079-2094 25. Reilly NA, Lutgens E, Kuiper J, Heijmans BT, Wouter JJ. **Effects of fatty acids on T cell function: role in atherosclerosis**. *Nat Rev Cardiol* (2021.0) **18** 824-837. PMID: 34253911 26. Lee LY, Oldham WM, He H, Wang R, Mulhern R, Handy DE. **Interferon-gamma impairs human coronary artery endothelial glucose metabolism by tryptophan catabolism and activates fatty acid oxidation**. *Circulation* (2021.0) **144** 1612-1628. PMID: 34636650 27. Wilhelm K, Happel K, Eelen G, Schoors S, Oellerich MF, Lim R. **FOXO1 couples metabolic activity and growth state in the vascular endothelium**. *Nature* (2016.0) **529** 216-220. PMID: 26735015 28. Lau S, Gossen M, Lendlein A, Jung F. **Venous and arterial endothelial cells from human umbilical cords: potential cell sources for cardiovascular research**. *Int J Mol Sci* (2021.0) **22** 978. PMID: 33478148 29. Choi RH, Tatum SM, Symons JD, Summers SA, Holland WL. **Ceramides and other sphingolipids as drivers of cardiovascular disease**. *Nat Rev Cardiol* (2021.0) **18** 701-711. PMID: 33772258 30. Hu T, Wu Q, Yao Q, Jiang K, Yu J, Tang Q. **Short-chain fatty acid metabolism and multiple effects on cardiovascular diseases**. *Ageing Res Rev* (2022.0) **81** 101706. PMID: 35932976 31. Chouchani ET, Pell VR, Gaude E, Aksentijevic D, Sundier SY, Robb EL. **Ischaemic accumulation of succinate controls reperfusion injury through mitochondrial ROS**. *Nature* (2014.0) **515** 431-435. PMID: 25383517 32. 32.Smith K, Swiderska A, Lock MC, Graham L, Iswari W, Choudhary T, et al. Chronic developmental hypoxia alters mitochondrial oxidative capacity and reactive oxygen species production in the fetal rat heart in a sex-dependent manner. J Pineal Res. 2022:e12821. 33. Neubauer S. **The failing heart–an engine out of fuel**. *N Engl J Med* (2007.0) **356** 1140-1151. PMID: 17360992 34. Lopaschuk GD, Karwi QG, Tian R, Wende AR, Abel ED. **Cardiac energy metabolism in heart failure**. *Circ Res* (2021.0) **128** 1487-1513. PMID: 33983836 35. Neinast M, Murashige D, Arany Z. **Branched chain amino acids**. *Annu Rev Physiol* (2019.0) **81** 139-164. PMID: 30485760 36. 36.Xie D, Xiong K, Su X, Wang G, Zou Q, Wang L, et al. Glutamate drives 'local Ca(2+) release' in cardiac pacemaker cells. Cell Res. 2022. 37. Li T, Zhang Z, Kolwicz SJ, Abell L, Roe ND, Kim M. **Defective branched-chain amino acid catabolism disrupts glucose metabolism and sensitizes the heart to ischemia-reperfusion injury**. *Cell Metab* (2017.0) **25** 374-385. PMID: 28178567 38. Lerman A, Suwaidi JA, Velianou JL. **L-Arginine: a novel therapy for coronary artery disease?**. *Expert Opin Inv Drug* (1999.0) **8** 1785-1793 39. Pipino C, Shah H, Prudente S, Di Pietro N, Zeng L, Park K. **Association of the 1q25 diabetes-specific coronary heart disease locus with alterations of the gamma-glutamyl cycle and increased methylglyoxal levels in endothelial cells**. *Diabetes* (2020.0) **69** 2206-2216. PMID: 32651240 40. Ussher JR, Elmariah S, Gerszten RE, Dyck JR. **The emerging role of metabolomics in the diagnosis and prognosis of cardiovascular disease**. *J Am Coll Cardiol* (2016.0) **68** 2850-2870. PMID: 28007146 41. Puetz A, Artati A, Adamski J, Schuett K, Romeo F, Stoehr R. **Non-targeted metabolomics identify polyamine metabolite acisoga as novel biomarker for reduced left ventricular function**. *ESC Heart Fail* (2022.0) **9** 564-573. PMID: 34811951 42. Lewis GD, Wei R, Liu E, Yang E, Shi X, Martinovic M. **Metabolite profiling of blood from individuals undergoing planned myocardial infarction reveals early markers of myocardial injury**. *J Clin Invest* (2008.0) **118** 3503-3512. PMID: 18769631 43. Yang RY, Wang SM, Sun L, Liu JM, Li HX, Sui XF. **Association of branched-chain amino acids with coronary artery disease: A matched-pair case-control study**. *Nutr Metab Cardiovasc Dis NMCD* (2015.0) **25** 937-942. PMID: 26231617 44. Carlstrom M. **Nitric oxide signalling in kidney regulation and cardiometabolic health**. *Nat Rev Nephrol* (2021.0) **17** 575-590. PMID: 34075241 45. Papapetropoulos A, Hobbs AJ, Topouzis S. **Extending the translational potential of targeting NO/cGMP-regulated pathways in the CVS**. *Br J Pharmacol* (2015.0) **172** 1397-1414. PMID: 25302549 46. Morris JL, Zaman AG, Smyllie JH, Cowan JC. **Nitrates in myocardial infarction: influence on infarct size, reperfusion, and ventricular remodelling**. *Br Heart J* (1995.0) **73** 310-319. PMID: 7756063 47. Kingery JR, Hamid T, Lewis RK, Ismahil MA, Bansal SS, Rokosh G. **Leukocyte iNOS is required for inflammation and pathological remodeling in ischemic heart failure**. *Basic Res Cardiol* (2017.0) **112** 19. PMID: 28238121 48. Wang S, Schianchi F, Neumann D, Wong LY, Sun A, van Nieuwenhoven FA. **Specific amino acid supplementation rescues the heart from lipid overload-induced insulin resistance and contractile dysfunction by targeting the endosomal mTOR-v-ATPase axis**. *Mol Metab* (2021.0) **53** 101293. PMID: 34265467 49. Salmani M, Alipoor E, Navid H, Farahbakhsh P, Yaseri M, Imani H. **Effect of l-arginine on cardiac reverse remodeling and quality of life in patients with heart failure**. *Clin Nutr* (2021.0) **40** 3037-3044. PMID: 33610421 50. Eisenberg T, Abdellatif M, Schroeder S, Primessnig U, Stekovic S, Pendl T. **Cardioprotection and lifespan extension by the natural polyamine spermidine**. *Nat Med* (2016.0) **22** 1428-1438. PMID: 27841876 51. Mao Y, Shi D, Li G, Jiang P. **Citrulline depletion by ASS1 is required for proinflammatory macrophage activation and immune responses**. *Mol Cell* (2022.0) **82** 527-541. PMID: 35016033 52. Gamble LD, Purgato S, Murray J, Xiao L, Yu D, Hanssen KM. **Inhibition of polyamine synthesis and uptake reduces tumor progression and prolongs survival in mouse models of neuroblastoma**. *Sci Transl Med* (2019.0) **11** eaau1099. PMID: 30700572 53. Digre A, Lindskog C. **The Human Protein Atlas-Spatial localization of the human proteome in health and disease**. *Protein Sci* (2021.0) **30** 218-233. PMID: 33146890 54. Dowling JK, Afzal R, Gearing LJ, Cervantes-Silva MP, Annett S, Davis GM. **Mitochondrial arginase-2 is essential for IL-10 metabolic reprogramming of inflammatory macrophages**. *Nat Commun* (2021.0) **12** 146. PMID: 33420015
--- title: Different effects of cardiometabolic syndrome on brain age in relation to gender and ethnicity authors: - Sung Hoon Kang - Mengting Liu - Gilsoon Park - Sharon Y. Kim - Hyejoo Lee - William Matloff - Lu Zhao - Heejin Yoo - Jun Pyo Kim - Hyemin Jang - Hee Jin Kim - Neda Jahanshad - Kyumgmi Oh - Seong-Beom Koh - Duk L. Na - John Gallacher - Rebecca F. Gottesman - Sang Won Seo - Hosung Kim journal: Alzheimer's Research & Therapy year: 2023 pmcid: PMC10061789 doi: 10.1186/s13195-023-01215-8 license: CC BY 4.0 --- # Different effects of cardiometabolic syndrome on brain age in relation to gender and ethnicity ## Abstract ### Background A growing body of evidence shows differences in the prevalence of cardiometabolic syndrome (CMS) and dementia based on gender and ethnicity. However, there is a paucity of information about ethnic- and gender-specific CMS effects on brain age. We investigated the different effects of CMS on brain age by gender in Korean and British cognitively unimpaired (CU) populations. We also determined whether the gender-specific difference in the effects of CMS on brain age changes depending on ethnicity. ### Methods These analyses used de-identified, cross-sectional data on CU populations from Korea and United Kingdom (UK) that underwent brain MRI. After propensity score matching to balance the age and gender between the Korean and UK populations, 5759 Korean individuals (3042 males and 2717 females) and 9903 individuals from the UK (4736 males and 5167 females) were included in this study. Brain age index (BAI), calculated by the difference between the predicted brain age by the algorithm and the chronological age, was considered as main outcome and presence of CMS, including type 2 diabetes mellitus (T2DM), hypertension, obesity, and underweight was considered as a predictor. Gender (males and females) and ethnicity (Korean and UK) were considered as effect modifiers. ### Results The presence of T2DM and hypertension was associated with a higher BAI regardless of gender and ethnicity ($p \leq 0.001$), except for hypertension in Korean males ($$p \leq 0.309$$). Among Koreans, there were interaction effects of gender and the presence of T2DM (p for T2DM*gender = 0.035) and hypertension (p for hypertension*gender = 0.046) on BAI in Koreans, suggesting that T2DM and hypertension are each associated with a higher BAI in females than in males. In contrast, among individuals from the UK, there were no differences in the effects of T2DM (p for T2DM*gender = 0.098) and hypertension (p for hypertension*gender = 0.203) on BAI between males and females. ### Conclusions Our results highlight gender and ethnic differences as important factors in mediating the effects of CMS on brain age. Furthermore, these results suggest that ethnic- and gender-specific prevention strategies may be needed to protect against accelerated brain aging. ## Background Aging is an important risk factor for cognitive impairment and dementia [1]. As aging progresses, brain atrophy also occurs at a mean volume reduction rate of $0.5\%$ per year after the age of 40 [2, 3]. Age-related brain atrophy is referred to as the brain age. Cardiometabolic syndrome (CMS), syndrome X, metabolic syndrome, and cardiometabolic dysfunction, composed of type 2 diabetes mellitus (T2DM), hypertension, and obesity, are critical modifiable risk factors for cognitive impairment. There is also a growing body of evidence that CMS has deleterious effects on brain atrophy [4] even in non-demented population [5, 6]. Previous studies have suggested that CMS may accelerate brain aging. Several cross-sectional studies have shown differences in brain volume between males and females in cognitively unimpaired (CU) populations [7–9]. Previously, changes in brain age or atrophy were shown to occur differently depending on gender [3, 10, 11]. Additionally, previous studies based on Hispanic or Korean populations suggested that CMS-associated brain atrophy was more extensive or prominent in females than in males [6, 10]. However, considering the differences in the prevalence of CMS and dementia between Korean and European populations, it would be reasonable to hypothesize that there might be a difference in the gender-specific relationship between CMS and brain age between Koreans and Europeans. Previous studies have analyzed various morphological features on brain magnetic resonance imaging (MRI), including cortical thickness [12], regional gray matter volume [11], and white matter hyperintensity [13] and integrity [14], and investigated the impact of CMS on brain structure in aging populations. Recently, various machine learning approaches have been developed to begin accurate prediction of brain age using the aforementioned brain imaging features [15–18] and provide a new metric called brain age index (BAI) to measure how old the brain age is compared to the chorological age at MRI scan. The difference between the predicted brain age using a deep learning-based algorithm and the chronological age is called the BAI, which explains how much older or younger an individual brain appears compared to the current age. A positive BAI is a novel marker of an older brain and has been shown to predict compromised brain health [19], earlier mortality [20], and cognitive impairment [21, 22]. In the present study, we investigated the different effects of CMS on BAI with respect to the sex of CU populations from Korea and United Kingdom (UK). Next, we determined whether CMS affects gender-specific BAI differently according to ethnicity. Considering that there are differences in incidence of CMS and cortical atrophy by gender and ethnicity, we hypothesized that there might be differences in the effects of CMS on the BAI in relation to gender and ethnicity. ## Study populations We enrolled CU participants aged ≥ 45 years from the Health Promotion Center of Samsung Medical Center (Seoul, Korea) who underwent a comprehensive health screening exam from September 1, 2008, to October 31, 2019. A total of 8227 eligible candidates underwent a full medical examination, which included cognitive assessment and 3.0-Tesla MRI, including high-resolution T1-weighted MRI, as part of a standard screening for dementia. The medical examination procedure used for the participants has been previously described [23]. We excluded participants who had any of the following conditions: 728 participants with missing data on years of education or Mini-Mental State Examination (MMSE) score [24]; 509 participants with significant cognitive impairment defined by MMSE scores below the 16th percentile in age-, gender-, and education-matched norms or through an interview conducted by a qualified neurologist; 312 participants with severe cerebral white matter hyperintensities (deep white matter ≥ 2.5 cm and caps or band ≥ 1.0 cm) or structural lesions such as territorial infarction, lobar hemorrhage, brain tumor, and hydrocephalus; 542 participants with missing information on DM, hypertension, or body mass index (BMI); and 377 participants with unreliable analyses of cortical thickness due to head motion, blurred MRI, inadequate registration to a standardized stereotaxic space, misclassification of tissue type, or inexact surface extraction. Finally, 5759 participants (3042 males and 2717 females) were included in this study. Similar data for people of British ancestry was obtained from the UK Biobank (UKB, http://www.ukbiobank.ac.uk), a population-based prospective cohort study of approximately 500,000 people in the UK [https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001779, https://www.nature.com/articles/s41586-018-0579-z]. Of these participants, approximately 40,000 attended an additional visit during which MRI brain imaging data was collected in addition to other health-related data [https://www.nature.com/articles/s41467-020-15948-9]. We included non-Hispanic White adults only in the present study. We excluded participants with a self-reported or hospital record-based history of dementia, Parkinson’s disease, or other central nervous system-related diseases. Finally, 9903 (4736 males and 5167 females) UKB participants were included after applying the inclusion/exclusion criteria and after random selection of a smaller subset of participants for brain imaging data processing. The institutional review board of the Samsung Medical Center approved this study and adhered to the principles of the Declaration of Helsinki. Written informed consent was obtained from all participants in the Health Promotion Center of Samsung Medical Center. Anonymous and deidentified data from the UKB was used for analysis and, therefore, the present study was exempted from obtaining informed consent. ## Measurement of cardiometabolic syndrome For populations from the Health Promotion Center at the Samsung Medical Center, a health screening exam was conducted by a well-trained medical professional using standardized protocols. Baseline workup included blood tests (complete blood cell count, liver/kidney/thyroid function test, and tumor markers), urine analyses, abdominal sonography, chest radiography, electrocardiogram, pulmonary function test, and gastroduodenoscopy. We classified each CMS component using the following criteria: T2DM was defined as a diagnostic history of T2DM or current use of any anti-diabetic medication; hypertension was defined as a diagnostic history of hypertension or current use of any antihypertensive medication; obesity and underweight were defined using the cut-off for BMI calculated by weight (kilograms)/height (meters) squared at the first visit. According to a previous study [10], populations with BMI < 18.5 kg/m2 were labeled as underweight, and those with BMI ≥ 27.5 kg/m2 were labeled as obese. For populations from the UKB, the classification of T2DM, hypertension, and obesity was determined based on a combination of a touchscreen-based questionnaire, a verbal interview, and linked hospital records. Specifically, T2DM was defined as either self-reported T2DM, a doctor’s diagnosis of T2DM (Data Field 2443), patients who were taking insulin (Data Fields 6177, 6153), or a hospital data-linked record of an individual with a diagnosis of T2DM. Hypertension was defined as either self-reported hypertension (Data-Field 20,002) or a hospital data-linked record of having a primary or secondary diagnosis of hypertension (Data-Fields 41,202, 41,204, 41,203, and 41,205). BMI was calculated using weight and height measurements in the same way as the Samsung Medical Center data. Populations with a BMI < 18.5 kg/m2 were categorized as underweight, whereas those with BMI ≥ 35 kg/m2 were categorized as obese [25]. ## Acquisition of brain MRI All Korean populations underwent a 3D volumetric brain MRI scan. An Achieva 3.0-Tesla MRI scanner (Philips, Best, the Netherlands) was used to acquire 3D T1 Turbo Field echo (TFE) MRI data using the following imaging parameters: sagittal slice thickness, 1.0 mm with $50\%$ overlap; no gap; repetition time of 9.9 ms; echo time of 4.6 ms; flip angle of 8; and matrix size of 240 × 240 pixels reconstructed to 480 × 480 over a field view of 240 mm. In the UKB populations, brain MRI scans were obtained at one of the three assessment sites using a 3.0 Tesla Siemens Skyra MRI Scanner. Among the six brain imaging modalities acquired was a T1-weighted, sagittal 3D magnetization prepared rapid gradient echo (MPRAGE) scan. The following imaging parameters were used in this T1-weighted acquisition: inversion time of 880 ms; repetition time of 2000 ms; 1 × 1 × 1 mm3 voxel size; 208 × 256 × 256 matrix size; and SENSE factor (R) of 2.0 [Miller, 2016: https://www.nature.com/articles/nn.4393]. ## Image processing and cortical surface extraction T1-weighted MRI scans from the Health Promotion Center in Korea and the UKB were used to reconstruct the inner and outer cortical boundaries using the CIVET pipeline developed at the Montreal Neurological Institute (http://www.bic.mni.mcgill.ca/ServicesSoftware/CIVET). Cortical morphology was quantitatively characterized by measuring cortical thickness, sulcal depth, and gray/white intensity ratio [26] on the cortical surface at 81,924 vertices (163,840 polygons). These features were further resampled to the surface template using the transformation obtained in the surface registration to allow for inter-subject comparisons. ## Development of prediction model for relative brain age As illustrated in Fig. 1, we did not use topology-varying surfaces because of the nature of the graph convolutional networks (GCN) model used in this study. Rather, we considered the cortical morphological changes that occur in relation to brain size and gyrification using cortical thickness, volume, and sulcal depth. The GCN employed in our study requires identical graph/mesh structures for all individual inputs, whereas the features of the nodes/vertices can vary. Another advantage of the topology-kept surface model is that surface nodes are registered across all individuals such that anatomical information is shared. Fig. 1The graph-based convolutional network for brain age prediction ## Brain age index After calculating the predicted brain age for each subject, we further calculated a metric that reflected the subject's relative brain health status, called the BAI. BAI was initially measured by subtracting the true brain age from the predicted brain age [27]. Due to regression dilution [28], however, it is also possible that regression models bias the predicted brain age toward the mean, underestimating the age of older subjects and overestimating the age of younger subjects [29]. When deriving the BAI, this bias must be corrected using a strategy introduced in other studies [15, 28]. We defined the new BAI as the difference between the individual BAI and the expected BAI (measurement fitted over the entire sample set using the regression model and leave-one-out cross-validation). The BAI was corrected such that the BAIs of the whole dataset analyzed became unbiased across all age ranges. ## Propensity score matching Propensity score matching was performed to minimize the differences in the demographics and cardiometabolic factors between the UK and KOR participants. The propensity score was obtained using multivariable logistic regression based on age, gender, T2DM, hypertension, and obesity. A total of 5541 KOR participants were matched with 9903 UK participants based on propensity scores using the 1:2 nearest-neighbor matching algorithm with caliper of 0.1. A good balance was achieved between the KOR and UK participants, with all standardized mean differences (age, gender, T2DM, hypertension, and obesity) below 0.1 after matching. ## Statistical analysis Independent t-tests and chi-squared tests were used to compare continuous and categorical variables, respectively. To explore the association between the presence of each CMS component and brain age in females and males among the Korean and UK populations, we performed a linear regression analysis with the presence of T2DM, hypertension, obesity, and underweight as covariates. To assess whether the association between the presence of each CMS component and brain age might differ by gender in the Korean and the UK populations, we performed linear regression analyses by adding each two-way interaction term (the presence of each CMS component*gender) to covariates in Korean and UK populations after controlling for the other CMS components. To assess whether the association between the presence of each CMS component and brain age might differ by gender and ethnicity, we performed linear regression analyses with the addition of each three-way interaction term (the presence of each CMS component*gender*ethnicity) to covariates after controlling for the other CMS components. False discovery rate (FDR) correction was conducted for all statistical analyses to control for p-values, and q-values were obtained after FDR correction. All reported p-values and q-values were two-sided and the significance level was set at 0.05. All analyses were performed using R version 4.3.0 (Institute for Statistics and Mathematics, Vienna, Austria; www.R-project.org). ## Demographics of cognitively unimpaired populations in the UK and Korea After propensity score matching, the demographic characteristics of the two ethnic datasets were similar (Table 1). Among the 5541 Korean populations, there were 2599 ($46.9\%$) females and 2942 ($53.1\%$) males. Among the 9903 UK populations, there were 5167 ($52.2\%$) females and 4736 ($47.8\%$) males. There were some differences in mean age (64.0 and 63.6 years, $p \leq 0.001$), female ratio (46.9 and $52.2\%$, $p \leq 0.001$), and the presence of T2DM (17.3 and $9.8\%$, $p \leq 0.001$), hypertension ($42.7\%$ and $40.6\%$, $$p \leq 0.011$$), obesity (10.7 and $7.9\%$, $p \leq 0.001$), and underweight (1.8 and $0.6\%$, $p \leq 0.001$) between Koreans and participants from the UK.Table 1Demographics of populations from UK Biobank and Health Promotion Center in KoreaVariablesKoreaUK p-value Females ($$n = 2599$$) Males ($$n = 2942$$) Total ($$n = 5541$$) Females ($$n = 5167$$) Males ($$n = 4736$$) Total ($$n = 9903$$) Age (years)a 63.2 ± 6.964.7 ± 6.564.0 ± 6.763.4 ± 7.163.8 ± 7.463.6 ± 7.20.007Hypertension (n, %)b 965 ($37.1\%$)1402 ($47.7\%$)2367 ($42.7\%$)1813 ($35.1\%$)2208 ($46.6\%$)4021 ($40.6\%$)0.1T2DM (n, %)b 273 ($10.5\%$)683 ($23.2\%$)956 ($17.3\%$)365 ($7.1\%$)602 ($12.7\%$)967 ($9.8\%$) < 0.001BMI (kg/m2)a 23.5 ± 2.924.5 ± 2.624.0 ± 2.826.8 ± 5.227.7 ± 4.427.2 ± 4.9 < 0.001Obesity (n, %)227 ($8.7\%$)336 ($12.4\%$)593 ($10.7\%$)450 ($8.7\%$)336 ($7.1\%$)786 ($7.9\%$) < 0.001Underweight (n, %)b 70 ($2.7\%$)32 ($1.1\%$)102 ($1.8\%$)49 ($0.9\%$)7 ($0.1\%$)56 ($0.6\%$) < 0.001Propensity score matching was performed to balance the age and gender between the Korean and UK populations, and 9903 out of 17,791 populations in the UK and 5541 out of 5759 populations in Korea were selected for the present studyAbbreviations: BMI, body mass index; T2DM, type2 diabetes mellitus; UK, United Kingdom aDistribution of age and BMI was compared between the populations of UK and Korea using independent t tests bDistribution of education level and presence of hypertension, T2DM, obesity, and underweight between the populations of UK and Korea were tested using the chi-squared test ## Effects of cardiometabolic syndrome components on brain age index As shown in Fig. 2, DM was associated with increased BAI for all participants, regardless of gender and ethnicity (q < 0.001 in the four groups) (Table 2). Hypertension was associated with a significantly higher BAI for all participants (q < 0.001), except for Korean males ($q = 0.309$). Obesity significantly increased the BAI for UK males ($q = 0.004$). Being underweight increased the BAI significantly only for UK females ($q = 0.002$).Fig. 2BAI distribution between groups regarding gender and ethnicity for healthy participants and participants with different CMS. BAI = 0 indicates that the chronological age is the same as the predicted brain age, with higher values indicating an older-appearing brain than chronological age. Asterisk symbol (*) indicates the following: q-values, FDR-corrected p-values, are lower than 0.05. BAI, brain age index; Kor, Korea; UK, United Kingdom; CMS, cardiometabolic syndromeTable 2Brain age index in controls and four CMS component groupsEthnicity/genderCMSAgeBAI β (SE) q-value* Korean femalesControl61.6 ± 8.1 − 0.67 ± 3.57T2DM66.9 ± 9.51.51 ± 4.441.73 (0.25) < 0.001Hypertension66.0 ± 8.00.39 ± 3.970.80 (0.20) < 0.001Obesity64.8 ± 8.50.31 ± 3.690.26 (0.94)0.347Underweight62.8 ± 7.60.30 ± 4.340.77 (0.48)0.145Korean malesControl64.1 ± 8.20.70 ± 3.55T2DM65.5 ± 8.61.82 ± 4.050.88 (0.19) < 0.001Hypertension65.3 ± 8.41.29 ± 4.130.19 (0.18)0.309Obesity63.4 ± 7.71.52 ± 4.030.37 (0.23)0.144Underweight67.5 ± 10.72.17 ± 3.631.24 (0.72)0.171UK femalesControl62.0 ± 7.2 − 0.17 ± 3.50T2DM64.4 ± 7.41.22 ± 3.871.08 (0.20) < 0.001Hypertension65.2 ± 6.60.45 ± 3.790.49 (0.13) < 0.001Obesity61.5 ± 6.70.53 ± 3.540.32 (0.19)0.088Underweight63.8 ± 7.01.60 ± 3.871.67 (0.53)0.002UK malesControl61.5 ± 7.50.25 ± 3.66T2DM65.8 ± 6.82.42 ± 3.901.55 (0.17) < 0.001Hypertension65.6 ± 6.81.35 ± 3.860.73 (0.13) < 0.001Obesity62.9 ± 7.21.72 ± 3.630.65 (0.22)0.004Underweight68.0 ± 6.3 − 1.21 ± 2.28 − 1.69 (1.42)0.235Values of age and BAI are presented as mean ± standard deviation Abbreviations: BAI, brain age index; CMS, cardiometabolic syndrome; T2DM, type2 diabetes mellitus; UK, United Kingdom*q-values, FDR-corrected p-values, were obtained using a linear regression analysis with the presence of hypertension, T2DM, obesity, and underweight as covariates in each group (Korean females, Korean males, UK females and UK males) ## Interactive effects of cardiometabolic syndrome components on brain age index in relation to gender and ethnicity We also investigated the interaction of the presence of each CMS component and gender with BAI in Koreans and participants from the UK. Among Koreans, there were interactions between T2DM and gender with BAI ($q = 0.035$) and between hypertension and gender with BAI ($q = 0.046$), suggesting that the effects of T2DM and hypertension on BAI were more prominent in females than in males (Table 3, Fig. 3). Among British participants, however, there were no interactions of any CMSs and gender with BAI (q range 0.098 to 0.203, Table 3, Fig. 3). In fact, there were interactions between gender and ethnicity for T2DM ($q = 0.004$) and hypertension ($q = 0.011$, Table 3, Fig. 3).Table 3Interaction effect on the difference in BAI between participants with each CMS and those with control Ethnicity Two-way interaction (each CMS component*gender) β (SE) q-value*KoreaT2DM*gender0.84 (0.32)0.035Hypertension*gender0.61 (0.27)0.046Obesity*gender − 0.11 (0.36)0.770Underweight*gender − 0.82 (0.85)0.450UKT2DM*gender − 0.52 (0.26)0.098Hypertension*gender − 0.26 (0.18)0.203Obesity*gender − 0.37 (0.28)0.191Underweight*gender3.51 (1.50)0.076 Three-way interaction(each CMS component*gender*ethnicity) β (SE) q-value¥BothT2DM*gender*ethnicity1.36 (0.41)0.004Hypertension*gender*ethnicity0.89 (0.32)0.011 Abbreviations: BAI, brain age index; CMS, cardiometabolic syndrome; T2DM, type2 diabetes mellitus; UK, United Kingdom*q-values, FDR-corrected p-values, were obtained using linear regression analyses with adding each two-way interaction term (the presence of each CMS component*gender) to covariates in Korean and UK populations after controlling for the other CMS components¥q-values, FDR-corrected p-values, were obtained using linear regression analyses with additionally adding each three-way interaction term (the presence of each CMS component*gender*ethnicity) to covariates after controlling for the other CMS componentsFig. 3Ethnic- and gender-specific difference in BAI between participants with and without T2DM and HTN. Values depicted in the bar plot represent the mean of BAI, and values depicted in the error bar represent the standard error of mean. BAI = 0 indicates that the chronological age is the same as the predicted brain age, with higher values indicating an older-appearing brain than chronological age. Asterisk (*) symbol indicates the following: q-values, FDR-corrected p-values, were obtained using linear regression analyses with adding each two-way interaction term (the presence of each CMS component*gender) to covariates in Korean and UK populations after controlling for the other CMS components. Yen (¥) symbol indicates the following: q-values, FDR-corrected p-values, were obtained using linear regression analyses with additionally adding each three-way interaction term (the presence of each CMS component*gender*ethnicity) to covariates after controlling for the other CMS components. BAI, brain age index; Kor, Korea; UK, United Kingdom; T2DM, type 2 diabetes mellitus; HTN, hypertension ## Discussion In the present study, we systematically investigated the different effects of CMS on BAI in relation to gender and ethnic differences in a large sample of Korean and UK CU populations. Our major findings are as follows: first, among Koreans, the effects of DM and hypertension on BAI were higher in females than in males. This indicated interaction effects of gender and the presence of T2DM and hypertension on BAI in Korean population. Second, among the UK population, there were no differences in the effects of T2DM and hypertension on BAI between males and females. Overall, there was evidence that ethnicity modified the gender-specific relationship of T2DM and hypertension with BAI. Taken together, our findings suggest that CMS exerts different effects on brain age depending on gender and ethnicity. Therefore, ethnic- and gender-specific prevention strategies may be necessary to protect against accelerated brain aging. We found that the presence of T2DM and hypertension was associated with a higher BAI regardless of gender and ethnicity, except for hypertension in Korean males. T2DM and hypertension are well-known risk factors for brain atrophy, which is an important indicator of brain age [30]. T2DM may have deleterious effects on the brain via various mechanisms, including cerebrovascular complications, glucose toxicity due to insulin resistance, and chronic inflammation [31]. Similarly, the positive association between hypertension and BAI may be due to several possible mechanisms including cerebral hypoperfusion, micro- and macrovascular damage in white matter, and cerebral microinfarcts [32, 33]. Our first major finding was that Korean females suffered more deleterious effects of T2DM and hypertension on brain age than Korean males. Although the underlying mechanisms for the gender-specific effects of T2DM and hypertension on brain age are not fully understood, our findings might be related to the complex effects of biological and socioeconomic differences [34]. Previous studies have suggested that hypertension exerts worse effects on multiple organs in females than in males. This was attributed to differences in sex hormones. There are stronger associations of hypertension with autonomic dysfunction in females than in males [35]. Similar associations are witnessed in the cases of microalbuminuria [36] and reduction of heart function [37]. In particular, females uniquely experience menopause transition, which might accelerate cardiometabolic syndrome, brain aging, or cognitive impairment via several mechanisms including changes in the availability of estrogen [38], estrogen receptor activity, and/or estrogen-regulated neural networks [39]. Specifically, estrogen deficiency in postmenopausal females leads to inflammatory process and vasoconstriction via the dysfunction of the renin-angiotensin system [40–42]. In fact, a growing body of evidence shows that menopause has a deleterious impact on cognitive function, which may contribute to the higher prevalence of dementia in females than in males [43–47]. Additionally, several studies have shown that females tend to maintain lifestyles that are more favorable for brain health, with overall lower drinking and smoking rates [48–51]. Therefore, our findings might be also related to differences in stress, alcohol consumption, smoking, and dietary habits according to gender. Our second major finding was that there were interactive effects of the presence of T2DM and hypertension, gender, and ethnicity on BAI. That is, unlike Koreans, there were no differences in the effects of T2DM and hypertension on BAI between males and females in the UK population. A few studies have found that brain age differs depending on ethnicity [52, 53]. However, gender- and ethnicity-specific differences in the effects of T2DM and hypertension on brain age have not been extensively investigated. These differences might be related to the biological and socioeconomic differences between the Korean and UK populations. Previously, a higher frequency of CMS in Korean populations compared to Europeans has been explained by the fact that Asians have higher visceral fat and lower subcutaneous fat than Europeans with the same BMI [54]. This might increase the complication rate of CMS because visceral fat has more deleterious effects on arteriosclerosis and brain health than subcutaneous fat. In fact, Asians are more likely to develop CMS-related complications such as coronary artery disease [55], stroke [56], dementia [57–59], or mortality [60]. Another potential explanation is that there were fewer differences in socioeconomic status and years of education between males and females in the UK than in Korea. Further studies are needed to investigate the pathomechanism to explain gender differences according to ethnicity. ## Limitations The strengths of our study include a large sample size from two different cohorts, well-balanced clinical demographics between the two cohorts after propensity score matching, and a novel measurement of brain age that is sensitive to neurodegenerative changes in gray and white matter. However, our study had some limitations. First, owing to the cross-sectional study design, the causal or temporal relationship of the effects of CMS on brain aging was not determined. In addition, the study did not have information on exposure time or changes in the status of risk factors. Longitudinal studies are needed to identify whether there are dynamic differences from mid-adulthood to old age in the effects of risk factors on brain aging in elderly CU populations. Second, differences in subject selection methods between the two cohorts may have confounded the ethnic differences. Third, the presence of T2DM and hypertension was determined through the patient history of diagnosis or medications and not through clinical examinations including measurement of systolic blood pressure and fasting glucose. Fourth, obesity was defined using BMI only rather than waist circumstance, which has relevance to central obesity according to the International Diabetes Federation. We also used different criteria for diagnosing obesity according to ethnicity-specific BMI. This was, however, done to abide by a previous consensus on the definition of obesity according to ethnicity [25]. Finally, we did not consider the brain pathology markers of Alzheimer’s disease, lacunes, micro-cortical infarcts, and white matter hyperintensities, which can also be associated with brain age. Further studies are needed to identify the effects of CMS on brain aging in relation to the pathophysiological processes. Despite the aforementioned limitations, our study is the first report to compare the gender- and ethnicity-specific effects of CMS on brain age. ## Conclusions In the present study, we highlight gender and ethnic differences in the effects of CMS on brain age. Furthermore, our findings suggest that different measures may be needed to prevent accelerated brain aging by CMS in terms of gender and ethnic differences. In conclusion, CMS exerted different effects on brain age according to the gender and ethnicity of the individuals. Our study shows that it is important to control for T2DM and hypertension to prevent brain aging. Since the effects of T2DM and hypertension on brain age were the largest among Korean females, more careful treatment of these CMS components would be more effective to prevent or mitigate fast brain aging in Korean females. Therefore, ethnic- and gender-specific prevention strategies may be needed to protect against accelerated brain aging. ## References 1. Wahl D, Solon-Biet SM, Cogger VC, Fontana L, Simpson SJ, Le Couteur DG. **Aging, lifestyle and dementia**. *Neurobiol Dis* (2019) **130** 104481. DOI: 10.1016/j.nbd.2019.104481 2. Fotenos AF, Snyder AZ, Girton LE, Morris JC, Buckner RL. **Normative estimates of cross-sectional and longitudinal brain volume decline in aging and AD**. *Neurology* (2005) **64** 1032-1039. DOI: 10.1212/01.WNL.0000154530.72969.11 3. Lee JS, Kim S, Yoo H, Park S, Jang YK, Kim HJ. **Trajectories of physiological brain aging and related factors in people aged from 20 to over-80**. *J Alzheimers Dis* (2018) **65** 1237-1246. DOI: 10.3233/JAD-170537 4. Söderlund H, Nyberg L, Nilsson LG. **Cerebral atrophy as predictor of cognitive function in old, community-dwelling individuals**. *Acta Neurol Scand* (2004) **109** 398-406. DOI: 10.1111/j.1600-0404.2004.00239.x 5. Leritz EC, Salat DH, Williams VJ, Schnyer DM, Rudolph JL, Lipsitz L. **Thickness of the human cerebral cortex is associated with metrics of cerebrovascular health in a normative sample of community dwelling older adults**. *Neuroimage* (2011) **54** 2659-2671. DOI: 10.1016/j.neuroimage.2010.10.050 6. Kotkowski E, Price LR, DeFronzo RA, Franklin CG, Salazar M, Garrett AS. **Metabolic syndrome predictors of brain gray matter volume in an age-stratified community sample of 776 Mexican-American adults: results from the genetics of brain structure image archive**. *Front Aging Neurosci* (2022) **14** 999288. DOI: 10.3389/fnagi.2022.999288 7. Cosgrove KP, Mazure CM, Staley JK. **Evolving knowledge of sex differences in brain structure, function, and chemistry**. *Biol Psychiatry* (2007) **62** 847-855. DOI: 10.1016/j.biopsych.2007.03.001 8. Lüders E, Steinmetz H, Jäncke L. **Brain size and grey matter volume in the healthy human brain**. *NeuroReport* (2002) **13** 2371-2374. DOI: 10.1097/00001756-200212030-00040 9. Ritchie SJ, Cox SR, Shen X, Lombardo MV, Reus LM, Alloza C. **Sex differences in the adult human brain: evidence from 5216 UK Biobank participants**. *Cereb Cortex* (2018) **28** 2959-2975. DOI: 10.1093/cercor/bhy109 10. Kim SE, Lee JS, Woo S, Kim S, Kim HJ, Park S. **Sex-specific relationship of cardiometabolic syndrome with lower cortical thickness**. *Neurology* (2019) **93** e1045-e1057. DOI: 10.1212/WNL.0000000000008084 11. Suzuki H, Venkataraman AV, Bai W, Guitton F, Guo Y, Dehghan A. **Associations of regional brain structural differences with aging, modifiable risk factors for dementia, and cognitive performance**. *JAMA Netw Open* (2019) **2** e1917257. DOI: 10.1001/jamanetworkopen.2019.17257 12. Choi YY, Lee JJ, Choi KY, Seo EH, Choo IH, Kim H. **The Aging slopes of brain structures vary by ethnicity and sex: evidence from a large magnetic resonance imaging dataset from a single scanner of cognitively healthy elderly people in Korea**. *Front Aging Neurosci* (2020) **12** 233. DOI: 10.3389/fnagi.2020.00233 13. Scharf EL, Graff-Radford J, Przybelski SA, Lesnick TG, Mielke MM, Knopman DS. **Cardiometabolic health and longitudinal progression of white matter hyperintensity: the Mayo Clinic Study of Aging**. *Stroke* (2019) **50** 3037-3044. DOI: 10.1161/STROKEAHA.119.025822 14. Tamura Y, Shimoji K, Ishikawa J, Matsuo Y, Watanabe S, Takahashi H. **Subclinical atherosclerosis, vascular risk factors, and white matter alterations in diffusion tensor imaging findings of older adults with cardiometabolic diseases**. *Front Aging Neurosci* (2021) **13** 712385. DOI: 10.3389/fnagi.2021.712385 15. Smith SM, Vidaurre D, Alfaro-Almagro F, Nichols TE, Miller KL. **Estimation of brain age delta from brain imaging**. *Neuroimage* (2019) **200** 528-539. DOI: 10.1016/j.neuroimage.2019.06.017 16. Jonsson BA, Bjornsdottir G, Thorgeirsson TE, Ellingsen LM, Walters GB, Gudbjartsson DF. **Brain age prediction using deep learning uncovers associated sequence variants**. *Nat Commun* (2019) **10** 5409. DOI: 10.1038/s41467-019-13163-9 17. Cole JH, Leech R, Sharp DJ. **Alzheimer's Disease Neuroimaging Initiative Prediction of brain age suggests accelerated atrophy after traumatic brain injury**. *Ann Neurol.* (2015) **77** 571-81. DOI: 10.1002/ana.24367 18. Shokri-Kojori E, Bennett IJ, Tomeldan ZA, Krawczyk DC, Rypma B. **Estimates of brain age for gray matter and white matter in younger and older adults: insights into human intelligence**. *Brain Res* (2021) **1763** 147431. DOI: 10.1016/j.brainres.2021.147431 19. Franke K, Gaser C. **Ten years of brainAGE as a neuroimaging biomarker of brain aging: what insights have we gained?**. *Front Neurol* (2019) **10** 789. DOI: 10.3389/fneur.2019.00789 20. Cole JH, Ritchie SJ, Bastin ME, Valdés Hernández MC, Muñoz Maniega S, Royle N. **Brain age predicts mortality**. *Mol Psychiatry* (2018) **23** 1385-1392. DOI: 10.1038/mp.2017.62 21. Liem F, Varoquaux G, Kynast J, Beyer F, KharabianMasouleh S, Huntenburg JM. **Predicting brain-age from multimodal imaging data captures cognitive impairment**. *Neuroimage* (2017) **148** 179-188. DOI: 10.1016/j.neuroimage.2016.11.005 22. Franke K, Gaser C. **Longitudinal changes in individual BrainAGE in healthy aging, mild cognitive impairment, and Alzheimer’s disease**. *GeroPsych.* (2012) **25** 235-45. DOI: 10.1024/1662-9647/a000074 23. Lee JS, Shin HY, Kim HJ, Jang YK, Jung NY, Lee J. **Combined effects of physical exercise and education on age-related cortical thinning in cognitively normal individuals**. *Sci Rep* (2016) **6** 24284. DOI: 10.1038/srep24284 24. Kang SH, Park YH, Lee D, Kim JP, Chin J, Ahn Y. **The cortical neuroanatomy related to specific neuropsychological deficits in Alzheimer’s continuum**. *Dement Neurocogn Disord* (2019) **18** 77-95. DOI: 10.12779/dnd.2019.18.3.77 25. **Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies**. *Lancet* (2004) **363** 157-163. DOI: 10.1016/S0140-6736(03)15268-3 26. Lewis JD, Evans AC, Tohka J. **T1 white/gray contrast as a predictor of chronological age, and an index of cognitive performance**. *Neuroimage* (2018) **173** 341-350. DOI: 10.1016/j.neuroimage.2018.02.050 27. Cole JH, Poudel RPK, Tsagkrasoulis D, Caan MWA, Steves C, Spector TD. **Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker**. *Neuroimage* (2017) **163** 115-124. DOI: 10.1016/j.neuroimage.2017.07.059 28. Ning K, Zhao L, Matloff W, Sun F, Toga AW. **Association of relative brain age with tobacco smoking, alcohol consumption, and genetic variants**. *Sci Rep* (2020) **10** 10. DOI: 10.1038/s41598-019-56089-4 29. Brown CJ, Moriarty KP, Miller SP, Booth BG, Zwicker JG, Grunau RE, Descoteaux M, Maier-Hein L, Franz A, Jannin P, Collins DL, Duchesne S. **Prediction of brain network age and factors of delayed maturation in very preterm infants**. *Medical Image Computing and Computer-Assisted Intervention - MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11–13, 2017, Proceedings, Part I* (2017) 84-91 30. Whitmer RA. **Type 2 diabetes and risk of cognitive impairment and dementia**. *Curr Neurol Neurosci Rep* (2007) **7** 373-380. DOI: 10.1007/s11910-007-0058-7 31. Ninomiya T. **Diabetes mellitus and dementia**. *Curr Diab Rep* (2014) **14** 487. DOI: 10.1007/s11892-014-0487-z 32. Walker KA, Power MC, Gottesman RF. **Defining the relationship between hypertension, cognitive decline, and dementia: a review**. *Curr Hypertens Rep* (2017) **19** 24. DOI: 10.1007/s11906-017-0724-3 33. Gąsecki D, Kwarciany M, Nyka W, Narkiewicz K. **Hypertension, brain damage and cognitive decline**. *Curr Hypertens Rep* (2013) **15** 547-558. DOI: 10.1007/s11906-013-0398-4 34. Rocca WA, Mielke MM, Vemuri P, Miller VM. **Sex and gender differences in the causes of dementia: a narrative review**. *Maturitas* (2014) **79** 196-201. DOI: 10.1016/j.maturitas.2014.05.008 35. Sevre K, Lefrandt JD, Nordby G, Os I, Mulder M, Gans RO. **Autonomic function in hypertensive and normotensive subjects: the importance of gender**. *Hypertension* (2001) **37** 1351-1356. DOI: 10.1161/01.HYP.37.6.1351 36. Palatini P, Mos L, Santonastaso M, Saladini F, Benetti E, Mormino P. **Premenopausal women have increased risk of hypertensive target organ damage compared with men of similar age**. *J Womens Health (Larchmt)* (2011) **20** 1175-1181. DOI: 10.1089/jwh.2011.2771 37. Lim JG, Shapiro EP, Vaidya D, Najjar SS, Turner KL, Bacher AC. **Sex differences in left ventricular function in older persons with mild hypertension**. *Am Heart J* (2005) **150** 934-940. DOI: 10.1016/j.ahj.2005.01.013 38. Brinton RD, Yao J, Yin F, Mack WJ, Cadenas E. **Perimenopause as a neurological transition state**. *Nat Rev Endocrinol* (2015) **11** 393-405. DOI: 10.1038/nrendo.2015.82 39. Scheyer O, Rahman A, Hristov H, Berkowitz C, Isaacson RS, Diaz Brinton R. **Female sex and Alzheime’'s risk: the menopause connection**. *J Prev Alzheimers Dis* (2018) **5** 225-230. PMID: 30298180 40. Chedraui P, Jaramillo W, Pérez-López FR, Escobar GS, Morocho N, Hidalgo L. **Pro-inflammatory cytokine levels in postmenopausal women with the metabolic syndrome**. *Gynecol Endocrinol* (2011) **27** 685-691. DOI: 10.3109/09513590.2010.521270 41. Pfeilschifter J, Köditz R, Pfohl M, Schatz H. **Changes in proinflammatory cytokine activity after menopause**. *Endocr Rev* (2002) **23** 90-119. DOI: 10.1210/edrv.23.1.0456 42. Macova M, Armando I, Zhou J, Baiardi G, Tyurmin D, Larrayoz-Roldan IM. **Estrogen reduces aldosterone, upregulates adrenal angiotensin II AT2 receptors and normalizes adrenomedullary Fra-2 in ovariectomized rats**. *Neuroendocrinology* (2008) **88** 276-286. DOI: 10.1159/000150977 43. Weber MT, Maki PM, McDermott MP. **Cognition and mood in perimenopause: a systematic review and meta-analysis**. *J Steroid Biochem Mol Biol* (2014) **142** 90-98. DOI: 10.1016/j.jsbmb.2013.06.001 44. Luine VN. **Estradiol and cognitive function: past, present and future**. *Horm Behav* (2014) **66** 602-618. DOI: 10.1016/j.yhbeh.2014.08.011 45. Kilpi F, Soares ALG, Fraser A, Nelson SM, Sattar N, Fallon SJ. **Changes in six domains of cognitive function with reproductive and chronological ageing and sex hormones: a longitudinal study in 2411 UK mid-life women**. *BMC Womens Health* (2020) **20** 177. DOI: 10.1186/s12905-020-01040-3 46. Yaffe K, Barnes D, Lindquist K, Cauley J, Simonsick EM, Penninx B. **Endogenous sex hormone levels and risk of cognitive decline in an older biracial cohort**. *Neurobiol Aging* (2007) **28** 171-178. DOI: 10.1016/j.neurobiolaging.2006.10.004 47. Yaffe K, Sawaya G, Lieberburg I, Grady D. **Estrogen therapy in postmenopausal women: effects on cognitive function and dementia**. *JAMA* (1998) **279** 688-695. DOI: 10.1001/jama.279.9.688 48. Emslie C, Hunt K, Macintyre S. **How similar are the smoking and drinking habits of men and women in non-manual jobs?**. *Eur J Public Health* (2002) **12** 22-28. DOI: 10.1093/eurpub/12.1.22 49. Bray RM, Fairbank JA, Marsden ME. **Stress and substance use among military women and men**. *Am J Drug Alcohol Abuse* (1999) **25** 239-256. DOI: 10.1081/ADA-100101858 50. Thun M, Peto R, Boreham J, Lopez AD. **Stages of the cigarette epidemic on entering its second century**. *Tob Control* (2012) **21** 96-101. DOI: 10.1136/tobaccocontrol-2011-050294 51. Cifkova R, Pitha J, Krajcoviechova A, Kralikova E. **Is the impact of conventional risk factors the same in men and women? Plea for a more gender-specific approach**. *Int J Cardiol* (2019) **286** 214-219. DOI: 10.1016/j.ijcard.2019.01.039 52. Brickman AM, Schupf N, Manly JJ, Luchsinger JA, Andrews H, Tang MX. **Brain morphology in older African Americans, Caribbean Hispanics, and whites from northern Manhattan**. *Arch Neurol* (2008) **65** 1053-1061. DOI: 10.1001/archneur.65.8.1053 53. Zahodne LB, Manly JJ, Narkhede A, Griffith EY, DeCarli C, Schupf NS. **Structural MRI predictors of late-life cognition differ across African Americans, Hispanics, and Whites**. *Curr Alzheimer Res* (2015) **12** 632-639. DOI: 10.2174/1567205012666150530203214 54. Nazare JA, Smith JD, Borel AL, Haffner SM, Balkau B, Ross R. **Ethnic influences on the relations between abdominal subcutaneous and visceral adiposity, liver fat, and cardiometabolic risk profile: the International Study of Prediction of Intra-Abdominal Adiposity and Its Relationship With Cardiometabolic Risk/Intra-Abdominal Adiposity**. *Am J Clin Nutr* (2012) **96** 714-726. DOI: 10.3945/ajcn.112.035758 55. McKeigue PM, Miller GJ, Marmot MG. **Coronary heart disease in south Asians overseas: a review**. *J Clin Epidemiol* (1989) **42** 597-609. DOI: 10.1016/0895-4356(89)90002-4 56. Eastwood SV, Tillin T, Chaturvedi N, Hughes AD. **Ethnic differences in associations between blood pressure and stroke in South Asian and European men**. *Hypertension* (2015) **66** 481-488. DOI: 10.1161/HYPERTENSIONAHA.115.05672 57. Park JE, Kim BS, Kim KW, Hahm BJ, Sohn JH, Suk HW. **Decline in the incidence of all-cause and Alzheimer’s disease dementia: a 12-year-later rural cohort study in Korea**. *J Korean Med Sci* (2019) **34** e293. DOI: 10.3346/jkms.2019.34.e293 58. Niu H, Álvarez-Álvarez I, Guillén-Grima F, Aguinaga-Ontoso I. **Prevalence and incidence of Alzheimer’s disease in Europe: a meta-analysis**. *Neurologia* (2017) **32** 523-532. DOI: 10.1016/j.nrl.2016.02.016 59. Jang J-W, Park JH, Kim S, Lee S-H, Lee S-H, Kim Y-J. **Prevalence and incidence of dementia in South Korea: a nationwide analysis of the National Health Insurance Service senior cohort**. *J Clin Neurol* (2021) **17** 249-256. DOI: 10.3988/jcn.2021.17.2.249 60. Wild SH, Fischbacher C, Brock A, Griffiths C, Bhopal R. **Mortality from all causes and circulatory disease by country of birth in England and Wales 2001–2003**. *J Public Health (Oxf)* (2007) **29** 191-198. DOI: 10.1093/pubmed/fdm010
--- title: Characterization of host and escherichia coli strains causing recurrent urinary tract infections based on molecular typing authors: - Cheng-Yen Kao - Yen-Zheng Zhang - Deng-Chi Yang - Pek Kee Chen - Ching-Hao Teng - Wei-Hung Lin - Ming-Cheng Wang journal: BMC Microbiology year: 2023 pmcid: PMC10061793 doi: 10.1186/s12866-023-02820-1 license: CC BY 4.0 --- # Characterization of host and escherichia coli strains causing recurrent urinary tract infections based on molecular typing ## Abstract ### Background Escherichia coli is the leading pathogen responsible for urinary tract infection (UTI) and recurrent UTI (RUTI). Few studies have dealt with the characterization of host and bacteria in RUTI caused by E. coli with genetically identical or different strains. This study aimed to investigate the host and bacterial characteristics of E. coli RUTI based on molecular typing. ### Results Patients aged 20 years or above who presented with symptoms of UTI in emergency department or outpatient clinics between August 2009 and December 2010 were enrolled. RUTI was defined as patients had 2 or more infections in 6 months or 3 or more in 12 months during the study period. Host factors (including age, gender, anatomical/functional defect, and immune dysfunction) and bacterial factors (including phylogenicity, virulence genes, and antimicrobial resistance) were included for analysis. There were 41 patients ($41\%$) with 91 episodes of E. coli RUTI with highly related PFGE (HRPFGE) pattern (pattern similarity > $85\%$) and 58 ($59\%$) patients with 137 episodes of E. coli RUTI with different molecular typing (DMT) pattern, respectively. There was a higher prevalence of phylogenetic group B2 and neuA and usp genes in HRPFGE group if the first episode of RUTI caused by HRPFGE E. coli strains and all episodes of RUTI caused by DMT E. coli strains were included for comparison. The uropathogenic E. coli (UPEC) strains in RUTI were more virulent in female gender, age < 20 years, neither anatomical/ functional defect nor immune dysfunction, and phylogenetic group B2. There were correlations among prior antibiotic therapy within 3 months and subsequent antimicrobial resistance in HRPFGE E. coli RUTI. The use of fluoroquinolones was more likely associated with subsequent antimicrobial resistance in most types of antibiotics. ### Conclusions This study demonstrated that the uropathogens in RUTI were more virulent in genetically highly-related E. coli strains. Higher bacterial virulence in young age group (< 20 years) and patients with neither anatomical/functional defect nor immune dysfunction suggests that virulent UPEC strains are needed for the development of RUTI in healthy populations. Prior antibiotic therapy, especially the fluoroquinolones, within 3 months could induce subsequent antimicrobial resistance in genetically highly-related E. coli RUTI. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12866-023-02820-1. ## Background Urinary tract infection (UTI) is a common infectious disease in the urinary tract. Nearly half of all women experience a UTI in their lifetime, and up to $27\%$-$50\%$ of these patients will have a recurrent infection in the following 6 months [1–3]. Recurrent UTI (RUTI) occurs due to bacterial persistence or bacterial reinfection, and *Escherichia coli* is one of the dominant pathogens responsible for RUTI [4]. Bacterial persistence is defined by the same bacteria strain not being eradicated within the host 2 weeks after antibiotic treatment. Reinfection is a recurrence with a different microorganism, the same microorganism in more than 2 weeks, or a sterile intervening culture [5]. The increasing prevalence and growing problem of antibiotic resistance among uropathogens present a critical challenge to the clinical management of RUTI [6]. There are three possible mechanisms responsible for correctly treated infections with subsequent gain of resistance: evolution of resistance through mutations, through dedicated resistance genes, and through reinfection with a different strain resistant to antibiotics [7]. Prior antimicrobial drug exposure is a risk factor for resistant UTI, especially after receiving multiple courses of antibiotics for recurrent infections [8, 9]. There were several studies investigating the bacterial characteristics in RUTI caused by uropathogenic E. coli (UPEC) with genetically identical or different strains. Regarding the RUTI in adults with community-acquired pyelonephritis caused by E. coli, Kärkkäinen et al. reported that genotype comparisons by random amplified polymorphic DNA (RAPD)-PCR analysis showed that $75\%$ of the original and recurrent strains were genetically non-identical. Virulence factors were evenly distributed among E. coli isolates of index episodes, independent of the recurrences. Lindblom et al. reported that half of the patients with E. coli RUTI were infected with ST131 isolates, and Clade C2 were the dominant subsets among ST131 isolates and more common in patients with RUTI than sporadic UTI [10, 11]. The aims of this study were to investigate the host characteristics, bacterial virulence, and antimicrobial resistance in genetically highly-related and genetically discordant E. coli strains of RUTI based on molecular typing. ## Results A total of 99 patients with 228 episodes of RUTI (including the first episode) caused by E. coli were included for analysis (Fig. 1A). Four primers, namely 1247, 1254, 1283, and 1290, were used in the time-saving and cost-saving random amplification of polymorphic DNA (RAPD)-PCR assay to determine the clonality of E. coli isolated from a single patient. The results showed that 46 of 99 RUTI patients (a total of 102 isolates) were suspected to be infected by the closely related clones (Fig. 1A). Pulsed-field gel electrophoresis (PFGE) was performed on 102 strains isolated from 46 patients to validate RAPD-PCR results (Fig. 1B). Strains isolated from a single patient showing PFGE patterns > $85\%$ identity with a tolerance of $0.9\%$ and an optimization parameter of $0.9\%$ by GelCompar II software were defined as highly related strains (Fig. 1B). There were 41 patients ($41\%$) with 91 episodes of E. coli RUTI with highly related PFGE (HRPFGE) pattern and 58 ($59\%$) patients with 137 episodes of E. coli RUTI with different molecular typing (DMT) pattern, respectively (Fig. 1A & 1B). Interestingly, three UPEC strains (U128, U1321, U1535) with two PFGE patterns were isolated from patient 21. Female gender was predominant ($74\%$). The bacterial characteristics in relation to molecular typing grouping in patients with RUTI are shown in Table 1. There was no significant difference in phylogenicity and bacterial virulence between HRPFGE and DMT E. coli strains in first episode of RUTI. If first episode of RUTI caused by HRPFGE E. coli strains and all episodes of RUTI caused by DMT E. coli strains were included for comparison, there was a higher prevalence of phylogenetic group B2 and neuA and usp genes in HRPFGE group. The host characteristics in relation to molecular typing grouping in 99 patients with E. coli RUTI are shown in Table 2. There was no significant difference in age, anatomical/functional defect, or immune dysfunction between PFGE identical and molecular typing different groups; there was a higher prevalence of male gender in the HRPFGE group. Fig. 1PFGE analysis to determine the clonality ofE. colistrains isolates from 99 patients with recurrent UTI. ( A). Experimental flow chart procedures of E. coli collection and clonality determination. ( B). PFGE patterns of 102 strains isolated from 46 RUTI patients. Eleven strains shown in red were considered as negative controls with different PFGE patterns. The black dotted line is the $85\%$ similarity line. Table 1Bacterial characteristics in relation to molecular typing grouping in patients with recurrent urinary tract infection (total 178 isolates)CharacteristicHighly related PFGE pattern,first episode($$n = 41$$)n (%)Different molecular typing pattern,first episode($$n = 58$$)n (%)Different molecular typing pattern,all episodes($$n = 137$$)n (%)P-value aP-value bPhylogenetic group0.01630.0003A0 [0]4 [7]12 [9]B11 [2]9 [16]28 [20]B228 [68]27 [47]59 [43]D10 [24]18 [31]38 [28]Untypable2 [5]00Virulence factor neuA 12 [29]9 [16]19 [14]0.13460.0332 papG I 000---- papG II 8 [20]12 [21]25 [18]1.00000.8223 papG III 5 [12]9 [16]19 [14]0.77321.0000 sfa 2 [5]4 [7]7 [5]1.00001.0000 foc 2 [5]5 [9]8 [6]0.69621.0000 cnf1 7 [17]8 [14]15 [11]0.77760.2903 aer 27 [66]40 [69]86 [63]0.82840.8536 usp 28 [68]28 [48]60 [44]0.06420.0074 iha 16 [39]13 [22]36 [26]0.11570.1220 ompT 33 [80]42 [72]88 [64]0.47610.0575 afa 14 [34]30 [52]56 [41]0.10220.4716 iroN 11 [27]24 [41]51 [37]0.20010.2644 fimH 39 [95]55 [95]126 [92]1.00000.7352 hlyA 11 [27]8 [14]19 [14]0.12480.0600 sat 15 [37]12 [21]35 [26]0.10890.1720PFGE: pulsed-field gel electrophoresisa Highly related PFGE pattern, first episode versus different molecular typing pattern, first episodeb Highly related PFGE pattern, first episode versus different molecular typing pattern, all episodes Table 2Host characteristics in relation to molecular typing grouping in 99 patients with recurrent urinary tract infection (first episode)CharacteristicHighly related PFGE pattern($$n = 41$$)n (%)Different molecular typing pattern($$n = 58$$)n (%)P-valueAge (year)64 ± 2459 ± 260.3061Gender (male)18 [44]8 [14]0.0011Anatomical/functional defect18 [44]21 [36]0.5320Immune dysfunction16 [39]29 [50]0.3111Both anatomical/functional defects and immune dysfunction6 [15]5 [9]0.5178Neither anatomical/functional defect nor immune dysfunction13 [32]13 [22]0.3568PFGE: pulsed-field gel electrophoresis The bacterial characteristics in relation to gender in 99 RUTI patients showed no difference in phylogenicity or virulence genes (Table S1). The bacterial characteristics in relation to age in 99 RUTI patients showed a higher prevalence of foc and cnf1 genes in the age < 20 years group (Table S2). The bacterial characteristics in relation to anatomical/ functional defect and immune dysfunction in 99 RUTI patients showed a higher prevalence of papG III, sfa, and hlyA genes in neither anatomical/ functional defect nor immune dysfunction group (Table 3). The bacterial characteristics in relation to phylogenetic group B2 in 99 RUTI patients showed a higher prevalence of neuA, sfa, cnf1, usp, iha, ompT, afa, hlyA, and sat genes (Table 4). Table 3Bacterial characteristics in relation to anatomical/ functional defect and immune dysfunction in 99 patients with recurrent urinary tract infection (first episode)CharacteristicEither anatomical/ functional defect or immune dysfunction($$n = 73$$)n (%)Neither anatomical/ functional defect nor immune dysfunction($$n = 26$$)n (%)P-valuePhylogenetic group0.6586A3 [4]1 [4]B18 [11]2 [8]B238 [52]17 [65]D23 [32]5 [19]Untypable1 [1]1 [4]Virulence factor neuA 14 [19]7 [27]0.4136 papG I 00-- papG II 16 [22]4 [15]0.5784 papG III 6 [8]8 [31]0.0084 sfa 2 [3]4 [15]0.0396 foc 3 [4]4 [15]0.0752 cnf1 9 [12]6 [23]0.2110 aer 52 [71]15 [58]0.2285 usp 39 [53]17 [65]0.3596 iha 21 [29]8 [31]1.0000 ompT 52 [71]23 [88]0.1099 afa 36 [49]8 [31]0.1142 iroN 22 [30]13 [50]0.0944 fimH 70 [96]24 [92]0.6043 hlyA 10 [14]9 [35]0.0389 sat 20 [27]7 [27]1.0000 Table 4Bacterial characteristics in relation to phylogenetic group B2 in 99 patients with recurrent urinary tract infection (first episode)CharacteristicPhylogenetic group B2($$n = 55$$)n (%)Non-phylogenetic group B2($$n = 44$$)n (%)P-valueVirulence factor neuA 20 [36]1 [2]< 0.0001 papG I 00-- papG II 15 [27]5 [11]0.0766 papG III 6 [11]8 [18]0.3875 sfa 6 [11]00.0322 foc 6 [11]1 [2]0.1280 cnf1 14 [25]1 [2]0.0013 aer 39 [71]28 [64]0.5186 usp 52 [95]4 [9]< 0.0001 iha 25 [45]4 [9]< 0.0001 ompT 55 [100]20 [45]< 0.0001 afa 19 [35]25 [57]0.0413 iroN 22 [40]13 [30]0.2990 fimH 53 [96]41 [93]0.6530 hlyA 15 [27]4 [9]0.0380 sat 24 [44]3 [7]< 0.0001 The antimicrobial susceptibility in RUTI related to HRPFGE E. coli (41 patients, 91 episodes) is shown in Table S3. There was no significant difference in antimicrobial susceptibility of most antibiotics between the first episode and second episode of RUTI E. coli strains. The serial antimicrobial susceptibility in recurrent urinary tract infections related to HRPFGE E. coli strains is shown in Table S4. The relationships among prior antibiotic therapy within 3 months and antimicrobial resistance in subsequent 91 episodes of RUTI related to HRPFGE E. coli, are shown in Table S5. There were correlations among prior antibiotic therapy within 3 months and subsequent antimicrobial resistance in HRPFGE E. coli RUTI, and the use of fluoroquinolones was associated with more antimicrobial resistance of UPEC in the following RUTI. The use of flomoxef, 1st generation cephalosporins, ampicillin or ampicillin/sulbactam, and trimethoprim/sulfamethoxazole was not associated with antimicrobial resistance in all types of antibiotics during the following 3 months. The use of fluoroquinolones was more likely associated with antimicrobial resistance in most types of antibiotics [flomoxef, piperacillin/tazobactam, cephalosporins (1st generation, 2nd generation, and 3rd generation) and fluoroquinolones] during the following 3 months. The use of 2nd generation and 3rd generation cephalosporin was associated with subsequent antimicrobial resistance in flomoxef, and the use of aminoglycosides was associated with subsequent antimicrobial resistance in gentamicin and trimethoprim/sulfamethoxazole during the following 3 months. ## Discussion RUTI may be caused by repeated ascending infections or chronic/persistent infections in the bladder [1]. E. coli is the leading pathogen responsible for RUTI. RUTI may be caused by the same or different E. coli strains. There have been several studies presenting the bacterial characteristics (phylogenicity, virulence factors, and biofilm), similarity and difference, and genomic variation in E. coli RUTI [10, 12, 13]. However, there have been scarce reports dealing with the host and bacterial characteristics based on the molecular typing in E. coli RUTI. Our study demonstrated and compared the patterns of host characteristics and serial bacterial characteristics between genetically highly-related and different E. coli strains in RUTI. The UPEC strains in RUTI were more virulent in female gender, age < 20 years, neither anatomical/functional defect nor immune dysfunction, and phylogenetic group B2. In HRPFGE E. coli RUTI, there were correlations among prior antibiotic therapy within 3 months and subsequent antimicrobial resistance in HRPFGE E. coli RUTI. The use of fluoroquinolones was more likely to have antimicrobial resistance in most types of antibiotic during the following 3 months. There were several bacterial characteristics contributing to the development of E. coli UTI, the phylogenicity, virulence factors, and antimicrobial resistance of UPEC strains varied from region to region [14–19]. The study dealing with uncomplicated community-acquired UTI in women by PFGE showed that $77\%$ after Pivmecillinam treatment had a relapse with the primary infecting E. coli strains [20]. Several studies demonstrated the recurrent rate of highly related strains in RUTI varied from 34–$82\%$ based on PFGE [6, 21–24]. Nielsen et al. reported that RUTI E. coli isolates did not cluster distinct from non-RUTI isolates in a single nucleotide polymorphism (SNP) phylogeny [13]. Our study showed that $41\%$ of UPEC strains in RUTI were generically highly related. Phylogenetic group B2 was the most predominant. There was no significant difference in phylogenicity and virulence profile between HRPFGE and DMT E. coli strains in the first episode of RUTI. Whereas increased bacterial virulence was present in HRPFGE E. coli strains if all episodes of RUTI are included for comparison. Repeated ascending infection and chronic/persistent infection in the bladder are the two possible mechanisms of RUTI. It has been suggested that RUTI is a consequence of complex host–pathogen interactions involving bacterial factors and deficiency in host defense [25–27]. Several host factors have been associated with UTI and RUTI, which include anatomic and functional disorders (e.g., female gender, post-menopause, vaginal infection, diabetes, urinary obstruction, urinary retention, immunosuppression, renal failure, renal transplantation, pregnancy, urolithiasis, and indwelling catheters or other drainage devices) [13, 26, 28]. There have been few studies investigating the host characteristics in relation to molecular typing in RUTI. This study showed that there was a higher prevalence of male gender in the HRPFGE group compared to that in the DMT group. Overall, there was no significant difference in phylogenicity and virulence between HRPFGE and DMT groups. There was a significantly higher bacterial virulence (foc and cnf1 genes) in the young age group (< 20 years), and a significantly lower bacterial virulence (papGIII, sfa and, iroN genes) in patients with either anatomical/functional defect or immune dysfunction. Phylogenetic group B2 prevailed in UPEC strains of UTI and RUTI [6, 16, 17, 29–31]. A study by Ejrnæs et al. showed that E. coli isolates causing persistence or relapse were more often of phylogenetic group B2, and were characterized by a higher prevalence of virulence factors. No specific combination of presence/absence of virulence factors could serve as a marker to predict RUTI [12]. Luo et al. reported that the persistence strains had more phylogenetic group B2 and virulence genes than the reinfection strains in E. coli RUTI [23]. Our study revealed that phylogenetic group B2 was the most predominant group and harbored more virulence genes in virulence profiles than the other phylogenetic groups in E. coli RUTI. There was an increasing trend in antimicrobial resistance associated with more RUTI episodes [32]. Genomic surveillance of antibiotic-resistant uropathogens shows that drug-resistant clones persisted within and transmitted between the intestinal and urinary tracts of patients affected by RUTI [33]. Among women with recurrent UTI receiving prophylaxis, the susceptibility pattern of E. coli strains within one month before a symptomatic E. coli UTI could be used to make informed choices for empirical antibiotic treatment [34]. The impact of antimicrobial resistance on the development of RUTI remains controversial. Luo et al. reported that the antimicrobial susceptibilities of UPEC isolates had little effect on the RUTI [23]. A study by Ormeño et al. showed that there were high rates of antibiotic resistance to the usual antibiotics in E. coli causing UTI, which emerged as a risk factor for the development of RUTI [35]. This study demonstrated that there was no significant increase in antimicrobial resistance of most antibiotics between the first and second episodes of HRPFGE E. coli RUTI. There were correlations among prior antibiotic therapy within 3 months and subsequent antimicrobial resistance in HRPFGE E. coli RUTI, and the use of fluoroquinolones was associated with more antimicrobial resistance of UPEC in the following RUTI. After machine-learning analysis of UTI and wound infections, Stracy et al. suggested that selection for existing resistant strains rather than de novo evolution is the predominant mechanism of treatment-induced emergence of resistance [7]. There are several limitations in our study. First, this was a single-center study with retrospective design and a relatively small sample size was enrolled. Therefore, a multicenter prospective study with a larger sample size is needed to verify the observations of our study. Second, we did not include all important characteristics of patients and E. coli in our analyzes. Third, the duration of antibiotic therapy and the severity of UTI were not included in the analysis. Fourth, the determination of genetic relatedness among E. coli strains isolated from a single patient was based on molecular typing, not whole genome sequencing. ## Conclusions This study provides a profile of host and bacterial characteristics of E. coli strains in RUTI based on the molecular typing. Compared to the overall genetically different strains, the uropathogens were more virulent in genetically highly related E. coli strains in RUTI. Higher bacterial virulence in young age group (< 20 years) and patients with neither anatomical/functional defect nor immune dysfunction suggests that more virulent UPEC strains are needed for the development of RUTI in healthy populations. Prior antibiotic therapy within 3 months could induce subsequent antimicrobial resistance in genetically highly related E. coli RUTI. ## Sample collection This is a single-center retrospective cohort study. The study enrolled patients aged 20 years or above who presented with symptoms of UTI in emergency department (ED) or outpatient clinics of National Cheng Kung University Hospital (NCKUH) between August 2009 and December 2010. Data regarding clinical and demographic characteristics, comorbidities, and prescribed medication were collected from the electronic medical record. RUTI was defined as patients had 2 or more infections in 6 months or 3 or more in 12 months during the study period [5]. Each episode of UTI presented with UTI symptoms including pain on urination, lumbago or fever and a bacterial count of more than 105 colony-forming units/mL from a urine specimen (collected from midstream or catheterized urine). The duration between two episodes of E. coli RUTI in this study was more than 2 weeks. Anatomical/functional defects included urinary tract obstruction, neurogenic bladder, urolithiasis, urinary tract tumor, vesicoureteral reflux, kidney transplantation, and indwelling catheters or drainage devices; immune dysfunction included diabetes, cirrhosis, malignancy, autoimmune disease, renal failure, and immunosuppression. This study was reviewed and approved by the Institutional Review Board of National Cheng Kung University Hospital, Tainan, Taiwan (B-ER-109-565). All procedures and methods were performed in accordance with the relevant guidelines and regulations. ## DNA extraction and random amplified polymorphic DNA-PCR Genomic DNA for E. coli was prepared using the Qiagen DNeasy Blood and Tissue kit (California, USA), according to the manufacturer’s instructions. Four primers, namely 1247, 1254, 1283, and 1290 [36], were used in RAPD-PCR assay to determine the clonality of E. coli isolated from a single patient. RAPD-profiles varying from each other in the positions of up to three bands were considered closely related. ## Pulsed-field gel electrophoresis typing PFGE of XbaI-digested genomic DNA was performed with a CHEF Mapper XA apparatus (Bio-Rad Laboratories, Inc., Hercules, CA, United States) using a $1\%$ agarose gel (Seakem Gold agarose; FMC Bio Products) in 0.5× Tris-Borate-EDTA for 19 h at 14ºC with pulsed times ranging from 5 to 35 s at 6 V/cm. The gels were stained with ethidium bromide and photographed with UV transillumination. PFGE profiles were subjected to data processing using the GelCompar II software, version 2.0 (Unimed Healthcare, Inc., Houston, TX, United States), with a tolerance of $0.9\%$ and an optimization parameter of $0.9\%$. Strains were considered to be genetically highly-related if they possessed > $85\%$ similarity to the restriction fragment patterns of DNA [10, 37]. ## Phylogenetic analysis The phylogenetic grouping of the E. coli isolates was determined by an algorithm of PCR-based method proposed by Clermont et al [38]. E. coli isolates were assigned to one of the four main phylogenetic groups (A, B1, B2, and D) according to the presence of chuA, yjaA, and the DNA fragment TSPE4.C2 [12, 39]. ## Detection of virulence genes Sixteen uropathogenic virulence factor genes of E. coli were determined using PCR. Primer pairs specific for K1 capsule gene (neuA), 3 PapG adhesion genes (papG class I to III) of P-fimbriae, and genes for type 1 fimbrial adhesins (fimH), S-/F1C-fimbriae (sfa/foc), afimbrial adhesins (afa), iron regulated gene A homologue adhesins (iha), hemolysin (hlyA), cytotoxic necrotizing factor 1 (cnf1), catecholate siderophore receptor (iroN), aerobactin receptor (iutA), outer membrane protease T (ompT), and uropathogenic specific protein (usp) have been described previously [13, 16, 18, 40–42]. Positive and negative control clinical isolates derived from our previous study [42] for each gene were also used in each assay. ## Determination of antimicrobial susceptibility The minimum inhibitory concentrations (MICs) to flomoxef (FLO), ampicillin-sulbactam (SAM), piperacillin/tazobactam (TZP), cefazolin (CZ), cefuroxime (CXM), cefmetazole (CMZ), ceftazidime (CAZ), ceftriaxone (CRO), cefoperazone/sulbactam (CFS), cefepime (FEP), ertapenem (ETP), imipenem (IPM), amikacin (AN), gentamicin (GM), ciprofloxacin (CIP), levofloxacin (LVX), tigecycline (TGC), and trimethoprim/sulfamethoxazole (SXT) by Vitek 2 testing using software version 5.04 and the AST-GN69 and AST-XN06 cards, according to the manufacturer’s instructions. E. coli ATCC 25922 was used as a quality control strain. The interpretation of resistance was determined according to the recommendations of the Clinical and Laboratory Standards Institute (CLSI) guideline [17, 43]. ## Statistical analysis The Chi-square test or Fisher’s exact test (two-tailed) was used for the comparison of categorical factors, whereas the Wilcoxon rank-sum test or Pearson’s Chi-squared test was used for the comparison of continuous factors between groups. A p value < 0.05 was considered to be statistically significant. All statistical analyses were performed using JMP software version 7.0 (SAS Institute Inc., Cary, NC, USA). ## Electronic Supplementary Material Below is the link to the electronic supplementary material Supplementary Material 1 Supplementary Material 2 ## References 1. Glover M, Moreira CG, Sperandio V, Zimmern P. **Recurrent urinary tract infections in healthy and nonpregnant women**. *Urol Sci* (2014.0) **25** 1-8. DOI: 10.1016/j.urols.2013.11.007 2. Ikaheimo R, Siitonen A, Heiskanen T, Karkkainen U, Kuosmanen P, Lipponen P. **Recurrence of urinary tract infection in a primary care setting: analysis of a 1-year follow-up of 179 women**. *Clin Infect Dis* (1996.0) **22** 91-9. DOI: 10.1093/clinids/22.1.91 3. Sihra N, Goodman A, Zakri R, Sahai A, Malde S. **Nonantibiotic prevention and management of recurrent urinary tract infection**. *Nat Rev Urol* (2018.0) **15** 750-76. DOI: 10.1038/s41585-018-0106-x 4. Kodner CM, Thomas Gupton EK. **Recurrent urinary tract infections in women: diagnosis and management**. *Am Fam Physician* (2010.0) **82** 638-43. PMID: 20842992 5. Dason S, Dason JT, Kapoor A. **Guidelines for the diagnosis and management of recurrent urinary tract infection in women**. *Can Urol Assoc J* (2011.0) **5** 316-22. DOI: 10.5489/cuaj.11214 6. Silverman JA, Schreiber HLt, Hooton TM, Hultgren SJ. **From physiology to pharmacy: developments in the pathogenesis and treatment of recurrent urinary tract infections**. *Curr Urol Rep* (2013.0) **14** 448-56. DOI: 10.1007/s11934-013-0354-5 7. Stracy M, Snitser O, Yelin I, Amer Y, Parizade M, Katz R. **Minimizing treatment-induced emergence of antibiotic resistance in bacterial infections**. *Science* (2022.0) **375** 889-94. DOI: 10.1126/science.abg9868 8. Metlay JP, Strom BL, Asch DA. **Prior antimicrobial drug exposure: a risk factor for trimethoprim-sulfamethoxazole-resistant urinary tract infections**. *J Antimicrob Chemother* (2003.0) **51** 963-70. DOI: 10.1093/jac/dkg146 9. Yelin I, Snitser O, Novich G, Katz R, Tal O, Parizade M. **Personal clinical history predicts antibiotic resistance of urinary tract infections**. *Nat Med* (2019.0) **25** 1143-52. DOI: 10.1038/s41591-019-0503-6 10. Karkkainen UM, Ikaheimo R, Katila ML, Siitonen A. **Recurrence of urinary tract infections in adult patients with community-acquired pyelonephritis caused by E. coli: a 1-year follow-up**. *Scand J Infect Dis* (2000.0) **32** 495-9. DOI: 10.1080/003655400458767 11. Lindblom A, Kiszakiewicz C, Kristiansson E, Yazdanshenas S, Kamenska N, Karami N. **The impact of the ST131 clone on recurrent ESBL-producing E. coli urinary tract infection: a prospective comparative study**. *Sci Rep* (2022.0) **12** 10048. DOI: 10.1038/s41598-022-14177-y 12. Ejrnaes K, Stegger M, Reisner A, Ferry S, Monsen T, Holm SE. **Characteristics of Escherichia coli causing persistence or relapse of urinary tract infections: phylogenetic groups, virulence factors and biofilm formation**. *Virulence* (2011.0) **2** 528-37. DOI: 10.4161/viru.2.6.18189 13. 13.Nielsen KL, Stegger M, Kiil K, Lilje B, Ejrnaes K, Leihof RF, et al. Escherichia coli Causing Recurrent Urinary Tract Infections: Comparison to Non-Recurrent Isolates and Genomic Adaptation in Recurrent Infections. Microorganisms. 2021;9(7). 10.3390/microorganisms9071416. 14. Bunduki GK, Heinz E, Phiri VS, Noah P, Feasey N, Musaya J. **Virulence factors and antimicrobial resistance of uropathogenic Escherichia coli (UPEC) isolated from urinary tract infections: a systematic review and meta-analysis**. *BMC Infect Dis* (2021.0) **21** 753. DOI: 10.1186/s12879-021-06435-7 15. 15.Lin WH, Zhang YZ, Liu PY, Chen PS, Wang S, Kuo PY, et al. Distinct Characteristics of Escherichia coli Isolated from Patients with Urinary Tract Infections in a Medical Center at a Ten-Year Interval. Pathogens. 2021;10(9). 10.3390/pathogens10091156. 16. Ny S, Edquist P, Dumpis U, Grondahl-Yli-Hannuksela K, Hermes J, Kling AM. **Antimicrobial resistance of Escherichia coli isolates from outpatient urinary tract infections in women in six European countries including Russia**. *J Glob Antimicrob Resist* (2019.0) **17** 25-34. DOI: 10.1016/j.jgar.2018.11.004 17. Rezatofighi SE, Mirzarazi M, Salehi M. **Virulence genes and phylogenetic groups of uropathogenic Escherichia coli isolates from patients with urinary tract infection and uninfected control subjects: a case-control study**. *BMC Infect Dis* (2021.0) **21** 361. DOI: 10.1186/s12879-021-06036-4 18. Wang MC, Tseng CC, Wu AB, Lin WH, Teng CH, Yan JJ. **Bacterial characteristics and glycemic control in diabetic patients with Escherichia coli urinary tract infection**. *J Microbiol Immunol Infect* (2013.0) **46** 24-9. DOI: 10.1016/j.jmii.2011.12.024 19. Yi-Te C, Shigemura K, Nishimoto K, Yamada N, Kitagawa K, Sung SY. **Urinary tract infection pathogens and antimicrobial susceptibilities in Kobe, Japan and Taipei, Taiwan: an international analysis**. *J Int Med Res* (2020.0) **48** 300060519867826. DOI: 10.1177/0300060519867826 20. Ejrnaes K, Sandvang D, Lundgren B, Ferry S, Holm S, Monsen T. **Pulsed-field gel electrophoresis typing of Escherichia coli strains from samples collected before and after pivmecillinam or placebo treatment of uncomplicated community-acquired urinary tract infection in women**. *J Clin Microbiol* (2006.0) **44** 1776-81. DOI: 10.1128/JCM.44.5.1776-1781.2006 21. Foxman B, Gillespie B, Koopman J, Zhang L, Palin K, Tallman P. **Risk factors for second urinary tract infection among college women**. *Am J Epidemiol* (2000.0) **151** 1194-205. DOI: 10.1093/oxfordjournals.aje.a010170 22. Foxman B, Zhang L, Tallman P, Palin K, Rode C, Bloch C. **Virulence characteristics of Escherichia coli causing first urinary tract infection predict risk of second infection**. *J Infect Dis* (1995.0) **172** 1536-41. DOI: 10.1093/infdis/172.6.1536 23. Luo Y, Ma Y, Zhao Q, Wang L, Guo L, Ye L. **Similarity and divergence of phylogenies, antimicrobial susceptibilities, and virulence factor profiles of Escherichia coli isolates causing recurrent urinary tract infections that persist or result from reinfection**. *J Clin Microbiol* (2012.0) **50** 4002-7. DOI: 10.1128/JCM.02086-12 24. Skjot-Rasmussen L, Hammerum AM, Jakobsen L, Lester CH, Larsen P, Frimodt-Moller N. **Persisting clones of Escherichia coli isolates from recurrent urinary tract infection in men and women**. *J Med Microbiol* (2011.0) **60** 550-4. DOI: 10.1099/jmm.0.026963-0 25. Ching C, Schwartz L, Spencer JD, Becknell B. **Innate immunity and urinary tract infection**. *Pediatr Nephrol* (2020.0) **35** 1183-92. DOI: 10.1007/s00467-019-04269-9 26. Flores-Mireles AL, Walker JN, Caparon M, Hultgren SJ. **Urinary tract infections: epidemiology, mechanisms of infection and treatment options**. *Nat Rev Microbiol* (2015.0) **13** 269-84. DOI: 10.1038/nrmicro3432 27. Soric Hosman I, Cvitkovic Roic A, Lamot L. **A Systematic Review of the (Un)known Host Immune Response Biomarkers for Predicting Recurrence of Urinary Tract Infection**. *Front Med (Lausanne)* (2022.0) **9** 931717. DOI: 10.3389/fmed.2022.931717 28. Stapleton AE. **Urinary tract infection pathogenesis: host factors**. *Infect Dis Clin North Am* (2014.0) **28** 149-59. DOI: 10.1016/j.idc.2013.10.006 29. Halaji M, Fayyazi A, Rajabnia M, Zare D, Pournajaf A, Ranjbar R. **Phylogenetic Group Distribution of Uropathogenic Escherichia coli and Related Antimicrobial Resistance Pattern: A Meta-Analysis and Systematic Review**. *Front Cell Infect Microbiol* (2022.0) **12** 790184. DOI: 10.3389/fcimb.2022.790184 30. Hyun M, Lee JY, Kim HA. **Differences of virulence factors, and antimicrobial susceptibility according to phylogenetic group in uropathogenic Escherichia coli strains isolated from Korean patients**. *Ann Clin Microbiol Antimicrob* (2021.0) **20** 77. DOI: 10.1186/s12941-021-00481-4 31. Norouzian H, Katouli M, Shahrokhi N, Sabeti S, Pooya M, Bouzari S. **The relationship between phylogenetic groups and antibiotic susceptibility patterns of Escherichia coli strains isolated from feces and urine of patients with acute or recurrent urinary tract infection**. *Iran J Microbiol* (2019.0) **11** 478-87. PMID: 32148679 32. Opatowski M, Brun-Buisson C, Touat M, Salomon J, Guillemot D, Tuppin P. **Antibiotic prescriptions and risk factors for antimicrobial resistance in patients hospitalized with urinary tract infection: a matched case-control study using the French health insurance database (SNDS)**. *BMC Infect Dis* (2021.0) **21** 571. DOI: 10.1186/s12879-021-06287-1 33. 33.Thanert R, Reske KA, Hink T, Wallace MA, Wang B, Schwartz DJ, et al. Comparative Genomics of Antibiotic-Resistant Uropathogens Implicates Three Routes for Recurrence of Urinary Tract Infections. mBio. 2019;10(4). 10.1128/mBio.01977-19. 34. Beerepoot MA, den Heijer CD, Penders J, Prins JM, Stobberingh EE, Geerlings SE. **Predictive value of Escherichia coli susceptibility in strains causing asymptomatic bacteriuria for women with recurrent symptomatic urinary tract infections receiving prophylaxis**. *Clin Microbiol Infect* (2012.0) **18** E84-90. DOI: 10.1111/j.1469-0691.2012.03773.x 35. Ormeno MA, Ormeno MJ, Quispe AM, Arias-Linares MA, Linares E, Loza F. **Recurrence of Urinary Tract Infections due to Escherichia coli and Its Association with Antimicrobial Resistance**. *Microb Drug Resist* (2022.0) **28** 185-90. DOI: 10.1089/mdr.2021.0052 36. Hopkins KL, Hilton AC. **Use of multiple primers in RAPD analysis of clonal organisms provides limited improvement in discrimination**. *Biotechniques* (2001.0) **30** 1262. DOI: 10.2144/01306st03 37. Hao M, Shen Z, Ye M, Hu F, Xu X, Yang Y. **Outbreak Of Klebsiella pneumoniae Carbapenemase-Producing Klebsiella aerogenes Strains In A Tertiary Hospital In China**. *Infect Drug Resist* (2019.0) **12** 3283-90. DOI: 10.2147/IDR.S221279 38. Clermont O, Christenson JK, Denamur E, Gordon DM. **The Clermont Escherichia coli phylo-typing method revisited: improvement of specificity and detection of new phylo-groups**. *Environ Microbiol Rep* (2013.0) **5** 58-65. DOI: 10.1111/1758-2229.12019 39. Wang MC, Tseng CC, Wu AB, Huang JJ, Sheu BS, Wu JJ. **Different roles of host and bacterial factors in Escherichia coli extra-intestinal infections**. *Clin Microbiol Infect* (2009.0) **15** 372-9. DOI: 10.1111/j.1469-0691.2009.02708.x 40. Kao CY, Lin WH, Tseng CC, Wu AB, Wang MC, Wu JJ. **The complex interplay among bacterial motility and virulence factors in different Escherichia coli infections**. *Eur J Clin Microbiol Infect Dis* (2014.0) **33** 2157-62. DOI: 10.1007/s10096-014-2171-2 41. Lin WH, Tseng CC, Wu AB, Chang YT, Kuo TH, Chao JY. **Clinical and microbiological characteristics of peritoneal dialysis-related peritonitis caused by Escherichia coli in southern Taiwan**. *Eur J Clin Microbiol Infect Dis* (2018.0) **37** 1699-707. DOI: 10.1007/s10096-018-3302-y 42. Lin WH, Wang MC, Liu PY, Chen PS, Wen LL, Teng CH. **Escherichia coli urinary tract infections: Host age-related differences in bacterial virulence factors and antimicrobial susceptibility**. *J Microbiol Immunol Infect* (2022.0) **55** 249-56. DOI: 10.1016/j.jmii.2021.04.001 43. 43.Clinical & Laboratory Standards Institute (CLSI). Performance Standards for Antimicrobial Susceptibility Testing, 31th Edition. CLSI supplement M100-S31 Wayne: CLSI., 2021. 2021.
--- title: Correlation between Heart rate recovery and Left Atrial phasic functions evaluated by 2D speckle-tracking Echocardiography after Acute Myocardial infarction authors: - Behruz Mashayekhi - Reza Mohseni-Badalabadi - Ali Hosseinsabet - Tahereh Ahmadian journal: BMC Cardiovascular Disorders year: 2023 pmcid: PMC10061796 doi: 10.1186/s12872-023-03194-y license: CC BY 4.0 --- # Correlation between Heart rate recovery and Left Atrial phasic functions evaluated by 2D speckle-tracking Echocardiography after Acute Myocardial infarction ## Abstract ### Background Heart rate recovery (HRR) in the exercise test is the index of cardiac autonomic system function and sympathovagal balance impaired in patients with myocardial infarction (MI). An instance is left atrial (LA) phasic function, which is impaired in such patients. In this study, we investigated the role of HRR in predicting LA phasic functions in patients with MI. ### Methods The present study recruited 144 consecutive patients with ST-elevation MI. A symptom-limited exercise test was performed about 5 weeks after MI, with echocardiography conducted just before the exercise test. The patients were divided into abnormal and normal HRR at 60 s (HRR60) and again into abnormal and normal HRR at 120 s (HRR120) after the exercise test. LA phasic functions, evaluated by 2D speckle-tracking echocardiography, were compared between the 2 groups. ### Results Patients with abnormal HRR120 had lower LA strain values and strain rates during the reservoir, conduit, and contraction phases, while those with abnormal HRR60 had lower LA strain values and strain rates during the reservoir and conduit phases. The differences were lost after adjustments for possible confounders, except for LA strain and strain rate during the conduit phase, in patients with abnormal HRR120. ### Conclusions Abnormal HRR120 in the exercise test can independently predict decreased LA conduit function in patients with ST-elevation MI. ## Introduction Myocardial infarction (MI) affects approximately $3\%$ of the population over 20 years of age in the United States, where every 40 s, 1 MI occurs. [ 1] The left atrial (LA) walls are rich in terms of the presence of sympathetic and parasympathetic neurons. MI can lead to an imbalance in sympathovagal output, contributing to post-MI ventricular and atrial arrhythmias. [ 2, 3] In addition, LA phasic functions are affected after MI [4] and are predictive of adverse clinical events. [ 5–7] LA phasic functions are associated with exercise capacity in various conditions ranging from normal conditions to various types of heart failure, including post-MI scenarios. [ 8–12] Heart rate recovery (HRR) in the exercise test is defined as the difference between the maximal heart rate at the exercise time and the heart rate at a defined recovery time. HRR is the index of cardiac autonomic system function and sympathovagal balance. [ 13] *It is* a prognostic factor in the general population and patients with coronary artery disease. [ 14–16] Heart rate variability (HRV) is another marker of cardiac autonomic system function and is evaluated by electrocardiography monitoring. Although the association between HRR and HRV has been presented in some studies, this association is not strong [17, 18], which may suggest different aspects of cardiac autonomic system function assessed by these markers. [ 19] HRV is associated with LA phasic functions in patients with hypertension and diabetes. [ 20, 21] Nonetheless, the data regarding the association between HRR and LA phasic functions are scarce. [ 22] In comparison with HRV, HRR is rapidly obtained from the exercise test. Additionally, the exercise test alone provides much information regarding the cardiovascular system. Two-dimensional speckle-tracking echocardiography (2DSTE) is widely used to evaluate LA phasic functions. It assesses the deformation of the LA myocardium during the cardiac cycle, especially in the longitudinal direction, enabling the evaluation of the 3 LA phasic functions: reservoir, conduit, and contraction. In brief, the blood is reserved in the LA during systole; then, it is directed into the left ventricle (LV) at early diastole before it is pushed into the LV by LA contraction at late diastole. [ 23] We hypothesized that cardiac autonomic system function was associated with LA phasic functions in patients with recent acute MI and aimed to evaluate the association between LA phasic functions, assessed by 2DSTE, and HRR in symptom-limited exercise tests in patients with a history of recent acute MI. The findings should further contribute to our understanding of the interaction between cardiac autonomic system function and cardiac mechanics. ## Study population From September 2020 through July 2021, the present study included patients who underwent successful primary percutaneous coronary intervention due to acute MI (thrombolysis in MI grade I or 0) in our hospital. Acute MI was defined in accordance with the fourth universal definition for ST-elevation MI. [ 24] The exclusion criteria were composed of inability to do the exercise test according to the patient’s expression, neglected MI, atrial fibrillation (AF) rhythm, left bundle branch block, moderate and more-than-moderate valvular regurgitation, any degree of valvular stenosis, a history of previous MI, percutaneous coronary intervention, cardiac surgery, pacemaker implantation, congenital heart disease, cancer, autoimmune disease, hepatic failure, creatinine > 1.5 mg/dL, uncontrolled thyroid disease, cardiomyopathies, and poor echocardiography windows. Finally, 144 consecutive patients were included in our study. Overnight fasting venous blood was drained in the morning after admission for biochemistry and blood cell count. The patients were treated according to validated recommendations. [ 25–27] Hypertension was defined as antihypertensive drug consumption or a history of blood pressure > $\frac{140}{90}$ mm Hg in 2 isolated measurements. Diabetes was defined as the consumption of an antidiabetic drug or insulin, hemoglobin A1c levels > $6.4\%$, or a history of fasting blood glucose levels ≥ 126 mg/dL in 2 separate samples. The post-discharge echocardiographic examination and symptom-limited exercise test, scheduled approximately 5 weeks after discharge, were compatible with the first post-discharge outpatient visits in our hospital. Echocardiography was followed instantly by the exercise test. The drugs used by the patients at echocardiography time were recorded. The research proposal was approved by our hospital’s review board, and written informed consent was obtained from all the patients. ## Standard echocardiography A cardiologist with nearly a decade of experience in advanced echocardiography performed all standard and 2DSTE examinations. Echocardiography was conducted with the patients in the left lateral decubitus position. One-lead electrocardiography monitoring was done continuously. A commercial echocardiography machine (Philips, Affinity 70 C, Andover, MA, USA) with an S5-1 probe was used. LV end-systolic and end-diastolic volumes were measured in the apical 2 and 4-chamber views according to the modified Simpson method; subsequently, the left ventricular ejection fraction (LVEF) was calculated. Pulsed-wave Doppler was applied to record the mitral inflow wave and the pulmonary vein flow. The peak of the mitral early and late diastolic waves (E and A, respectively), the deceleration time of the E wave, and the peak of the pulmonary vein flow in systole and diastole (S and D, respectively) were measured in 3 consecutive cardiac cycles, and their average was presented. Pulsed-wave tissue Doppler was utilized to record myocardial velocities at the septal and lateral mitral annuli. The peak velocities in systole, early diastole, and late diastole (s′, e′, and a′, respectively) in 3 consecutive cardiac cycles were measured, and the average velocity of the septal and lateral mitral annuli was demonstrated. Next, the E/averaged e′ ratio was computed. All the measurements, including diastolic dysfunction severity, [28] were done following the recommendations of the American Society of Echocardiography. [ 29, 30] ## 2DSTE For 2DSTE on the LA, 3 cardiac cycles in the 2 and 4-chamber views in the expiratory phase were obtained, with maximal efforts applied to avoid the foreshortening of the LA and the inclusion of the LA appendage and the pulmonary vein orifice. The echocardiography movies had a rate of 48 ± 6 frames per second. The aCMQ option in the QLAB 13.0 package was employed to evaluate longitudinal deformation markers in the LA myocardium. First, the endocardial and epicardial borders were traced as 5 mm segments, and the endocardial layer strain and the strain curve were selected for visualization. Next, at end-diastole, via the 3-click method, the 2 sides of the mitral annulus and the center of the LA roof were pointed. Afterward, the endocardial and epicardial borders of the LA were automatically traced by software and divided into 6 segments. The operator manually adjusted the traced border with the actual endocardial and epicardial borders if required. In the next step, with the aid of the compute option, the strain and strain rate curves were illustrated while the peak of the R wave was set as level 0. If 1 of the segment curves was noisy, the aforementioned steps were repeated. The global strain curve was composed of 3 parts: 1 positive peak at systole, 1 plateau at early diastole, and 1 negative peak at late diastole. The difference between a positive peak and a negative peak was presented as LASr, the difference between a positive peak and a plateau as LAScd, and the difference between a plateau and a negative peak as LASct. The strain rate curve had 1 positive peak at systole and 2 negative peaks at early and late diastole, and it was presented as pLASRr, pLASRcd, and pLASRct, respectively (Fig. 1). These parameters were measured in 3 cardiac cycles, and their mean value was presented. LASr and pLASRr were the indices of the LA reservoir function, LAScd and pLASRcd were the indices of the LA conduit function, and LAScd and pLASRcd were the indices of the LA contraction function. LA 2DSTE was done following the recommendations of the American Society of Echocardiography. [ 31] Fig. 1The image illustrates the 2D speckle-tracking echocardiography of the left atrium in the 4-chamber apical view. ( A) Strain curves (B) Strain rate curves LAScd, Left atrial longitudinal strain during the conduit phase; LASct, Left atrial longitudinal strain during the contraction phase; LASr, Left atrial longitudinal strain during the reservoir phase; pLASRcd, Peak left atrial longitudinal strain rate during the conduit phase; pLASRct, Peak left atrial longitudinal strain rate during the contraction phase; pLASRr, Peak left atrial longitudinal strain rate during the reservoir phase The aCMQ option provides curve volume changes during the cardiac cycle. Hence, we measured maximal, minimal, and pre-P LA volumes before computing the volumetric parameters of LA functions. The left atrial total emptying volume (LATEV) was considered the difference between maximal and minimal LA volumes. The left atrial passive emptying volume (LAPEV) was considered the difference between maximal and pre-P LA volumes. The left atrial active emptying volume (LAAEV) was considered the difference between pre-P and minimal LA volumes. LATEV multiplied by 100 and divided by LA maximal volume provides the LA total emptying fraction, and LATEV multiplied by 100 and divided by LA minimal volume yields the expansion index as 2 markers of the LA reservoir function. LAPEV multiplied by 100 and divided by maximal LA volume provides the LA passive emptying fraction, and LAPEV divided by LATEV yields the passive emptying percentage of total emptying as 2 markers of the LA conduit function. LAAEV divided by pre-P LA volume yields the LA active emptying fraction, and LAAEV divided by LATEV provides the booster active emptying percentage of total emptying as 2 markers of the LA contraction function. The inter and intraobserver variabilities of the 2DSTE-derived markers of LA phasic functions were calculated 3 months after the termination of the analysis. Twenty-four ($17\%$) patients were randomly selected for the analysis of inter and intraobserver variabilities. Another cardiologist, highly experienced in advanced echocardiography, and the previously mentioned cardiologist evaluated the interobserver variability independently. ## The exercise test The exercise test was done under an experienced nurse’s observation and in accordance with the Bruce protocol, including a 2-stage cooldown, with a commercial setting (Phillips, STi80 Stress Testing System, Andover, USA). The patients were asked to do the exercise until the completion of the protocol or the appearance of symptoms such as dyspnea, dizziness, chest pain, and ST depression > 1 mm. Continuous 12-lead electrocardiography monitoring was conducted during the exercise test, in conjunction with blood pressure monitoring at the end of each stage. The exercise duration, the achieved metabolic equivalent, the maximal heart rate at the exercise time, and the heart rate at 60 and 120 s were recorded. Next, HRR at 60 s (HRR60) and HRR at 120 s (HRR120) were computed. HRR60 values ≤ 12 bpm and HRR values < 22 bpm were considered abnormal. [ 32] Forty-five patients had abnormal HRR60, 35 patients had abnormal HRR120, 99 patients had normal HRR60, and 109 patients had normal HRR120. ## Statistical analysis Categorical data were shown as frequencies and percentages and compared using the χ2 test or the Fisher exact test, whichever was appropriate. Normally distributed continuous data were demonstrated as mean values and standard deviations and compared using the independent Student t test; otherwise, they were presented as median values and interquartile ranges (25th–75th) and compared using the Mann–Whitney U test. Variables that were different between the 2 groups ($P \leq 0.05$) were considered potential confounders and entered into a multivariable regression analysis if they were physiologically supported and compatible with the assumptions of the multivariable regression analysis. If the dependent variables were not normally distributed, they were first transformed logarithmically; then, the logarithm of that variable was included in the multivariable regression analysis. Inter and intraobserver variabilities were evaluated using intraclass correlation coefficients. A P value < 0.05 was considered statistically significant, and all the statistical analyses were done using IBM SPSS Statistics for Windows, version 24 (Armonk, NY: IBM Corp). ## Results First, the characteristics, laboratory, and echocardiography data were compared between patients with HRR60 ≤ 12 bpm and those with HRR60 > 12 bpm (abnormal vs. normal). Then, these data were compared between patients with HHR120 bpm < 22 bpm and HRR120 ≥ 22 bpm (abnormal vs. normal). The results of these comparisons are presented in Tables 1, 2, 3 and 4. All the patients used antiplatelet agents. Table 1Demographic, clinical, and laboratory data of the studied groupsVariablesHRR60 ≤ 12 bpm($$n = 45$$)HRR60 > 12 bpm($$n = 99$$)P valueHRR120 < 22 bpm($$n = 35$$)HRR120 ≥ 22 bpm($$n = 109$$)P valueAge (y)58.8 ± 9.453.2 ± 8.90.00160.6 ± 8.553.1 ± 8.9< 0.001Male sex (%)36 [80]90 [91]0.06730 [86]96 [88]0.770Body mass index (kg/m2)27.8 ± 4.427.5 ± 3.80.72427.7 ± 4.127.6 ± 4.00.819Body surface area (m2)1.8 ± 0.21.9 ± 0.20.0521.8 ± 0.21.9 ± 0.20.054Obesity (%)12 [27]22 [22]0.56110 [29]24 [22]0.427Hypertension (%)21 [47]26 [26]0.01616 [46]31 [28]0.058Diabetes (%)23 [51]26 [26]0.00418 [51]31 [28]0.013Cigarette smoking (%)21 [47]56 [57]0.27016 [46]61 [56]0.290Family history of CAD (%)14 [31]32 [32]0.88512 [34]34 [31]0.733History of ACEI/ARB usage (%)41 [91]93 [94]0.50430 [86]104 [95]0.063History of β-blocker usage (%)41 [91]95 [96]0.25732 [91]104 [95]0.403History of calcium channel blocker usage (%)4 [9]7 [7]0.7403 [9]8 [7]0.729History of nitrate usage (%)23 [51]43 [43]0.39116 [46]50 [46]0.987History of statin usage (%)42 [93]95 [96]0.67832 [91]105 [96]0.361History of diuretic usage (%)8 [18]19 [19]0.8408 [23]19 [17]0.474History of oral antidiabetic agent usage (%)21 [47]23 [23]0.00517 [49]27 [25]0.008History of insulin usage (%)1 [2]2 [1]> 0.9990 [0]3 [3]> 0.999LAD culprit lesion (%)30 [67]53 [54]0.13922 [63]61 [56]0.473LCX culprit lesion (%)5 [11]13 [13]0.7342 [6]16 [15]0.242RCA culprit lesion (%)10 [22]33 [33]0.17711 [31]32 [29]0.816Single-vessel disease (%)19 [42]52 [53]0.36915 [43]57 [52]0.331Two-vessel disease (%)16 [36]25 [25]0.20415 [43]26 [24]0.030Three-vessel disease (%)9 [20]22 [22]0.7645 [14]26 [24]0.231FBS (mg/dL)138 (109–220)113 (98–142)0.007134 (107–183)114 (99–147)0.006Creatinine (mg/dL)1.0 ± 0.21.0 ± 0.20.5841.1 ± 0.11.0 ± 0.20.401Hemoglobin (g/dL)14.7 ± 1.715.4 ± 1.40.02114.9 ± 1.615.3 ± 1.50.192Triglyceride (mg/dL)130 (100–173)131 (102–184)0.602114 (98–173)134 (107–183)0.271Cholesterol (mg/dL)163 ± 35169 ± 460.442164± 34168 ± 450.647HDL (mg/dL)43 ± 1039 ± 90.01939 ± 944 ± 90.014LDL (mg/dL)103 ± 25108 ± 320.395104 ± 25107 ± 310.558ACEI/ARB, Angiotensin-converting enzyme inhibitor/angiotensin-receptor blocker; CAD, Coronary artery disease; FBS, Fasting blood sugar; HDL, High-density lipoprotein; LAD, Left anterior descending artery; LCX, Left circumflex artery; LDL, Low-density lipoprotein; RCA, Right coronary artery Table 2Standard echocardiography data and volumetric parameters of the left atrium in the studied groupsVariablesHRR60 ≤ 12 bpm($$n = 45$$)HRR60 > 12 bpm($$n = 99$$)P valueHRR120 < 22 bpm($$n = 35$$)HRR120 ≥ 22 bpm($$n = 109$$)P valueLVEDV index (mL/m2)53 ± 1152 ± 120.84555 ± 1352 ± 120.232LVESV index (mL/m2)26 ± 923 ± 80.13824 (19–34)22 (19–26)0.063LVEF (%)47 ± 1051 ± 90.01847 ± 1151 ± 90.041E (cm/s)68 ± 2261 ± 140.05665 ± 2162 ± 160.415 A (cm/s)74 ± 2162 ± 160.00275 ± 2263 ± 160.001E/A ratio1.0 ± 0.41.0 ± 0.40.2750.9 (0.6–1.1)0.9 (0.8–1.3)0.039DT (ms)216 ± 56219 ± 540.800224 ± 61216 ± 520.433 S (cm/s)56 ± 1252 ± 100.08655 ± 1253 ± 100.271D (cm/s)43 ± 1640 ± 120.26041 ± 1241 ± 120.774 S/D ratio1.4 ± 0.41.4 ± 0.40.7131.4 ± 0.41.4 ± 0.40.301Mean s´(cm/s)7.4 ± 1.68.5 ± 1.7< 0.0017.3 ±1.68.5 ± 1.7< 0.001Mean e´(cm/s)7.4 ± 1.78.6 ± 1.8< 0.0017.0 ± 1.78.5 ± 1.8< 0.001Mean a´(cm/s)9.1 ± 1.79.4 ± 1.50.3979.0 ± 1.99.4 ± 1.50.235Average e´/a´0.8 ± 0.20.9 ± 0.20.0140.8 ± 0.20.9 ± 0.20.007E/(average e´)9.0 (6.6 - 11.6)7.0 (5.9–8.3)< 0.0019.0 (6.5–12.0)7.0 (6.0–8.0)0.004Systolic pulmonary arterial pressure (mm Hg)*29 ± 627 ± 70.16629 ± 627 ± 70.139Diastolic dysfunction grades II and III**7 [16]4 [4]0.0225 [14]6 [6]0.095LA enlargement [LA maximal volume index > 35 (mL/m2)]15 [33]19 [19]0.06415 [43]19 [17]0.002Maximal LA volume index (mL/m2)31.2 ± 8.328.7 ± 7.20.07033.3 ± 7.628.3 ± 7.2< 0.001Minimal LA volume (mL/m2)11.3 (9.4–16.2)10.8 (8.0-12.9)0.03414.5 (10.5–17.3)10.3 (7.9–12.6)< 0.001Pre-A LA volume (mL/m2)23.4 ± 7.221.0 ± 6.10.04425.8 ± 6.220.4 ± 6.1< 0.001LA total emptying fraction (%)60 ± 862 ± 80.08457 ± 763 ± 8< 0.001Expansion index (%)157 (118–197)164 (138–204)0.096130 (116–163)171 (141–210)< 0.001LA passive emptying fraction (%)26 ± 827 ± 80.24123 ± 628 ± 6< 0.001Passive emptying percentage of total emptying (%)43 ± 1144 ± 100.61740 ± 1045 ± 100.009LA active emptying fraction (%)46 ± 948 ± 80.14144 ± 848 ± 80.016Booster active emptying percentage of total emptying (%)57 ± 1156 ± 110.61760 ± 1055 ± 100.009**Systolic pulmonary artery pressure was measurable in 25 patients with HRR ≤ 12 bpm, 56 patients with HRR > 12 bpm, 25 patients with HRR < 22 bpm, and 62 patients with HRR ≥ 22 bpm.**One patient was in the indeterminate categoryHRR60; Heart rate recovery at 60 s after exercise termination (Maximal heart rate – Heart rate at 60 s after exercise termination), HRR120; Heart rate recovery at 120 s after exercise termination (Maximal heart rate – Heart rate at 120 s after exercise termination);DT, Deceleration time; LA, Left atrium; LV, Left ventricle; LVEDV, Left ventricular end-diastolic volume; LVEF, Left ventricular ejection fraction; LVESV, Left ventricular end-systolic volume Table 3Exercise test data in the studied groupsVariablesHRR60 ≤ 12 bpm($$n = 45$$)HRR60 > 12 bpm($$n = 99$$)P valueHRR120 < 22 bpm($$n = 35$$)HRR120 ≥ 22 bpm($$n = 109$$)P valueMyocardial infarction time to echocardiography and exercise test time interval (d)36 ± 834 ± 60.28036 ± 1034 ± 50.255Rest heart rate (bpm)83 ± 1781 ± 130.44783 ± 1781 ± 130.395Maximal heart rate (bpm)140 ± 19145 ± 160.064134 ± 19147 ± 150.001Systolic blood pressure (mm Hg)123 ± 16119 ± 160.175124 ± 16119 ± 160.085Diastolic blood pressure (mm Hg)76 ± 776 ± 80.98276 ± 776 ± 80.992Exercise time (min)6.2 ± 2.07.8 ± 2.1< 0.0015.8 ± 2.07.8 ± 2.0< 0.001Metabolic equivalents7.5 ± 2.39.4 ± 2.1< 0.0017.3 ± 2.19.3 ± 2.2< 0.001HRR607 (3–10)19 (15–23)< 0.0017 (3–10)18 (15–23)< 0.001HRR12019 (12–24)34 (29–39)< 0.00117 (11–19)34 (27–38)< 0.001HRR60; Heart rate recovery at 60 s after exercise termination (Maximal heart rate – Heart rate at 60 s after exercise termination), HRR120; Heart rate recovery at 120 s after exercise termination (Maximal heart rate – Heart rate at 120 s after exercise termination) Table 4Mean and standard deviation of the 2D speckle-tracking echocardiography-derived parameters of the longitudinal deformation of the left atrial myocardium in the studied groupsVariablesHRR60 ≤ 12 bpm($$n = 45$$)HRR60 > 12 bpm($$n = 99$$)P valueHRR120 < 22 bpm($$n = 35$$)HRR120 ≥ 22 bpm($$n = 109$$)P valueLASr (%)27.9 ± 7.630.4 ± 6.30.03825.2 ± 5.231.0 ± 6.6< 0.001LAScd (%)9.5 (7.2–13.2)11.5 (9.4–13.9)0.0248.7 ± 2.712.4 ± 4.4< 0.001LASct (%)17.2 ± 4.918.5 ± 4.50.13916.5 ± 4.718.6 ± 4.50.022pLASRr (s− 1)2.7 ± 0.63.0 ± 0.70.0242.6 ± 0.63.0 ± 0.70.002pLASRcd (s− 1)2.3 ± 0.82.8 ± 0.90.0012.0 ± 0.62.9 ± 0.9< 0.001pLASRct (s− 1)3.8 ± 1.34.3 ± 1.30.0723.7 ± 1.34.3 ± 1.30.030LAScd, Left atrial longitudinal strain during the conduit phase; LASct, Left atrial longitudinal strain during the contraction phase; LASr, Left atrial longitudinal strain during the reservoir phase; pLASRcd, Peak left atrial longitudinal strain rate during the conduit phase; pLASRct, Peak left atrial longitudinal strain rate during the contraction phase; pLASRr, Peak Left atrial longitudinal strain rate during the reservoir phase ## HRR60 ≤ 12 bpm vs. HRR60 > 12 bpm Patients with abnormal HRR60 were older than those with normal HRR60 ($$P \leq 0.001$$). The prevalence of hypertension and diabetes was higher in patients with abnormal HRR60 ($$P \leq 0.016$$ and $$P \leq 0.004$$, respectively) (Table 1). In patients with abnormal HRR60, the E/e′ ratio ($P \leq 0.001$) and the prevalence of grades II and III LV diastolic dysfunction were higher ($$P \leq 0.022$$), whereas LVEF was lower ($$P \leq 0.018$$). The pre-P LA volume index and the minimal LA volume index were higher in patients with abnormal HRR60 ($$P \leq 0.044$$ and $$P \leq 0.034$$, respectively) (Table 2). The mean duration of the exercise test was shorter among patients with abnormal HRR60 than among patients with normal HRR60 ($P \leq 0.001$) (Table 3). LASr ($30.4\%$±6.3 vs. $27.9\%$±7.6; $$P \leq 0.038$$), pLASRr (3.0 s− 1±0.7 vs. 2.7 s− 1 ±0.6; $$P \leq 0.024$$), LAScd ($11.5\%$ [9.4–13.9] vs. $9.5\%$ [7.2–13.2]; $$P \leq 0.024$$), and pLASRcd (2.8 s− 1±0.9 vs. 2.3 s− 1±0.8; $$P \leq 0.001$$) were lower in patients with abnormal HRR60. The interval between MI occurrence and post-discharge echocardiography and the exercise test was not significantly different between the 2 groups. The multivariable regression analysis after adjustments for potential confounders, consisting of age, hypertension, diabetes, LVEF, the E/e′ ratio, and the exercise duration, demonstrated that the differences between the 2 groups regarding LASr, pLASRr, logLAScd, and pLASRcd were lost. ## HRR120 < 22 bpm vs. HRR120 ≥ 22 bpm Patients with normal HRR120 were younger than those with abnormal HRR120 ($P \leq 0.001$). The prevalence of diabetes was lower in patients with normal HRR120 ($$P \leq 0.013$$) (Table 1). In patients with normal HRR120, the E/e′ ratio was lower ($$P \leq 0.004$$). The maximal LA volume index, the pre-P LA volume index, and the minimal LA volume index were lower in patients with normal HRR120 (all Ps < 0.001). All the volumetric parameters of LA phasic functions were higher in patients with normal HRR120, except for the booster active emptying percentage of total emptying, which was less in patients with normal HRR120 (all Ps < 0.05) (Table 2). The mean duration of the exercise test was longer among patients with normal HRR120 than patients with abnormal HRR120, with the former group having a maximal heart rate at exercise time ($P \leq 0.001$ and $$P \leq 0.001$$, respectively) (Table 3). All the longitudinal deformation markers of the LA myocardium were higher in patients with normal HRR120 (all Ps < 0.05) (Table 4). The time interval between MI occurrence and post-discharge echocardiography and the exercise test was not significantly different between the 2 groups. The multivariable regression analysis after adjustments for potential confounders, consisting of age, diabetes, LVEF, the E/e′ ratio, the exercise duration, and the maximal LA volume index, indicated that the difference between the 2 groups remained regarding LAScd (β = 0.193; $$P \leq 0.017$$) and pLAScd (β = 0.198; $$P \leq 0.019$$). The results concerning inter and intraobserver variabilities are presented in Table 5. Table 5Intra and interobserver variabilities for the 2D speckle-tracking echocardiography-derived parameters of left atrial myocardial functionVariablesIntraobserverInterobserverICC$95\%$ limit of agreementICC$95\%$ limit of agreementLASr (%)0.9820.947–0.9930.9150.803–0.964LAScd (%)0.8400.632–0.9300.8170.556–0.923LASct (%)0.9370.530–0.9810.8240.600–0.923pLASRr (s− 1)0.9880.939–0.9960.9520.891–0.979pLASRcd (s− 1)0.9950.987–0.9980.9920.982–0.997pLASRct (s− 1)0.9940.985–0.9970.9530.891–0.980ICC, Intraclass correlation coefficient; LAScd, Left atrial longitudinal strain during the conduit phase; LASct, Left atrial longitudinal strain during the contraction phase; LASr, Left atrial longitudinal strain during the reservoir phase; pLASRcd, Peak left atrial longitudinal strain rate during the conduit phase; pLASRct, Peak left atrial longitudinal strain rate during the contraction phase; pLASRr, Peak left atrial longitudinal strain rate during the reservoir phase ## Discussion In the present study, for the first time, we employed 2DSTE to evaluate LA phasic functions in patients with recent acute MI with normal and abnormal HRR at 60 and 120 s. We found that the LA reservoir function markers, namely LASr and pLASRr, and the LA conduit function markers, namely LAScd and pLASRcd, were low in patients with abnormal HRR60 in comparison with patients with normal HRR60. Nevertheless, these differences were lost after adjustments for potential confounders. Additionally, there was a decline in the LA reservoir function markers, namely LASr and pLASRr, the LA conduit function markers, namely LAScd and pLASRcd, and the LA contraction function markers, namely LASct and LASRct, in patients with abnormal HRR120 compared with patients with normal HRR120. However, after adjustments for potential confounders, only the difference between the 2 groups remained statistically significant in terms of the LA conduit function markers. Tadic et al. [ 20] in 2014 assessed LA phasic functions and HRV indices in patients with normal LVEF divided into groups with and without hypertension. Their results indicated a correlation between LASr and several indices of HRV, including the indices of the sympathetic and parasympathetic systems. They excluded patients aged > 60 years and those with diabetes or coronary artery disease. In contrast, our patients were affected by MI, and we did not exclude patients with diabetes and reduced LVEF. Tadic et al. [ 21] in 2017 investigated LA phasic functions and HRV parameters in subjects with normal LVEF divided into groups with and without diabetes and revealed that a parameter of the parasympathetic system was correlated with LASr. Our study population, in contrast to theirs, included patients with reduced LVEF, hypertension, and a recent history of MI. Vukomanovic et al. [ 22] in 2020 evaluated LA phasic functions and exercise capacity in patients with normal LVEF and without a history of coronary artery disease divided into groups with and without diabetes. Their results demonstrated a correlation between LASr and HHR60. In comparison with their study, we included patients with a history of recent MI, hypertension, and reduced LVEF. This group of researchers, in another study, found a correlation between endocardial right ventricular strain and HRR60 in a study population similar to that in their previous study. [ 33] Exercise induces sympathetic system activation, accompanied by the withdrawal of the parasympathetic system. The recovery phase includes an initial fast phase, mainly dependent on the reactivation of the parasympathetic system, and a late slow phase, principally dependent on sympathetic withdrawal. [ 34] HHR60 and HHR120 are indices of these phases, respectively. [ 13] Our study results may indicate that the LA conduit function has a higher correlation with the late slow phase of HRR, suggesting sluggish sympathetic inactivation after adjustments for exercise duration. This conclusion is in alignment with the findings of a previous study indicating the superiority of HHR120 over HHR60 as a predictor of mortality. [ 35] Furthermore, it has been previously demonstrated that the LA conduit function is correlated with functional capacity in patients after MI, [12] patients with heart failure, [36, 37] and subjects with normal structural heart. [ 11] However, in the current study, we revealed that HRR120, independent of functional capacity, was correlated with the LA conduit function, which may point to the significance of autonomic dysfunction beyond functional capacity as an indicator of the LA conduit function. Inflammation could lead to autonomic dysfunction [38] and LA phasic function in the presence of impaired systemic inflammation. [ 39] MI leads to the creation of an inflammatory milieu, increased cytokine levels, and increased oxidative stress [40], which can result in autonomic dysfunction and LA phasic dysfunction. Moreover, MI can lead to sympathetic overactivity, which persists for several months and is accompanied by decreased HRR. [ 2, 34] MI can also result in LV systolic dysfunction, accompanied by increased sympathetic activity and decreased parasympathetic activity. The increased sympathetic activity can be detrimental to myocardial function. [ 34] Further, MI can lead to LV diastolic dysfunction, which is one of the determinants of LA phasic functions, including the conduit function. [ 41] In the presence of MI, the activation of the renin-angiotensin-aldosterone axis occurs, with the increased aldosterone level associated with the LA conduit function. [ 42] There are possible pathophysiological mechanisms that can explain our findings. Some studies have indicated the prognostic role and clinical significance of the LA conduit function. Diminished LA conduit function is present in patients with recent MI. The impairment of the LA conduit function assessed by cardiac magnetic resonance imaging is considered a predictor of major adverse cardiac events in patients with MI. [ 6, 43] *In a* prior study on patients with dilated cardiomyopathy, the LA conduit function was a predictor of a composite of sudden or cardiac death, heart failure hospitalization, and life-threatening arrhythmias. [ 44] In another investigation, the LA conduit function was a predictor of all-cause mortality and heart transplantation, and a composite of all-cause mortality, heart transplantation, heart failure readmission, and aborted sudden cardiac death. [ 45] In older adult subjects, the LA conduit function can augment accuracy in the prediction of death and heart failure with reduced or preserved EFs. [ 46] *In a* prior investigation, impaired LA conduit function was associated with the occurrence of AF in patients with MI after about 5 years of follow-up. [ 47] In addition, the LA conduit function can predict AF recurrence after electrical cardioversion and catheter ablation. [ 48, 49] On the other hand, autonomic dysfunction is associated with ventricular and atrial arrhythmias, such as AF, [34, 50] and increased risks of all-cause and cardiovascular mortality. [ 51] In the prevention of cardiac autonomic dysfunction, it seems that general public health recommendations, such as weight reduction, healthy diets, increased physical activity, better behavioral stress management, and better control of cardiovascular risk factors (e.g., hypertension, diabetes, and dyslipidemia) should be considered. Moreover, some drugs used in treating patients after MI, such as β-blockers, angiotensin-converting enzyme inhibitors, and angiotensin receptor blockers, have been suggested in some studies. [ 51] In some cases, cardiac sympathetic denervation, renal denervation, and parasympathetic stimulation can be drawn upon. [ 50] Subjects with some of these risk factors for cardiac autonomic dysfunction have lower LA conduit function. [ 52] Hence, it seems that more appropriate treatment of factors that can lead to cardiac autonomic dysfunction may contribute to better LA conduit function. [ 53] *From a* clinical perspective, our study indicates that a post-MI exercise test, which does not require sophisticated software and trained personnel, may be useful in providing information regarding the LA conduit function, functional capacity, and autonomic function. It, therefore, seems that the exercise test may be a time and cost-saving procedure, especially in the absence of advanced technology for the anticipation of impaired LA conduit function. ## Study Limitations Our cross-sectional study indicated a correlation between 2 variables and did not indicate a causal relation. The single-center small-size study was another shortcoming, as a result of which some 2DSTE-derived markers of LA phasic functions that were significantly different between the 2 groups could have been rendered nonsignificant after adjustments for possible confounders. However, we could not evaluate LA functions by cardiac magnetic resonance imaging or 3D echocardiography. In addition, we used software initially designed for the assessment of LV deformation markers. ## Conclusions In our patients with ST elevation MI treated by primary percutaneous intervention, HRR120 was correlated with the 2DSTE-derived markers of the LA conduit function after adjustments for possible confounding factors, including the exercise duration. ## References 1. -Virani SS, Alonso A, Aparicio HJ. **Heart disease and stroke statistics-2021 update: a report from the American Heart Association**. *Circulation* (2021.0) **143** e254-e743. DOI: 10.1161/CIR.0000000000000950 2. -Wu P, Vaseghi M. **The autonomic nervous system and ventricular arrhythmias in myocardial infarction and heart failure**. *Pacing Clin Electrophysiol* (2020.0) **43** 172-80. DOI: 10.1111/pace.13856 3. -Sagnard A, Guenancia C, Mouhat B. **Involvement of autonomic nervous system in new-onset atrial fibrillation during acute myocardial infarction**. *J Clin Med* (2020.0) **9** 1481. DOI: 10.3390/jcm9051481 4. -Davarpasand T, Hosseinsabet A, Omidi F. **Interaction effect of diabetes and acute myocardial infarction on the left atrial function as evaluated by 2-D speckle-tracking echocardiography**. *Ultrasound Med Biol* (2020.0) **46** 1490-503. DOI: 10.1016/j.ultrasmedbio.2020.02.008 5. -Nayyar D, Nguyen T, Pathan F. **Cardiac magnetic resonance derived left atrial strain after ST-elevation myocardial infarction: an independent prognostic indicator**. *Cardiovasc Diagn Ther* (2021.0) **11** 383-93. DOI: 10.21037/cdt-20-879 6. -Leng S, Ge H, He J. **Long-term prognostic value of cardiac MRI left atrial strain in ST-segment elevation myocardial infarction**. *Radiology* (2020.0) **296** 299-309. DOI: 10.1148/radiol.2020200176 7. -Chu AA, Wu TT, Zhang L. **The prognostic value of left atrial and left ventricular strain in patients after ST-segment elevation myocardial infarction treated with primary percutaneous coronary intervention**. *Cardiol J* (2021.0) **28** 678-89. DOI: 10.5603/CJ.a2020.0010 8. -Maffeis C, Rossi A, Cannata L. **Left atrial strain predicts exercise capacity in heart failure independently of left ventricular ejection fraction**. *ESC Heart Fail* (2022.0) **9** 842-52. DOI: 10.1002/ehf2.13788 9. -Ye Z, Miranda WR, Yeung DF. **Left atrial strain in evaluation of heart failure with preserved ejection fraction**. *J Am Soc Echocardiogr* (2020.0) **33** 1490-9. DOI: 10.1016/j.echo.2020.07.020 10. -Bekfani T, Hamadanchi A, Ijuin S. **Relation of left atrial function with exercise capacity and muscle endurance in patients with heart failure**. *ESC Heart Fail* (2021.0) **8** 4528-38. DOI: 10.1002/ehf2.13656 11. -Leite L, Mendes SL, Baptista R. **Left atrial mechanics strongly predict functional capacity assessed by cardiopulmonary exercise testing in subjects without structural heart disease**. *Int J Cardiovasc Imaging* (2017.0) **33** 635-42. DOI: 10.1007/s10554-016-1045-3 12. -Fontes-Carvalho R, Sampaio F, Teixeira M. **Left atrial deformation analysis by speckle tracking echocardiography to predict exercise capacity after myocardial infarction**. *Rev Port Cardiol (Engl Ed)* (2018.0) **37** 821-30. DOI: 10.1016/j.repc.2017.10.018 13. -Peçanha T, Silva-Júnior ND, Forjaz CL. **Heart rate recovery: autonomic determinants, methods of assessment and association with mortality and cardiovascular diseases**. *Clin Physiol Funct Imaging* (2014.0) **34** 327-39. DOI: 10.1111/cpf.12102 14. -Hernesniemi JA, Sipilä K, Tikkakoski A. **Cardiorespiratory fitness and heart rate recovery predict sudden cardiac death independent of ejection fraction**. *Heart* (2020.0) **106** 434-40. DOI: 10.1136/heartjnl-2019-315198 15. -Tabachnikov V, Saliba W, Aker A. **Heart rate response to exercise and recovery: independent prognostic measures in patients without known major cardiovascular disease**. *J Cardiopulm Rehabil Prev* (2022.0) **42** E34-E41. DOI: 10.1097/HCR.0000000000000679 16. -Peçanha T, Bartels R, Brito LC. **Methods of assessment of the post-exercise cardiac autonomic recovery: a methodological review**. *Int J Cardiol* (2017.0) **227** 795-802. DOI: 10.1016/j.ijcard.2016.10.057 17. - Bechke E, Kliszczewicz B, McLester C. **An examination of single day vs. multi-day heart rate variability and its relationship to heart rate recovery following maximal aerobic exercise in females**. *Sci Rep* (2020.0) **10** 14760. DOI: 10.1038/s41598-020-71747-8 18. - Ha D, Malhotra A, Ries AL. **Heart rate variability and heart rate recovery in lung cancer survivors eligible for long-term cure**. *Respir Physiol Neurobiol* (2019.0) **269** 103264. DOI: 10.1016/j.resp.2019.103264 19. - Kappus RM, Ranadive SM, Yan H. **Sex differences in autonomic function following maximal exercise**. *Biol Sex Differ* (2015.0) **6** 28. DOI: 10.1186/s13293-015-0046-6 20. -Tadic M, Cuspidi C, Pencic B. **The association between heart rate variability and biatrial phasic function in arterial hypertension**. *J Am Soc Hypertens* (2014.0) **8** 699-708. DOI: 10.1016/j.jash.2014.07.032 21. -Tadic M, Vukomanovic V, Cuspidi C. **Left atrial phasic function and heart rate variability in asymptomatic diabetic patients**. *Acta Diabetol* (2017.0) **54** 301-8. DOI: 10.1007/s00592-016-0962-x 22. -Vukomanovic V, Suzic-Lazic J, Celic V. **Is there association between left atrial function and functional capacity in patients with uncomplicated type 2 diabetes?**. *Int J Cardiovasc Imaging* (2020.0) **36** 15-22. DOI: 10.1007/s10554-019-01680-z 23. -Kupczyńska K, Mandoli GE, Cameli M. **Left atrial strain - a current clinical perspective**. *Kardiol Pol* (2021.0) **79** 955-64. DOI: 10.33963/KP.a2021.0105 24. -Thygesen K, Alpert JS, Jaffe AS. **Fourth universal definition of myocardial infarction (2018)**. *Circulation* (2018.0) **138** e618-51. DOI: 10.1161/CIR.0000000000000617 25. -Ibanez B, James S, Agewall S. **2017 ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation: the Task Force for the management of acute myocardial infarction in patients presenting with ST-segment elevation of the European Society of Cardiology (ESC)**. *Eur Heart J* (2018.0) **39** 119-77. DOI: 10.1093/eurheartj/ehx393 26. 26.-Levine GN, Bates ER, Blankenship JC, ACC/AHA/SCAI Focused Update on Primary Percutaneous Coronary Intervention for Patients With ST-Elevation Myocardial Infarction. 2015 : An Update of the 2011 ACCF/AHA/SCAI guideline for percutaneous coronary intervention and the 2013 ACCF/AHA guideline for the management of ST-elevation myocardial infarction: A report of the American College of Cardiology/American Heart Association task force on clinical practice guidelines and the Society for Cardiovascular Angiography and Interventions. Circulation 2016;133:1135-47. 27. -O’Gara PT, Kushner FG, Ascheim DD. **2013 ACCF/AHA guideline for the management of ST-elevation myocardial infarction: executive summary: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines: developed in collaboration with the American College of Emergency Physicians and Society for Cardiovascular Angiography and Interventions**. *Catheter Cardiovasc Interv* (2013.0) **82** E1-27. DOI: 10.1002/ccd.24776 28. -Nagueh SF, Smiseth OA, Appleton CP. **Recommendations for the evaluation of left ventricular diastolic function by echocardiography: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging**. *J Am Soc Echocardiogr* (2016.0) **29** 277-314. DOI: 10.1016/j.echo.2016.01.011 29. - Lang RM, Badano LP, Mor-Avi V. **Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging**. *J Am Soc Echocardiogr* (2015.0) **28** 1-39e14. DOI: 10.1016/j.echo.2014.10.003 30. -Nagueh SF, Appleton CP, Gillebert TC. **Recommendations for the evaluation of left ventricular diastolic function by echocardiography**. *J Am Soc Echocardiogr* (2009.0) **22** 107-33. DOI: 10.1016/j.echo.2008.11.023 31. -Badano LP, Kolias TJ, Muraru D. **Standardization of left atrial, right ventricular, and right atrial deformation imaging using two-dimensional speckle tracking echocardiography: a consensus document of the EACVI/ASE/Industry Task Force to standardize deformation imaging**. *Eur Heart J Cardiovasc Imaging* (2018.0) **19** 591-600. DOI: 10.1093/ehjci/jey042 32. -Sharma K, Kohli P, Gulati M. **An update on exercise stress testing**. *Curr Probl Cardiol* (2012.0) **37** 177-202. DOI: 10.1016/j.cpcardiol.2011.11.004 33. - Vukomanovic V, Suzic-Lazic J, Celic V. **Cardiorespiratory fitness and right ventricular mechanics in uncomplicated diabetic patients: is there any relationship?**. *Acta Diabetol* (2020.0) **57** 425-31. DOI: 10.1007/s00592-019-01449-9 34. -Goldberger JJ, Arora R, Buckley U. **Autonomic nervous system dysfunction: JACC focus seminar**. *J Am Coll Cardiol* (2019.0) **73** 1189-206. DOI: 10.1016/j.jacc.2018.12.064 35. -Shetler K, Marcus R, Froelicher VF. **Heart rate recovery: validation and methodologic issues**. *J Am Coll Cardiol* (2001.0) **38** 1980-7. DOI: 10.1016/S0735-1097(01)01652-7 36. -Maffeis C, Morris DA, Belyavskiy E. **Left atrial function and maximal exercise capacity in heart failure with preserved and mid-range ejection fraction**. *ESC Heart Fail* (2021.0) **8** 116-28. DOI: 10.1002/ehf2.13143 37. von Roeder M, Rommel KP, Kowallick JT. **Exercise capacity and left ventricular function in patients with heart failure and preserved ejection fraction**. *Circ Cardiovasc Imaging* (2017.0) **10** e005467. DOI: 10.1161/CIRCIMAGING.116.005467 38. - Oliveira PWC, de Sousa GJ, Birocale AM. **Chronic metformin reduces systemic and local inflammatory proteins and improves hypertension-related cardiac autonomic dysfunction**. *Nutr Metab Cardiovasc Dis* (2020.0) **30** 274-81. DOI: 10.1016/j.numecd.2019.09.005 39. - Cauwenberghs N, Sabovčik F, Vandenabeele E. **Subclinical heart dysfunction in relation to metabolic and inflammatory markers: a community-based study**. *Am J Hypertens* (2021.0) **34** 46-55. DOI: 10.1093/ajh/hpaa150 40. - Oliveira JB, Soares AASM, Sposito AC. **Inflammatory response during myocardial infarction**. *Adv Clin Chem* (2018.0) **84** 39-79. DOI: 10.1016/bs.acc.2017.12.002 41. - Thomas L, Marwick TH, Popescu BA. **Left atrial structure and function, and left ventricular diastolic dysfunction: JACC state-of-the-art review**. *J Am Coll Cardiol* (2019.0) **73** 1961-77. DOI: 10.1016/j.jacc.2019.01.059 42. - Wang D, Xu JZ, Chen X. **Left atrial myocardial dysfunction in patients with primary aldosteronism as assessed by speckle-tracking echocardiography**. *J Hypertens* (2019.0) **37** 2032-40. DOI: 10.1097/HJH.0000000000002146 43. -Schuster A, Backhaus SJ, Stiermaier T. **Left atrial function with MRI enables Prediction of Cardiovascular events after myocardial infarction: insights from the AIDA STEMI and TATORT NSTEMI trials**. *Radiology* (2019.0) **293** 292-302. DOI: 10.1148/radiol.2019190559 44. -Raafs AG, Vos JL, Henkens MTHM. **Left atrial strain has superior prognostic value to ventricular function and delayed-enhancement in dilated cardiomyopathy**. *JACC Cardiovasc Imaging* (2022.0) **15** 1015-26. DOI: 10.1016/j.jcmg.2022.01.016 45. -Li Y, Xu Y, Tang S. **Left atrial function predicts outcome in dilated cardiomyopathy: fast long-axis strain analysis derived from MRI**. *Radiology* (2022.0) **302** 72-81. DOI: 10.1148/radiol.2021210801 46. -Inciardi RM, Claggett B, Minamisawa M. **Association of left atrial structure and function with heart failure in older adults**. *J Am Coll Cardiol* (2022.0) **79** 1549-61. DOI: 10.1016/j.jacc.2022.01.053 47. 47.- Svartstein AW, Lassen MH, Skaarup KG et al. Predictive value of left atrial strain in relation to atrial fibrillation following acute myocardial infarction.Int J Cardiol2022:S0167-5273(22)00736-7. 48. -Giubertoni A, Boggio E, Ubertini E. **Atrial conduit function quantitation precardioversion predicts early arrhythmia recurrence in persistent atrial fibrillation patients**. *J Cardiovasc Med (Hagerstown)* (2019.0) **20** 169-79. DOI: 10.2459/JCM.0000000000000756 49. Pilichowska-Paszkiet E, Baran J, Kułakowski P. **Echocardiographic assessment of left atrial function for prediction of efficacy of catheter ablation for atrial fibrillation**. *Med (Baltim)* (2021.0) **100** e27278. DOI: 10.1097/MD.0000000000027278 50. -Manolis AA, Manolis TA, Apostolopoulos EJ. **The role of the autonomic nervous system in cardiac arrhythmias: the neuro-cardiac axis, more foe than friend?**. *Trends Cardiovasc Med* (2021.0) **31** 290-302. DOI: 10.1016/j.tcm.2020.04.011 51. -Spallone V. **Update on the impact, diagnosis and management of cardiovascular autonomic neuropathy in diabetes: what is defined, what is new, and what is unmet**. *Diabetes Metab J* (2019.0) **43** 3-30. DOI: 10.4093/dmj.2018.0259 52. -Jain V, Ghosh R, Gupta M. **Contemporary narrative review on left atrial strain mechanics in echocardiography: cardiomyopathy, valvular heart disease and beyond**. *Cardiovasc Diagn Ther* (2021.0) **11** 924-38. DOI: 10.21037/cdt-20-461 53. - Serhiyenko VA, Serhiyenko AA. **Cardiac autonomic neuropathy: risk factors, diagnosis and treatment**. *World J Diabetes* (2018.0) **9** 1-24. DOI: 10.4239/wjd.v9.i1.1
--- title: Behavioral phenotype, intestinal microbiome, and brain neuronal activity of male serotonin transporter knockout mice authors: - Hirotaka Shoji - Kazutaka Ikeda - Tsuyoshi Miyakawa journal: Molecular Brain year: 2023 pmcid: PMC10061809 doi: 10.1186/s13041-023-01020-2 license: CC BY 4.0 --- # Behavioral phenotype, intestinal microbiome, and brain neuronal activity of male serotonin transporter knockout mice ## Abstract The serotonin transporter (5-HTT) plays a critical role in the regulation of serotonin neurotransmission. *Mice* genetically deficient in 5-HTT expression have been used to study the physiological functions of 5-HTT in the brain and have been proposed as a potential animal model for neuropsychiatric and neurodevelopmental disorders. Recent studies have provided evidence for a link between the gut-brain axis and mood disorders. However, the effects of 5-HTT deficiency on gut microbiota, brain function, and behavior remain to be fully characterized. Here we investigated the effects of 5-HTT deficiency on different types of behavior, the gut microbiome, and brain c-Fos expression as a marker of neuronal activation in response to the forced swim test for assessing depression-related behavior in male 5-HTT knockout mice. Behavioral analysis using a battery of 16 different tests showed that 5-HTT−/− mice exhibited markedly reduced locomotor activity, decreased pain sensitivity, reduced motor function, increased anxiety-like and depression-related behavior, altered social behavior in novel and familiar environments, normal working memory, enhanced spatial reference memory, and impaired fear memory compared to 5-HTT+/+ mice. 5-HTT+/− mice showed slightly reduced locomotor activity and impaired social behavior compared to 5-HTT+/+ mice. Analysis of 16S rRNA gene amplicons showed that 5-HTT−/− mice had altered gut microbiota abundances, such as a decrease in Allobaculum, Bifidobacterium, *Clostridium sensu* stricto, and Turicibacter, compared to 5-HTT+/+ mice. This study also showed that after exposure to the forced swim test, the number of c-Fos-positive cells was higher in the paraventricular thalamus and lateral hypothalamus and was lower in the prefrontal cortical regions, nucleus accumbens shell, dorsolateral septal nucleus, hippocampal regions, and ventromedial hypothalamus in 5-HTT−/− mice than in 5-HTT+/+ mice. These phenotypes of 5-HTT−/− mice partially recapitulate clinical observations in humans with major depressive disorder. The present findings indicate that 5-HTT-deficient mice serve as a good and valid animal model to study anxiety and depression with altered gut microbial composition and abnormal neuronal activity in the brain, highlighting the importance of 5-HTT in brain function and the mechanisms underlying the regulation of anxiety and depression. ### Supplementary Information The online version contains supplementary material available at 10.1186/s13041-023-01020-2. ## Introduction The serotonin transporter (5-HTT) is expressed on the presynaptic membrane of serotonergic neurons and reuptakes serotonin (5-HT) from the synaptic cleft into presynaptic terminals to recycle 5-HT for future release, playing a critical role in the regulation of 5-HT neurotransmission [1]. Many studies have suggested that dysregulation of the 5-HT system is associated with neuropsychiatric and neurodevelopmental disorders, such as anxiety and depression, and 5-HTT is a major target for antidepressants and anxiolytics [2–5]. Genetic association studies have reported that genetic variants of 5-HTT, well known as the 5-HTT gene-linked polymorphic region (5-HTTLPR), are associated with increased neuroticism, anxiety, and depression [6–8]. In addition, the short variant of the polymorphism is suggested to reduce the transcriptional efficiency of the 5-HTT gene promoter, resulting in decreased 5-HTT expression and 5-HT uptake [6, 9, 10]. Genetic animal models of 5-HTT deficiency offer promising opportunities to investigate the causal relationship between 5-HTT deficiency and brain function. Earlier and subsequent studies have focused on anxiety and depression phenotypes, reporting that mice genetically deficient in 5-HTT expression exhibited decreased locomotor activity and increased anxiety-like and depression-related behaviors [11–29], which vary with their genetic and environmental background [11, 12, 19, 20]. In contrast, a relatively small number of studies have examined other behavioral domains in 5-HTT knockout (KO) mice, reporting normal sensory function [14, 18], reduced motor performance [11, 14, 18, 19], normal or decreased social behavior [23, 27, 28, 30], and altered memory function [27, 31–33], which await further investigation under various test conditions to confirm the behavioral consequences of 5-HTT deficiency. The gut microbiota plays an essential role in shaping and modulating the gut-brain axis. There is increasing evidence that the imbalance of the microbiota-gut-brain axis leads to dysregulation of neurotransmission, inflammation, the immune system, and the endocrine system [34, 35], which may be associated with neuropsychiatric and neurodevelopmental disorders, including depression [36–38]. Approximately $95\%$ of the total 5-HT in the body is found in the gastrointestinal tract. 5-HTT mediates 5-HT reuptake in platelets, which allows 5-HT to be distributed to peripheral tissues and contributes to various physiological processes, such as bone remodeling and energy metabolism [39–42]. A recent study reported that 5-HTT−/− mice had higher abundances of Bacilli species and lower abundances of Bifidobacterium species and *Akkermansia muciniphilia* than 5-HTT+/+ mice in fecal and cecal samples [43]. These findings suggest that altered gut microbial composition via 5-HTT deficiency is associated with changes in physiological and behavioral functions. *Various* genetic and environmental factors can influence the gut microbial composition [44, 45], and are potential confounding variables that may lead to different outcomes between laboratories. Therefore, further investigation and replication of the association between 5-HTT deficiency and gut microbiota is needed. Several lines of evidence indicate specific brain neural circuits responsible for emotional and mood regulation [46–48]. In animal studies, c-Fos protein expression has been widely used as a marker of neuronal activity in response to behavioral tests to map functional brain regions associated with anxiety and depression [49–52]. The forced swim test is one of the extensively used behavioral paradigms for screening for new drugs with potential antidepressant effects and assessing depression-related behavior in rodents [53, 54]. In the behavioral test, 5-HTT−/− mice showed increased depression-related behaviors [11, 14]. Brain mapping of c-Fos after exposure to the forced swim test may provide valuable insights into the brain circuits involved in the depressive state induced by 5-HTT deficiency. There are advantages to using a multifaceted approach to behavioral phenotyping through a battery of behavioral tests, which allows us to characterize various domains of behavior in different test situations, thus minimizing inappropriate interpretation of behavioral data by multiple tests [55–57]. In the present study, to understand the brain function of 5-HTT and to evaluate 5-HTT-deficient mice as an animal model for neuropsychiatric and neurodevelopmental disorders, we first assessed various domains of behavior in male 5-HTT heterozygous KO and homozygous KO mice and their wild-type controls using a battery of behavioral tests, including 16 different tests assessing locomotor activity in novel and familiar environments, sensory and motor function, anxiety-like and depression-related behaviors, social behaviors in novel and familiar environments, prepulse inhibition, working memory, spatial reference memory, contextual and cued fear memory, and remote memory. Although many studies have performed tests for behavioral phenotyping of 5-HTT-deficient mice, some of the behavioral tests and specific behavioral measures (e.g., social interaction test in a novel and familiar environments, some major measures for the acquisition session and probe trial in Barnes maze test, contextual fear conditioning test, and remote memory test) have not been investigated. Thus, our behavioral analysis allows us to reevaluate and further explore the behavioral phenotypes of 5-HTT-deficient mice in different testing situations. Second, we analyzed the fecal microbiota in 5-HTT homozygous KO mice and their wild-type controls to investigate genotype-dependent changes in gut microbial composition in our environment and to explore the potential relationships between 5-HTT deficiency, gut microbiota, and behavior. Finally, to investigate the stress response and brain regions associated with increased depression-related behavior caused by deletion of the 5-HTT gene, plasma corticosterone levels and brain c-Fos expression after the forced swim test were assessed in 5-HTT homozygous KO mice and their wild-type controls. ## Animals 5-HTT KO mice were derived from the colony of the originally generated mutants as previously described [13, 15]. After backcrossing to C57BL/6JJcl mice (CLEA Japan, Inc., Tokyo, Japan) for at least ten generations, the mutants were maintained by crossing male heterozygous KO mice and female heterozygous KO mice. In this study, 5-HTT homozygous KO (5-HTT−/−), 5-HTT heterozygous KO (5-HTT+/−), and wild-type (5-HTT+/+) males were obtained from the litters produced by crossing male and female 5-HTT+/− mice in the same manner. Some of the litters used in this study did not contain WT males. Therefore, some WT males used as controls were not littermates of the heterozygous KO mice and/or the homozygous KO mice (for detailed information about the genotype of each mouse in each litter, see “Mouse Phenotype Database”, http://www.mouse-phenotype.org/). After weaning at approximately 4 weeks of age, mice were genotyped and housed in groups (two to four per cage) of the same genotype and sex in plastic cages (250 × 182 × 139 mm) with paper chips for bedding (Paper Clean; Japan SLC, Inc., Shizuoka, Japan). Rooms were maintained on a 12-h light/dark cycle (lights on at 7:00 am) at 23 ± 2 °C. Food (CRF-1, Oriental Yeast Co., Ltd., Tokyo, Japan) and water were provided ad libitum. A total of 33 male 5-HTT homozygous KO (5-HTT−/−), 16 male 5-HTT heterozygous KO (5-HTT+/−), and 36 male wild-type control (5-HTT+/+) mice were used in this study. All experimental procedures were approved by the Institutional Animal Care and Use Committee of Fujita Health University. ## Behavioral tests Male 5-HTT homozygous KO (5-HTT−/−, $$n = 15$$), 5-HTT heterozygous KO (5-HTT+/−, $$n = 16$$), and their wild-type control (5-HTT+/+, $$n = 15$$) mice, 16–22-weeks (4–5-months) old at the beginning of the experiment, were subjected to a battery of behavioral tests in the following order (see Additional file 4: Table S1): general health and neurological screen, light/dark transition, open field, elevated plus maze, hot plate, social interaction, rotarod, startle response/prepulse inhibition, Porsolt forced swim, three-chamber social approach, T-maze spontaneous alternation, Barnes maze, tail suspension, sucrose preference, contextual and cued fear conditioning, and home cage social interaction tests, as previously described [58]. After each test, the floors and walls of the testing apparatuses were cleaned with $70\%$ ethanol solution and hypochlorous acid water to prevent a bias based on olfactory cues. With the exception of the sucrose preference test, behavioral tests were conducted between 9:00 am and 5:00 pm. ## General health and neurological screen Physical characteristics, including body weight and rectal temperature, were recorded. Neuromuscular strength was assessed by the grip strength and wire hang tests. Forelimb grip strength was measured using a grip strength meter (O’Hara & Co., Tokyo, Japan). Mice were lifted by their tails to grasp a wire grid with their forelimbs. They were then gently pulled back until they released the grid. The peak force of the grip strength was recorded in Newtons (N). In the wire hang test, mice were placed on a wire mesh (O’Hara & Co., Tokyo, Japan) which was then gently inverted so that the mice grasped the wire. The latency to fall from the wire was recorded with a 60-s cut-off time. ## Light/dark transition test The light/dark transition test, developed by Crawley and colleagues [59] to assess anxiety-like behavior, was performed as previously described [60]. The apparatus consisted of a cage (21 × 42 × 25 cm) divided into two equal chambers by a partition with a door (O’Hara & Co., Tokyo, Japan). One chamber had white plastic walls and was brightly lit (390 lx) by lights mounted above the ceiling of the chamber. The other chamber had black plastic walls and was dark (2 lx). Both chambers had a white plastic floor. Mice were placed in the dark chamber and allowed to move freely between the two chambers for 10 min with the door open. Distance traveled (cm), the number of transitions, latency to enter the light chamber (s), and time spent in the light chamber (s) were automatically recorded using the ImageLD software (see “Image analysis”). ## Open field test The open field test was conducted to assess anxiety-like behavior, habituation to a novel environment, and locomotor activity [61–64] by measuring behaviors for 120 min in the open field apparatus with the VersaMax activity monitoring system (Accuscan Instruments, Columbus, OH, USA). The open field arena was made of acrylic with transparent walls and a white floor (40 × 40 × 30 cm). The floor of the central area, defined as 20 cm × 20 cm, was illuminated at 100 lx. Each mouse was placed in one corner of an open field and allowed to explore freely for 120 min. Distance traveled (cm), vertical activity (rearing measured by counting the number of photobeam interruptions), time spent in the center area (s), and stereotypic counts (beam-break counts for stereotyped behaviors) were measured in each 5-min block. ## Elevated plus maze test The elevated plus maze test, which is widely used to assess anxiety [65], was performed as previously described [66, 67]. The apparatus consisted of two open arms (25 × 5 cm) and two closed arms of the same size with 15-cm-high transparent walls and a central square (5 × 5 cm) connecting the arms (O’Hara & Co., Tokyo, Japan). The floor of the apparatus was made of white plastic plates and was elevated to 55 cm above the floor. The open arms were surrounded by a raised ledge (3-mm thick and 3-mm high) to prevent mice from falling off the arms. Arms of the same type were located opposite one another. The illumination level at the central area was 100 lx. Each mouse was placed in the central square of the maze facing one of the closed arms. The number of arm entries, distance traveled (cm), percentage of entries into open arms, and percentage of time spent in open arms were measured during a 10-min test period. Data acquisition and analysis were performed automatically using the ImageEP software. ## Hot plate test The hot plate test was used to assess sensitivity to a painful stimulus. Each mouse was placed on a hot plate (55.0 ± 0.1 °C; Columbus Instruments, Columbus, OH, USA), and the latency to the first paw response (s) was recorded with a cut-off time of 15 s. The paw response was defined as either a paw lick or a foot shake. ## Social interaction test The social interaction test was used to assess social behavior in a novel environment. Weight-matched mice (mean ± SD for differences in body weight: 5-HTT+/+, 2.15 ± 1.32 g; 5-HTT+/−, 1.87 ± 0.97 g; 5-HTT−/−, 1.43 ± 1.34 g) of the same genotype, housed in different cages, were placed together in a white plastic box (40 × 40 × 30 cm) and allowed to explore freely for 10 min. The central area of the box was illuminated at 100 lx. The total number of contacts, total duration of contacts (s), total duration of active contacts (s), mean duration per contact (s), and total distance traveled (cm) were measured automatically using the ImageSI software. The active contact was measured when the two mice made contact and one or both mice moved with a velocity of at least 10 cm/s. ## Rotarod test Motor coordination and balance were assessed using the rotarod test. Mice were placed on a rotating drum (3 cm diameter, UGO Basile Accelerating Rotarod). They were given three trials per day for two consecutive days. The latency to fall off the rod (s) was measured. The speed of the rotarod accelerated from 4 to 40 rpm over 300 s. ## Three-chamber social approach test The apparatus consisted of a rectangular, three-chambered box and a lid with a video camera (O’Hara & Co., Tokyo, Japan). Each chamber was made of white plastic (20 × 40 × 47 cm) and the partitions were made of transparent acrylic with a small square opening (5 × 3 cm). The three-chamber test was conducted as previously described [68]. First, each test mouse was placed in the center chamber of the apparatus, in which empty wire cages (9 cm in diameter, 11 cm in height, with vertical bars 0.5 cm apart) were placed in the corners of each side chamber, and was allowed to explore for 10 min (habituation session). Next, an unfamiliar C57BL/6 J male mouse (stranger 1; 9–10 weeks old; strangers were obtained from Charles River Laboratories Japan, Inc., Kanagawa, Japan) that had no prior contact with the test mice was placed in the wire cage located in one of the side chambers. The location of the stranger mouse in the left or right chamber was systematically alternated between trials. The test mouse was placed in the center chamber and allowed to explore for a 10-min session to assess sociability (sociability test). Then, a second stranger mouse (stranger 2; 9–10 weeks old) was placed in the wire cage that had been empty during the first 10-min session to assess social preference for the new stranger (social novelty preference test). Thus, the test mouse had a choice between the first, already-investigated, now-familiar mouse (stranger 1) and the new, unfamiliar mouse (stranger 2). Time spent in each chamber and around each cage was automatically calculated from video images using the ImageCSI software. ## Acoustic startle response/prepulse inhibition test Startle response and prepulse inhibition tests were performed using a startle reflex measurement system (O’Hara & Co., Tokyo, Japan). Mice were placed in a Plexiglas cylinder and left undisturbed for 10 min. A loud sound stimulus (110 or 120 dB, white noise, 40 ms) was presented as a startle stimulus. A prepulse sound (74 or 78 dB, white noise, 20 ms) was presented 100 ms before the startle stimulus to assess prepulse inhibition. A test session consisted of six trial types (i.e., two types of startle stimulus-only trials, and four types of prepulse inhibition trials: 74–110, 78–110, 74–120, and 78–120 dB). Six blocks of the six trial types were presented in a pseudo-random order such that each trial type was presented once within a block. The average interval was 15 s (range: 10–20 s). A 70-dB white noise was presented as a background noise during the test. The peak amplitude of the startle responses to the stimuli was recorded for 400 ms from the onset of the prepulse stimulus. The percent PPI was calculated for each mouse using the following formula: percent PPI = 100 × [1 − (startle response amplitude in prepulse + startle trial)/(startle response amplitude in startle stimulus-only trial)]. ## Porsolt forced swim test The Porsolt forced swim test, developed by Porsolt and colleagues [53], was used to assess depression-related behavior. Mice were placed in a clear plastic cylinder (20 cm height × 10 cm diameter, O’Hara & Co., Tokyo, Japan) filled with water (approximately 21 °C) to a depth of 8 cm for 10 min. The percentage of immobility time was automatically calculated using the ImagePS/TS software as previously described [58, 69]. ## T-maze spontaneous alternation test The T-maze spontaneous alternation test was conducted to assess spatial working memory using a modified automatic T-maze apparatus (O'Hara & Co.), as previously described [58, 70]. The apparatus consisted of white plastic runways with 25-cm-high walls. It was partitioned into six areas: the stem of the T, a straight runway, left and right arms, and the passageways connecting the arms to the stem of the T. Mice were subjected to a session of 10 trials per day for two days (a cut-off time of 50 min). Each trial consisted of a forced-choice followed by a free choice (inter-trial interval, 60 s). In the forced-choice trial, mice were forced to enter either the left or right arm of the T-maze and were held in the arm for 10 s. After the 10-s period, the doors of the passageway connecting the arm to the stem of the T were opened and the mouse was allowed to return to the start compartment. Three seconds after the mice entered the start compartment, a free-choice trial began. The mice were allowed to choose one of the arms in the free-choice trial, and if the mice entered the arm opposite to the arm they entered in the forced-choice trial, the response was recorded as a correct response. The mean percentage of correct responses across two sessions was calculated. Data acquisition and analysis were performed automatically using the ImageTM software (see "Image analysis"). ## Barnes maze test The Barnes circular maze test [71] was used to test spatial reference memory on ‘dry land’, a white circular surface 1.0 m in diameter with 12 holes equally spaced around the perimeter (O’Hara & Co., Tokyo, Japan). The circular open field was elevated 75 cm above the floor. A black Plexiglas escape box (17 × 13 × 7 cm) was placed under one of the holes (target hole). The acquisition session consisted of two trials per day for nine days. In each trial, a mouse was placed in the center of the maze and allowed to explore. If a mouse did not enter the escape box within a maximum of 5 min, it was gently guided to the escape hole. After entering the escape hole, the mouse remained in the escape box for 30 s before returning to the holding cage. The location of the target was constant for a given mouse, but randomized across mice. The maze was rotated daily, but the spatial location of the target was kept constant for the distal visual cues to avoid bias due to an olfactory cue or proximal cues. The latency to reach the target hole (s), the number of errors to reach the target hole, and the distance traveled first to reach the target hole (cm) were automatically recorded by the ImageBM software. One day and 28 days after the last acquisition session, probe trials without the escape box were performed to assess spatial reference memory. In the probe test, the time spent around each hole (s) was measured using the ImageBM software. ## Tail suspension test The tail suspension test was performed to evaluate depression-related behavior [72]. Mice were suspended 30 cm above the floor in a visually isolated area using adhesive tape placed approximately 1 cm from the tip of the tail. Immobility time was measured for a 10-min test period using the ImagePS/TS software in the same manner as for the forced swim test. ## Sucrose preference test After the tail suspension test, mice were individually housed in plastic cages (250 × 182 × 139 mm) with fresh paper chips as bedding and provided with two bottles of filtered tap water. After one day of acclimation to the housing conditions, mice were given one bottle of water and a second bottle of $1\%$ sucrose solution, with the left/right position counterbalanced across genotypes of animals. The bottles were weighed at approximately 24-h intervals to measure water and sucrose intake on each day of the four-day session, with the left–right position changed daily. Sucrose preference was expressed as 100 × [(sucrose intake in grams)/(sucrose intake in grams + water intake in grams)]. ## Contextual and cued fear conditioning test The contextual and cued fear conditioning test was conducted to assess fear memory using an automated video analysis system [73]. First, mice were placed in a conditioning chamber (26 × 34 × 29 cm) in a sound-attenuated room and allowed to explore freely for 2 min. Next, the animals were presented with an auditory cue (55 dB white noise) that served as a conditioned stimulus (CS) for 30 s. During the last 2 s of the CS, the mice received a mild footshock (0.3 mA, 2 s) as an unconditioned stimulus (US). Two more CS-US pairings were then presented at 120-s intervals. One day and 29 days after the conditioning session, a context test was performed in the conditioning chamber. More than 3 h after the context test, a cued test in an altered context was performed in a triangular box (35 × 35 × 40 cm) made of opaque white plastic in another sound-attenuated room. In the cued test, after the initial 3-min period without CS presentation, the CS was presented during the last 3-min period. In each test, video images were recorded at one frame per second. Freezing time (%) and distance traveled (cm) in each trial were measured automatically from the images using the ImageFZ software, as previously described [73]. Images were also recorded at a rate of 4 frames per second for 6 s from 2 s before delivery of a 2 s footshock until 2 s after the footshock, and distance traveled (cm) was measured as an index of footshock sensitivity. ## Home cage social interaction test The home cage social interaction test was conducted to assess social behavior and activity levels under familiar conditions in a home cage. This test was performed 11 days after the context and cued tests on day 2 of the fear conditioning test (the second session of context and cued tests, 29 days after the conditioning, was performed seven days after the home cage social interaction test). The social interaction monitoring system consisted of a home cage with paper chips for bedding and a cage top with an infrared video camera (25 × 15 × 23.5 cm, internal dimensions). Two mice of the same genotype housed in separate cages were placed together in the cage. Video images were recorded at a rate of 1 frame per second. Social interaction was measured by automatically counting the number of animals detected in each frame using the ImageHA software. The activity level of animals was quantified by measuring the number of pixels that changed between each pair of consecutive images. The mean number of animals and total activity level in each 1-h bin were calculated for 1 week. ## Image analysis for behavioral test Image analysis software (ImageLD/EP/SI/CSI/PS/TS/TM/BM/FZ/HA) were used to automatically analyze mouse behaviors, as previously described [60, 66, 70, 73]. The software, based on the public domain ImageJ program (developed by Wayne Rasband at the National Institute of Mental Health, Bethesda), were developed and modified for each test by Tsuyoshi Miyakawa. The ImageLD/EP/TM/FZ programs can be freely downloaded from the “Mouse Phenotype Database” (http://www.mouse-phenotype.org/). ## Fecal sample collection, 16S rRNA gene sequencing, and data analysis Fecal samples were collected from an independent group of naïve male 5-HTT−/− and 5-HTT+/+ mice ($$n = 8$$, each genotype; two mice per cage were used; in each genotype), which were individually placed in an empty cage (250 × 182 × 139 mm) precleaned with $65\%$ ethanol. The feces were quickly collected in a tube and stored at − 80 °C. DNA from each fecal sample was extracted by a bead-based method, as previously performed [74]. Prokaryote universal primers (Pro341F and Pro805R) with the sample-specific 8-bp dual-index barcode sequences were used to amplify V3 and V4 regions of 16S rDNA genes by polymerase chain reaction [74, 75]. The barcoded amplicons were paired-end sequenced on the Illumina MiSeq platform using the MiSeq reagent kit v3 (600 cycles, 2 × 284-bp cycle; Illumina, San Diego, CA, USA). Paired-end reads were joined using the fastq-join program [76]. The joined reads with a quality value score ≥ 20 for > $99\%$ of the sequence were extracted using FASTX-Toolkit [77] and were used for further analysis. The chimeric sequences were removed using uSearch61 software [78, 79]. Taxonomy assignment from the sequence reads was performed using Metagenome@KIN software (World Fusion, Tokyo, Japan) and the database RDP MultiClassifier ver.2.11 [80] with an $80\%$ confidence level. The amplicon sequencing and taxonomic assignment were performed by TechnoSuruga Laboratory Co., Ltd. (Shizuoka, Japan). The read counts were analyzed to assess α-diversity (species richness and evenness from the rarefied counts; observed number of microbial genera, Chao1 index, Shannon index, and Simpson index) using R packages 'vegan' ver. 2.5–7 [81]. In addition, β-diversity was visualized with principal coordinate analysis (PCoA) with Bray–Curtis dissimilarity, and permutational multivariate analysis of variance (PERMANOVA) was used to assess microbial differences using the adonis function in the R package 'vegan'. The relative abundance of microbiota at the genus level was analyzed using the linear discriminant analysis (LDA) effect size (LEfSe) method [82] to identify taxonomic features characterizing the differences between the genotypes (LDA score > 3, $p \leq 0.05$). ## Plasma corticosterone measurement Blood samples were collected from the facial vein or submandibular vein of an independent group of naïve male 5-HTT−/− ($$n = 5$$) and 5-HTT+/+ ($$n = 6$$) mice using a Goldenrod Animal Lancet (MEDIpoint, Inc., NY, USA) to measure basal plasma corticosterone levels during the light phase (11:30–12:30). Samples were placed in tubes containing 1 unit of sodium heparin (Wako Pure Chemical Industries Ltd., Osaka, Japan) and centrifuged at 3000×g for 10 min at 4 °C. Supernatants were collected and stored at − 80 °C until measurement. Two days after the first blood collection, mice were subjected to the forced swim test for 10 min (11:00–12:30). Blood samples were collected immediately and 90 min after the test to assess stress-induced corticosterone levels and their time-dependent decline after stress exposure. Plasma corticosterone (CORT) concentrations were determined using an enzyme immunoassay kit (Assay Designs Inc., MI, USA) according to the manufacturer’s protocol. ## c-Fos immunohistochemistry Immediately after the blood sampling 90 min after exposure to the forced swim test, 5-HTT−/− ($$n = 5$$) and 5-HTT+/+ ($$n = 6$$) mice were deeply anesthetized and transcardially perfused with saline followed by $4\%$ paraformaldehyde in 0.1 M phosphate buffer (PB). Mice not subjected to the forced swim test and blood sampling as non-exposed groups (5-HTT−/−, $$n = 5$$; 5-HTT+/+, $$n = 7$$) were treated identically immediately after removal from their home cages. Brain samples were removed and fixed in the same fixative overnight at 4 °C. After the post-fixation, the brains were soaked in $30\%$ sucrose in phosphate buffer saline (PBS) at 4 °C for at least 3 days, then embedded in Tissue-Tek OCT compound (Sakura Finetek Japan Co., Ltd., Tokyo, Japan) under liquid nitrogen, and stored at − 80 °C. Brains were cut into 30-μm-thick coronal sections on a cryostat (CM1850; Leica Biosystems, Wetzlar, Germany). Sections were stored at − 20 °C in a cryoprotectant solution ($25\%$ glycerol, $25\%$ ethylene glycol in 0.1 M PB) until use. Floating sections were washed with PBS and incubated in $0.3\%$ H2O2 for 20 min. After washing with PBS, the sections were soaked in normal horse serum (S-2000; Vector Laboratories, CA, USA) in PBS containing $0.3\%$ Triton X-100 for 30 min at room temperature and then were incubated overnight at 4 °C with goat polyclonal anti-c-Fos antibody (1:1000, sc-52G; Santa Cruz Biotechnology, CA, USA). The sections were then washed with PBS and incubated with biotinylated horse anti-goat secondary antibody (1:500, BA-9500; Vector Laboratories, CA, USA) for 30 min, followed by incubation with reagents from the VECTASTAIN Elite ABC kit (PK-6100; Vector Laboratories, CA, USA) for 30 min. Sections were immersed in DAB peroxidase substrate solution (ImmPACT, SK-4105; Vector Laboratories, CA, USA) for 2 min. Sections were then mounted on glass slides, dehydrated in ethanol, cleared in xylene, and coverslipped with Multi Mount 480 (Matsunami Glass Ind., Ltd., Osaka, Japan). Images of the stained sections were collected using a light microscope (BZ-9000; Keyence, Osaka, Japan). At least six sections per animal were used for image analysis of each brain region. The number of c-Fos-positive cells was quantified in 200 μm × 200 μm of brain regions bilaterally (see Fig. 6b). Anatomical localization of brain regions was aided by the use of the illustrations in a stereotaxic atlas ([83]; see Fig. 6b and Additional file 4: Table S5). Cells were counted manually in ImageJ in a blinded fashion. ## Statistical analysis Statistical analyses were performed using SAS Studio (SAS OnDemand for Academics; SAS Institute, Cary, NC, USA) and R (version 3.6.3). Behavioral data were analyzed by one-way ANOVAs with genotype as the between-subject variable or two-way repeated measures ANOVAs with genotype as between-subject variables and time/trial/block as the within-subject variable. When a genotype × time interaction was significant, simple main effects were analyzed to examine the effect of genotype at each time point. Comparisons of microbial composition between genotypes were performed using Wilcoxon rank sum test. Plasma corticosterone levels were analyzed using two-way repeated measures ANOVAs with genotype and phase (before exposure to the forced swim test, immediately after the test, and 90 min after the test). Brain c-Fos expression was analyzed using a two-way ANOVA with genotype and exposure (no exposure and forced swim exposure). Values in graphs are expressed as the mean ± SEM. Uncorrected p-values are shown. ## Physical characteristics and neurological functions in 5-HTT-deficient mice The statistical results of behavioral data of 5-HTT−/−, 5-HTT+/−, and 5-HTT+/+ mice subjected to a behavioral test battery are summarized in Additional file 4: Table S2. There were significant effects of genotype on the body weight (Fig. 1a), wire hang latency (Fig. 1d), hot plate latency (Fig. 1e), rotarod latency (Fig. 1f), and acoustic startle responses to 110 dB and 120 dB stimuli (Fig. 1g). No significant effects of genotype were found on the rectal temperature (Fig. 1b), grip strength (Fig. 1c), and prepulse inhibition in any of the trial types (Fig. 1h). Post hoc analysis revealed that 5-HTT−/− mice were significantly heavier than 5-HTT+/+ and 5-HTT+/− mice (Fig. 1a; vs. +/+, $$p \leq 0.0017$$; vs. +/−, $$p \leq 0.0359$$), while body weights were not significantly different between 5-HTT+/+ and 5-HTT+/− mice ($$p \leq 0.2238$$). In the wire hand test (Fig. 1d), 5-HTT−/− mice exhibited a shorter latency to fall off the wire, which is indicative of reduced muscular strength, than 5-HTT+/+ mice ($$p \leq 0.0002$$), and the wire hang latency of 5-HTT+/− mice was intermediate between that of the other two genotypes (vs. +/+, $$p \leq 0.0804$$; vs. −/−, $$p \leq 0.0206$$). The hot plate latency was higher in 5-HTT−/− mice than in 5-HTT+/+ and 5-HTT+/− mice (Fig. 1e; vs. +/+, $$p \leq 0.0122$$; vs. +/−, $$p \leq 0.0223$$), and there were no significant differences between 5-HTT+/+ and 5-HTT+/− mice ($$p \leq 0.7733$$), suggesting reduced pain sensitivity in 5-HTT−/− mice. In the rotarod test (Fig. 1f), 5-HTT−/− mice spent less time on the rotating rod compared with other genotypes, suggesting decreased motor function (vs. +/+, $$p \leq 0.0037$$; vs. +/−, $$p \leq 0.0032$$), while no difference was found between 5-HTT+/− and 5-HTT+/+ mice ($$p \leq 0.9919$$). Acoustic startle responses were decreased in 5-HTT−/− mice compared to other genotypes in response to 110 dB stimulus (vs. +/+, $$p \leq 0.0038$$; vs. +/−, $$p \leq 0.0006$$) and 120 dB stimulus (vs. +/+, $$p \leq 0.0009$$; vs. +/−, $$p \leq 0.0236$$). There were no significant differences in the startle responses between 5-HTT+/− and 5-HTT+/+ mice (110 dB, $$p \leq 0.5560$$; 120 dB, $$p \leq 0.2095$$).Fig. 1General health and sensory and motor functions in 5-HTT-deficient mice. a Body weight (g), b body temperature (°C), c grip strength (Newton, N), d wire hang latency (s), e latency to paw lick or foot shake (s) in the hot plate test, f latency to fall off a rotating rod (s) in the rotarod test, g acoustic startle response to sound stimuli (110 and 120 dB white noise), and h prepulse inhibition (%) of the startle response with 74 and 78 dB prepulse stimuli. Values are means ± SEM. Asterisks and daggers indicate statistically significant differences between groups (5-HTT−/− vs. 5-HTT+/+, *$p \leq 0.05$ and **$p \leq 0.01$; 5-HTT−/− vs. 5-HTT+/−, †$p \leq 0.05$ and ††$p \leq 0.01$) ## Decreased locomotor activity and increased anxiety-like behavior in 5-HTT-deficient mice In the light/dark transition test, 5-HTT−/− mice traveled shorter distances in the dark chamber (Fig. 2a: −/− vs. +/+, $$p \leq 0.0007$$; −/− vs. +/−, $$p \leq 0.0015$$) and light chamber (Fig. 2a: −/− vs. +/+, $$p \leq 0.0002$$; −/− vs. +/−, $$p \leq 0.0011$$) and spent less time in the light chamber (Fig. 2c: −/− vs. +/+, $p \leq 0.0001$; −/− vs. +/−, $p \leq 0.0001$) than the other genotypes of mice. These behaviors were not significantly different between 5-HTT+/− and 5-HTT+/+ mice. There were no significant effects of genotype on the number of transitions (Fig. 2b) and the latency to enter the light chamber (Fig. 2d). These observations indicate that 5-HTT−/− mice showed reduced locomotor activity and increased anxiety-like behavior compared to 5-HTT+/+ and 5-HTT+/− mice. Fig. 2Anxiety-like and depression-related behaviors in 5-HTT-deficient mice. a–d Light/dark transition test: a distance traveled (cm) in the dark and light chambers, b number of transitions, c time spent in the light chamber (s), and d latency to enter the light chamber (s). e–h Open field test: e distance traveled (cm), f vertical activity, g center time (s), and h stereotypic counts for each 5-min block of testing. i–l *Elevated plus* maze test: i distance traveled (cm), j number of arm entries, k time spent in open arms (%), and l entries into open arms (%). m, n Porsolt forced swim test: m immobility time (%) and n distance traveled (cm) for each 1 min-block. o Immobility time (%) for each 1-min block in the tail suspension test. ( p–r) Sucrose preference test: p water intake and q $1\%$ sucrose intake, and r sucrose preference (%). Values are means ± SEM. Asterisks and daggers indicate statistically significant differences between groups (5-HTT−/− vs. 5-HTT+/+, *$p \leq 0.05$ and **$p \leq 0.01$; 5-HTT−/− vs. 5-HTT+/−, †$p \leq 0.05$ and ††$p \leq 0.01$; 5-HTT+/− vs. 5-HTT+/+, § $p \leq 0.05$ and §§$p \leq 0.01$) In the open field test, there were significant main effects of genotype and significant genotype × time interactions on the distance traveled, vertical activity, center time, and stereotypic counts (Fig. 2e–h). For these behavioral measures, significant simple main effects of genotype were found in every 5-min block except for the 23rd block for the distance traveled (all $p \leq 0.05$). In almost all 5-min blocks, 5-HTT−/− mice exhibited significantly shorter distance traveled, less vertical activity, and fewer stereotypic behaviors than 5-HTT+/+ and 5-HTT+/− mice ($p \leq 0.05$), indicating that 5-HTT−/− mice had reduced locomotor activity. 5-HTT−/− mice also spent significantly less time in the center area, suggestive of increased anxiety-like behavior, than 5-HTT+/+ and 5-HTT+/− mice ($p \leq 0.05$). 5-HTT+/− mice showed slightly decreased distance traveled in the 10th and 17th blocks, shorter center time in the 17th and 23rd blocks, and fewer stereotypic counts in the 10th, 14th, 16th, 17th, 19th, 20th, and 23rd blocks than 5-HTT+/+ mice (all $p \leq 0.05$). In the elevated plus maze test, there were significant genotype effects on distance traveled, number of total arm entries, percentage of open arm entries, and percentage of time on open arms (Fig. 2i–l). 5-HTT−/− mice exhibited decreased distance traveled, fewer total arm entries, a lower percentage of open arm entries, and a lower percentage of time on open arms than 5-HTT+/+ and 5-HTT+/− mice (all $p \leq 0.05$). There were no significant differences in these behavioral measures between 5-HTT+/− and 5-HTT+/+ mice. Taken together, these data obtained from three different types of behavioral tests indicate that 5-HTT−/− mice showed reduced locomotor activity and increased anxiety-like behavior compared to 5-HTT+/− and 5-HTT+/+ mice. ## Increased depression-related behavior in 5-HTT-deficient mice In the forced swim test, significant main effects of genotype and significant genotype × time interactions were found on the percentage of immobility time and distance traveled on days 1 and 2 (Fig. 2m, n). On day 1, 5-HTT−/− mice displayed increased immobility compared to 5-HTT+/+ mice (2nd, 4th, and 6th time blocks, all $p \leq 0.05$) and 5-HTT+/− mice (2nd, 3rd, and 4th time blocks, all $p \leq 0.05$), while 5-HTT−/− mice traveled shorter distances than 5-HTT+/+ mice (2n and 4th time blocks, all $p \leq 0.05$) and 5-HTT+/− mice (2nd, 3rd, and 4th time blocks, all $p \leq 0.05$). 5-HTT+/− mice did not differ from 5-HTT+/+ mice in immobility and distance traveled in any time blocks. Similarly, on day 2, 5-HTT−/− mice showed increased immobility compared to 5-HTT+/+ mice (3rd, 4th, and 5th time blocks, all $p \leq 0.05$) and 5-HTT+/− mice (3rd, 4th, and 5th time blocks, all $p \leq 0.05$), while 5-HTT−/− mice traveled shorter distances than 5-HTT+/+ mice in the 3rd time block ($$p \leq 0.0122$$). There were no significant differences in immobility and distance traveled between 5-HTT+/+ and 5-HTT+/− mice. These data suggest that 5-HTT−/− mice showed increased depression-related behavior than other genotypes of mice. In the tail suspension test, there was no significant main effect of genotype and no significant genotype × time interaction on the immobility (Fig. 2o). In the sucrose preference test, there was no significant main effect of genotype and no significant genotype × session interaction on water intake (Fig. 2p). There was a significant main effect of genotype was found on sucrose intake (Fig. 2q: F2,42 = 7.04, $$p \leq 0.0023$$) and percent sucrose preference (Fig. 2r; F2,42 = 3.60, $$p \leq 0.0362$$). There were no significant genotype × session interactions on sucrose intake and percent sucrose preference, indicating that the genotype effects were not dependent on the test session (Fig. 2q, r). These data suggest that mice of each genotype did not show avoidance of the sucrose solution from test day 1 and that there was no apparent genotype difference in the process of the habituation to the sucrose solution. Post hoc analysis showed that 5-HTT−/− and/or 5-HTT+/− mice had reduced sucrose intake (−/− vs. +/+, $$p \leq 0.0471$$; +/− vs. +/+, $$p \leq 0.0005$$) and sucrose preference (−/− vs. +/+, $$p \leq 0.1530$$; +/− vs. +/+, $$p \leq 0.0105$$) compared to 5-HTT+/+ mice. ## Altered social behavior in 5-HTT-deficient mice In the one-chamber social interaction test in a novel environment, there were significant effects of genotype on all the behavioral measures (Fig. 3a–e). Compared to 5-HTT+/+ and 5-HTT+/− mice, 5-HTT−/− mice exhibited fewer number of contacts (vs. +/+, $$p \leq 0.0003$$; vs. +/−, $$p \leq 0.0010$$), longer time of contacts (vs. +/+, $$p \leq 0.0008$$; vs. +/−, $$p \leq 0.0005$$), shorter time of active contacts (vs. +/+, $$p \leq 0.0002$$; vs. +/−, $$p \leq 0.0009$$), longer mean duration per contact (vs. +/+, $$p \leq 0.0013$$; vs. +/−, $$p \leq 0.0010$$), and shorter distance traveled (vs. +/+, $$p \leq 0.0004$$; vs. +/−, $$p \leq 0.0020$$). No significant differences in these measures were observed between 5-HTT+/+ and 5-HTT+/− mice. Fig. 3Social behavior in 5-HTT-deficient mice. a–e Social interaction test in a novel environment: a the number of contacts, b total duration of contacts (s), c total duration of active contacts (s), d mean duration per contact (s), and distance traveled (cm). f–i Three-chamber social approach test: f time spent in the chamber with an empty cage, the center chamber, and the chamber with a cage containing a stranger mouse (stranger 1). g Time spent around the empty cage and the cage with stranger 1. h Time spent in the chamber with the cage containing stranger 1, the center chamber, and the chamber with a cage containing a novel unfamiliar mouse (stranger 2). i Time spent around the cage containing stranger 1 and the cage containing stranger 2. j–m Home cage social interaction test: j mean number of particles calculated for each 1-h period over 7 days, k mean number of particles averaged over the last 3 days, l mean activity level (arbitrary unit, A.U.) for each 1-h period over 7 days, and m mean activity level (A.U.) averaged over the last 3 days. Values are means ± SEM. a–e, j–m Asterisks and daggers indicate statistically significant differences between groups (5-HTT−/− vs. 5-HTT+/+, *$p \leq 0.05$ and **$p \leq 0.01$; 5-HTT−/− vs. 5-HTT+/−, †$p \leq 0.05$ and ††$p \leq 0.01$). f–i Asterisks represent statistical significance with paired t tests (**$p \leq 0.01$) In the three-chamber sociability test, 5-HTT−/− mice showed no significant differences between time spent in the chamber with stranger 1 and time spent in the chamber with an empty cage (Fig. 3f; t14 = 0.97, $$p \leq 0.3488$$) and between time spent around the cage containing stranger 1 and time spent around the empty cage (Fig. 3g; t14 = 1.16, $$p \leq 0.2663$$). Although 5-HTT+/− mice spent more time around the cage with stranger 1 than time around the empty cage (Fig. 3g; t15 = 3.33, $$p \leq 0.0045$$), they showed no significant difference between time spent in the chamber with stranger 1 and time spent in the chamber with the empty cage (Fig. 3f; t15 = 2.09, $$p \leq 0.0536$$). In contrast, 5-HTT+/+ mice spent more time in the chamber with stranger 1 than in the chamber with empty cage (t14 = 5.05, $$p \leq 0.0002$$) and spent more time around the cage containing stranger 1 than around the empty cage (t14 = 5.24, $$p \leq 0.0001$$). In the social novelty preference test, although there were no significant differences between the time spent in the side chambers with stranger 1 and stranger 2 and between the time spent around the cages containing stranger 1 and stranger 2 (Fig. 3h, i), 5-HTT+/+ mice showed a tendency toward increased time spent around the cage containing stranger 2 compared to the cage containing stranger 1 (Fig. 3i; $$p \leq 0.0700$$). The results of the sociability test and the one-chamber social interaction test indicate the reduced social behavior in 5-HTT−/− mice. In the home cage social interaction test, pairs of mice of identical genotypes were housed in a cage for seven days. There were significant main effects of genotype and genotype × time interactions on the number of particles (one particle indicates contact between the two mice, and two particles indicate that the mice are not in contact with each other; Fig. 3j) and activity levels (Fig. 3l). For the total period, the number of particles was lower in 5-HTT−/− mice than in other genotypes (vs. 5-HTT+/+, $$p \leq 0.0028$$; vs. 5-HTT+/−, $$p \leq 0.0073$$), suggesting increased physical contact with each other in 5-HTT−/− mice, and activity levels were lower in 5-HTT−/− mice than in other genotypes (vs. 5-HTT+/+, $$p \leq 0.0011$$; vs. 5-HTT+/−, $$p \leq 0.0061$$). Overall, there were no significant differences in these behavioral measures between 5-HTT+/+ and 5-HTT+/− mice. Behavioral data averaged over the last three days were analyzed to assess social behavior and locomotor activity in the familiar condition. Two-way repeated measures ANOVAs revealed that there was a significant genotype × time interaction on the number of particles (Fig. 3k), and there was a significant main effect of genotype and a significant genotype × time interaction on activity levels (Fig. 3m). A lower number of particles was observed in 5-HTT−/− mice than in 5-HTT+/+ mice during the dark phase (8 pm, 10 pm, and 0 am; all $p \leq 0.05$) and in 5-HTT+/− mice at 0 am ($$p \leq 0.0186$$). 5-HTT−/− mice showed lower activity levels than other genotypes during the dark phase (all $p \leq 0.05$, for 8 pm, 10 pm to 2 am, 6 am, 2 pm except for the case of comparison between 5-HTT−/− and 5-HTT+/+ mice at 2 pm and between 5-HTT−/− and 5-HTT+/− mice at 8 pm and 2 am). No significant differences in behavioral data averaged over the last three days were found between 5-HTT+/+ and 5-HTT+/− mice. In the social interaction test, which has been used to assess anxiety in rodents [96], the decreased distance traveled in 5-HTT−/− mice was observed, possibly resulting in decreased social contact. Our results suggest an increased anxiety-like phenotype in a social situation in 5-HTT−/− mice. Interestingly, 5-HTT−/− mice spent more time in physical contact with a novel conspecific than 5-HTT+/+ mice. Similar results for increased social contacts in 5-HTT−/− mice were observed at the beginning of the home cage social interaction test. These behavioral outcomes in a novel environment in 5-HTT−/− mice may be partly due to their hypoactive phenotype. Together with the reduced number of social contacts in the reciprocal social interaction test, the results of the three-chamber paradigm in the present study confirmed the reduced social preference in 5-HTT−/− and 5-HTT+/− mice, as shown in the previous study [28]. These findings suggest that 5-HTT deficiency result in reduced social behavior. ## Normal working memory and altered spatial memory in 5-HTT-deficient mice The T-maze spontaneous alternation test was performed to evaluate working memory. The percentage of correct responses in the T-maze did not differ between the three genotypes (Fig. 4a), suggesting normal working memory in 5-HTT−/− mice. Fig. 4Memory functions in 5-HTT-deficient mice. a Correct responses (%) in the T-maze spontaneous alternation test. b–k Barnes maze test: b the number of errors to first reach the target hole, c latency to reach the target hole, d distance traveled to first reach the target hole, and e number of omissions during the acquisition session. f–k Time spent around the target hole in the probe trial 1 day (f) and 28 days (i) after the last acquisition session. In each probe trial, time spent around the target hole was compared to averaged time spent around 11 non-target holes (g, j) and averaged time spent around two holes adjacent to the target hole (h, k). l–p Contextual and cued fear conditioning test: freezing (%) in the conditioning session (l conditioned stimulus, CS, 55-dB white noise, 30 s; unconditioned stimulus, US, 0.3-mA footshock, 2 s) and in the context test (m) and cued test (n) one day after the conditioning. Freezing (%) was also measured in the context test (o) and cued test (p) 29 days after the conditioning. Values are means ± SEM. a–f, i, l–p Asterisks and daggers indicate statistically significant differences between groups (5-HTT−/− vs. 5-HTT+/+, *$p \leq 0.05$ and **$p \leq 0.01$; 5-HTT−/− vs. 5-HTT+/−, †$p \leq 0.05$ and ††$p \leq 0.01$; 5-HTT+/− vs. 5-HTT+/+, §$p \leq 0.05$ and §§$p \leq 0.01$). g, h, j Asterisks represent statistical significance with paired t tests (**$p \leq 0.01$) In the Barnes maze test of spatial memory, there were no significant main effects of genotype and genotype × block interactions on the number of errors, the distance traveled to first reach the target hole, and the number of omission errors during the acquisition sessions (Fig. 4b, d, e). A significant genotype × block interaction (F16,328 = 2.04, $$p \leq 0.0105$$) and a significant simple main effect of genotype were observed for latency to reach the target hole in the first block (Fig. 4c; +/− vs. −/−, $$p \leq 0.0168$$), and there were no genotype differences in the latency to reach the target hole from the second to the last block. In the probe test conducted one day after the last acquisition session to assess memory retention, a significant effect of genotype was found on the time spent around the target hole (Fig. 4f), and 5-HTT−/− mice spent longer time around the target hole than other genotypes, although the difference between 5-HTT−/− and 5-HTT+/+ mice did not reach significance (−/− vs. +/+, $$p \leq 0.0793$$; −/− vs. +/−, $$p \leq 0.0057$$; +/− vs. +/+, $$p \leq 0.2973$$). Each genotype of mouse spent more time around the target hole than the mean time spent around the non-target holes (Fig. 4g; +/+, t13 = 4.53, $$p \leq 0.0006$$; +/−, t15 = 5.42, $p \leq 0.0001$; −/−, t13 = 6.03, $p \leq 0.0001$) and mean time spent around the holes adjacent to the target (Fig. 4h; +/+, t13 = 3.70, $$p \leq 0.0027$$; +/−, t15 = 3.87, $$p \leq 0.0015$$; −/−, t13 = 4.59, $$p \leq 0.0005$$). The probe test data suggest that 5-HTT−/− mice may have an increased motivation to escape from the maze due to their heightened anxiety-like phenotype or may have an enhanced recent spatial memory. In the second probe test 28 days after the acquisition, there was no significant effect of genotype on the time spent around the target hole (Fig. 4i). In the second probe test, each genotype of mice spent longer time around the target hole than the mean time spent around the non-target holes (Fig. 4j; +/+, t13 = 3.50, $$p \leq 0.0039$$; +/−, t15 = 3.99, $$p \leq 0.0012$$; −/−, t13 = 3.04, $$p \leq 0.0095$$). There was no significant difference between the time spent around the target and adjacent holes in 5-HTT−/− mice (t13 = 1.04, $$p \leq 0.3169$$), and 5-HTT+/+ and 5-HTT+/− mice tended to spend more time around the target hole than time around the adjacent holes (Fig. 4k; +/+, t13 = 2.01, $$p \leq 0.0652$$; +/−, t15 = 1.99, $$p \leq 0.0653$$). ## Impaired fear memory in 5-HTT-deficient mice In the conditioning session of the fear conditioning test, there were significant main effects of genotype on freezing and distance traveled (Fig. 4l and Additional file 1: Fig. S1a). A significant genotype × time interaction was found on the distance traveled (Additional file 1: Fig. S1a). 5-HTT−/− mice showed more freezing than other genotypes (vs. +/+, $$p \leq 0.0102$$; vs. +/−, $$p \leq 0.0030$$), while 5-HTT+/− and 5-HTT+/+ mice did not differ in freezing ($$p \leq 0.6683$$). In addition, 5-HTT−/− mice traveled a shorter distance than other genotypes at the 1st and 2nd time blocks (all $p \leq 0.05$). 5-HTT+/− mice traveled a longer distance than 5-HTT+/+ mice during the 2nd time block ($$p \leq 0.0089$$). Distance traveled for 4 s during and after 2-s footshocks was analyzed to assess shock sensitivity. A significant main effect of genotype was found on the distance traveled in response to the first footshock, but not to the second and third footshocks (Additional file 1: Fig. S1f–h). 5-HTT−/− traveled a shorter distance in response to the first footshock than 5-HTT+/+ and 5-HTT+/− mice ($$p \leq 0.0491$$ and $$p \leq 0.0016$$, respectively; 5-HTT+/− vs. 5-HTT+/+, $$p \leq 0.1880$$). In the context test, approximately 24 h after conditioning, there were significant main effects of genotype and significant genotype × time interactions on freezing and distance traveled (Fig. 4m and Additional file 1: Fig. S1b). 5-HTT−/− and 5-HTT+/− mice showed less freezing and traveled longer distances than 5-HTT+/+ mice in the first and second blocks (all $p \leq 0.05$), while 5-HTT−/− displayed more freezing and shorter distance traveled than other genotypes in the 5th time block. In the cued test without CS presentation, there were no significant main effects of genotype and no significant interactions on freezing and distance traveled (Fig. 4n and Additional file 1: Fig. S1c). During the last 3 min with CS in the cued test, there were significant genotype × time interactions in freezing and distance traveled. In the last time block with CS, 5-HTT+/− mice showed less freezing and traveled a longer distance than other genotypes (all $p \leq 0.05$). The context test and the cued test were conducted 29 days after conditioning to further assess remote memory. In the second trial of the context test, there was a significant genotype × time interaction on freezing but not on distance traveled (Fig. 4o and Additional file 1: Fig. S1d). During the 5th time block, 5-HTT−/− mice exhibited more freezing than 5-HTT+/+ and 5-HTT+/− mice ($$p \leq 0.0205$$ and $$p \leq 0.0407$$, respectively). In the cued test 29 days after conditioning, there were no significant main effects of genotype and no significant genotype × time interactions on freezing and distance traveled during the first 3 min without CS (Fig. 4p and Additional file 1: Fig. S1e). During the last 3 min of the cued test with CS, there were significant genotype × time interactions on freezing and distance traveled. 5-HTT−/− mice traveled a longer distance than 5-HTT+/+ mice in the 4th and 5th time block (all $p \leq 0.05$), and 5-HTT+/− mice exhibited less freezing and traveled a longer distance traveled than 5-HTT+/+ and 5-HTT−/− mice in the 6th time block (all $p \leq 0.05$). ## Altered composition of intestinal microbiome in 5-HTT-deficient mice The microbial taxa with a mean relative abundance greater than $1\%$ in any group at the phylum and genus level are shown in Figs. 5a and b (taxonomies with a relative abundance less than $1\%$ or not identified were included in 'Others'; for details, see Additional file 4: Tables S3 and S4). The most abundant taxa at the phylum level were Actinobacteria, Bacteroidetes, and Firmicutes. Statistical analysis showed that 5-HTT+/+ mice had a higher abundance of Actinobacteria than 5-HTT−/− mice ($p \leq 0.05$). Next, alpha and beta diversity were examined using data from the identified 259 taxa at genus level. For alpha diversity indices (Fig. 5c–f), there were no significant effects of genotype on observed species, Chao1 index, Shannon index, and Simpson index ($p \leq 0.05$). Beta diversity, representing differences in microbial composition, differed between genotypes when visualized with principal coordinate analysis (PCoA) using Bray–Curtis dissimilarity (Fig. 5g), and permutational multivariate analysis of variance (PERMANOVA) showed a tendency toward a difference in Bray–Curtis dissimilarity (F1,14 = 2.1756, $$p \leq 0.098$$). Further analysis using the linear discriminant analysis (LDA) effect size (LEfSe) method revealed that there were significant effects of genotype on 14 genera (Fig. 5h: LDA score > 3, $p \leq 0.05$), showing that 5-HTT−/− mice had reduced abundances of 8 genera (Allobaculum, Bifidobacterium, *Clostridium sensu* stricto, Turicibacter, Gardnerella, Olsenella, Atopobacter, Desulfovibrio) and increased abundances of 6 genera (Dorea, Schwartzia, Filibacter, Anaerofustis, Prevotella, and Gemella) compared with 5-HTT+/+ mice (Additional file 2: Fig. S2).Fig. 5Intestinal microbial composition in 5-HTT-deficient mice. Relative abundance (%) of microbiota at the phylum level (a) and at the genus level (b) in fecal sample in each mouse (taxonomies with relative abundance of less than $1\%$ or not identified were included in 'Others'). c–f Alpha diversity of the microbiota at the genus level: c observed number of microbial genera, d Chao1 index, e Shannon index, and f Simpson index. Values are means ± SEM. g Beta diversity of the microbiota at the genus level was visualized with principal coordinate analysis (PCoA) using Bray–Curtis dissimilarity. h Taxa differentially abundant between 5-HTT−/− and 5-HTT+/+ mice was identified by the linear discriminant analysis (LDA) effect size (LEfSe) method (LDA score > 3, $p \leq 0.05$) ## Plasma corticosterone levels There was no significant main effect of genotype and no significant genotype × phase interaction on plasma corticosterone levels (genotype effect, F1,9 = 0.50, $$p \leq 4981$$; interaction, F2,18 = 0.33, $$p \leq 0.7237$$). 5-HTT−/− and 5-HTT+/+ mice did not differ in corticosterone levels before exposure to the forced swim test, immediately after the test and 90 min after the test (Additional file 3: Fig. S3). ## Brain c-Fos expression To examine genotype differences in neuronal activation in brain regions associated with depression-related behavior, we performed immunohistochemistry for the protein of the immediate early gene c-fos in the brain of mice exposed or not exposed to the forced swim test (Fig. 6a–c and Additional file 4: Table S5). Two-way ANOVAs revealed significant main effects of test exposure on c-Fos expression in all brain regions examined in this study except the dorsal dentate gyrus (dDG), where c-Fos expression was increased by forced swim exposure. There were no significant main effects of genotype and no significant genotype × exposure interactions on c-Fos expressions in some brain regions (Additional file 4: Table S5), including the paraventricular nucleus of the hypothalamus (PVN) and dorsomedial hypothalamus (DMH), which are related to the regulation of circulating corticosterone levels. Significant main effects of genotype and/or significant genotype × exposure interactions were found in the cingulate cortex (Cg), primary motor cortex (M1), secondary motor cortex (M2), infralimbic cortex (IL), piriform cortex (Pir), nucleus accumbens shell (AcbSh), dorsal part of the lateral septal nucleus (LSD), lateral hypothalamus (LH), paraventricular nucleus of the thalamus (PVT), CA1, CA3, dorsal DG (dDG), ventral DG (vDG), and ventromedial hypothalamus (VMH), although genotype × exposure interactions were marginally significant in the IL and dDG (Additional file 4: Table S5). 5-HTT−/− mice showed an increased number of c-Fos-positive cells in the PVT and LH, and a decreased number of c-Fos-positive cells in the Cg, M1, M2, Pir, IL, AcbSh, LSD, CA1, CA3, dDG, vDG, and VMH, after exposure to the forced swim test, compared to 5-HTT+/+ mice (Fig. 6d–n).Fig. 6Brain c-Fos expression in 5-HTT-deficient mice. a Experimental design for brain sampling. 5-HTT−/− and 5-HTT+/+ mice were either exposed to the Porsolt forced swim test (PS, Exposure) or were left undisturbed until sacrifice without any exposure to behavioral testing (No exposure). b Schematic diagram of the brain areas in which c-Fos expression was quantified (brain images were adapted with permission from ref. [ 83]). c Representative photomicrographs of c-Fos immunoreactivity on brain regions (cingulate cortex, Cg; paraventricular thalamus, PVT). d–n Number of c-Fos-positive cells (/mm2) in the brain areas: (d) cingulate cortex (Cg), e infralimbic cortex (IL), f nucleus accumbens shell (AcbSh), g dorsolateral septum (LSD), h lateral hypothalamus (LH), i paraventricular thalamus (PVT), j ventromedial hypothalamus (VMH), k dorsal dentate gyrus (dDG), l ventral dentate gyrsu (vDG), m CA3, and n CA1. Scale bar, 200 μm. Values are means ± SEM. Asterisks indicate statistically significant differences between 5-HTT−/− and 5-HTT+/+ mice (*$p \leq 0.05$ and **$p \leq 0.01$) ## Discussion The present study revealed the behavioral profiles of 5-HTT−/− mice with a C57BL/6JJcl genetic background. Some behavioral phenotypes, especially anxiety-like and depression-related behaviors, have been well studied in the previous studies, while the other domains of behavior have not been fully investigated; thus, our study not only replicated the previous findings that 5-HTT−/− mice show increased anxiety-like and depression-related behaviors, demonstrating that 5-HTT−/− mice serve as a reliable animal model for depression, but also, to our knowledge, revealed novel behavioral phenotypes of 5-HTT-deficient mice, such as decreased social behavior in novel and familiar environments, no obvious deficits in recent and remote spatial memory, no generalized fear response, and altered contextual fear memory. Analysis of 16S rRNA gene amplicons revealed that 5-HTT−/− mice had altered gut microbiota compared to 5-HTT+/+ mice. Brain c-Fos expression analysis showed that the number of c-Fos-positive cells was higher in the paraventricular thalamus and lateral hypothalamus and lower in the prefrontal cortical regions, nucleus accumbens shell, dorsolateral septal nucleus, hippocampal regions, and ventromedial hypothalamus after exposure to the behavioral paradigm assessing depression-related behavior in 5-HTT−/− mice than in 5-HTT+/+ mice. These data indicate that genetic deletion of 5-HTT causes alterations in various domains of behavior, gut microbial composition, and brain c-Fos expression in brain regions associated with the regulation of anxiety and depression. ## Increased body weight and possible sensory and motor dysfunction in 5-HTT−/− mice The results of the present study for physical characteristics and sensory and motor functions are consistent with previous studies reporting that 5-HTT−/− mice showed an increased body weight [11, 16, 19], normal body temperature [18, 84], a tendency toward increased hot plate latency [18], decreased wire-hang latency [11], and decreased rotarod latency [11], compared to 5-HTT+/+ and 5-HTT+/− mice. Our study found no genotype difference in grip strength, indicating normal forepaw muscle strength in 5-HTT-deficient mice. The decreased wire-hang and rotarod latencies were observed even when their body weights were considered as a covariate. These findings suggest two possibilities: one possibility is that 5-HTT−/− mice had lower whole-body muscle strength and reduced motor function, and the other possibility is that the reduced latencies to fall off may reflect a depressive state [85, 86]. Mice homozygous deficient in 5-HTT showed reduced startle responses to loud noises compared to 5-HTT+/+ mice, consistent with a recent study ([29]; but see ref. [ 18, 19]). The altered startle responses would not be due to loss of auditory function because the normal hearing was observed in 5-HTT−/− mice [29], although the possibility that their decreased motor functions and hypoactive phenotype lead to the reduced startle responses could not be excluded. Our data suggest that 5-HTT+/− mice have normal physical, sensory, and motor functions. ## Increased anxiety-like and depression-related behaviors in 5-HTT−/− mice The present study confirmed that 5-HTT−/− mice showed increased anxiety-like behaviors in different types of tests, including the open field, light/dark transition, and elevated plus maze tests, as reported in previous studies (see Additional file 4: Table S6). An early study reported that depression-related phenotypes in 5-HTT KO mice were dependent on the genetic background [11]. 5-HTT−/− mice on the 129S6 background exhibited increased immobility in the forced swim test and decreased immobility in the tail suspension test [11, 14], whereas 5-HTT−/− mice on the C57BL/6 J background showed no alternation in immobility time in the forced swim and tail suspension tests [11]. Our data showed that 5-HTT−/− mice on the C57BL/6JJcl background exhibited increased immobility in the forced swim test. There are several factors influencing behavior in the forced swim test, such as test conditions and prior test experience [54, 87]. For example, repeated exposure to the forced swim test resulted in greater immobility in 5-HTT−/− mice on the C57BL/6 J background than in wild-type mice [25, 31]. In the present study, previous testing experience across the battery of behavioral tests might have led to heightened depression-related behavior in 5-HTT−/− mice, even in the first trial. In addition, 5-HTT−/− mice showed an increase in immobility from the second minute of the test compared to 5-HTT+/+ mice on the first day of the forced swim test, and the immobility in all three genotypes of mice reached a plateau after the sixth minute of the test. These results indicate that immobility increased earlier in 5-HTT−/− mice than in 5-HTT+/+ mice. The second test confirmed the finding of increased immobility observed in 5-HTT−/− mice on the first day of testing. Immobility in the first minute of the second test was higher than in the first test, suggesting that a learned fear to the unavoidable situation was normal in 5-HTT−/− mice. The tail suspension test is conceptually similar to but methodologically different from the forced swim test for assessing depression-related behavior. A previous study showed that exposure to the tail suspension test did not induce changes in brain monoamine concentrations, whereas the forced swim test did, suggesting that the two tests involve different neural mechanisms [88]. In our previous studies, the results of genotype difference in immobility observed in the forced swim test were not necessarily consistent with those found in the tail suspension test in some strains of mutant mice [89–94], and it is not rare that the two tests yield seemingly inconsistent results. The exact reason why significant differences in immobility were observed between 5-HTT−/− and 5-HTT+/+ mice in the forced swim test but not in the tail suspension test remains unclear. Therefore, further study is needed to understand the mechanisms underlying the link between 5-HTT deficiency and immobility in the tests. 5-HTT−/− and 5-HTT+/− mice consumed less sucrose solution and showed a lower sucrose preference than 5-HTT+/+ mice, although there was no significant difference in sucrose preference between 5-HTT−/− and 5-HTT+/+ mice, which is partially consistent with the previous study in mice and rats [22, 95]. These results suggest a trend toward an anhedonia-like phenotype in 5-HTT-deficient mice. Although 5-HTT−/− mice showed a marked decrease in distance traveled in novel environments such as the open field, there were no genotype differences in locomotor activity in a home cage in a familiar environment during the light phase when other behavioral tests were performed. It is suggested that the hypoactive phenotype is specific to novel environments. Thus, increased anxiety-like and depression-related behaviors could not be explained solely by changes in motor function and general activity. The present study also indicates that locomotor activity and anxiety-like and depression-related behaviors of 5-HTT+/− mice do not differ from those of 5-HTT+/+ mice. ## No apparent deficits in working and spatial memory and altered fear memory in 5-HTT−/− mice Enhanced spatial working memory, as indicated by increased spontaneous alternation behavior in the T-maze task, was reported in 5-HTT−/− mice [27]. A similar trend for genotype effect on spontaneous alternation was observed in the present study, although our results did not reach statistical significance. The inconsistent results may be due to methodological differences between the studies, such as testing procedures and genetic background. The procedure used in the previous study consisted of a forced-choice run followed by 14 consecutive free-choice trials [27], in which there would be inter-trial interference due to the continuous nature of the task, similar to the Y-maze task, potentially increasing cognitive demands [97]. In our study, each trial consisted of one forced and one free choice run with an inter-trial interval of 60 s, which is expected to reduce the inter-trial interference and the task difficulty. *The* genetic background of the animals was C57BL/6N background in the previous study [27] and C57BL/6JJcl background in our study. C57BL/6N showed fewer different arm choices in the first eight arm entries, which is indicative of decreased working memory, compared to C57BL/6J in the eight-arm radial maze test [98]. The lower basal level (approximately $50\%$ chance level) of spontaneous alternation observed in 5-HTT+/+ mice with C57BL/6N background [27] might allow for detecting the possible improving effect of 5-HTT deficiency on working memory. As evidenced by previous studies on anxiety-like and depression-related behaviors [11, 12], our findings also highlight the importance of considering genetic background when studying learning and memory in 5-HTT-deficient mice. In the Barnes maze test, 5-HTT−/− mice spent slightly more time around the target hole in the probe trial one day after the acquisition session, but showed no difference in time in the target 28 days after the acquisition compared to 5-HTT+/+ mice. The Barnes maze test is based on the innate motivation of rodents to avoid the aversive open and brightly lit environment. Thus, although the results suggest that 5-HTT deficiency may contribute to an improvement in recent spatial memory as seen in the T-maze test, the increased time spent in the target area in 5-HTT−/− mice could be explained by an increased motivation to escape the maze due to an enhanced anxiety-like phenotype. Decreased freezing and increased distance traveled were observed in 5-HTT−/− and 5-HTT+/− mice during the first 2 min of the context test, suggesting impaired contextual fear memory induced by 5-HTT deficiency. Interestingly, 5-HTT−/− mice exhibited increased freezing during the last minute of the context test. The time-dependent changes in freezing might reflect an initial increase in the flight and escape response due to their heightened fear and subsequently their hypoactive phenotype or delayed fear memory retrieval. In the cued test with altered context one day after conditioning, the present study showed no genotype differences in overall freezing during no CS presentation and CS presentation, consistent with previous studies [31, 32]. These results suggest that 5-HTT may not be involved in the control of generalized fear and recent cued fear memory. However, the reason why 5-HTT+/− mice showed reduced freezing during the last minute of the test remains unclear. In the retest 29 days after conditioning, 5-HTT−/− mice also showed higher levels of freezing during the last minute of the context test, suggesting that deletion of 5-HTT gene does not interfere with retrieval of remote contextual fear memory. ## 5-HTT deficiency-induced alteration in intestinal microbial composition Accumulating evidence suggests that neuropsychiatric disorders, including bipolar disorder and major depression, are associated with an altered gut microbiota composition in preclinical and clinical studies [99–101]. There is a recent report on the gut microbial composition in 5-HTT−/− mice, which showed higher abundances of Bacilli species, including the genera Lactobacillus, Streptococcus, Enterococcus and Listeria, and lower abundances of Bifidobacterium species and *Akkermansia muciniphilia* than 5-HTT+/+ mice [43]. The present study showed that 5-HTT−/− mice had higher abundance of *Streptococcus and* lower abundance of Bifidobacterium than 5-HTT+/+ mice, replicating the key findings of the previous report for altered gut microbial composition in 5-HTT−/− mice [43]. Our study also identified altered abundances of some other microbial genera which were not reported in the previous study [43], showing that 5-HTT−/− mice had decreased abundances of Allobaculum and *Clostridium sensu* stricto in fecal samples. Depression and obesity tend to co-occur in individuals [102]. Allobaculum and *Clostridium sensu* stricto are negatively associated with inflammation, insulin resistance, lipid metabolism, and obesity [103–107]. Such microbial composition may explain increased depressive and obesity phenotypes such as increased body weight, possible insulin resistance, and hyperglycemia in 5-HTT−/− mice [108]. In this study, decreased abundance of Bifidobacterium and Turicibacter and increased abundance of Prevotella were also observed in 5-HTT−/− mice. Similar changes in relative abundances of Bifidobacterium, Turicibacter, and Prevotella have been reported in human patients with depression compared to healthy controls [37, 109–111], while there are inconsistencies in the gut microbial compositions between human studies of depression [100, 112]. The present findings strengthen the notion that 5-HTT−/− mice with altered gut microbial composition can be used as an animal model of depression. ## Possible brain regions involved in the depressive state induced by 5-HTT deficiency Stress can trigger the hypothalamus–pituitary–adrenal (HPA) axis response through the activation of neurons in some brain regions, including the paraventricular nucleus of the hypothalamus (PVN) and dorsomedial hypothalamus (DMH), resulting in elevations in circulating corticosterone [113]. Our results of brain c-Fos expression suggest that PVN and DMH neurons may be activated at a similar level between 5-HTT−/− and 5-HTT+/+ mice, which seems to consistent with the results of this study and a previous study showing that no differences in plasma corticosterone levels after exposure to the forced swim test as well as immobilization stress were observed in the two genotypes of mice [114]. These findings suggest a normal endocrine response involving the adrenal glands after stress exposure in 5-HTT−/− mice, although some reports showed that 5-HTT−/− mice have reduced basal corticosterone and increased adrenocorticotropic hormone [115, 116]. Brain mapping of neuronal activation by c-Fos expression in response to the forced swim test may allow us to understand the neural circuit involved in the induction and facilitation of the depressive state induced by 5-HTT deficiency. The present results of c-Fos expression analysis suggest hypoactivation of the PFC (cingulate, infralimbic, piriform, and motor cortex), nucleus accumbens shell, dorsolateral septum, ventromedial hypothalamus, and hippocampus (CA1, CA3, and dentate gyrus) and hyperactivation of the paraventricular thalamus (PVT) and lateral hypothalamus (LH) in 5-HTT−/− mice. Functional brain imaging studies of human patients with depression have reported that the PFC (including the anterior cingulate cortex and ventromedial prefrontal cortex), striatum, hippocampus, and amygdala have important implications for the neurobiological mechanisms of depression and anxiety [117, 118]. The rodent homolog of the ventromedial prefrontal cortex is the infralimbic cortex [119], which sends a dense projection to the nucleus accumbens shell [120, 121]. The infralimbic cortex and its projection to the nucleus accumbens shell have been reported to play a role in affective processing and the suppressing learned negative emotional states [122, 123]. The ventromedial hypothalamus and the septo-hippocampal circuit are involved in escape and defensive responses, mood, and motivation [124–129]. The PVT participates in the control of emotional behavior, with its projections to the PFC and amygdala promoting anxiety-like and depression-related behaviors and fear memory [130–134], and to the accumbens inhibiting anxiety-like behavior ([135, 136]; for reviews, see ref. [ 137]). Activation of the LH bidirectionally modulates anxiety-like behavior and feeding via GABAergic and orexin neurons [138, 139]. Given the functional interactions between these brain structures, altered neuronal activation in the brain regions in 5-HTT−/− mice may be characteristic of the increased depression-related behavior observed in the forced swim test. However, there are limitations in considering c-Fos expression as a marker of neuronal activity [140, 141], and therefore it needs to be further confirmed whether c-Fos expression truly reflects the differences in neuronal activity during the forced swim test between 5-HTT−/− and 5-HTT+/+ mice. For example, alpha-CaMKII heterozygous knockout mice have an immature dentate gyrus (iDG) phenotype, in which the DG neurons are in a pseudo-immature status [142, 143]. The mutants showed dramatically reduced c-Fos and Arc expressions in the brain after electric footshock or a memory task compared to wild-type mice [142, 144], while an in vivo calcium imaging study showed that the estimated firing rate from calcium signals in the DG neurons did not differ between the mutants and wild-type mice [145]. Thus, c-Fos immunohistochemistry has low temporal resolution but may be useful to identify brain regions activated by exposure to the behavioral test. Another limitation is that the present study could not exclude the possibility of the influence of stress on c-Fos expression by blood collection after the forced swim test. ## 5-HT hypothesis of depression The 5-HT hypothesis of depression is one of the widely studied biological hypotheses that depression is caused by reduced 5-HT activity or concentration. Recent meta-analytic studies and a systematic umbrella review provide no consistent evidence of an association between 5-HT, 5-HTT gene polymorphism, and depression and no support for the hypothesis [146]. On the other hand, some meta-analytic studies showed weak and inconsistent evidence of reduced 5-HTT binding in some brain areas [147–149], which would lead to an increased possibility of synaptic availability of 5-HT, in people with depression [146]. 5-HTT−/− mice had increased levels of extracellular 5-HT [150, 151] and decreased tissue concentrations of 5-HT and its metabolite 5-HIAA [152] in the brain compared to 5-HTT+/+ mice. Thus, 5-HTT−/− mice may resemble humans with a subtype of depression with serotonergic dysregulation. Anxiety and depression are a co-occurring symptom in individuals with autism spectrum disorder (ASD) with a core symptom of social deficits [153, 154]. ASD has been reported to be associated with elevated blood 5-HT levels and reduced brain 5-HT/5-HTT binding [155–159]. A previous study [28] and the present findings indicate that 5-HTT deficient mice may also be a useful animal model for studying ASD with anxiety and depression. ## Conclusions The present study shows that 5-HTT−/− mice exhibit various behavioral phenotypes, such as decreased locomotor activity, heightened anxiety-like and depression-related behaviors, decreased social behavior in novel environments, no deficits in recent and remote spatial memory, normal generalized fear response and cued fear memory, and altered contextual fear memory. The behavioral results of previous studies [11, 14, 18, 19, 24, 25] and the present study indicate that significant increases in anxiety-like and depression-related behaviors are a robust, well-replicated behavioral phenotype in 5-HTT−/− mice. These findings reveal the role of 5-HTT on brain function and strengthen the notion that 5-HTT-deficient mice have a high face and construct validity of the animal model for studying anxiety and depression with altered serotonergic function. This study also showed 5-HTT deficiency-induced changes in the abundance of some microbial genera in feces, offering novel targets for further research on the role of gut microbiota in behavioral changes induced by 5-HTT deficiency. Furthermore, brain c-*Fos analysis* helps to understand the brain circuits and neuronal activation associated depressive state induced by 5-HTT deficiency. Our results suggest a potential link between 5-HTT deficiency-induced changes in gut microbiota, brain neuronal activation, and behavior, although further research is needed to elucidate the causal relationship between the gut-brain-behavior. Taken together, the results of the present study highlight the importance of 5-HTT in regulating brain function, possibly in part via the gut-brain axis, and also provide useful insights into the understanding of neuronal and circuitry mechanisms underlying brain dysfunction caused by dysregulated serotonin neurotransmission. ## Supplementary Information Additional file 1: Figure S1. Distance traveled in fear conditioning test in 5-HTT-deficient mice. ( a–h) Contextual and cued fear conditioning test: distance traveled (cm) in the conditioning session (a: conditioned stimulus, CS, 55-dB white noise, 30 s; unconditioned stimulus, US, 0.3-mA footshock, 2 s) and in the context test (b) and cued test (c) one day after the conditioning. Distance traveled (cm) was also measured in the context test (d) and cued test (e) 29 days after the conditioning. In the conditioning session, to assess footshock sensitivity, distance traveled (cm) was measured from images recorded at high frame rate for 6 s from 2 s before electric footshock (2-s period) to 2 s after exposure to footshock. Values are means ± SEM. ( a–f, i, l–p) Asterisks and daggers indicate statistically significant differences between groups (5-HTT−/− vs. 5-HTT+/+, * $p \leq 0.05$ and ** $p \leq 0.01$; 5-HTT−/− vs. 5-HTT+/−, † $p \leq 0.05$ and †† $p \leq 0.01$; 5-HTT+/− vs. 5-HTT+/+, § $p \leq 0.05$ and §§ $p \leq 0.01$).Additional file 2: Figure S2. Relative abundance of intestinal microbiota at the genus level in 5-HTT-deficient mice. ( a–n) Relative abundance (%) of microbiota at the genus level in fecal samples of 5-HTT−/− and 5-HTT+/+ mice. Taxa differentially abundant between 5-HTT−/− and 5-HTT+/+ mice, which was identified by the linear discriminant analysis (LDA) effect size (LEfSe) method (LDA score > 3, $p \leq 0.05$). ( a) Allobaculum, (b) Bifidobacterium, (c) *Clostridium sensu* stricto, (d) Turicibacter, (e) Gardnerella, (f) Olsenella, (g) Atopobacter, (h) Desulfovibrio, (i) Dorea, (j) Schwartzia, (k) Filibacter, (l) Anaerofustis, (m) Prevotella, and (n) Gemella. Values are means ± SEM. Asterisks indicate statistically significant differences between groups (* $p \leq 0.05$, ** $p \leq 0.01$, and *** $p \leq 0.001$).Additional file 3: Figure S3. Plasma corticosterone levels in 5-HTT-deficient mice. Blood were collected from 5-HTT−/− and 5-HTT+/+ mice 2 days before the Porsolt forced swim test (PS), immediately after the PS test, and 90 min after the PS test. Corticosterone levels (ng/mL) in the blood samples were measured. Values are means ± SEM.Additional file 4: Table S1. A battery of behavioral tests in 5-HTT-deficient and wild-type mice. Table S2. Statistical analysis of behavioral data of 5-HTT-deficient and wild-type mice. Table S3. Relative abundance of intestinal microbiota at the phylum level in 5-HTT−/− and 5-HTT+/+ mice. Table S4. Relative abundance of intestinal microbiota at the genus level in 5-HTT−/− and 5-HTT+/+ mice. Table S5. Number of c-Fos-positive cells in the brain in 5-HTT−/− and 5-HTT+/+ mice. Table S6. Behavioral study of 5-HTT-deficient animals ## References 1. Murphy DL, Lerner A, Rudnick G, Lesch KP. **Serotonin transporter: gene, genetic disorders, and pharmacogenetics**. *Mol Interv* (2004.0) **4** 109. DOI: 10.1124/mi.4.2.8 2. Murphy DL, Andrews AM, Wichems CH, Li Q, Tohda M, Greenberg B. **Brain serotonin neurotransmission: an overview and update with an emphasis on serotonin subsystem heterogeneity, multiple receptors, interactions with other neurotransmitter systems, and consequent implications for understanding the actions of serotonergic drugs**. *J Clin Psychiatry* (1998.0) **59** 4-12. PMID: 9786305 3. Naughton M, Mulrooney JB, Leonard BE. **A review of the role of serotonin receptors in psychiatric disorders**. *Hum Psychopharmacol Clin Exp* (2000.0) **15** 397-415. DOI: 10.1002/1099-1077(200008)15:6<397::AID-HUP212>3.0.CO;2-L 4. Nordquist N, Oreland L. **Serotonin, genetic variability, behaviour, and psychiatric disorders-a review**. *Upsala J Med Sci* (2010.0) **115** 2-10. DOI: 10.3109/03009730903573246 5. Gross C, Hen R. **The developmental origins of anxiety**. *Nat Rev Neurosci* (2004.0) **5** 545-552. DOI: 10.1038/nrn1429 6. Lesch KP, Bengel D, Heils A, Sabol SZ, Greenberg BD, Petri S, Benjamin J, Müller CR, Hamer DH, Murphy DL. **Association of anxiety-related traits with a polymorphism in the serotonin transporter gene regulatory region**. *Science* (1996.0) **274** 1527-1531. DOI: 10.1126/science.274.5292.1527 7. Mazzanti CM, Lappalainen J, Long JC, Bengel D, Naukkarinen H, Eggert M, Virkkunen M, Linnoila M, Goldman D. **Role of the serotonin transporter promoter polymorphism in anxiety-related traits**. *Arch Gen Psychiatry* (1998.0) **55** 936-940. DOI: 10.1001/archpsyc.55.10.936 8. Greenberg BD, Li Q, Lucas FR, Hu S, Sirota LA, Benjamin J, Lesch KP, Hamer D, Murphy DL. **Association between the serotonin transporter promoter polymorphism and personality traits in a primarily female population sample**. *Am J Med Genet* (2000.0) **96** 202-216. DOI: 10.1002/(SICI)1096-8628(20000403)96:2<202::AID-AJMG16>3.0.CO;2-J 9. Greenberg BD, Tolliver TJ, Huang SJ, Li Q, Bengel D, Murphy DL. **Genetic variation in the serotonin transporter promoter region affects serotonin uptake in human blood platelets**. *Am J Med Genet* (1999.0) **88** 83-87. DOI: 10.1002/(SICI)1096-8628(19990205)88:1<83::AID-AJMG15>3.0.CO;2-0 10. Little KY, McLaughlin DP, Zhang L, Livermore CS, Dalack GW, McFinton PR, DelProposto ZS, Hill E, Cassin BJ, Watson SJ, Cook EH. **Cocaine, ethanol, and genotype effects on human midbrain serotonin transporter binding sites and mRNA levels**. *Am J Psychiatry* (1998.0) **155** 207-213. DOI: 10.1176/ajp.155.2.207 11. Holmes A, Yang RJ, Murphy DL, Crawley JN. **Evaluation of antidepressant-related behavioral responses in mice lacking the serotonin transporter**. *Neuropsychopharmacology* (2002.0) **27** 914-923. DOI: 10.1016/S0893-133X(02)00374-3 12. Holmes A, Li Q, Murphy DL, Gold E, Crawley JN. **Abnormal anxiety-related behavior in serotonin transporter null mutant mice: the influence of genetic background**. *Genes Brain Behav* (2003.0) **2** 365-380. DOI: 10.1046/j.1601-1848.2003.00050.x 13. Holmes A, Yang RJ, Lesch KP, Crawley JN, Murphy DL. **Mice lacking the serotonin transporter exhibit 5-HT1A receptor-mediated abnormalities in tests for anxiety-like behavior**. *Neuropsychopharmacology* (2003.0) **28** 2077-2088. DOI: 10.1038/sj.npp.1300266 14. Lira A, Zhou M, Castanon N, Ansorge MS, Gordon JA, Francis JH, Bradley-Moore M, Lira J, Underwood MD, Arango V, Kung HF, Hofer MA, Hen R, Gingrich JA. **Altered depression-related behaviors and functional changes in the dorsal raphe nucleus of serotonin transporter-deficient mice**. *Biol Psychiatry* (2003.0) **54** 960-971. DOI: 10.1016/S0006-3223(03)00696-6 15. Bengel D, Murphy DL, Andrews AM, Wichems CH, Feltner D, Heils A, Mössner R, Westphal H, Lesch KP. **Altered brain serotonin homeostasis and locomotor insensitivity to 3, 4-methylenedioxymethamphetamine (“Ecstasy”) in serotonin transporter-deficient mice**. *Mol Pharmacol* (1998.0) **53** 649-655. DOI: 10.1124/mol.53.4.649 16. Sora I, Hall FS, Andrews AM, Itokawa M, Li XF, Wei HB, Wichems C, Lesch KP, Murphy DL, Uhl GR. **Molecular mechanisms of cocaine reward: combined dopamine and serotonin transporter knockouts eliminate cocaine place preference**. *Proc Natl Acad Sci USA* (2001.0) **98** 5300-5305. DOI: 10.1073/pnas.091039298 17. Li Q, Holmes A, Ma L, Van de Kar LD, Garcia F, Murphy DL. **Medial hypothalamic 5-hydroxytryptamine (5-HT) 1A receptors regulate neuroendocrine responses to stress and exploratory locomotor activity: application of recombinant adenovirus containing 5-HT1A sequences**. *J Neurosci* (2004.0) **24** 10868-10877. DOI: 10.1523/JNEUROSCI.3223-04.2004 18. Zhao S, Edwards J, Carroll J, Wiedholz L, Millstein RA, Jaing C, Murphy DL, Lanthorn TH, Holmes A. **Insertion mutation at the C-terminus of the serotonin transporter disrupts brain serotonin function and emotion-related behaviors in mice**. *Neuroscience* (2006.0) **140** 321-334. DOI: 10.1016/j.neuroscience.2006.01.049 19. Adamec R, Burton P, Blundell J, Murphy DL, Holmes A. **Vulnerability to mild predator stress in serotonin transporter knockout mice**. *Behav Brain Res* (2006.0) **170** 126-140. DOI: 10.1016/j.bbr.2006.02.012 20. Jansen F, Heiming RS, Lewejohann L, Touma C, Palme R, Schmitt A, Lesch KP, Sachser N. **Modulation of behavioural profile and stress response by 5-HTT genotype and social experience in adulthood**. *Behav Brain Res* (2010.0) **207** 21-29. DOI: 10.1016/j.bbr.2009.09.033 21. Jennings KA, Loder MK, Sheward WJ, Pei Q, Deacon RM, Benson MA, Olverman HJ, Hastie ND, Harmar AJ, Shen S, Sharp T. **Increased expression of the 5-HT transporter confers a low-anxiety phenotype linked to decreased 5-HT transmission**. *J Neurosci* (2006.0) **26** 8955-8964. DOI: 10.1523/JNEUROSCI.5356-05.2006 22. Kalueff AV, Gallagher PS, Murphy DL. **Are serotonin transporter knockout mice ‘depressed’?: hypoactivity but no anhedonia**. *NeuroReport* (2006.0) **17** 1347-1351. DOI: 10.1097/01.wnr.0000230514.08962.76 23. Kalueff AV, Fox MA, Gallagher PS, Murphy DL. **Hypolocomotion, anxiety and serotonin syndrome-like behavior contribute to the complex phenotype of serotonin transporter knockout mice**. *Genes Brain Behav* (2007.0) **6** 389-400. DOI: 10.1111/j.1601-183X.2006.00270.x 24. Kalueff AV, Jensen CL, Murphy DL. **Locomotory patterns, spatiotemporal organization of exploration and spatial memory in serotonin transporter knockout mice**. *Brain Res* (2007.0) **1169** 87-97. DOI: 10.1016/j.brainres.2007.07.009 25. Carroll JC, Boyce-Rustay JM, Millstein R, Yang R, Wiedholz LM, Murphy DL, Holmes A. **Effects of mild early life stress on abnormal emotion-related behaviors in 5-HTT knockout mice**. *Behav Genet* (2007.0) **37** 214-222. DOI: 10.1007/s10519-006-9129-9 26. Line SJ, Barkus C, Coyle C, Jennings KA, Deacon RM, Lesch KP, Sharp T, Bannerman DM. **Opposing alterations in anxiety and species-typical behaviours in serotonin transporter overexpressor and knockout mice**. *Eur Neuropsychopharmacol* (2011.0) **21** 108-116. DOI: 10.1016/j.euroneuro.2010.08.005 27. Sakakibara Y, Kasahara Y, Hall FS, Lesch KP, Murphy DL, Uhl GR, Sora I. **Developmental alterations in anxiety and cognitive behavior in serotonin transporter mutant mice**. *Psychopharmacology* (2014.0) **231** 4119-4133. DOI: 10.1007/s00213-014-3554-x 28. Tanaka M, Sato A, Kasai S, Hagino Y, Kotajima-Murakami H, Kashii H, Takamatsu Y, Nishito Y, Inagaki M, Mizuguchi M, Hall FS, Uhl GR, Murphy D, Sora I, Ikeda K. **Brain hyperserotonemia causes autism-relevant social deficits in mice**. *Mol Autism* (2018.0) **9** 1-14. DOI: 10.1186/s13229-018-0243-3 29. Pan W, Lyu K, Zhang H, Li C, Chen P, Ying M, Chen F, Tang J. **Attenuation of auditory mismatch negativity in serotonin transporter knockout mice with anxiety-related behaviors**. *Behav Brain Res* (2020.0) **379** 112387. DOI: 10.1016/j.bbr.2019.112387 30. Holmes A, Murphy DL, Crawley JN. **Reduced aggression in mice lacking the serotonin transporter**. *Psychopharmacology* (2002.0) **161** 160-167. DOI: 10.1007/s00213-002-1024-3 31. Wellman CL, Izquierdo A, Garrett JE, Martin KP, Carroll J, Millstein R, Lesch KP, Murphy DL, Holmes A. **Impaired stress-coping and fear extinction and abnormal corticolimbic morphology in serotonin transporter knock-out mice**. *J Neurosci* (2007.0) **27** 684-691. DOI: 10.1523/JNEUROSCI.4595-06.2007 32. Narayanan V, Heiming RS, Jansen F, Lesting J, Sachser N, Pape HC, Seidenbecher T. **Social defeat: impact on fear extinction and amygdala-prefrontal cortical theta synchrony in 5-HTT deficient mice**. *PLoS ONE* (2011.0) **6** e22600. DOI: 10.1371/journal.pone.0022600 33. Karabeg MM, Grauthoff S, Kollert SY, Weidner M, Heiming RS, Jansen F, Popp S, Kaiser S, Lesch KP, Sachser N, Schmitt AG, Lewejohann L. **5-HTT deficiency affects neuroplasticity and increases stress sensitivity resulting in altered spatial learning performance in the Morris water maze but not in the Barnes maze**. *PLoS ONE* (2013.0) **8** e78238. DOI: 10.1371/journal.pone.0078238 34. Sampson TR, Mazmanian SK. **Control of brain development, function, and behavior by the microbiome**. *Cell Host Microbe* (2015.0) **17** 565-576. DOI: 10.1016/j.chom.2015.04.011 35. Sherwin E, Sandhu KV, Dinan TG, Cryan JF. **May the force be with you: the light and dark sides of the microbiota–gut–brain axis in neuropsychiatry**. *CNS Drugs* (2016.0) **30** 1019-1041. DOI: 10.1007/s40263-016-0370-3 36. Jiang H, Ling Z, Zhang Y, Mao H, Ma Z, Yin Y, Wang W, Tang W, Tan Z, Shi J, Li L, Ruan B. **Altered fecal microbiota composition in patients with major depressive disorder**. *Brain Behav Immun* (2015.0) **48** 186-194. DOI: 10.1016/j.bbi.2015.03.016 37. Aizawa E, Tsuji H, Asahara T, Takahashi T, Teraishi T, Yoshida S, Ota M, Koga N, Hattori K, Kunugi H. **Possible association of Bifidobacterium and Lactobacillus in the gut microbiota of patients with major depressive disorder**. *J Affect Disord* (2016.0) **202** 254-257. DOI: 10.1016/j.jad.2016.05.038 38. Cenit MC, Sanz Y, Codoñer-Franch P. **Influence of gut microbiota on neuropsychiatric disorders**. *World J Gastroenterol* (2017.0) **23** 5486. DOI: 10.3748/wjg.v23.i30.5486 39. Gershon MD, Tack J. **The serotonin signaling system: from basic understanding to drug development for functional GI disorders**. *Gastroenterology* (2007.0) **132** 397-414. DOI: 10.1053/j.gastro.2006.11.002 40. Yadav VK, Ryu JH, Suda N, Tanaka KF, Gingrich JA, Schütz G, Glorieux FH, Chiang CY, Zajac JD, Insogna KL, Mann JJ, Hen R, Ducy P, Karsenty G. **Lrp5 controls bone formation by inhibiting serotonin synthesis in the duodenum**. *Cell* (2008.0) **135** 825-837. DOI: 10.1016/j.cell.2008.09.059 41. Baganz NL, Blakely RD. **A dialogue between the immune system and brain, spoken in the language of serotonin**. *ACS Chem Neurosci* (2013.0) **4** 48-63. DOI: 10.1021/cn300186b 42. Sumara G, Sumara O, Kim JK, Karsenty G. **Gut-derived serotonin is a multifunctional determinant to fasting adaptation**. *Cell Metab* (2012.0) **16** 588-600. DOI: 10.1016/j.cmet.2012.09.014 43. Singhal M, Turturice BA, Manzella CR, Ranjan R, Metwally AA, Theorell J, Huang Y, Alrefai WA, Dudeja PK, Finn PW, Perkins DL, Gill RK. **Serotonin transporter deficiency is associated with dysbiosis and changes in metabolic function of the mouse intestinal microbiome**. *Sci Rep* (2019.0) **9** 1-11. DOI: 10.1038/s41598-019-38489-8 44. Nguyen TLA, Vieira-Silva S, Liston A, Raes J. **How informative is the mouse for human gut microbiota research?**. *Dis Models Mech* (2015.0) **8** 1-16. DOI: 10.1242/dmm.017400 45. Laukens D, Brinkman BM, Raes J, De Vos M, Vandenabeele P. **Heterogeneity of the gut microbiome in mice: guidelines for optimizing experimental design**. *FEMS Microbiol Rev* (2016.0) **40** 117-132. DOI: 10.1093/femsre/fuv036 46. Ressler KJ, Mayberg HS. **Targeting abnormal neural circuits in mood and anxiety disorders: from the laboratory to the clinic**. *Nat Neurosci* (2007.0) **10** 1116-1124. DOI: 10.1038/nn1944 47. Krishnan V, Nestler EJ. **The molecular neurobiology of depression**. *Nature* (2008.0) **455** 894-902. DOI: 10.1038/nature07455 48. Russo SJ, Nestler EJ. **The brain reward circuitry in mood disorders**. *Nat Rev Neurosci* (2013.0) **14** 609-625. DOI: 10.1038/nrn3381 49. Duncan GE, Knapp DJ, Breese GR. **Neuroanatomical characterization of Fos induction in rat behavioral models of anxiety**. *Brain Res* (1996.0) **713** 79-91. DOI: 10.1016/0006-8993(95)01486-1 50. Hale MW, Bouwknecht JA, Spiga F, Shekhar A, Lowry CA. **Exposure to high-and low-light conditions in an open-field test of anxiety increases c-Fos expression in specific subdivisions of the rat basolateral amygdaloid complex**. *Brain Res Bull* (2006.0) **71** 174-182. DOI: 10.1016/j.brainresbull.2006.09.001 51. Singewald N, Salchner P, Sharp T. **Induction of c-Fos expression in specific areas of the fear circuitry in rat forebrain by anxiogenic drugs**. *Biol Psychiatry* (2003.0) **53** 275-283. DOI: 10.1016/S0006-3223(02)01574-3 52. Muigg P, Hoelzl U, Palfrader K, Neumann I, Wigger A, Landgraf R, Singewald N. **Altered brain activation pattern associated with drug-induced attenuation of enhanced depression-like behavior in rats bred for high anxiety**. *Biol Psychiatry* (2007.0) **61** 782-796. DOI: 10.1016/j.biopsych.2006.08.035 53. Porsolt RD, Bertin A, Jalfre M. **Behavioral despair in mice: a primary screening test for antidepressants**. *Arch Int Pharmacodyn Ther* (1977.0) **229** 327-336. PMID: 596982 54. Petit-Demouliere B, Chenu F, Bourin M. **Forced swimming test in mice: a review of antidepressant activity**. *Psychopharmacology* (2005.0) **177** 245-255. DOI: 10.1007/s00213-004-2048-7 55. Crawley JN, Paylor R. **A proposed test battery and constellations of specific behavioral paradigms to investigate the behavioral phenotypes of transgenic and knockout mice**. *Horm Behav* (1997.0) **31** 197-211. DOI: 10.1006/hbeh.1997.1382 56. Bailey KR, Rustay NR, Crawley JN. **Behavioral phenotyping of transgenic and knockout mice: practical concerns and potential pitfalls**. *ILAR J* (2006.0) **47** 124-131. DOI: 10.1093/ilar.47.2.124 57. Crawley JN. **Behavioral phenotyping strategies for mutant mice**. *Neuron* (2008.0) **57** 809-818. DOI: 10.1016/j.neuron.2008.03.001 58. Shoji H, Miyakawa T. **Age-related behavioral changes from young to old age in male mice of a C57BL/6J strain maintained under a genetic stability program**. *Neuropsychopharmacol Rep* (2019.0) **39** 100-118. DOI: 10.1002/npr2.12052 59. Crawley J, Goodwin FK. **Preliminary report of a simple animal behavior model for the anxiolytic effects of benzodiazepines**. *Pharmacol Biochem Behav* (1980.0) **13** 167-170. DOI: 10.1016/0091-3057(80)90067-2 60. Takao K, Miyakawa T. **Light/dark transition test for mice**. *J Vis Exp* (2006.0) **1** e104 61. Cabib S, Algeri S, Perego C, Puglisi-Allegra S. **Behavioral and biochemical changes monitored in two inbred strains of mice during exploration of an unfamiliar environment**. *Physiol Behav* (1990.0) **47** 749-753. DOI: 10.1016/0031-9384(90)90089-M 62. Bolivar VJ, Caldarone BJ, Reilly AA, Flaherty L. **Habituation of activity in an open field: a survey of inbred strains and F1 hybrids**. *Behav Genet* (2000.0) **30** 285-293. DOI: 10.1023/A:1026545316455 63. Prut L, Belzung C. **The open field as a paradigm to measure the effects of drugs on anxiety-like behaviors: a review**. *Eur J Pharmacol* (2003.0) **463** 3-33. DOI: 10.1016/S0014-2999(03)01272-X 64. Karlsson RM, Hefner KR, Sibley DR, Holmes A. **Comparison of dopamine D1 and D5 receptor knockout mice for cocaine locomotor sensitization**. *Psychopharmacology* (2008.0) **200** 117-127. DOI: 10.1007/s00213-008-1165-0 65. Lister RG. **The use of a plus-maze to measure anxiety in the mouse**. *Psychopharmacology* (1987.0) **92** 180-185. DOI: 10.1007/BF00177912 66. Komada M, Takao K, Miyakawa T. **Elevated plus maze for mice**. *J Vis Exp* (2008.0) **22** e1088 67. Shoji H, Miyakawa T. **Effects of test experience, closed-arm wall color, and illumination level on behavior and plasma corticosterone response in an elevated plus maze in male C57BL/6J mice: a challenge against conventional interpretation of the test**. *Mol Brain* (2021.0) **14** 1-12. DOI: 10.1186/s13041-020-00721-2 68. Moy SS, Nadler JJ, Perez A, Barbaro RP, Johns JM, Magnuson TR, Piven J, Crawley JN. **Sociability and preference for social novelty in five inbred strains: an approach to assess autistic-like behavior in mice**. *Genes Brain Behav* (2004.0) **3** 287-302. DOI: 10.1111/j.1601-1848.2004.00076.x 69. Nakao A, Miki T, Shoji H, Nishi M, Takeshima H, Miyakawa T, Mori Y. **Comprehensive behavioral analysis of voltage-gated calcium channel beta-anchoring and-regulatory protein knockout mice**. *Front Behav Neurosci* (2015.0) **9** 141. DOI: 10.3389/fnbeh.2015.00141 70. Shoji H, Hagihara H, Takao K, Hattori S, Miyakawa T. **T-maze forced alternation and left-right discrimination tasks for assessing working and reference memory in mice**. *J Vis Exp* (2012.0) **60** e3300 71. Barnes CA. **Memory deficits associated with senescence: a neurophysiological and behavioral study in the rat**. *J Comp Physiol Psychol* (1979.0) **93** 74. DOI: 10.1037/h0077579 72. Steru L, Chermat R, Thierry B, Simon P. **The tail suspension test: a new method for screening antidepressants in mice**. *Psychopharmacology* (1985.0) **85** 367-370. DOI: 10.1007/BF00428203 73. Shoji H, Takao K, Hattori S, Miyakawa T. **Contextual and cued fear conditioning test using a video analyzing system in mice**. *J Vis Exp* (2014.0) **85** e50871 74. Takahashi S, Tomita J, Nishioka K, Hisada T, Nishijima M. **Development of a prokaryotic universal primer for simultaneous analysis of Bacteria and Archaea using next-generation sequencing**. *PLoS ONE* (2014.0) **9** e105592. DOI: 10.1371/journal.pone.0105592 75. Hisada T, Endoh K, Kuriki K. **Inter-and intra-individual variations in seasonal and daily stabilities of the human gut microbiota in Japanese**. *Arch Microbiol* (2015.0) **197** 919-934. DOI: 10.1007/s00203-015-1125-0 76. Aronesty E. **Comparison of sequencing utility programs**. *Open Bioinformatics J* (2013.0) **7** 1-8. DOI: 10.2174/1875036201307010001 77. 77.Gordon A, Hannon GJ. Fastx-toolkit. FASTQ/A short-reads pre-processing tools. 2010. http://hannonlab.cshl.edu/fastx_toolkit/index.html. 78. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Peña AG, Goodrich JK, Gordon JI, Huttley GA, Kelley ST, Knights D, Koenig JE, Ley RE, Lozupone CA, McDonald D, Muegge BD, Pirrung M, Reeder J, Sevinsky JR, Turnbaugh PJ, Walters WA, Widmann J, Yatsunenko T, Zaneveld J, Knight R. **QIIME allows analysis of high-throughput community sequencing data**. *Nat Methods* (2010.0) **7** 335-336. DOI: 10.1038/nmeth.f.303 79. Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. **UCHIME improves sensitivity and speed of chimera detection**. *Bioinformatics* (2011.0) **27** 2194-2200. DOI: 10.1093/bioinformatics/btr381 80. Wang Q, Garrity GM, Tiedje JM, Cole JR. **Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy**. *Appl Environ Microbiol* (2007.0) **73** 5261-5267. DOI: 10.1128/AEM.00062-07 81. 81.Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, Minchin PR, O’Hara RB, Simpson GL, Solymos P, Stevens MHH, Szoecs E, Wagner H. Vegan: community ecology package (version 2.5–6). 2019. http://www.cran.r-project.org/package=vegan 82. Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, Huttenhower C. **Metagenomic biomarker discovery and explanation**. *Genome Biol* (2011.0) **12** 1-18. DOI: 10.1186/gb-2011-12-6-r60 83. Paxinos G, Franklin KBJ. *The mouse brain in stereotaxic coordinates* (2001.0) 84. Li Q, Wichems C, Heils A, Lesch KP, Murphy DL. **Reduction in the density and expression, but not G-protein coupling, of serotonin receptors (5-HT1A) in 5-HT transporter knock-out mice: gender and brain region differences**. *J Neurosci* (2000.0) **20** 7888-7895. DOI: 10.1523/JNEUROSCI.20-21-07888.2000 85. Mizoguchi K, Yuzurihara M, Ishige A, Sasaki H, Tabira T. **Chronic stress impairs rotarod performance in rats: implications for depressive state**. *Pharmacol Biochem Behav* (2002.0) **71** 79-84. DOI: 10.1016/S0091-3057(01)00636-0 86. Mizoguchi K, Shoji H, Ikeda R, Tanaka Y, Tabira T. **Persistent depressive state after chronic stress in rats is accompanied by HPA axis dysregulation and reduced prefrontal dopaminergic neurotransmission**. *Pharmacol Biochem Behav* (2008.0) **91** 170-175. DOI: 10.1016/j.pbb.2008.07.002 87. Bogdanova OV, Kanekar S, D'Anci KE, Renshaw PF. **Factors influencing behavior in the forced swim test**. *Physiol Behav* (2013.0) **118** 227-239. DOI: 10.1016/j.physbeh.2013.05.012 88. Renard CE, Dailly E, David DJ, Hascoet M, Bourin M. **Monoamine metabolism changes following the mouse forced swimming test but not the tail suspension test**. *Fundam Clin Pharmacol* (2003.0) **17** 449-455. DOI: 10.1046/j.1472-8206.2003.00160.x 89. Abbas MG, Shoji H, Soya S, Hondo M, Miyakawa T, Sakurai T. **Comprehensive behavioral analysis of male Ox1r-/- mice showed implication of orexin receptor-1 in mood, anxiety, and social behavior**. *Front Behav Neurosci* (2015.0) **9** 324. DOI: 10.3389/fnbeh.2015.00324 90. Matsuda I, Shoji H, Yamasaki N, Miyakawa T, Aiba A. **Comprehensive behavioral phenotyping of a new Semaphorin 3 F mutant mouse**. *Mol Brain* (2016.0) **9** 1-13. DOI: 10.1186/s13041-016-0196-4 91. Hayashi S, Inoue Y, Hattori S, Kaneko M, Shioi G, Miyakawa T, Takeichi M. **Loss of X-linked Protocadherin-19 differentially affects the behavior of heterozygous female and hemizygous male mice**. *Sci Rep* (2017.0) **7** 1-15. DOI: 10.1038/s41598-017-06374-x 92. Imai H, Shoji H, Ogata M, Kagawa Y, Owada Y, Miyakawa T, Sakimura K, Terashima T, Katsuyama Y. **Dorsal forebrain-specific deficiency of Reelin-Dab1 signal causes behavioral abnormalities related to psychiatric disorders**. *Cereb Cortex* (2017.0) **27** 3485-3501. DOI: 10.1093/cercor/bhv334 93. Katano T, Takao K, Abe M, Yamazaki M, Watanabe M, Miyakawa T, Sakimura K, Ito S. **Distribution of Caskin1 protein and phenotypic characterization of its knockout mice using a comprehensive behavioral test battery**. *Mol Brain* (2018.0) **11** 1-20. DOI: 10.1186/s13041-018-0407-2 94. Okuda K, Takao K, Watanabe A, Miyakawa T, Mizuguchi M, Tanaka T. **Comprehensive behavioral analysis of the Cdkl5 knockout mice revealed significant enhancement in anxiety-and fear-related behaviors and impairment in both acquisition and long-term retention of spatial reference memory**. *PLoS ONE* (2018.0) **13** e0196587. DOI: 10.1371/journal.pone.0196587 95. Olivier JDA, Van Der Hart MGC, Van Swelm RPL, Dederen PJ, Homberg JR, Cremers T, Deen PM, Cuppen E, Cools AR, Ellenbroek BA. **A study in male and female 5-HT transporter knockout rats: an animal model for anxiety and depression disorders**. *Neuroscience* (2008.0) **152** 573-584. DOI: 10.1016/j.neuroscience.2007.12.032 96. File SE, Seth P. **A review of 25 years of the social interaction test**. *Eur J Pharmacol* (2003.0) **463** 35-53. DOI: 10.1016/S0014-2999(03)01273-1 97. Deacon RM, Rawlins JNP. **T-maze alternation in the rodent**. *Nat Protoc* (2006.0) **1** 7-12. DOI: 10.1038/nprot.2006.2 98. Matsuo N, Takao K, Nakanishi K, Yamasaki N, Tanda K, Miyakawa T. **Behavioral profiles of three C57BL/6 substrains**. *Front Behav Neurosci* (2010.0) **4** 29. PMID: 20676234 99. Cheung SG, Goldenthal AR, Uhlemann AC, Mann JJ, Miller JM, Sublette ME. **Systematic review of gut microbiota and major depression**. *Front Psychiatry* (2019.0) **10** 34. DOI: 10.3389/fpsyt.2019.00034 100. Barandouzi ZA, Starkweather AR, Henderson WA, Gyamfi A, Cong XS. **Altered composition of gut microbiota in depression: a systematic review**. *Front Psychiatry* (2020.0) **11** 541. DOI: 10.3389/fpsyt.2020.00541 101. Morais LH, Schreiber HL, Mazmanian SK. **The gut microbiota–brain axis in behaviour and brain disorders**. *Nat Rev Microbiol* (2021.0) **19** 241-255. DOI: 10.1038/s41579-020-00460-0 102. Milaneschi Y, Simmons WK, van Rossum EF, Penninx BW. **Depression and obesity: evidence of shared biological mechanisms**. *Mol Psychiatry* (2019.0) **24** 18-33. DOI: 10.1038/s41380-018-0017-5 103. Zhang X, Zhao Y, Zhang M, Pang X, Xu J, Kang C, Li M, Zhang C, Zhang Z, Zhang Y, Li X, Ning G, Zhao L. **Structural changes of gut microbiota during berberine-mediated prevention of obesity and insulin resistance in high-fat diet-fed rats**. *PLoS ONE* (2012.0) **7** e42529. DOI: 10.1371/journal.pone.0042529 104. Everard A, Lazarevic V, Gaïa N, Johansson M, Ståhlman M, Backhed F, Delzenne NM, Schrenzel J, François P, Cani PD. **Microbiome of prebiotic-treated mice reveals novel targets involved in host response during obesity**. *ISME J* (2014.0) **8** 2116-2130. DOI: 10.1038/ismej.2014.45 105. Wang J, Wang P, Li D, Hu X, Chen F. **Beneficial effects of ginger on prevention of obesity through modulation of gut microbiota in mice**. *Eur J Nutr* (2020.0) **59** 699-718. DOI: 10.1007/s00394-019-01938-1 106. Chen Z, Radjabzadeh D, Chen L, Kurilshikov A, Kavousi M, Ahmadizar F, Ikram MA, Uitterlinden AG, Zhernakova A, Fu J, Kraaij R, Voortman T. **Association of insulin resistance and type 2 diabetes with gut microbial diversity: a microbiome-wide analysis from population studies**. *JAMA Netw Open* (2021.0) **4** e2118811-e2118811. DOI: 10.1001/jamanetworkopen.2021.18811 107. Zheng Z, Lyu W, Ren Y, Li X, Zhao S, Yang H, Xiao Y. **Allobaculum involves in the modulation of intestinal ANGPTLT4 expression in mice treated by high-fat diet**. *Front Nutr* (2021.0) **8** 242. DOI: 10.3389/fnut.2021.690138 108. Murphy DL, Lesch KP. **Targeting the murine serotonin transporter: insights into human neurobiology**. *Nat Rev Neurosci* (2008.0) **9** 85-96. DOI: 10.1038/nrn2284 109. Liu Y, Zhang L, Wang X, Wang Z, Zhang J, Jiang R, Wang X, Wang K, Liu Z, Xia Z, Xu Z, Nie Y, Lv X, Wu X, Zhu H, Duan L. **Similar fecal microbiota signatures in patients with diarrhea-predominant irritable bowel syndrome and patients with depression**. *Clin Gastroenterol Hepatol* (2016.0) **14** 1602-1611. DOI: 10.1016/j.cgh.2016.05.033 110. Lin P, Ding B, Feng C, Yin S, Zhang T, Qi X, Lv H, Guo X, Dong K, Zhu Y, Li Q. **Prevotella and Klebsiella proportions in fecal microbial communities are potential characteristic parameters for patients with major depressive disorder**. *J Affect Disord* (2017.0) **207** 300-304. DOI: 10.1016/j.jad.2016.09.051 111. Liu P, Gao M, Liu Z, Zhang Y, Tu H, Lei L, Wu P, Zhang A, Yang C, Li G, Sun N, Zhang K. **Gut microbiome composition linked to inflammatory factors and cognitive functions in first-episode, drug-naive major depressive disorder patients**. *Front Neurosci* (2021.0) **15** 800764. DOI: 10.3389/fnins.2021.800764 112. Sanada K, Nakajima S, Kurokawa S, Barceló-Soler A, Ikuse D, Hirata A, Yoshizawa A, Tomizawa Y, Salas-Valero M, Noda Y, Mimura M, Iwanami A, Kishimoto T. **Gut microbiota and major depressive disorder: a systematic review and meta-analysis**. *J Affect Disord* (2020.0) **266** 1-13. DOI: 10.1016/j.jad.2020.01.102 113. Ulrich-Lai YM, Herman JP. **Neural regulation of endocrine and autonomic stress responses**. *Nat Rev Neurosci* (2009.0) **10** 397-409. DOI: 10.1038/nrn2647 114. Tjurmina OA, Armando I, Saavedra JM, Goldstein DS, Murphy DL. **Exaggerated adrenomedullary response to immobilization in mice with targeted disruption of the serotonin transporter gene**. *Endocrinology* (2002.0) **143** 4520-4526. DOI: 10.1210/en.2002-220416 115. Li Q, Wichems C, Heils A, Van de Kar LD, Lesch KP, Murphy DL. **Reduction of 5-hydroxytryptamine (5-HT) 1A-mediated temperature and neuroendocrine responses and 5-HT1A binding sites in 5-HT transporter knockout mice**. *J Pharmacol Exp Ther* (1999.0) **291** 999-1007. PMID: 10565817 116. Li Q, Wichems CH, Ma L, Van de Kar LD, Garcia F, Murphy DL. **Brain region-specific alterations of 5-HT2A and 5-HT2C receptors in serotonin transporter knockout mice**. *J Neurochem* (2003.0) **84** 1256-1265. DOI: 10.1046/j.1471-4159.2003.01607.x 117. Hulvershorn LA, Cullen K, Anand A. **Toward dysfunctional connectivity: a review of neuroimaging findings in pediatric major depressive disorder**. *Brain Imaging Behav* (2011.0) **5** 307-328. DOI: 10.1007/s11682-011-9134-3 118. Kerestes R, Davey CG, Stephanou K, Whittle S, Harrison BJ. **Functional brain imaging studies of youth depression: a systematic review**. *NeuroImage Clin* (2014.0) **4** 209-231. DOI: 10.1016/j.nicl.2013.11.009 119. Uylings HB, Groenewegen HJ, Kolb B. **Do rats have a prefrontal cortex?**. *Behav Brain Res* (2003.0) **146** 3-17. DOI: 10.1016/j.bbr.2003.09.028 120. Berendse HW, Graaf YGD, Groenewegen HJ. **Topographical organization and relationship with ventral striatal compartments of prefrontal corticostriatal projections in the rat**. *J Comp Neurol* (1992.0) **316** 314-347. DOI: 10.1002/cne.903160305 121. Heidbreder CA, Groenewegen HJ. **The medial prefrontal cortex in the rat: evidence for a dorso-ventral distinction based upon functional and anatomical characteristics**. *Neurosci Biobehav Rev* (2003.0) **27** 555-579. DOI: 10.1016/j.neubiorev.2003.09.003 122. Reynolds SM, Berridge KC. **Positive and negative motivation in nucleus accumbens shell: bivalent rostrocaudal gradients for GABA-elicited eating, taste “liking”/“disliking” reactions, place preference/avoidance, and fear**. *J Neurosci* (2002.0) **22** 7308-7320. DOI: 10.1523/JNEUROSCI.22-16-07308.2002 123. Hurley SW, Carelli RM. **Activation of infralimbic to nucleus accumbens shell pathway suppresses conditioned aversion in male but not female rats**. *J Neurosci* (2020.0) **40** 6888-6895. DOI: 10.1523/JNEUROSCI.0137-20.2020 124. Silveira MCL, Graeff FG. **Defense reaction elicited by microinjection of kainic acid into the medial hypothalamus of the rat: antagonism by a GABAA receptor agonist**. *Behav Neural Biol* (1992.0) **57** 226-232. DOI: 10.1016/0163-1047(92)90192-7 125. Sheehan TP, Chambers RA, Russell DS. **Regulation of affect by the lateral septum: implications for neuropsychiatry**. *Brain Res Rev* (2004.0) **46** 71-117. DOI: 10.1016/j.brainresrev.2004.04.009 126. Santos JM, Macedo CE, Brandão ML. **Gabaergic mechanisms of hypothalamic nuclei in the expression of conditioned fear**. *Neurobiol Learn Mem* (2008.0) **90** 560-568. DOI: 10.1016/j.nlm.2008.06.007 127. Trent NL, Menard JL. **The ventral hippocampus and the lateral septum work in tandem to regulate rats' open-arm exploration in the elevated plus-maze**. *Physiol Behav* (2010.0) **101** 141-152. DOI: 10.1016/j.physbeh.2010.04.035 128. De Paula DC, Torricelli AS, Lopreato MR, Nascimento JOG, Viana MDB. **5-HT2A receptor activation in the dorsolateral septum facilitates inhibitory avoidance in the elevated T-maze**. *Behav Brain Res* (2012.0) **226** 50-55. DOI: 10.1016/j.bbr.2011.08.044 129. Silva MS, Souza TM, Pereira BA, Ribeiro DA, Céspedes IC, Bittencourt JC, Viana MB. **The blockage of ventromedial hypothalamus CRF type 2 receptors impairs escape responses in the elevated T-maze**. *Behav Brain Res* (2017.0) **329** 41-50. DOI: 10.1016/j.bbr.2017.04.030 130. Penzo MA, Robert V, Tucciarone J, De Bundel D, Wang M, Van Aelst L, Darvas M, Parada LF, Palmiter R, He M, Huang J, Li B. **The paraventricular thalamus controls a central amygdala fear circuit**. *Nature* (2015.0) **519** 455-459. DOI: 10.1038/nature13978 131. Kasahara T, Takata A, Kato TM, Kubota-Sakashita M, Sawada T, Kakita A, Mizukami H, Kaneda D, Ozawa K, Kato T. **Depression-like episodes in mice harboring mtDNA deletions in paraventricular thalamus**. *Mol Psychiatry* (2016.0) **21** 39-48. DOI: 10.1038/mp.2015.156 132. Kato TM, Fujimori-Tonou N, Mizukami H, Ozawa K, Fujisawa S, Kato T. **Presynaptic dysregulation of the paraventricular thalamic nucleus causes depression-like behavior**. *Sci Rep* (2019.0) **9** 1-9. DOI: 10.1038/s41598-019-52984-y 133. Chen M, Bi LL. **Optogenetic long-term depression induction in the PVT-CeL circuitry mediates decreased fear memory**. *Mol Neurobiol* (2019.0) **56** 4855-4865. DOI: 10.1007/s12035-018-1407-z 134. Pliota P, Böhm V, Grössl F, Griessner J, Valenti O, Kraitsy K, Kaczanowska J, Pasieka M, Lendl T, Deussing JM, Haubensak W. **Stress peptides sensitize fear circuitry to promote passive coping**. *Mol Psychiatry* (2020.0) **25** 428-441. DOI: 10.1038/s41380-018-0089-2 135. Barson JR, Leibowitz SF. **GABA-induced inactivation of dorsal midline thalamic subregions has distinct effects on emotional behaviors**. *Neurosci Lett* (2015.0) **609** 92-96. DOI: 10.1016/j.neulet.2015.10.029 136. Cheng J, Wang J, Ma X, Ullah R, Shen Y, Zhou YD. **Anterior paraventricular thalamus to nucleus accumbens projection is involved in feeding behavior in a novel environment**. *Front Mol Neurosci* (2018.0) **11** 202. DOI: 10.3389/fnmol.2018.00202 137. Barson JR, Mack NR, Gao WJ. **The paraventricular nucleus of the Thalamus is an important node in the emotional processing network**. *Front Behav Neurosci* (2020.0) **14** 598469. DOI: 10.3389/fnbeh.2020.598469 138. James MH, Campbell EJ, Dayas CV. **Role of the orexin/hypocretin system in stress-related psychiatric disorders**. *Curr Top Behav Neurosci* (2017.0) **33** 197-219. DOI: 10.1007/7854_2016_56 139. Cassidy RM, Lu Y, Jere M, Tian JB, Xu Y, Mangieri LR, Felix-Okoroji B, Selever J, Xu Y, Arenkiel BR, Tong Q. **A lateral hypothalamus to basal forebrain neurocircuit promotes feeding by suppressing responses to anxiogenic environmental cues**. *Sci Adv* (2019.0) **5** eaav1640. DOI: 10.1126/sciadv.aav1640 140. Kovács KJ. **Invited review c-Fos as a transcription factor: a stressful (re) view from a functional map**. *Neurochem Int* (1998.0) **33** 287-297. DOI: 10.1016/S0197-0186(98)00023-0 141. Kovács KJ. **Measurement of immediate-early gene activation-c-fos and beyond**. *J Neuroendocrinol* (2008.0) **20** 665-672. DOI: 10.1111/j.1365-2826.2008.01734.x 142. Yamasaki N, Maekawa M, Kobayashi K, Kajii Y, Maeda J, Soma M, Takao K, Tanda K, Ohira K, Toyama K, Kanzaki K, Fukunaga K, Sudo Y, Ichinose H, Ikeda M, Iwata N, Ozaki N, Suzuki H, Higuchi M, Suhara T, Yuasa S, Miyakawa T. **Alpha-CaMKII deficiency causes immature dentate gyrus, a novel candidate endophenotype of psychiatric disorders**. *Mol Brain* (2008.0) **1** 1-21. DOI: 10.1186/1756-6606-1-6 143. Hagihara H, Takao K, Walton NM, Matsumoto M, Miyakawa T. **Immature dentate gyrus: an endophenotype of neuropsychiatric disorders**. *Neural Plast* (2013.0) **2013** 318596. DOI: 10.1155/2013/318596 144. Matsuo N, Yamasaki N, Ohira K, Takao K, Toyama K, Eguchi M, Yamaguchi S, Miyakawa T. **Neural activity changes underlying the working memory deficit in alpha-CaMKII heterozygous knockout mice**. *Front Behav Neurosci* (2009.0) **3** 20. DOI: 10.3389/neuro.08.020.2009 145. Murano T, Nakajima R, Nakao A, Hirata N, Amemori S, Murakami A, Kamitani Y, Yamamoto J, Miyakawa T. **Multiple types of navigational information are independently encoded in the population activities of the dentate gyrus neurons**. *Proc Natl Acad Sci USA* (2022.0) **119** e2106830119. DOI: 10.1073/pnas.2106830119 146. Moncrieff J, Cooper RE, Stockmann T, Amendola S, Hengartner MP, Horowitz MA. **The serotonin theory of depression: a systematic umbrella review of the evidence**. *Mol Psychiatry* (2022.0). DOI: 10.1038/s41380-022-01661-0 147. Gryglewski G, Lanzenberger R, Kranz GS, Cumming P. **Meta-analysis of molecular imaging of serotonin transporters in major depression**. *J Cereb Blood Flow Metab* (2014.0) **34** 1096-1103. DOI: 10.1038/jcbfm.2014.82 148. Kambeitz JP, Howes OD. **The serotonin transporter in depression: meta-analysis of in vivo and post mortem findings and implications for understanding and treating depression**. *J Affect Disord* (2015.0) **186** 358-366. DOI: 10.1016/j.jad.2015.07.034 149. Nikolaus S, Müller HW, Hautzel H. **Different patterns of 5-HT receptor and transporter dysfunction in neuropsychiatric disorders–a comparative analysis of in vivo imaging findings**. *Rev Neurosci* (2016.0) **27** 27-59. DOI: 10.1515/revneuro-2015-0014 150. Mathews TA, Fedele DE, Coppelli FM, Avila AM, Murphy DL, Andrews AM. **Gene dose-dependent alterations in extraneuronal serotonin but not dopamine in mice with reduced serotonin transporter expression**. *J Neurosci Methods* (2004.0) **140** 169-181. DOI: 10.1016/j.jneumeth.2004.05.017 151. Shen HW, Hagino Y, Kobayashi H, Shinohara-Tanaka K, Ikeda K, Yamamoto H, Yamamoto T, Lesch KP, Murphy DL, Hall FS, Uhl GR, Sora I. **Regional differences in extracellular dopamine and serotonin assessed by in vivo microdialysis in mice lacking dopamine and/or serotonin transporters**. *Neuropsychopharmacology* (2004.0) **29** 1790-1799. DOI: 10.1038/sj.npp.1300476 152. Kim DK, Tolliver TJ, Huang SJ, Martin BJ, Andrews AM, Wichems C, Holmes A, Lesch KP, Murphy DL. **Altered serotonin synthesis, turnover and dynamic regulation in multiple brain regions of mice lacking the serotonin transporter**. *Neuropharmacology* (2005.0) **49** 798-810. DOI: 10.1016/j.neuropharm.2005.08.010 153. Magnuson KM, Constantino JN. **Characterization of depression in children with autism spectrum disorders**. *J Dev Behav Pediatr* (2011.0) **32** 332. DOI: 10.1097/DBP.0b013e318213f56c 154. Uljarević M, Hedley D, Rose-Foley K, Magiati I, Cai RY, Dissanayake C, Richdale A, Trollor J. **Anxiety and depression from adolescence to old age in autism spectrum disorder**. *J Autism Dev Disord* (2020.0) **50** 3155-3165. DOI: 10.1007/s10803-019-04084-z 155. Mulder EJ, Anderson GM, Kema IP, De Bildt A, Van Lang ND, Den Boer JA, Minderaa RB. **Platelet serotonin levels in pervasive developmental disorders and mental retardation: diagnostic group differences, within-group distribution, and behavioral correlates**. *J Am Acad Child Adolesc Psychiatry* (2004.0) **43** 491-499. DOI: 10.1097/00004583-200404000-00016 156. Nakamura K, Sekine Y, Ouchi Y, Tsujii M, Yoshikawa E, Futatsubashi M, Tsuchiya KJ, Sugihara G, Iwata Y, Suzuki K, Matsuzaki H, Suda S, Sugiyama T, Takei N, Mori N. **Brain serotonin and dopamine transporter bindings in adults with high-functioning autism**. *Arch Gen Psychiatry* (2010.0) **67** 59-68. DOI: 10.1001/archgenpsychiatry.2009.137 157. Oblak A, Gibbs TT, Blatt GJ. **Reduced serotonin receptor subtypes in a limbic and a neocortical region in autism**. *Autism Res* (2013.0) **6** 571-583. DOI: 10.1002/aur.1317 158. Gabriele S, Sacco R, Persico AM. **Blood serotonin levels in autism spectrum disorder: a systematic review and meta-analysis**. *Eur Neuropsychopharmacol* (2014.0) **24** 919-929. DOI: 10.1016/j.euroneuro.2014.02.004 159. Muller CL, Anacker AM, Veenstra-VanderWeele J. **The serotonin system in autism spectrum disorder: from biomarker to animal models**. *Neuroscience* (2016.0) **321** 24-41. DOI: 10.1016/j.neuroscience.2015.11.010
--- title: Clinical and biological heterogeneities in triple-negative breast cancer reveals a non-negligible role of HER2-low authors: - Xi′e Hu - Ping Yang - Songhao Chen - Gang Wei - Lijuan Yuan - Zhenyu Yang - Li Gong - Li He - Lin Yang - Shujia Peng - Yanming Dong - Xianli He - Guoqiang Bao journal: 'Breast Cancer Research : BCR' year: 2023 pmcid: PMC10061837 doi: 10.1186/s13058-023-01639-y license: CC BY 4.0 --- # Clinical and biological heterogeneities in triple-negative breast cancer reveals a non-negligible role of HER2-low ## Abstract ### Background HER2-low could be found in some patients with triple-negative breast cancer (TNBC). However, its potential impacts on clinical features and tumor biological characteristics in TNBC remain unclear. ### Methods We enrolled 251 consecutive TNBC patients retrospectively, including 157 HER2-low (HER2low) and 94 HER2-negtive (HER2neg) patients to investigate the clinical and prognostic features. Then, we performed single-cell RNA sequencing (scRNA-seq) with another seven TNBC samples (HER2neg vs. HER2low, 4 vs. 3) prospectively to further explore the differences of tumor biological properties between the two TNBC phenotypes. The underlying molecular distinctions were also explored and then verified in the additional TNBC samples. ### Results Compared with HER2neg TNBC, HER2low TNBC patients exhibited malignant clinical features with larger tumor size ($$P \leq 0.04$$), more lymph nodes involvement ($$P \leq 0.02$$), higher histological grade of lesions ($P \leq 0.001$), higher Ki67 status ($P \leq 0.01$), and a worse prognosis ($P \leq 0.001$; HR [CI $95\%$] = 3.44 [2.10–5.62]). Cox proportional hazards analysis showed that neoadjuvant systemic therapy, lymph nodes involvement and Ki67 levels were prognostic factors in HER2low TNBC but not in HER2neg TNBC patients. ScRNA-seq revealed that HER2low TNBC which showed more metabolically active and aggressive hallmarks, while HER2neg TNBC exhibited signatures more involved in immune activities with higher expressions of immunoglobulin-related genes (IGHG1, IGHG4, IGKC, IGLC2); this was further confirmed by immunofluorescence in clinical TNBC samples. Furthermore, HER2low and HER2neg TNBC exhibited distinct tumor evolutionary characteristics. Moreover, HER2neg TNBC revealed a potentially more active immune microenvironment than HER2low TNBC, as evidenced by positively active regulation of macrophage polarization, abundant CD8+ effector T cells, enriched diversity of T-cell receptors and higher levels of immunotherapy-targeted markers, which contributed to achieve immunotherapeutic response. ### Conclusions This study suggests that HER2low TNBC patients harbor more malignant clinical behavior and aggressive tumor biological properties than the HER2neg phenotype. The heterogeneity of HER2 may be a non-negligible factor in the clinical management of TNBC patients. Our data provide new insights into the development of a more refined classification and tailored therapeutic strategies for TNBC patients. ### Supplementary Information The online version contains supplementary material available at 10.1186/s13058-023-01639-y. ## Introduction Breast cancer (BC) is the most frequently diagnosed tumor in women worldwide, and it remains as the second leading cause of cancer-related death [1]. Triple-negative BC (TNBC) accounts for 15–$20\%$ of all BC cases which is usually defined as the absence of estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) [2]. TNBC is an intertwined disease characterized by its early onset, increased metastatic risk and poor prognosis [3–5]. It exhibits highly clinical and molecular inherent heterogeneity consisting of different intron subtypes with distinguishing biological characteristics, treatment sensitivities and clinical outcomes [6, 7]. Therefore, the lack of therapeutic targets and its malignant biological features render it a challenging issue in the clinical management of BC. The difference in HER2 expression levels is one of the apparent heterogeneous properties of TNBC which can be assessed in clinical practice [8]. According to pathological assessments of HER2 expression levels by immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH), TNBC can be divided into two different categories: HER2-negative/HER2-zero TNBC (HER2neg TNBC; IHC 0) and HER2-low TNBC (HER2low TNBC; IHC1 +, or IHC2 + and FISH-negative). Traditionally, this differentiation seems to be less pivotal for patients’ clinical treatment options, but recent studies have suggested a potential efficacy of novel HER2-targeted antibody drug conjugates (ADCs) in the treatment of HER2low BC [9, 10], which opens up an emerging era for evaluating the implicit role of HER2-low in the clinical setting of TNBC. To date, several studies have investigated the clinical features and biological hallmarks of HER2low BC [8, 11–17], which highlight the potential effect of HER2-low on the treatment response and clinical outcomes of TNBC. However, current studies on the significance of HER2-low are still inconclusive [8, 13, 15–17], and the underlying pathogenic role of HER2-low in TNBC is less explored. Furthermore, the potential biological differences between HER2low and HER2neg TNBC categories also remain poorly understood. Therefore, a more comprehensive analysis of the biological influence of HER2-low on TNBC is urgently needed, which may lay a foundation for future TNBC target-based therapy, especially in the new era of precision therapy. Single-cell RNA sequencing (scRNA-seq) is a promising technique for defining tumor subpopulations and identifying potential treatment targets [7] and has a potential to detect subtle discrepancies between different tumor subtypes for TNBC. Hence, we employed this technique to better understand the underlying features of HER2low TNBC and HER2neg TNBC. In this study, we investigated the clinical characteristics between 157 HER2low TNBC and 94 HER2neg TNBC patients; then, scRNA-seq was prospectively performed on 36,168 cells from three HER2low and four HER2neg TNBC patients for the further in-deep exploration. The aims of this study were as following: a) to investigate the clinical significance of HER2-low in TNBC patients; b) to delineate the transcriptome patterns of HER2low and HER2neg TNBC; and c) to explore the potential impacts of HER2 status on tumor behaviors and microenvironment properties in TNBC. This study contributes to a better understanding of the potential clinical and biological heterogeneities of HER2low and HER2neg TNBC and may provide novel clues for the development of more refined classification and tailored therapeutic strategies for TNBC. ## Clinical patients and sample collection To investigate the clinical characteristics of TNBC with different HER2 status, a total of 251 female patients consecutively confirmed as TNBC pathologically in our institution from Jan. 2013 to Dec. 2020 were incorporated in this study, including 157 HER2low and 94 HER2neg TNBC patients. The clinicopathological data were retrospectively collected from medical records of the institutional database. In this study, all the determinations of ER, PR, and HER2 status were performed according to the ASCO/CAP guidelines [18, 19]. Briefly, the TNBC lesion was defined as ER < $1\%$, PR < $1\%$, and HER2 IHC 0 or IHC 1 + /2 + with a negative result of HER2 amplification by FISH. HER2 IHC 1 + and 2 + (FISH-negative) were considered as HER2-low. The overall survival was defined as the time from TNBC diagnosis to the time of the last follow-up time (Jan 1st, 2021), the time of death or the time lost to follow-up. The follow-up data of the patients were collected based on medical records or telephone interviews. Males, patients who refused follow-up, or patients with missing clinicopathological data were excluded from this study. The detailed inclusion and exclusion process of patients is shown in Fig. 1A, and the detailed data of the patients are shown in Additional file 8: Table S1.Fig. 1Investigations of clinical features in TNBC patients with different HER2 status. ( A) Flow chart of study population enrollment. ( B) Overall survival curves of HER2low and HER2neg TNBC patients diagnosed at our institution in the past seven years, using logrank test. $P \leq 0.05$ indicates statistical significance. The detailed survival data are attached in Additional file 8: Table S1. ( C) Forest plots showing the prognostic effects of clinical features in all TNBC, HER2low, and HER2neg TNBC patients, respectively The patient recruitment time for scRNA-seq was from Jan. 2021 to Jan. 2022, and all patients included provided with written informed consent. Seven enrolled patients with pathologically confirmed TNBC were all female, range 38 to 49 years old and not pregnant. All enrolled patients were treatment-naïve with a unilateral lesion and consented to receive ultrasound-guided pathological puncture. Notably, P3, P4 and P5 were HER2low TNBC, and P1, P2, P6 and P7 were HER2neg TNBC (Fig. 1A). P1, P2 and P7 received anti-PD-1 immunotherapy (Tislelizumab Injection from BeiGene, Ltd) combined with chemotherapy (nab-paclitaxel) as neoadjuvant systematic therapy (NST) every three weeks (Q3W) for four cycles. Finally, 7 treatment-naïve puncture samples and 3 postoperative specimens of P1, P2 and P7 (after NST) were obtained. The basic demographic characteristics, clinical profiles and sampling information of the patients are presented in Additional file 9: Table S2. This prospective study was conducted under a protocol approved by the Institutional Ethics Committee of The Second Affiliated Hospital of Air Force Medical University (No. K202010-04) and in accordance with the Declaration of Helsinki. ## Tissue dissociation and preparation Fresh breast tissues were stored in Tissue Preservation Solution (Singleron Biotechnologies, Nanjing, China) and placed on ice after the biopsy within 30 min. The specimens were washed three times and then cut into slices of 1 to 2 mm. Subsequently, the tissue pieces were digested in a 15 ml centrifuge tube at 37 °C with continuous agitation for 15 min. Then, they were centrifuged at 500 × g for 5 min and suspended gently with PBS (HyClone, USA). Finally, the samples were stained with trypan blue (Sigma, USA), and cell viability was evaluated under a phase-contrast microscope (Nikon, Japan). ## Library preparation Single-cell suspensions (1 × 105 cells/ml) with PBS (HyClone, USA) were loaded into microfluidic devices using the Singleron Matrix® Single Cell Processing System (Singleron). Subsequently, the scRNA-seq library was established according to the protocol of the GEXSCOPE® Single Cell RNA Library Kit (Singleron) [20]. Libraries for individuals were diluted to 4 nM and pooled for sequencing. Finally, the pools were sequenced with 150 bp paired-end reads on an Illumina HiSeq X instrument. ## Quality control and pre-processing Cells were filtered by gene counts between 200 and 5,000 and unique molecular identifier (UMI) counts below 30,000. Consistent with previous studies [21–24], we removed the cells with over $50\%$ mitochondrial content in order to optimize keeping cells and removing dead and dying cells. After filtering, we used functions from Seurat v3.1.2 for dimension reduction and clustering. The raw reads were processed to generate gene expression profiles. Briefly, the cell barcode and UMI were extracted after filtering read one without poly-T tails. We trimmed (fastp V1) the adapters and poly-A tails before aligning read two to GRCh38 with Ensemble v92 gene annotation (fastp 2.5.3a and featureCounts 1.6.2) [25]. Reads of the same cell barcode, UMIs and genes were combined to count the number of UMIs in each cell. UMI count tables in each cell barcode were applied for further analysis. ## Dimensionality reduction The Read10 × function was applied to process the Seurat object with individual gene expression data. For each sample, gene expression was expressed as a fraction of the genes, which were then multiplied by 10,000. *These* genes were converted into natural logarithms after the addition of 1 and normalized to avoid obtaining logarithms of 0. Before we performed principal component analysis (PCA) based on these standardized expression matrices, we identified the top 3000 highly variable genes (HVGS) from the standardized expression matrix and concentrated and scaled them. The batch effects were removed by the Harmony package (version 1.0) of R/Rstudio software (version 3.6.1) based on the identity of the top 50 PCA components [26]. ## scRNA-seq-based copy number variation (CNV) detection The InferCNV package [27] was used to detect CNV in malignant breast cells. Non-malignant breast cells were identified as the baseline to evaluate the CNVs of malignant cells. Genes expressed in more than 20 cells were sorted according to their loci on each chromosome. The relative expression value was centered at 1, and a total of 1.5 standard deviations from the residual standardized expression value were considered the ceiling. The sliding window size of 101 genes was used to smooth the relative expression of each chromosome to eliminate the influence of gene-specific expression. ## Intratumoral heterogeneity (ITH) score calculation The ITH score was calculated by the algorithm described in BC. The ITH score was defined as the average Euclidean distance between the individual cells and all other cells, according to the first 20 principal components derived from the normalized expression levels of highly variable genes. The highly variable gene was identified by the Seurat package with the default parameters. ## Analysis of differential expression genes (DEGs) and cell type annotations Genes expressed in more than $10\%$ of the cells in a cluster and with an average log (fold change) of greater than 0.25 were selected as DEGs by Seurat v3.1.2 based on the Wilcox likelihood-ratio test with default parameters. Cell type identification and clustering analysis were performed by the Seurat program [28, 29]. Furthermore, the Seurat program (http://satijalab.org/seurat/) was applied for the analysis of RNA-seq data. UMI count tables were loaded into R by “read.table” function. Afterward, the parameter resolution to 2.0 was set for the “Find Clusters” function for clustering analyses. For subclustering of various cell types, we set the resolution at 1.2. The UMAP algorithm was used to visualize cells in a two-dimensional space. The cell type of each cluster was identified according to the expression of typical markers in DEGs using the SynEcoSys database. Doublet cells were mainly judged based on marker gene expression, which would commonly express marker genes of two or more cell types that already exist on the clustering map and have no differentiation relationship. The doublet gene expression profile may affect the results of cell subtype clustering, cell differentiation status analysis, as well as cell subtype functional enrichment analysis, resulting in biased understanding of the biological significance [30]. Therefore, annotated doublet is generally removed in this study to reduce the possibility of errors in subsequent analysis. Detailed information on the cell markers is shown in Additional file 10: Table S3. ## Pathway enrichment analysis To investigate the potential functions of DEGs, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were applied with the “ClusterProfiler” R package 3.6.1. In this study, the gene sets in the “biological process (BP)” of GO pathway were mainly considered. GO and KEGG functional enrichment analyses were conducted to explore biological functions or pathways significantly associated with the specifically expressed genes [31]. Gene set enrichment analysis (GSEA) was performed on genes expressed in tumor clusters. For GSVA pathway enrichment analysis, the average gene expression of each tumor cell in every TNBC group was used as input data using the GSVA package. ## Trajectory analysis To map the differentiation of tumor cells in the two TNBC groups, pseudotime trajectory analysis was performed by Monocle v2 [32]. To construct the trajectory, the highly variable genes were selected from tumor clusters 1 to 15 via the Seurat v3.1.2 Find Variable Features function. The dimension reduction was performed by DDRTree. The trajectory was subsequently visualized and the dynamic changes in gene expression over pseudotime were displayed. The differentiation status of each tumor cell subcluster was detected by CytoTRACE [33]. ## Single-cell T-cell receptor-sequencing (scTCR-seq) scTCR-seq libraries were constructed according to the protocol of the GEXSCOPE Single Cell Immuno-TCR Kit (Singleron Biotechnologies). Briefly, the magnetic beads with molecular labels captured the poly-A tail of mRNAs and TCR region of immune cells after the cells were lysed. Subsequently, the magnetic beads in the chip were collected, and then mRNAs captured by the magnetic beads were reverse transcribed into complementary DNA (cDNA) and amplified. Sequencing libraries suitable for the Illumina sequencing platform were constructed after partial cDNA fragments and splicing. The remaining cDNA was enriched for TCR, and the enriched products were amplified by PCR to construct a sequencing library suitable for the Illumina sequencing platform. Finally, each library was sequenced on an Illumina HiSeq X platform with 150 bp paired-end reads. ## Cell–cell interaction analysis Cell–cell interactions (CCIs) between the eight cell types were predicted by Cellphone DB version [34] based on known ligand–receptor pairs. The permutation number was set to 1000 to calculate the null distribution of average ligand–receptor pair expression in randomized cell identities. The threshold cut-off of individual ligand or receptor expression was based on the average log of the gene expression distribution for all genes of each cell type. Predicted interaction pairs were visualized by the circlize (0.4.10) R package, and a p value < 0.05 and average log expression > 0.1 were considered significant pairs. ## Kaplan–Meier-plotter database analysis Kaplan–Meier-plotter database (http://kmplot.com/analysis/) was applied to compare the overall survival of TNBC patients with different mRNA expression levels of IGKC, IGHG1, IGHG4, SCGB2A1, PTN and MUCL1. The population included in the analysis were patients with TNBC, and the detailed datasets of the included population are shown in Additional file 11: Table S4. The cutoff value of each molecule expression was determined by the median expression level in the population. ## GEO datasets analysis TNBC datasets (GSE76124, GSE95700, GSE103091, GSE135565, GSE157284 and GSE167213) from the GEO database (https://www.ncbi.nlm.nih.gov/geo/) were applied to explore the associations of the expressions of ERBB2, immunoglobulin-related molecules (IGHG1, IGHG4, IGKC and IGLC2), and immunotherapy related targets (PDCD1, CD274, CD47, CTLA4, CDK6 and DDR2). The R package “sva” was applied to normalize the expression in different batches after merging the six GEO datasets. The correlation between expressions of immunoglobulin-related genes and the functions of immune microenvironment was also explored using Spearman test. ## IHC, FISH and immunofluorescence For immunohistochemical analysis of ER, PR and HER2, the tissue paraffin blocks from seven TNBC puncture specimens were sectioned for analysis of ER, PR, and HER2 via IHC. Briefly, a 4-μm-thick tissue was deparaffinized, rehydrated and blocked by peroxidase after antigen retrieval, and a primary antibody (ER, Roche Diagnostics GmbH, Germany; PR, Roche Diagnostics GmbH, Germany; HER2, Roche Diagnostics GmbH, Germany) was incubated at room temperature for 3 h. Then, the slides were incubated with corresponding secondary antibody at 37 °C for 30 min, rinsed with PBS and stained with DAB substrate. Subsequently, routine dehydration, transparency, drying and sealing of tablets were conducted. Finally, the stained tablets were observed under a microscope (Olympus IX73), and images were evaluated independently by two experienced pathologists according to ASCO/CAP guidelines [18, 19]. In this study, only P2 was HER2 IHC 2 +, so FISH detection of HER2 was further conducted. For fluorescence in situ hybridization (FISH) of HER2, the sections were detected using the HER2 FISH detection kit (Beijing Jinbojia Biotechnology Co., Ltd., China) following the manufacturer’s instruction. In brief, sections were routinely dewaxed in xylene, rehydrated with graded ethanol, treated with acidic sodium sulfite, digested with protease, soaked in $1\%$ HCl, dehydrated with graded ethanol, fixed in acetone, baked at 56 °C for 5 min, added with probe working solution on the tissue sections, and denatured at 73 °C for 5 min. After that, the hybridization was performed overnight at 42 °C in a wet box for 16 h. Then, sections were rinsed with $50\%$ formamide, citrate buffer, $0.1\%$ NP-40 and $70\%$ ethanol. Subsequently, the sections were dried naturally and counterstained with DAPI stain. After placing in the dark for 20 min, the sections were observed under a fluorescence microscope (Olympus IX73). The results were evaluated according to ASCO/CAP guidelines [18, 19]. For immunofluorescence (IF) analysis of IGHG4, IGKC, APOD, and MUCL1, before the antibody incubation, 4-μm-thick clinical paraffin section samples were deparaffinized and rehydrated, and the antigens were repaired through the microwave heating method. Then, tissue sections were blocked with $10\%$ goat serum for 1 h in room temperature. After incubation of the primary antibody (IGHG4, 1:100, Proteintech, 66,408–1-Ig; IGKC, 1:200, Bioss Antibodies, Bs-3800R; APOD, 1:100, AB Clonal Technology, A15639; MUCL1, 1:200, Bioss Antibodies, Bs-17247R) at 4 times. Image quantification and analysis of each sample was done using Image J software. The staining intensity of each sample was the average staining intensity of 5 non-overlapping representative fields (× 200). ## Statistics Descriptive statistics were used to delineate the clinicopathological characteristics of the retrospective study population. Continuous variables were presented as median and interquartile range and were compared using Wilcoxon rank-sum test. Categorical variables were expressed as counts and percentages and compared using the Fisher’s exact test. We used Kaplan–Meier and logrank test to compare the overall survival of HER2low and HER2neg TNBC patients diagnosed in our institution during the study period. We applied the Cox proportional hazards model to determine independent clinical risk factors for overall survival. In the prospective study of scRNA-seq, statistical analyses were performed with R software (version 3.6.1). Comparisons of the mean proportions of the eight cell types between the two TNBC groups were calculated using Student’s t test. Student’s t test was also used to quantitatively analyze the staining intensity of IGKC, IGHG4, APOD and MUCL1 in breast tumor tissues (HER2low TNBC, $$n = 5$$, HER2neg TNBC, $$n = 5$$) of the two groups. Logrank test was also applied to compare the overall survival of TNBC patients with different mRNA expression levels of IGKC, IGHG1, IGHG4, SCGB2A1, PTN and MUCL1. All the correlation analyses were performed by Spearman test. The expression levels of key immunotherapeutic biomarkers in the two TNBC groups were used Wilcoxon’s rank-sum test and corrected for multiple testing using Bonferroni’s test. $P \leq 0.05$ indicates statistical significance in this study. ## Patients with HER2low TNBC show more malignant clinical features compared with HER2neg phenotype A total of 284 consecutive patients diagnosed with TNBC based on preoperative pathology were enrolled in this study. Finally, 251 patients met the inclusion criteria and entered in the further analysis, including 157 HER2low ($62.5\%$) and 94 HER2neg TNBC patients ($37.5\%$) (Fig. 1A). The clinicopathological characteristics and prognosis of the two groups were investigated. It showed that compared with HER2neg TNBC patients, HER2low patients were more prone to have larger tumor size ($$P \leq 0.04$$), lymph node involvement ($$P \leq 0.02$$), higher status of Ki67 ($P \leq 0.001$) and higher histological grade of lesions ($P \leq 0.01$); however, HER2neg TNBC patients were more likely to be diagnosed at a younger age (< 45 vs. ≥ 45 years, $$P \leq 0.03$$, OR ($95\%$ CI) = 1.97 (1.09–3.57); Table 1). Notably, the Kaplan–*Meier analysis* revealed that HER2low TNBC patients had a shorter overall survival ($P \leq 0.001$, HR (CI $95\%$) = 3.44 (2.10–5.62); Fig. 1B). Moreover, the Cox proportional hazards analysis showed that receiving the neoadjuvant systemic therapy, lymph node metastasis, Ki67 level ≥ $30\%$, tumor size > 2 cm and the higher histological grade (grade III) were significantly associated with the inferior prognosis of TNBC patients (Fig. 1C). Stratified analysis showed that the first three clinical characteristics above were significantly associated with the prognosis in HER2low TNBC patients; however, these results were not observed in the HER2neg group (Fig. 1C). Taken together, these clinical data indicated that TNBC patients with different HER2 phenotypes show distinct clinical features; HER2low TNBC patients exhibit more malignant clinical behavior. Table 1The clinical characteristics of HER2neg and HER2low TNBC patientsClinical characteristicsTNBC grouping ($$n = 251$$)OR$95\%$ CIPHER2negHER2lown = 94 ($37.5\%$)$$n = 157$$ ($62.5\%$)Age (years)1.971.09–3.570.03 < 4529 (30.9)29 (18.5) ≥ 4565 (69.1)128 (81.5)Menstrual status0.680.39–1.170.17 Pre59 ($62.8\%$)112 ($71.3\%$) Post35 ($37.2\%$)45 ($28.7\%$)Lesion0.950.57–1.580.90 Left46 ($48.9\%$)79 ($50.3\%$) Others48 ($51.1\%$)78 ($49.7\%$)NST0.910.54–1.510.80 No48 ($51.1\%$)84 ($53.5\%$) Yes46 ($48.9\%$)73 ($46.5\%$)Tumor size1.931.04–3.570.04 < 2 cm26 ($27.7\%$)26 ($16.6\%$) ≥ 2 cm68 ($72.3\%$)131 ($83.4\%$)Lymph node metastasis1.851.10–3.130.02 Negative45 ($47.9\%$)52 ($33.1\%$) Positive49 ($52.1\%$)105 ($66.9\%$)Distant metastasis0.850.31–2.300.8 Negative87 ($92.6\%$)147 ($93.6\%$) Positive7 ($7.4\%$)10 ($6.4\%$)Pathological type1.530.85–2.770.16 Ductal73 ($77.7\%$)109 ($69.4\%$) Others21 ($22.3\%$)48 ($35.5\%$)Ki67, %5.973.16–11.31< 0.001 < 3041 ($43.6\%$)18 ($11.5\%$) ≥ 3053 ($56.4\%$)139 ($88.5\%$)Histological grade2.681.41–5.12< 0.01 I/II79 ($84.0\%$)104 ($66.2\%$) III15 ($16.0\%$)53 ($33.8\%$)NST, neoadjuvant systemic therapyP values in bold indicate statistical significance ## Tumor cell clusters in HER2low TNBC have more aggressive signatures than HER2neg TNBC revealed by scRNA-seq High-quality data of 36,168 single cells totally were obtained by scRNA-seq from seven initial TNBC samples (HER2low, $$n = 3$$ vs. HER2neg, $$n = 4$$; Fig. 2A, B; Additional file 9: Table S2), including epithelial cells, stromal cells and immune cells according to their unique gene markers (Fig. 2C; Additional file 1: Fig. S1A; Additional file 10: Table S3). The cellular compositions were different between HER2low TNBC and HER2neg TNBC (Fig. 2D). Compared with HER2low TNBC, HER2neg patients had a relatively lower proportion of endothelial cells (ECs) ($1.8\%$ vs. $8.1\%$, $P \leq 0.05$) and fibroblasts ($2.1\%$ vs. $17.5\%$, $P \leq 0.01$) but more B cells ($25.4\%$ vs. $0.3\%$, $P \leq 0.05$) and T cells ($6.1\%$ vs. $0.7\%$, $P \leq 0.05$; Fig. 2D; Additional files 12, 13: Table S5, S6).Fig. 2Different subclusters and signatures of tumor cells between HER2low TNBC and HER2neg TNBC. ( A) Graphic view of the study design. ( B) Representative IHC and FISH images showing different HER2 status (IHC 0, 1 +, 2 +) in tumor tissues from three primary TNBC patients (P7, P4 and P5). Scale bars: IHC, 50 μm; FISH, 20 μm. ( C) UMAP plot of all the single cells in seven TNBC samples, containing eight identified cell types. SMCs, smooth muscle cells; ECs, endothelial cells. ( D) Comparisons of the mean proportions of the eight cell types between the two TNBC groups. P value, Student’s t test (*$P \leq 0.05$; **$P \leq 0.01$). EC, endothelial cell; SMC, smooth muscle cell. See Additional file 13: Table S6 for details. ( E) UMAP plot of tumor cells of HER2low TNBC and HER2neg TNBC. Orange, HER2low TNBC; blue, HER2neg TNBC; green, the common tumor clusters shared in two groups. ( F) Heatmap of pathway activation scores by GSVA in two TNBC groups of the unique tumor cell clusters. Shown are GSVA scores from a lineal model. Tumor cell clusters are indicated on the top. The scores were estimated using SCENIC analysis with a Wilcoxon rank-sum test The tumor cells identified by CNV (Additional file 1: Fig. S1B) exhibited substantial heterogeneity among patients according to intratumoral heterogeneity (ITH) scoring and the UMAPs based on different patients (Additional file 1: Fig. S1C; Additional file 2: Fig. S2A). Fifteen subclusters of tumor cells were further subdivided based on their characteristic gene expression profiles which showed distinct clusters distribution between HER2low and HER2neg TNBC (Fig. 2E; Additional file 1: Fig. S1D). Although patients within the same TNBC group had different tumor subclusters, these subclusters exhibited similar functional characteristics (Fig. 2F; Additional file 2: Fig. S2B). Specifically, apart from the commonly shared cluster 1, 3 and 13, the HER2low group (P3, P4 and P5) contained seven unique tumor subclusters, including clusters 2, 3, 8, 9, 11, 12 and 14, which highly expressed hallmarks of angiogenesis, EMT, and biological metabolic processing (Fig. 2F; Additional file 2: Fig. S2B), suggesting the aggressive signature of tumor cells in this group. By contrast, the HER2neg TNBC group (P1, P2, P6 and P7) was consisted of signature tumor clusters 4, 5, 6, 7, 10, and 15, which were associated with cell proliferation and immune response process (Fig. 2F; Additional file 2: Fig. S2B); particularly, the major clusters (cluster 5 and 6) were responsible for antigen processing and presentation. Altogether, these findings suggest HER2low and HER2neg TNBC have heterogeneity in tumor cluster subdivisions and characteristic functional signatures; HER2low TNBC have more aggressive tumor cell clusters than HER2neg TNBC. ## HER2low TNBC and HER2neg TNBC have different tumor evolutionary characteristics We found tumor clusters in the two TNBC groups had different differentiative status. The clusters in HER2low TNBC were at a higher differentiative state but maintained a lower tumor stemness level (Fig. 3A, B). However, main clusters in HER2neg TNBC presented the earliest state of differentiation (lineage 1; Fig. 3A) which showed the upregulation of immune activation (such as GO term “antigen processing and presentation”; Additional file 3: Fig. S3A). Furthermore, the HER2low TNBC group exhibited two cell lineages with different differentiation states: one lineage included clusters 2, 9, and 14 (lineage 2; Fig. 3A) at a moderate differentiation stage; the other lineage (lineage 3) containing clusters 11 and 12 was in the latest stage (Fig. 3A), playing a part in metabolism-related activities (Additional file 3: Fig. S3A). Moreover, our further analysis showed that the higher expression level of ERBB2, the higher differentiation stage of tumor cells, indicating a possible connection between ERBB2 expression and tumor development in TNBC (Additional file 3: Fig. S3B).Fig. 3Different dynamic evolutionary characteristics of tumor cells in HER2low and HER2neg TNBC. ( A) Density of tumor cells along pseudotime in two TNBC groups. The vertical axis is the pseudotime process from bottom to top, the horizontal axis is the composition of cell clusters at different pseudotime state (displayed by TNBC groups). The main tumor clusters of the two groups have been annotated, diverging from early to late lineages 1,2, and 3, respectively. ( B) The box plot shows the differentiation status of each tumor cell subcluster using CytoTRACE. Tumor clusters marked in blue and orange fonts are specific clusters of HER2neg and HER2low TNBC, respectively, and the clusters marked in green font are shared by the two TNBC groups. ( C) Trajectory of the evolution of tumor cells predicted by monocle. The dashed arrows indicate the direction of evolution. ( D) Three states of tumor evolution in all tumor cells. From State 1 to State 3 indicates that tumor cell evolution from early to late stages. ( E) Heatmap indicates the gene expression signatures of the three evolutionary states. ( F) Trajectory reconstructions of tumor cells in the HER2low TNBC and (G) HER2neg TNBC, respectively, revealing three branches including pre-branch, fate 1, and fate 2. ( H) Heatmaps show upregulated or downregulated genes along with the two differentiation fates and the GO enrichment analysis in HER2low and (I) HER2neg TNBC groups, indicating active signature pathways for each branch in two groups. The abscissa is from the middle to the left and right sides (fate 1, fate 2), representing the tumor evolution process from early to late; the ordinate represents the gene, and each point represents the average expression of the specified gene in the pseudotime. Genes with similar expression patterns are clustered into one module Then, the developmental and evolutionary characteristics of the two groups were further investigated. Interestingly, the pseudotime trajectory almost began with HER2neg tumor cells and then split into two divergent differentiated branches (Fig. 3C). One terminal end of the trajectory was the mixture of HER2neg and a part of HER2low tumor cells; in contrast, the other terminal end was full of HER2low tumor cells (Fig. 3C). Then, all tumor cells were divided into three states according to the pseudotime trajectory (Fig. 3D). Intriguingly, tumor in state 1 exhibited an activation of immune-related signatures (IGLC2, IGHG4, IGHG1 and IGKC; Fig. 3E, Additional file 4: Fig. S4), while the tumor cells in state 3 which were mostly contributed by HER2low cells possessed the characteristics of metabolic biological function with the highly expression of PTN, SCGB2A2, MUCL1, PIP, etc. ( Fig. 3E, Additional file 4: Fig. S4). Of note, some of the genes (PIP and APOD) are well-characterized androgen receptor (AR) target genes and the pathway analysis was enriched in lipid, fatty acid and cholesterol pathways that typically are elevated in luminal AR TNBC subtypes. Thus, we further evaluated AR and other AR target genes (FKBP5, PIP, APOD, ALCAM, DHCR24, FASN, CLDN8) in this trajectory to explore whether AR could contribute to the higher differentiation. Interestingly, our data appeared that almost all of these genes (except FKBP5) had similar expression patterns, with expression increasing gradually along the pseudotime trajectory (Additional file 5: Fig. S5A), especially in HER2-low TNBC (Fig. 3C; Additional file 5: Fig. S5B). Therefore, it would be worth staining HER2-low for AR and quantifying in TNBC patients. The two differentiation branches (fate 1 and fate 2) of the two groups were, respectively, presented (Fig. 3F, G). In HER2low TNBC group, the fate 1 branch was predominantly associated with module 2 genes (PTN, KRT15, S100A8, etc.) which suggested the tumor harbored the features of apoptosis, migration and metabolism (Fig. 3H). The fate 2 branch presented higher levels of module 1 genes enriched in immune and inflammatory process (such as PDIA3, HSPA5 and HLA-A), cell proliferation and migration (e.g., MMP2, COL3A1 and COL1A1), as well as cell stemness maintenance (e.g., DDX6, RIF1 and SMC3) (Fig. 3H). Likewise, in HER2neg TNBC group, two gene modules were observed during the two differentiated fates. Module 1 genes were mainly involved in cell proliferation (CCNK, CCNT1, CETN2, etc.) and DNA damage repair (BLM, HMGB1, FOXM, etc.), which were highly expressed during fate 2, whereas the module 2 genes, mainly elevated during fate 1, were enriched in immune response and tumor migration process, highly expressing TNF, CXCL1, JUN, etc. ( Fig. 3I). In summary, HER2neg and HER2low TNBC harbored distinct tumor clusters with distinct signatures as well as different evolutionary characteristics, indicating the biological heterogeneity of TNBC. To some degree, this is a probable cause of the clinical heterogeneity in TNBC patients with different HER2 status. ## HER2neg TNBC exhibits higher expressions of immunoglobulin-related genes linked with a favorable prognosis than HER2low TNBC To further explore the underlying differences in tumor biological hallmarks between the two TNBC groups, the DEGs of the tumor cells were further investigated (Additional file 6: Fig. S6A). Notably, the immunoglobulin-related genes (IGKC, IGHG1, IGHG4, IGLC2, etc.) were significantly upregulated in HER2neg TNBC, while APOD, MUCL1, SCGB2A1, PTN, etc., were upregulated in HER2low TNBC (Fig. 4A, B). Of note, GO analysis showed the immune activation function was upregulated in HER2neg TNBC, such as antigen processing and presentation, immune response-activating signal transduction and regulation of innate immune response (Fig. 4C). In addition, GSEA revealed that HER2neg TNBC tumor was mediating some processes involving in immune-related activities, such as IL-6-JAK-STAT3 signaling (Additional file 6: Fig. S6B).Fig. 4Different hallmarks of tumor between HER2low TNBC and HER2neg TNBC. ( A) Volcano map shows the DEGs between two TNBC groups of tumor cells. Each point represents a gene; genes marked in orange and blue are highly expressed genes in tumor cells of HER2low TNBC and HER2neg TNBC, respectively. ( B) The expression levels of four representative immunoglobulin-related genes in tumor cells of HER2neg and HER2low TNBC, respectively. ( C) GO (BP) enrichment pathway analysis of the highly expressed DEGs of tumor cells in the two TNBC groups. ( D) Representative images of fluorescent staining for the verification of IGHG4, IGKC, APOD and MUCL1 expressions in breast tumor tissues of two groups of clinical TNBC samples. All scale bars, 50 μm. ( E) *Quantitative analysis* of the intensity of staining of IGHG4, IGKC, APOD and MUCL1 expressed in two groups of breast tumor tissues (HER2low TNBC, $$n = 5$$) vs. HER2neg TNBC, $$n = 5$$) by fluorescent staining. P value, Student’s t test (∗$P \leq 0.05$, ∗$P \leq 0.01$, ∗∗$P \leq 0.001$, ∗∗∗$P \leq 0.0001$). Error bars show SEM of single patients. ( F) The overall survival of TNBC patients with different mRNA expression levels of IGHG4, IGKC, IGHG1, SCGB2A1, PTN and MUCL1 using Kaplan–Meier-plotter database. The cutoff values were set as the median expression values of all above genes, and $P \leq 0.05$ indicates statistical significance, using logrank test Then, we verified the expressions of IGHG4, IGKC, APOD and MUCL1 at the protein level in tumor sections of clinical TNBC patients (HER2low, $$n = 5$$ vs. HER2neg, $$n = 5$$) by IF staining. Strikingly, it showed that IGHG4 and IGKC were predominantly expressed in HER2neg TNBC group (in tumor tissues containing tumor cells and mesenchymal cells), whereas APOD and MUCL1 were highly expressed in HER2low group (Fig. 4D, E), which were consistent with the scRNA-seq data. Furthermore, the clinical cohort in Kaplan–Meier-plotter database demonstrated that the higher expression of IGKC, IGHG1 and IGHG4 (which were highly expressed in HER2neg TNBC) were significantly associated with favorable overall survival in TNBC patients; however, the high expressions of SCGB2A1 and PTN (highly expressed in HER2low TNBC) were linked to worse outcomes in TNBC patients (Fig. 4F). In summary, these findings suggested that HER2neg TNBC harbored distinct tumor properties from HER2low TNBC phenotype which exhibited more common expressions of immunoglobulin-related hallmarks. ## HER2neg TNBC reveals a potentially more active immune microenvironment than HER2low TNBC The two groups of TNBC showed different diversity of immune cells (Additional file 7: Fig. S7A). Specifically, CD8+ effector T cells, CD4+ proliferating T cells and naïve T cells seemed more common in HER2neg group. Single-cell T-cell receptor (TCR)-sequencing (scTCR-seq) showed that the HER2neg group had more enriched TCR diversity with abundant clonotypes of TCR (Fig. 5A). Importantly, HER2neg TNBC presented significantly higher macrophage infiltration (mainly M2 phenotypes) compared with HER2low group (Fig. 5B). Meanwhile, the additional GEO datasets showed that the expression of ERBB2 in TNBC was significantly positively correlated with M0 macrophages infiltration but negatively associated with the M1 macrophages infiltration which play an important role in in anti-tumor immune activities ($P \leq 0.001$; Fig. 5C). Furthermore, RNA-seq of TNBC cases revealed that IGHG1 and IGKC (which were highly expressed in the HER2neg TNBC) were both positively associated with CCR2, CCR5, CSF1R and ITGA4 (Fig. 5D) which have been explained as crucial markers of the recruitment of macrophages [35]. In addition, the TNBC dataset revealed that the lower level of ERBB2, the higher immune and TME scores (Fig. 5E). Moreover, the communication between tumor and immune cells was more widespread in the HER2neg group, especially in immune response-related crosstalk between tumor cells and myeloid cells (which was primarily comprised of macrophages; Additional file 7: Fig. S7B), such as “HLA-DPB1–TNFSF13B” and “TNFRSF1A–GRN” (Additional file 7: Fig. S7C). By contrast, HER2low TNBC appeared to lack interactions in the tumor microenvironment with scarce crosstalk between tumor cells and immune cells (Additional file 7: Fig. S7C). Collectively, these findings together indicated that TNBC with distinct HER2 phenotypes have different immune states of the tumor microenvironment; HER2neg TNBC reveals a more active state of immune microenvironment which is crucial for promoting the response of immunotherapies (Additional file 8).Fig. 5Distinct patterns of immune cell characteristics between HER2low TNBC and HER2neg TNBC. ( A) scTCR-seq analysis shows TCR diversity in HER2neg TNBC and HER2low TNBC. The horizontal axis lists T cell types, and the vertical axis shows individual samples and TNBC groupings. Different colors represent different frequencies of TCR clonotypes. Single, unique TCR clonotypes; medium, TCR clonotypes with a frequency between 1 and 10; large, TCR clonotypes with a frequency > 10.The size of the circle represents the number of T cells. ( B) Bar plot shows infiltration levels of different macrophages in HER2neg and HER2low TNBC groups. M, all macrophages; M1, M1 macrophages; M2, M2 macrophages. ( C) Correlations between ERBB2 mRNA expression level and various immune cell functions in TNBC (GSE76124, GSE95700, GSE103091, GSE135565, GSE157284 and GSE167213). P, Spearman correlation analysis; the numbers on the right are p values; p values in red indicate a significant positive correlation, while those in blue indicate a significant negative correlation. ( D) Correlations between the expressions of characteristic immunoglobulin genes (IGHG1, IGKC) and the signature molecules in macrophage recruitment (CCR2, CCR5, CSF1R, ITGA4), using Spearman correlation analysis; $P \leq 0.05$ indicates statistical significance. ( E) Comparison of immune scores in TNBC samples with different ERBB2 expression levels. The median expression level of ERBB2 was used as the cut-off value ## HER2neg TNBC exhibits preferable response with a pro-inflammatory state of immune microenvironment after immunotherapy After treatment, HER2neg TNBC presented preferable clinical response (Additional file 9: Table S2) with an enhanced antitumor capacity of immune microenvironment. Compared with the treatment-naïve patients, patients after immunotherapy had decreased levels of Tregs, accompanied by elevated levels of CD8+ T cells and CD4+helper T cells (Fig. 6A). These augment of potential tumor-reactive T cells implicated an enhanced antitumor capacity of T cells after immunotherapy in HER2neg TNBC patients, which was also manifested by both the upregulated functions of T cell involving in immune response after immunotherapy (Fig. 6B) and the exhibition of the higher TCR diversity than before (Fig. 6C) (Additional files 10, 11).Fig. 6Characteristic changes of immune microenvironment in HER2neg TNBC patients before and after neoadjuvant immunotherapy. ( A) Comparison of the abundance of the subclusters of T cells before and after treatment of HER2neg TNBC. CD8Teff, CD8+ effector T cells; HelperT, CD4+ helper T cells. ( B) Pathway enrichment analysis of up-regulated genes in T cells after neoadjuvant immunotherapy in HER2neg TNBC compared with treatment-naïve T cells. ( C) ScTCR-seq analysis in HER2neg TNBC before and after treatment by four different scoring methods. ( D) Comparison of the abundance of the subclusters of microphages before and after treatment in HER2neg TNBC patients. M1, M1 microphages; M2, M2 microphages; TAMs, tumor-associated microphages; MatureDCs, Mature dendritic cells; cDC, classical dendritic cells Of note, the abundance of M1 macrophages was increased, but M2 macrophages were decreased after NST (Fig. 6D), suggesting that NST may alter the degree of M1/M2 macrophage polarization in HER2neg TNBC, which is conducive to M1 macrophage polarization but detrimental to M2 macrophage polarization, exerting as an important role in anti-tumor functions. Altogether, our results suggests that the immune microenvironment may be more prone to a proinflammatory state after immunotherapy in HER2neg TNBC patients, which potentially contribute to the response to immunotherapies. ## HER2neg TNBC exhibits higher levels of immunotherapeutic biomarkers than HER2low TNBC Interestingly, the tumor cells in two TNBC groups displayed different expression patterns of the critical immunotherapeutic targets, such as PD-1/L1, CTLA4, CD47, CDK$\frac{4}{6}$, PARP$\frac{1}{2}$ and DDR$\frac{1}{2}$ (Fig. 7A; Additional file 14: Table S7). Of note, PDCD1 (PD-1) and CD274 (PD-L1) were highly expressed on T cells and myeloid cells in the HER2neg TNBC group but barely in the HER2low group (Fig. 7B). It has been reported that the remarkable expression of PD-L1 on myeloid cells in host would also increase the potential risk of tumor immune escape [36]. Thus, the application of immunotherapy seems more imperative for HER2-negative TNBC patients. Meanwhile, the TNBC dataset from GEO database confirmed that ERBB2 was negatively correlated with the expressions of CD274, CTLA4, CD47, CDK6 and DDR2. Furthermore, the expression of immunoglobulin-related molecules was significantly positively correlated with the expression of these immunotherapeutic targets (Fig. 7C).Fig. 7Different expression patterns of critical immunotherapeutic biomarkers in HER2low TNBC and HER2neg TNBC. ( A) Violin plot shows the expression levels of key immunotherapeutic biomarkers in the two TNBC groups. See Additional file 14: Table S7 for details. ( B) Color-coded UMAPs for expression levels (gray to red) of PDCD1 and CD274 in two TNBC groups. ( C) Correlations between ERBB2 expression and immunotherapeutic targets (CD274, CTLA4, CD47, CDK6, DDR2, PDCD1), using Spearman correlation analysis; $P \leq 0.05$ indicates statistical significance Overall, the immune microenvironments were probably at different states between HER2low and HER2neg TNBC showing as the distinctions on immune cell abundance, TCR diversity, as well as expression levels of critical immunotherapeutic targets. Therefore, it may deserve further consideration on HER2 status when immunotherapy is incorporated in TNBC patients. ## Discussions In this study, the potential clinical and biological heterogeneities of HER2neg and HER2low TNBC were explored by both the retrospective and prospective approaches. We found HER2neg TNBC patients harbored milder clinical features than HER2low phenotype with less lymph node involvement, lower histological grade of lesions, lower level of Ki67, and had a favorable outcome. Then, we further investigated the underlying differences of single-cell transcriptome profiling between HER2low and HER2neg TNBC (Fig. 8). Our data suggested these two TNBC phenotypes harbored distinct tumor properties with varying biological features. The HER2low tumors exhibited aggressive signatures associated with increased capacities for metabolism, proliferation and differentiation; while the HER2neg tumors highly expressed immunoglobulin-related genes and were more likely to play a role in immune activities. Additionally, tumors of these two distinct TNBC groups presented different evolutionary dynamic trajectories and hallmarks. Moreover, HER2neg TNBC exhibited enriched expression of immunotherapy-targeted genes and enhanced immunological activity with substantial CD8+ T cells and TCR diversity and would also affect the biological functions of macrophages. To the best of our knowledge, this is the first study to unveil the distinct biological properties of tumor and immune microenvironments between HER2low and HER2neg TNBC at single-cell RNA resolution. Altogether, our data revealed TNBC with different HER2 status harbored different patterns of tumor features as well as immune microenvironment characteristics. This study highlights an important role of HER2 in the heterogeneity of TNBC tumorigenesis and may provide new insights into the development of more refined clinical classification and novel tailored therapies for TNBC patients. Fig. 8Clinical and biological heterogeneities in HER2neg TNBC and HER2low TNBC. HER2low TNBC and HER2neg TNBC show distinct clinical features as well as different tumor properties, including tumor cell clusters, tumor hallmarks, and TMEs TNBC is known to exhibit heterogenous characteristics, so the identification of its intron subtypes is imperative for understanding the underlying biological behavior and facilitating personalized treatment strategies. Extensive efforts have been devoted to expound various potential subgroups of TNBC on the basis of its unique molecular characteristics, such as VICC [5], Baylor [37] and FUSCC types [38]. Notably, the FUSCC typing subdivided TNBC into four distinct subgroups, and its further clinical study (FUTURE trial, NCT03805399) suggested that the combination of pyrotinib and capecitabine was effective even if the expression of HER2 was clinically negative for some patients [39]. Therefore, IHC-based classifications of TNBC may enable us to better evaluate the therapeutic benefit, but it is not yet known whether HER2-negativity could serve as a contraindication criterion for HER2-targeted therapy. The signature of HER2 in BC has evolved dramatically over the past three decades, from a poor prognostic biomarker to one of the clinical targets for some anti-HER2 drugs, especially for patients with HER2-enriched tumors. The emergence of HER2-targeted drugs has improved the prognosis of BC patients with abnormal amplification or overexpression of ERBB2 [40], but at present, this agent has not yet been approved for patients with HER2low TNBC. Although patients with “low-expression of HER2” are diagnosed as “HER2neg” currently, different levels of HER2 are still expressed on the surface of tumor cells of TNBC [41], probably associated with tumor clinical features to some extent [42]. However, recent studies on the effect of HER2low-status on prognosis for TNBC patients were inconsistent. Some studies suggested that low-expression of HER2 did not affect the prognosis of TNBC [13, 17, 43], or it was linked to a better clinical outcome (compared with HER2neg TNBC) [8]. However, the data involved in these studies were mostly based on patients’ chemotherapy results, and the results of patients who received targeted therapy or immunotherapy were not included. Of note, some immunotherapeutic targeted regimes have been regarded as promising candidates for TNBC treatment which can improve the survival of TNBC patients with the combination with chemotherapeutic agents [44–47]. Moreover, a large number of clinical trials of novel targeted drugs are underway in patients with HER2low breast cancer [14]; the results are yet to be published to date, but some novel HER2-targeted agents (NCT02277717, NCT03734029) and antitumor vaccines (NCT01570036, NCT01570036) have shown promising activity in HER2low TNBC patients. Therefore, although the subdivisions of TNBC according to HER2 status in the future remains to be verified, the research on the difference between HER2low and HER2neg TNBC is of great significance. Hence, our investigations in this study are pivotal for TNBC scenario. Regarding the tumor characteristics, our data revealed TNBC with HER2low/HER2neg expressions have different tumor cellular compositions as well as functional hallmarks. The discrepancy in both composition and functions of tumor clusters could be observed between HER2low and HER2neg TNBCs, and they harbored different differentiation states, which may give a hint on the diverse tumor identities between these two TNBC groups. Recent studies indicated that HER2 0 and HER2 1 + /2 + (by IHC, the same below) BC had completely different intrinsic subtype distributions in PAM50 [11] as well as varying genetic backgrounds [8]. Likewise, our data showed HER2low TNBC actively participated in activities with regard to tumor metabolism and growth pathways with MUCL1, PTN, SCGB2A2 and APOD highly expressed, revealing an increased metabolic and proliferative capacity contained. In addition, the two TNBC groups also presented as distinct tumor evolutionary dynamics and characteristics. Compared with HER2neg TNBC, HER2low tumor was at a relatively later stage of evolution with a higher level of differentiation. The different gene expression patterns within the two groups of TNBC manifested as distinct differentiation pathways and functional characteristics, potentially give a hint to further treatment optimizing for different TNBC individuals. Importantly, tumor cells in HER2neg TNBC seemed more likely to be associated with activities involving immune responses, highly expressing immunoglobulin-related genes (such as IGKC, IGHG1, IGHG4 and IGLC2) which are associated with the favorable prognosis and have been verified in the clinical TNBC cases. Immunoglobulin is a class of globulin with antibody activity, which is an important component of the body to resist disease [48]. Traditionally, it is believed that only B lymphocytes and plasma cells can produce and secrete immunoglobulins [49, 50]. However, more and more studies have discovered that various tumor cells (e.g., breast, cervical, lung cancer) can also express immunoglobulins (especially IgG) which play an important role in the occurrence and development of cancer [51–56]. In this study, we found that tumor-derived immunoglobulins were expressed more common in HER2neg TNBC than in HER2low TNBC. In addition, the expression some immunoglobulins were positively correlated with the function of macrophage recruitment and expressions of critical therapeutic targets in TNBC, suggesting immunoglobulins may play a potential role in immune regulation processes. Therefore, further investigation of the implications of tumor-derived immunoglobulins in TNBC will be helpful to formulate new strategies for the refined diagnosis and treatment. To explore the impact of HER2low on immune activities in TNBC, we compared the difference in immune microenvironment between HER2low and HER2neg TNBC. Our results suggested the abundance in subdivisions of T cells, B cells and myeloid cells were different between these two groups; the expression of ERBB2 was correlated with functions of various immune cells as well as TME scores, which might collectively contribute to a heterogeneity in immune activation between these two TNBC patient groups. Of note, we found HER2neg TNBC presented higher M2 infiltration, which seems to contradict the common belief that M2 has a pro-tumor effect resulting in poor prognosis. Interestingly, although more M2 infiltration was estimated than M1 in the HER2neg group, the infiltrations of M1 and M2 in the HER2-negative group were both higher than that in the HER2low group. In addition, macrophage polarization is plastic and can switch with tumor progression via complicated regulatory mechanisms, and the activation of M1 or M2 is affected by many factors [57, 58]; thus, the role of macrophage polarization here still needs to be further explored. In light of the prominent role of key immune checkpoint genes, (such as PD-1/L1) in the immunosuppression of the immunosuppressant tumor microenvironment [59, 60], we also compared the expression patterns of these critical immune checkpoint genes between these two different TNBC groups. Intriguingly, both PDCD1 (PD-1) and CD274 (PD-L1) were highly expressed in HER2neg TNBC instead of HER2low group. Furthermore, we confirmed in a larger TNBC samples that ERBB2 level was reversely related to expressions of these immune therapeutic biomarkers. Altogether, these data suggested that the heterogeneity of HER2 in TNBC patients may affect the efficacy of immunotherapy; HER2neg TNBC would more likely to get a potential benefit from immunotherapy in the clinical management. Although it remains further explored, the HER2 heterogeneity may be a non-negligible factor when considering immunotherapy-based therapy for clinical treatment of TNBC patients in the future. The major limitation of this study included the relatively small sample size of patient biopsy samples, which might partially compromise the statistical significance of our findings. Additionally, due to the lack of well-established in vitro cell culture models of HER2low TNBC to date, further functional validations were hindered. To address this issue, the staining of additional clinical samples and further analyses with larger sample size based on public databases were conducted. At present, the clinical definition of “HER2-low” is only based on the histological level, and has not yet penetrated into the RNA level, so we used the median RNA expression level of ERBB2 in the TNBC population as the cut-off value, which might be biased. Moreover, in the process of our clinical data statistics, we found that some patients’ puncture results and postoperative pathological test results were inconsistent, especially for the drift of HER2 IHC1 + and 2 +. Although the definition of IHC1 + and 2 + will not have any impact on the conclusions of this study, more stringent requirements for the selection of patients with IHC1 + and 2 + should be employed if more refined studies are to be conducted in the future. Therefore, improvements in more accurate and standardized detection of HER2 expression are still urgent for TNBC patients. ## Conclusions Taken together, our data invoke a key issue that TNBC patients with different HER2 status harbor distinct clinical behavior and tumor biological properties. The heterogeneity of HER2 expression may be a non-negligible factor in the clinical management of TNBC patients. Our study has shed new light on the inherent heterogeneity of TNBC with different HER2 status, providing new clues to the development of a more refined classification and tailored therapeutic strategies for TNBC patients. ## Supplementary Information Additional file 1: Fig. S1. Showing the single-cell landscape of all TNBC samples in this study. Additional file 2: Fig. S2. Showing the UMAPs of tumor cells based on different patients and the expression of four key biological functions in different tumor clusters which suggests HER2neg TNBC and HER2low TNBC harbor distinct tumor biological properties. Additional file 3: Fig. S3. Showing tumor cell subclusters with different differentiation status had distinct features and ERBB2 levels. Additional file 4: Fig. S4. Showing GO enrichment pathways of highly expressed genes of all tumor cells at three evolutionary states during the pseudotime. Additional file 5: Fig. S5. Showing the expression characteristics of AR and AR target genes in the pseudotime trajectory. Additional file 6: Fig. S6. Showing different hallmarks of tumor cells in HER2low TNBC and HER2neg TNBC.Additional file 7: Fig. S7. Showing different immune microenvironments of HER2low TNBC and HER2neg TNBC.Additional file 8: Table S1. Showing the detailed clinical information and follow-up data of the enrolled TNBC population. Additional file 9: Table S2. Showing the clinical information of seven primary triple-negative breast cancer patients with different HER2 status. Additional file 10: Table S3. *Showing* gene markers of all cell types in this study. Additional file 11: Table S4. Showing TNBC population included for overall survival analysis in Kaplan–Meier database. Additional file 12: Table S5. Showing cellular composition of the seven TNBC samples. Additional file 13: Table S6. Showing the mean proportion of the eight main cell types in the two TNBC groups. Additional file 14: Table S7. Showing the expression level of key immunotherapeutic biomarkers in HER2neg TNBC and HER2low TNBC. ## References 1. Siegel RL, Miller KD, Fuchs HE, Jemal A. **Cancer statistics, 2021**. *CA Cancer J Clin* (2021) **71** 7-33. DOI: 10.3322/caac.21654 2. Guarneri V, Barbieri E, Dieci MV, Piacentini F, Conte P. **Anti-HER2 neoadjuvant and adjuvant therapies in HER2 positive breast cancer**. *Cancer Treat Rev* (2010) **36** S62-S66. DOI: 10.1016/S0305-7372(10)70022-0 3. Brown M, Tsodikov A, Bauer KR, Parise CA, Caggiano V. **The role of human epidermal growth factor receptor 2 in the survival of women with estrogen and progesterone receptor-negative, invasive breast cancer: the California Cancer Registry, 1999–2004**. *Cancer* (2008) **112** 737-747. DOI: 10.1002/cncr.23243 4. Dent R, Trudeau M, Pritchard KI, Hanna WM, Kahn HK, Sawka CA. **Triple-negative breast cancer: clinical features and patterns of recurrence**. *Clin Cancer Res* (2007) **13** 4429-4434. DOI: 10.1158/1078-0432.CCR-06-3045 5. Lehmann BD, Bauer JA, Chen X, Sanders ME, Chakravarthy AB, Shyr Y. **Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies**. *J Clin Invest* (2011) **121** 2750-2767. DOI: 10.1172/JCI45014 6. Gong Y, Ji P, Yang YS, Xie S, Yu TJ, Xiao Y. **Metabolic-pathway-based subtyping of triple-negative breast cancer reveals potential therapeutic targets**. *Cell Metab* (2021) **33** 51-64.e9. DOI: 10.1016/j.cmet.2020.10.012 7. Karaayvaz M, Cristea S, Gillespie SM, Patel AP, Mylvaganam R, Luo CC. **Unravelling subclonal heterogeneity and aggressive disease states in TNBC through single-cell RNA-seq**. *Nat Commun* (2018) **9** 3588. DOI: 10.1038/s41467-018-06052-0 8. Denkert C, Seither F, Schneeweiss A, Link T, Blohmer JU, Just M. **Clinical and molecular characteristics of HER2-low-positive breast cancer: pooled analysis of individual patient data from four prospective, neoadjuvant clinical trials**. *Lancet Oncol* (2021) **22** 1151-1161. DOI: 10.1016/S1470-2045(21)00301-6 9. Rinnerthaler G, Gampenrieder SP, Greil R. **HER2 directed antibody-drug-conjugates beyond T-DM1 in breast cancer**. *Int J Mol Sci* (2019) **20** 58. DOI: 10.3390/ijms20051115 10. Modi S, Park H, Murthy RK, Iwata H, Tamura K, Tsurutani J. **Antitumor activity and safety of Trastuzumab Deruxtecan in patients with HER2-low-expressing advanced breast cancer: results from a phase Ib study**. *J Clin Oncol* (2020) **38** 1887-1896. DOI: 10.1200/JCO.19.02318 11. Schettini F, Chic N, Brasó-Maristany F, Paré L, Pascual T, Conte B. **Clinical, pathological, and PAM50 gene expression features of HER2-low breast cancer**. *NPJ Breast Cancer* (2021) **7** 1. DOI: 10.1038/s41523-020-00208-2 12. Marchiò C, Annaratone L, Marques A, Casorzo L, Berrino E, Sapino A. **Evolving concepts in HER2 evaluation in breast cancer: Heterogeneity, HER2-low carcinomas and beyond**. *Semin Cancer Biol* (2021) **72** 123-135. DOI: 10.1016/j.semcancer.2020.02.016 13. Agostinetto E, Rediti M, Fimereli D, Debien V, Piccart M, Aftimos P. **HER2-low breast cancer: molecular characteristics and prognosis**. *Cancers (Basel).* (2021) **13** 140. DOI: 10.3390/cancers13112824 14. Tarantino P, Hamilton E, Tolaney SM, Cortes J, Morganti S, Ferraro E. **HER2-low breast cancer: pathological and clinical landscape**. *J Clin Oncol* (2020) **38** 1951-1962. DOI: 10.1200/JCO.19.02488 15. Rossi V, Sarotto I, Maggiorotto F, Berchialla P, Kubatzki F, Tomasi N. **Moderate immunohistochemical expression of HER-2 (2+) without HER-2 gene amplification is a negative prognostic factor in early breast cancer**. *Oncologist* (2012) **17** 1418-1425. DOI: 10.1634/theoncologist.2012-0194 16. Gilcrease MZ, Woodward WA, Nicolas MM, Corley LJ, Fuller GN, Esteva FJ. **Even low-level HER2 expression may be associated with worse outcome in node-positive breast cancer**. *Am J Surg Pathol* (2009) **33** 759-767. DOI: 10.1097/PAS.0b013e31819437f9 17. Eggemann H, Ignatov T, Burger E, Kantelhardt EJ, Fettke F, Thomssen C. **Moderate HER2 expression as a prognostic factor in hormone receptor positive breast cancer**. *Endocr Relat Cancer* (2015) **22** 725-733. DOI: 10.1530/ERC-15-0335 18. Allison KH, Hammond M, Dowsett M, McKernin SE, Carey LA, Fitzgibbons PL. **Estrogen and progesterone receptor testing in breast cancer: ASCO/CAP guideline update**. *J Clin Oncol* (2020) **38** 1346-1366. DOI: 10.1200/JCO.19.02309 19. Wolff AC, Hammond M, Allison KH, Harvey BE, Mangu PB, Bartlett J. **Human epidermal growth factor receptor 2 testing in breast cancer: American Society of Clinical Oncology/College of American Pathologists Clinical Practice Guideline Focused Update**. *J Clin Oncol* (2018) **36** 2105-2122. DOI: 10.1200/JCO.2018.77.8738 20. Dura B, Choi JY, Zhang K, Damsky W, Thakral D, Bosenberg M. **scFTD-seq: freeze-thaw lysis based, portable approach toward highly distributed single-cell 3' mRNA profiling**. *Nucleic Acids Res* (2019) **47** e16. DOI: 10.1093/nar/gky1173 21. Xu K, Wang R, Xie H, Hu L, Wang C, Xu J. **Single-cell RNA sequencing reveals cell heterogeneity and transcriptome profile of breast cancer lymph node metastasis**. *Oncogenesis* (2021) **10** 66. DOI: 10.1038/s41389-021-00355-6 22. Gambardella G, Viscido G, Tumaini B, Isacchi A, Bosotti R, di Bernardo D. **A single-cell analysis of breast cancer cell lines to study tumour heterogeneity and drug response**. *Nat Commun* (2022) **13** 1714. DOI: 10.1038/s41467-022-29358-6 23. Davis RT, Blake K, Ma D, Gabra M, Hernandez GA, Phung AT. **Transcriptional diversity and bioenergetic shift in human breast cancer metastasis revealed by single-cell RNA sequencing**. *Nat Cell Biol* (2020) **22** 310-320. DOI: 10.1038/s41556-020-0477-0 24. MacParland SA, Liu JC, Ma XZ, Innes BT, Bartczak AM, Gage BK. **Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations**. *Nat Commun* (2018) **9** 4383. DOI: 10.1038/s41467-018-06318-7 25. Liao Y, Smyth GK, Shi W. **featureCounts: an efficient general purpose program for assigning sequence reads to genomic features**. *Bioinformatics* (2014) **30** 923-930. DOI: 10.1093/bioinformatics/btt656 26. Korsunsky I, Millard N, Fan J, Slowikowski K, Zhang F, Wei K. **Fast, sensitive and accurate integration of single-cell data with Harmony**. *Nat Methods* (2019) **16** 1289-1296. DOI: 10.1038/s41592-019-0619-0 27. Guo M, Bao EL, Wagner M, Whitsett JA, Xu Y. **SLICE: determining cell differentiation and lineage based on single cell entropy**. *Nucleic Acids Res* (2017) **45** e54. PMID: 27998929 28. Satija R, Farrell JA, Gennert D, Schier AF, Regev A. **Spatial reconstruction of single-cell gene expression data**. *Nat Biotechnol* (2015) **33** 495-502. DOI: 10.1038/nbt.3192 29. Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. **Integrating single-cell transcriptomic data across different conditions, technologies, and species**. *Nat Biotechnol* (2018) **36** 411-420. DOI: 10.1038/nbt.4096 30. Singh M, Al-Eryani G, Carswell S, Ferguson JM, Blackburn J, Barton K. **High-throughput targeted long-read single cell sequencing reveals the clonal and transcriptional landscape of lymphocytes**. *Nat Commun* (2019) **10** 3120. DOI: 10.1038/s41467-019-11049-4 31. Yu G, Wang LG, Han Y, He QY. **clusterProfiler: an R package for comparing biological themes among gene clusters**. *OMICS* (2012) **16** 284-287. DOI: 10.1089/omi.2011.0118 32. Qiu X, Mao Q, Tang Y, Wang L, Chawla R, Pliner HA. **Reversed graph embedding resolves complex single-cell trajectories**. *Nat Methods* (2017) **14** 979-982. DOI: 10.1038/nmeth.4402 33. Gulati GS, Sikandar SS, Wesche DJ, Manjunath A, Bharadwaj A, Berger MJ. **Single-cell transcriptional diversity is a hallmark of developmental potential**. *Science* (2020) **367** 405-411. DOI: 10.1126/science.aax0249 34. Efremova M, Vento-Tormo M, Teichmann SA, Vento-Tormo R. **Cell PhoneDB: inferring cell-cell communication from combined expression of multi-subunit ligand-receptor complexes**. *Nat Protoc* (2020) **15** 1484-1506. DOI: 10.1038/s41596-020-0292-x 35. Denkert C, von Minckwitz G, Darb-Esfahani S, Lederer B, Heppner BI, Weber KE. **Tumour-infiltrating lymphocytes and prognosis in different subtypes of breast cancer: a pooled analysis of 3771 patients treated with neoadjuvant therapy**. *Lancet Oncol* (2018) **19** 40-50. DOI: 10.1016/S1470-2045(17)30904-X 36. Noguchi T, Ward JP, Gubin MM, Arthur CD, Lee SH, Hundal J. **Temporally distinct PD-L1 expression by tumor and host cells contributes to immune escape**. *Cancer Immunol Res* (2017) **5** 106-117. DOI: 10.1158/2326-6066.CIR-16-0391 37. Burstein MD, Tsimelzon A, Poage GM, Covington KR, Contreras A, Fuqua SA. **Comprehensive genomic analysis identifies novel subtypes and targets of triple-negative breast cancer**. *Clin Cancer Res* (2015) **21** 1688-1698. DOI: 10.1158/1078-0432.CCR-14-0432 38. Zhao S, Ma D, Xiao Y, Li XM, Ma JL, Zhang H. **Molecular subtyping of triple-negative breast cancers by immunohistochemistry: molecular basis and clinical relevance**. *Oncologist.* (2020) **25** e1481-1481e1491. DOI: 10.1634/theoncologist.2019-0982 39. Jiang YZ, Liu Y, Xiao Y, Hu X, Jiang L, Zuo WJ. **Molecular subtyping and genomic profiling expand precision medicine in refractory metastatic triple-negative breast cancer: the FUTURE trial**. *Cell Res* (2021) **31** 178-186. DOI: 10.1038/s41422-020-0375-9 40. Dawood S, Broglio K, Buzdar AU, Hortobagyi GN, Giordano SH. **Prognosis of women with metastatic breast cancer by HER2 status and trastuzumab treatment: an institutional-based review**. *J Clin Oncol* (2010) **28** 92-98. DOI: 10.1200/JCO.2008.19.9844 41. Onsum MD, Geretti E, Paragas V, Kudla AJ, Moulis SP, Luus L. **Single-cell quantitative HER2 measurement identifies heterogeneity and distinct subgroups within traditionally defined HER2-positive patients**. *Am J Pathol* (2013) **183** 1446-1460. DOI: 10.1016/j.ajpath.2013.07.015 42. Ogitani Y, Hagihara K, Oitate M, Naito H, Agatsuma T. **Bystander killing effect of DS-8201a, a novel anti-human epidermal growth factor receptor 2 antibody-drug conjugate, in tumors with human epidermal growth factor receptor 2 heterogeneity**. *Cancer Sci* (2016) **107** 1039-1046. DOI: 10.1111/cas.12966 43. Horisawa N, Adachi Y, Takatsuka D, Nozawa K, Endo Y, Ozaki Y. **The frequency of low HER2 expression in breast cancer and a comparison of prognosis between patients with HER2-low and HER2-negative breast cancer by HR status**. *Breast Cancer* (2022) **29** 234-241. DOI: 10.1007/s12282-021-01303-3 44. Schmid P, Rugo HS, Adams S, Schneeweiss A, Barrios CH, Iwata H. **Atezolizumab plus nab-paclitaxel as first-line treatment for unresectable, locally advanced or metastatic triple-negative breast cancer (IMpassion130): updated efficacy results from a randomised, double-blind, placebo-controlled, phase 3 trial**. *Lancet Oncol* (2020) **21** 44-59. DOI: 10.1016/S1470-2045(19)30689-8 45. Emens LA, Cruz C, Eder JP, Braiteh F, Chung C, Tolaney SM. **Long-term clinical outcomes and biomarker analyses of Atezolizumab therapy for patients with metastatic triple-negative breast cancer: a phase 1 study**. *JAMA Oncol* (2019) **5** 74-82. DOI: 10.1001/jamaoncol.2018.4224 46. Schmid P, Salgado R, Park YH, Muñoz-Couselo E, Kim SB, Sohn J. **Pembrolizumab plus chemotherapy as neoadjuvant treatment of high-risk, early-stage triple-negative breast cancer: results from the phase 1b open-label, multicohort KEYNOTE-173 study**. *Ann Oncol* (2020) **31** 569-581. DOI: 10.1016/j.annonc.2020.01.072 47. Schmid P, Cortes J, Pusztai L, McArthur H, Kümmel S, Bergh J. **Pembrolizumab for early triple-negative breast cancer**. *N Engl J Med* (2020) **382** 810-821. DOI: 10.1056/NEJMoa1910549 48. Panda S, Ding JL. **Natural antibodies bridge innate and adaptive immunity**. *J Immunol* (2015) **194** 13-20. DOI: 10.4049/jimmunol.1400844 49. Cyster JG, Allen C. **B cell responses: cell interaction dynamics and decisions**. *Cell* (2019) **177** 524-540. DOI: 10.1016/j.cell.2019.03.016 50. Nutt SL, Hodgkin PD, Tarlinton DM, Corcoran LM. **The generation of antibody-secreting plasma cells**. *Nat Rev Immunol* (2015) **15** 160-171. DOI: 10.1038/nri3795 51. Ma C, Wang Y, Zhang G, Chen Z, Qiu Y, Li J. **Immunoglobulin G expression and its potential role in primary and metastatic breast cancers**. *Curr Mol Med* (2013) **13** 429-437. PMID: 23331015 52. Yang B, Ma C, Chen Z, Yi W, McNutt MA, Wang Y. **Correlation of immunoglobulin G expression and histological subtype and stage in breast cancer**. *PLoS ONE* (2013) **8** e58706. DOI: 10.1371/journal.pone.0058706 53. Chen Z, Gu J. **Immunoglobulin G expression in carcinomas and cancer cell lines**. *FASEB J* (2007) **21** 2931-2938. DOI: 10.1096/fj.07-8073com 54. Wang J, Lin D, Peng H, Huang Y, Huang J, Gu J. **Cancer-derived immunoglobulin G promotes tumor cell growth and proliferation through inducing production of reactive oxygen species**. *Cell Death Dis* (2013) **4** e945. DOI: 10.1038/cddis.2013.474 55. Qiu X, Zhu X, Zhang L, Mao Y, Zhang J, Hao P. **Human epithelial cancers secrete immunoglobulin g with unidentified specificity to promote growth and survival of tumor cells**. *Cancer Res* (2003) **63** 6488-6495. PMID: 14559841 56. Wan X, Lei Y, Li Z, Wang J, Chen Z, McNutt M. **Pancreatic expression of immunoglobulin G in human pancreatic cancer and associated diabetes**. *Pancreas* (2015) **44** 1304-1313. DOI: 10.1097/MPA.0000000000000544 57. Sica A, Mantovani A. **Macrophage plasticity and polarization: in vivo veritas**. *J Clin Invest* (2012) **122** 787-795. DOI: 10.1172/JCI59643 58. Zhou D, Huang C, Lin Z, Zhan S, Kong L, Fang C. **Macrophage polarization and function with emphasis on the evolving roles of coordinated regulation of cellular signaling pathways**. *Cell Signal* (2014) **26** 192-197. DOI: 10.1016/j.cellsig.2013.11.004 59. Patel SP, Kurzrock R. **PD-L1 expression as a predictive biomarker in cancer immunotherapy**. *Mol Cancer Ther* (2015) **14** 847-856. DOI: 10.1158/1535-7163.MCT-14-0983 60. Li Y, Liang L, Dai W, Cai G, Xu Y, Li X. **Prognostic impact of programed cell death-1 (PD-1) and PD-ligand 1 (PD-L1) expression in cancer cells and tumor infiltrating lymphocytes in colorectal cancer**. *Mol Cancer* (2016) **15** 55. DOI: 10.1186/s12943-016-0539-x
--- title: Development of models to predict 10-30-year cardiovascular disease risk using the Da Qing IGT and diabetes study authors: - Fei Chen - Jinping Wang - Xiaoping Chen - Liping Yu - Yali An - Qiuhong Gong - Bo Chen - Shuo Xie - Lihong Zhang - Ying Shuai - Fang Zhao - Yanyan Chen - Guangwei Li - Bo Zhang journal: Diabetology & Metabolic Syndrome year: 2023 pmcid: PMC10061839 doi: 10.1186/s13098-023-01039-4 license: CC BY 4.0 --- # Development of models to predict 10-30-year cardiovascular disease risk using the Da Qing IGT and diabetes study ## Abstract ### Background This study aimed to develop cardiovascular disease (CVD) risk equations for Chinese patients with newly diagnosed type 2 diabetes (T2D) to predict 10-, 20-, and 30-year of risk. ### Methods Risk equations for forecasting the occurrence of CVD were developed using data from 601 patients with newly diagnosed T2D from the Da Qing IGT and Diabetes Study with a 30-year follow-up. The data were randomly assigned to a training and test data set. In the training data set, Cox proportional hazard regression was used to develop risk equations to predict CVD. Calibration was assessed by the slope and intercept of the line between predicted and observed probabilities of outcomes by quintile of risk, and discrimination was examined using Harrell’s C statistic in the test data set. Using the Sankey flow diagram to describe the change of CVD risk over time. ### Results Over the 30-year follow-up, corresponding to a 10,395 person-year follow-up time, 355 of 601 ($59\%$) patients developed incident CVD; the incidence of CVD in the participants was 34.2 per 1,000 person-years. Age, sex, smoking status, 2-h plasma glucose level of oral glucose tolerance test, and systolic blood pressure were independent predictors. The C statistics of discrimination for the risk equations were 0.748 ($95\%$CI, 0.710–0.782), 0.696 ($95\%$CI, 0.655–0.704), and 0.687 ($95\%$CI, 0.651–0.694) for 10-, 20-, and 30- year CVDs, respectively. The calibration statistics for the CVD risk equations of slope were 0.88 ($$P \leq 0.002$$), 0.89 ($$P \leq 0.027$$), and 0.94 ($$P \leq 0.039$$) for 10-, 20-, and 30-year CVDs, respectively. ### Conclusions The risk equations forecast the long-term risk of CVD in patients with newly diagnosed T2D using variables readily available in routine clinical practice. By identifying patients at high risk for long-term CVD, clinicians were able to take the required primary prevention measures. ### Supplementary Information The online version contains supplementary material available at 10.1186/s13098-023-01039-4. ## Background Reducing the cardiovascular disease (CVD) burden in diabetes mellitus is a major clinical imperative that should be prioritized in order to reduce premature death, improve quality of life, reduce individual economic burdens of associated morbidities, and reduce the high cost of medical care [1]. CVD is a major cause of mortality and disability in diabetes, especially in those with type 2 diabetes (T2D) who have a 2–4 fold increase in the risk for CVD when compared with the general population without diabetes [2]. Therefore, risk prediction models are needed for identifying patients with T2D at high risk for CVD, which is an important strategy in the primary prevention of CVD. Several CVD risk prediction models for patients with T2D have been developed to assist clinicians in estimating patient CVD risk and tailoring management to the needs of patients. Most of these models, however, were developed using data from predominantly Caucasian participants and do not perform well when applied to Chinese patients with T2D due to the ethnic differences in the prevalence of CVD events [3, 4]. For example, the Chinese population has a lower risk of coronary heart disease and heart failure than Caucasians, but a higher risk of stroke [5]. Such differences in prevalence may be explained by the differences in lifestyle behaviours, genetic factors, and environmental influences [6]. Moreover, a study highlighted that the risk prediction models developed from studies in one country or ethnic population might not be suitable for another country or ethnic population; therefore, localised risk prediction models should be developed [7]. Although some CVD risk prediction models have been published for Chinese with T2D, these models are inadequate because they focused on 5-year or 10-year follow-ups with a modest discriminative ability [8, 9]. Therefore, a robust model to accurately predict long-term CVD risk in Chinese T2D is still lacking and urgently needed to enable accurate risk stratification and management to prevent CVD complications in the world’s largest T2D population. Therefore, this study aimed to develop models to predict the 10-, 20-, and 30-year risk of CVD using datasets from the Da Qing IGT and Diabetes Study. ## Study design and participants The design and methods used in the Da Qing IGT and Diabetes Study have been reported elsewhere [10–12]. Briefly, 110,660 residents aged 25–74 years were selected as eligible for resident screening for diabetes in 1986. Finally, 3,956 participants received a 75-g oral glucose tolerance test (OGTT) which included the measurements of plasma glucose concentrations at fasting, after 1-h, and after 2-h. Based on the WHO criteria of 1985 [13], 630 participants were identified as newly diagnosed type 2 diabetes (T2D). The participants were required to undergo a baseline examination that included systolic (SBP) and diastolic (DBP) blood pressure, body mass index (BMI), a 12-lead electrocardiogram, plasma lipids, and OGTT. Details of the baseline examination were previously described [14, 15]. All the newly diagnosed T2D were informed of their diagnosis and received a guideline of available clinical treatment in the local clinic. Written informed consent was obtained from all study participants and proxy informants for the deceased. The study was approved by the WHO and China-Japan Friendship Hospital’s Institutional Review Board. Of the 630 newly diagnosed T2D participants who underwent baseline examination in 1986, we excluded 19 who had missing information at baseline examination, and 10 with a known history of cardiovascular disease at enrolment. Finally, 601 participants were included in the study. ## Follow-up and cardiovascular events All participants were tracked from their enrolment to the onset of CVD. Data were collected by personal interview, clinical examination, by trained staff for living participants, while a living spouse, sibling, or child were interviewed for the deceased with standardised questionnaires for the proxy informants. Those unable to attend the hospital because of ill health or living outside of Da Qing city were examined at home, interviewed by telephone, and examined in local hospitals. Data were then verified by review of the medical records and death certificate. CVD events were defined as the first occurrence of non-fatal or fatal myocardial infarctions, sudden death, and non-fatal or fatal stroke. The earliest date of recognition of the CVD event from medical records, interviews, or the 20- and 30-year follow-up examinations was used to define the onset of CVD. First occurrence of CVD of 355 participants were reported by December 31, 2016. Moreover, we could infer the CVD status at the 10-year follow-up based on the onset date of CVD. ## Statistical analysis Participants’ characteristics are shown as the mean (± standard deviation) for quantitative parameters and as a percentage for categorical variables. Descriptive statistics were compared between participants who developed CVD or never developed CVD within a 30-year follow-up. ANOVA tests and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\chi }^{2}$$\end{document} tests were used for normally distributed continuous variables and categorical variables, separately. CVD incidence rates were calculated by dividing the sum of the events by the sum of person-years. The participants’ follow-up person-years were calculated from date of enrolment to the first onset of CVD. The data were randomly assigned to two subsamples of roughly equal sizes: the training dataset ($$n = 300$$) and the test dataset ($$n = 301$$). Cox proportional hazard regression with the step forward algorithm was used to select predictors at baseline for incident CVD as long as the Akaike information criterion fell by at least the number of extra parameters. Finally, the prediction model included the baseline variables of age, sex, smoking status, plasma glucose levels 2 h after the oral glucose tolerance test (2 h-PG), and SBP. All the continuous variables were naturally logarithmically transformed to improve the discrimination and calibration of the models and to minimise the influence of extreme observations. Based on Cox proportional hazard regression, the risk score was =\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}_{1}\times {\beta }_{1}+{X}_{2}\times {\beta }_{2}\cdots +{X}_{n}\times {\beta }_{n}$$\end{document}. The probability of CVD over j years was: CVD risk probability = \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1-{S\left(j\right)}^{\text{exp}\left(risk score-mean of the risk score\right)},$$\end{document} where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}_{1},{ X}_{2},{\cdots X}_{n}$$\end{document} were baseline predictors and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{1},{\beta }_{2},{\cdots \beta }_{n}$$\end{document} were the estimated coefficients of baseline predictors, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$S\left(j\right)$$\end{document} was the survival probability over j years when the risk equation took the value of its mean. We evaluated the ability of the risk prediction model to discriminate participants who experience a CVD event from those who do not, using an overall C statistic [16] in the test set, expanding on a suggestion by Harrell et al. [ 17]. The C statistic is analogous to the area under the receiver-operating characteristic curve. Bootstrapping was performed 200 times for the estimation of the $95\%$ confidence intervals for the C statistic. We evaluated the calibration through the slope and intercept of the line between predicted and observed probabilities of each outcome by the quintile of risk [18]. Sankey flow diagrams [19] is a data visualisation technique that emphasises flow/movement/change from one state to another or one time to another, which is popular in economics, business, and science to examine complex multi-step processes. We used the Sankey flow diagrams to visualise the CVD risk of patients over time to identify patients at high risk who needed primary prevention. R software version 4.1.0 were used for all statistical analyses. A 2-tailed with $P \leq 0.05$ was set for the statistical significance level. ## Baseline characteristics During the 30-year follow-up, corresponding to 10,395 person-years of follow-up time, $59\%$ ($\frac{355}{601}$) of participants had a first CVD incident, conferring an incidence of 34.2 per 1,000 person-years. The mean age was 48.3 years (SD = 8.7), and $52.1\%$ ($\frac{313}{601}$) participants were females. First CVDs were occurred in 107 ($18\%$), 271 ($45\%$), and 355 ($59\%$) participants of 10-, 20-, and 30-year follow-ups, respectively. The baseline characteristics of people who progressed to CVD or never developed CVD within the 30-year follow-up were shown in Table 1. The participants who developed CVD were more likely to be male, older, with elevated CVD risk profiles such as smoking, elevated plasma glucose levels 1 h after the oral glucose tolerance test, and elevated SBP and DBP. Table 1Baseline characteristics of participants who progressed to CVD or never developed CVD within the 30-year follow-upNo CVDIncident CVD P N (%)246 ($40.9\%$)355 ($59.1\%$)Sex (male, %)104 ($42.3\%$)184 ($51.8\%$)0.026Age (years)46.9 (9.78)49.2 (7.79)0.001BMI (kg/\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${m}^{2}$$\end{document})25.2 (3.70)25.8 (3.55)0.051Current smoker (%)71 ($28.9\%$)138 ($38.9\%$)0.014Fasting plasma glucose (mmol/L)8.3 (2.9)8.8 (3.1)0.0551 h-PG (mmol/L)15.7 (3.4)16.3 (3.5)0.0292 h-PG (mmol/L)15.0 (3.5)15.5 (3.7)0.153Systolic Blood Pressure (mm Hg)131.9 (22.0)138.0 (24.8)0.002Diastolic Blood Pressure (mm Hg)85.7 (13.8)89.8 (14.6)0.001Data presented as mean (SD) for continuous variables or n (%) for categorical variables. CVD, cardiovascular disease; BMI, body mass index; 1 h-PG /2 h-PG, venous plasma glucose concentration 1 and 2 h after 75 g oral glucose load, respectively ## Developing CVD risk equations in participants with newly diagnosed T2D The multivariable-adjusted regression coefficients and hazard ratios for incident CVD events were presented in Table 2. The CVD risk equations included standard cardiovascular risk factors such as age, sex, smoking status, 2 h-PG, and SBP. We observed statistically significant relations of most risk factors in Table 2. The developed CVD risk score was 0.26 × sex (1 for male) + 2.36 × loge (age) + 0.22 × current smoker (1 for yes) + 0.94 × loge (OGTT 2h plasma glucose) + 1.37 × loge (systolic blood pressure). The average survival \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${s}_{0}$$\end{document} at 10-, 20-, and 30- year follow-up times were 0.812, 0.487, and 0.279, respectively. The risk probability of CVD = \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1-{s}_{0}^{exp(risk score-8.21)}$$\end{document}. The discrimination and calibration of CVD risk equations were moderate. The C statistics of discrimination for the risk equations were 0.748 ($95\%$CI, 0.710–0.782), 0.696 ($95\%$CI, 0.655–0.704), and 0.687 ($95\%$CI, 0.651–0.694) for 10-, 20-, and 30-year CVDs, respectively. The calibration statistics for the CVD prediction equations of slope were 0.88 ($$P \leq 0.002$$), 0.89 ($$P \leq 0.027$$), and 0.94 ($$P \leq 0.039$$) for 10-, 20-, and 30-year CVDs, respectively, indicating relative goodness of fit. Table 2Regression coefficients and hazard ratios for the CVD risk prediction modelVariablesBeta P Hazard Ratio$95\%$ CIS0[10] = 0.812S0[20] = 0.487S0[30] = 0.279Sex (male)0.260.0281.3(1.02–1.64)Log of age2.36< 0.00110.58(5.31–21.07)Current smoker0.220.0761.24(0.98–1.58)Log of 2 h-PG0.94< 0.0012.56(1.63–4.02)Log of systolic blood pressure1.37< 0.0013.95(2.01–7.78)S0[10], the average survival probability of the participants in 10-year; S0[20], the average survival probability of the participants in 20-year; S0[30], the average survival probability of the participants in 30-year ## The progression of CVD risk over time Through the CVD risk equations developed in this study, the CVD risk score could be calculated for every participant in 10-, 20-, and 30-year follow-ups, respectively. Based on the risk population proportion [20] and the prevalence of CVD with $18\%$ ($\frac{107}{601}$) in 10-year follow-ups in this study, participants with a 10-year CVD risk score ≥ $20\%$ were assigned to the high-risk group; those with a 10-year CVD risk score of < $10\%$ were assigned to the low-risk group based on the published study [21]; and those with a risk score between $10\%$ and $20\%$ were assigned to the intermediate-risk group (Fig. 1). We repeated a similar method while processing CVD risk scores of 20- and 30-year follow-ups. The prevalence of CVD with $45\%$ in 20-year follow-ups in this study, therefore, participants with a 20-year CVD risk score ≥ $45\%$ were assigned to high-risk group. The CVD risk score of 20 years with thresholds < $35\%$, and 35–$45\%$ were assigned to the low-risk, intermediate-risk based on the risk population proportion [20], respectively. In the same way, the CVD risk score of 30 years with the thresholds < $50\%$, 50–$60\%$, and > $60\%$ were assigned to the low-risk, intermediate-risk, and high-risk groups, respectively (Fig. 1). Based on the definitions of high-, intermediate-, and low- risk of 10-year CVD risk score, 233 participants, 238 participants, and 130 participants were assigned to 10-high-risk group, 10-intermediate-risk group, and 10-low-risk group, separately. Meanwhile, there were 380 participants in the 20-high-risk group, 88 participants in the 20-intermediate-risk group, and 133 participants in the 20-low-risk group. Moreover, 459 participants were assigned to 30-high-risk group, 64 participants were assigned to 30-intermediate-risk group, and 78 participants were assigned to 30-low-risk group. Through the number of high-risk participants, the CVD risk equations may overestimate the risk of CVD in newly diagnosed T2D population. Through the Sankey flow diagrams in Fig. 1, the high-risk group ($$n = 233$$) in 10-year follow-up remained high risk over 20- and 30-year follow-ups and needed to take primary prevention when diagnosed, followed by 235 of 238 participants in the intermediate-risk group who developed high risk in 30-year follow-ups and were overlooked in published CVD risk models because of not high-risk participants in risk models. Fig. 1Changes in the participants with low, intermediate, and high risks of CVD over time. 10-low-risk, 130 participants with the lower risk of CVD events in a 10-year follow-up in which the risk score is < $10\%$; 10-intermediate-risk, 238 participants with the intermediate risk of CVD events in a 10-year follow-up in which the risk score is between $10\%$ and $20\%$; 10-high-risk, 233 participants with the higher risk of CVD events in a 10-year follow-up in which the risk score is > $20\%$. The definitions of 20-low-risk, 20-intermediate-risk, 20-high-risk are similar with the 10-year with the thresholds of < $35\%$, 35-$45\%$, > $45\%$, separately. There are 380 participants in 20-high-risk groups, 88 participants in 20-intermediate-risk group, 133 participants in 20-low-ris group. In the same way, 459 participants with the risk score > $60\%$ were assigned to the 30-high-risk group, 64 participants with the risk score between $50\%$ and $60\%$ were assigned to the 30-intermediate-risk group, and 78 participants with the risk score < $50\%$ were assigned to the 30-low-risk group ## Discussion We developed risk equations to predict 10-, 20-, and 30-year CVD risk in participants with newly diagnosed T2D aged 25–74 years using data commonly available in clinical practice, such as age, sex, smoking status, 2 h-PG, and SBP. To our knowledge, these are the first long-term CVD risk equations developed in China. They are based on longer follow-up data and more comprehensively capture the progression of CVD. Our risk equations are based on newly diagnosed T2D patients without previous CVD events, and the models predict the risk of CVD as a primary event. In terms of model discrimination and calibration, the C statistics were 0.748 ($95\%$CI, 0.710–0.782), 0.696 ($95\%$CI, 0.655–0.704), and 0.687 ($95\%$CI, 0.651–0.694) and the calibration statistics for CVD prediction equations of slope were 0.88 ($$P \leq 0.002$$), 0.89 ($$P \leq 0.027$$), and 0.94 ($$P \leq 0.039$$) for 10-year CVD, 20-year CVD, and 30-year CVD, respectively. Through the Sankey flow diagram, we observed the risk scores of CVD over time and identified patients at high risk of CVD in 30 years for early prevention. Although the Chinese Multi-provincial Cohort Study and the China-PAR project had developed 10-year CVD risk prediction models for Chinese individuals to guide the prevention of CVD [22, 23], the study populations were the general population and not for individuals with T2D. Therefore, our focus was on T2D to develop the CVD risk prediction equations. Some published CVD-related models focused on the 5-year or 8-year follow-up in Chinese with T2D [3, 24, 25] and cannot predict the long-term CVD risk. However, we developed 10-, 20-, and 30-year CVD risk prediction equations that can predict the long-term CVD risk of patients with T2D. Through the long-term CVD risk prediction equations, we can implement the stratified management and advanced prevention of CVD in patients with newly diagnosed T2D. Treatment target recommendations regarding the risk factor control may need to be more aggressive in participants who have been identified as high-risk for CVD in either 10-, 20-, or 30-year follow-ups. This group of patients was focused on by clinicians and entailed much spending of medical resources. Moreover, patients who have been assigned to intermediate-risk for 10-year CVD risk and developed high risk in 20-, and 30-year periods were advised to maintain a healthy glycaemic level, lose weight, and increase physical activity to lower the risk of CVD with an appropriate expenditure of medical resources. Moreover, participants who were assigned to intermediate risk at 10 and 20 years and developed high-risk at 30 years also needed to step up exercise and to keep glycaemia, blood pressure and lipids on target, and reduce body weight if obese. To some extent, with the help of the CVD risk equations, patients with newly diagnosed and long-standing diabetes can reduce their CVD risk by maximizing the utilization of clinical resources [26, 27]. From a national perspective, China has the highest number of people with diabetes worldwide, and patients with diabetes who were assigned to low- and intermediate-risk at 30 years were advised to maintain their existing drug treatments and lifestyle, and other patients took precision treatments. In this way, the CVD risk equation can reduce national health insurance costs. We observed differences of 1 h-PG in baseline between participants who developed CVD and did not develop CVD. Moreover, the performance of the predictive models using 2 h-PG or 1 h-PG as a predictor was similar (Additional file 1: Table S1) which demonstrated that 1 h-PG also needed strict control. In addition, some studies showed that 1 h-PG predictive performance was similar to 2 h-PG in the prediction of T2D, complications and mortality [28, 29]. Based on the evidence from this study and the results of published studies [28, 29], we showed the 1 h-PG as a predictor in additional file 1 Table S1. This study emphasized the significance of 1 h-PG, which was frequently ignored and undervalued by clinicians. To facilitate the promotion of the model, we also showed the CVD risk equations which included 1 h-PG in additional file 1 Table S2. It has been established that lipid information, especially low-density lipoproteins, are important predictors for CVD [30, 31]. However, this study lacked the measurement of related indicators which was a major limitation. Without using blood lipid information, our CVD risk prediction models achieved moderate discrimination, which indicated a potentially wider use based on the five easy-access predictors. Through the performance of CVD risk models, the performance of CVD risk equations of 20-, and 30-years has declined. The possible reason being that the predictive ability of the baseline for long-term CVD was weak, and intermediate variables or drugs need to be added to improve the predictive ability. Although the performance to predict CVD risk at 20- and 30-years was moderate, the study provided a tool for long-term CVD prediction and showed the CVD risk change over time. This study has some limitations. First, we did not validate the CVD risk prediction models in an external dataset of T2D to evaluate the performance. The external dataset of T2D with a 30-year follow-up was lacking, which limited the validation of CVD risk prediction. Second, the CVD risk equation did not include lipid information because of lacking the measures of low-density lipoproteins and the $25\%$ missing rate of triglyceride; however, the performance of the risk equation was good and may have a wider application in clinical management. Third, although the smoking status was not significant in the model, the smoking status was included in the CVD risk equation model which was very important for CVD events. Smoking status was a risk factor for CVD events in the univariate model, however, after adjusting for age and sex, smoking status was not significant risk factor for CVD events. The reason for the smoking status not being significant was that sex, age and smoking status have some degree of correlation. Future research will verify the performance of this model in an external validation set, and further promote this model for clinical use. ## Conclusions This study developed long-term CVD risk equations for Chinese patients with newly diagnosed T2D with a 30-year follow-up. It offers a useful tool for the clinician faced with the increasing prevalence of CVD in T2D. It will aid decision-making to provide early appropriate action to decrease the risk of adverse outcomes as well as aid health service planning. ## Electronic Supplementary Material Below is the link to the electronic supplementary material. Supplementary Material 1 ## References 1. Low Wang CC, Hess CN, Hiatt WR, Goldfine AB. **Clinical update: Cardiovascular Disease in Diabetes Mellitus: atherosclerotic Cardiovascular Disease and Heart failure in type 2 Diabetes Mellitus – Mechanisms, Management, and clinical considerations**. *Circulation* (2016.0) **133** 2459-502. DOI: 10.1161/CIRCULATIONAHA.116.022194 2. Rawshani A, Rawshani A, Franzén S, Eliasson B, Svensson A-M, Miftaraj M. **Mortality and cardiovascular disease in type 1 and type 2 diabetes**. *N Engl J Med* (2017.0) **376** 1407-18. DOI: 10.1056/NEJMoa1608664 3. Wan EYF, Fong DYT, Fung CSC, Yu EYT, Chin WY, Chan AKC. **Development of a cardiovascular diseases risk prediction model and tools for chinese patients with type 2 diabetes mellitus: a population-based retrospective cohort study**. *Diabetes Obes Metab* (2018.0) **20** 309-18. DOI: 10.1111/dom.13066 4. Zhao D, Liu J, Wang M, Zhang X, Zhou M. **Epidemiology of cardiovascular disease in China: current features and implications**. *Nat Rev Cardiol* (2019.0) **16** 203-12. DOI: 10.1038/s41569-018-0119-4 5. Ma RCW. **Epidemiology of diabetes and diabetic complications in China**. *Diabetologia* (2018.0) **61** 1249-60. DOI: 10.1007/s00125-018-4557-7 6. 6.Mazimba S, Peterson PN. JAHA Spotlight on Racial and Ethnic Disparities in Cardiovascular Disease. J Am Heart Assoc. 2021;10:e023650. 7. Zhao D, Liu J, Xie W, Qi Y. **Cardiovascular risk assessment: a global perspective**. *Nat Rev Cardiol* (2015.0) **12** 301-11. DOI: 10.1038/nrcardio.2015.28 8. Dong W, Wan EYF, Bedford LE, Wu T, Wong CKH, Tang EHM. **Prediction models for the risk of cardiovascular diseases in chinese patients with type 2 diabetes mellitus: a systematic review**. *Public Health* (2020.0) **186** 144-56. DOI: 10.1016/j.puhe.2020.06.020 9. Dziopa K, Asselbergs FW, Gratton J, Chaturvedi N, Schmidt AF. **Cardiovascular risk prediction in type 2 diabetes: a comparison of 22 risk scores in primary care settings**. *Diabetologia* (2022.0) **65** 644-56. DOI: 10.1007/s00125-021-05640-y 10. Gong Q, Zhang P, Wang J, Ma J, An Y, Chen Y. **Morbidity and mortality after lifestyle intervention for people with impaired glucose tolerance: 30-year results of the Da Qing diabetes Prevention Outcome Study**. *Lancet Diabetes Endocrinol* (2019.0) **7** 452-61. DOI: 10.1016/S2213-8587(19)30093-2 11. Chen Y, Zhang P, Wang J, Gong Q, An Y, Qian X. **Associations of progression to diabetes and regression to normal glucose tolerance with development of cardiovascular and microvascular disease among people with impaired glucose tolerance: a secondary analysis of the 30 year Da Qing diabetes Prevention Outcome Study**. *Diabetologia* (2021.0) **64** 1279-87. DOI: 10.1007/s00125-021-05401-x 12. An Y, Zhang P, Wang J, Gong Q, Gregg EW, Yang W. **Cardiovascular and all-cause Mortality over a 23-Year period among chinese with newly diagnosed diabetes in the Da Qing IGT and Diabetes Study**. *Diabetes Care* (2015.0) **38** 1365-71. DOI: 10.2337/dc14-2498 13. **Diabetes mellitus. Report of a WHO study group**. *World Health Organ Tech Rep Ser* (1985.0) **727** 1-113. PMID: 3934850 14. Gong Q, Zhang P, Wang J, An Y, Gregg EW, Li H. **Changes in mortality in people with IGT before and after the Onset of Diabetes during the 23-Year follow-up of the Da Qing diabetes Prevention Study**. *Diabetes Care* (2016.0) **39** 1550-5. DOI: 10.2337/dc16-0429 15. He S, Wang J, Shen X, Qian X, An Y, Gong Q. **Cancer and its predictors in chinese adults with newly diagnosed diabetes and impaired glucose tolerance (IGT): a 30-year follow-up of the Da Qing IGT and Diabetes Study**. *Br J Cancer* (2022.0) **127** 102-8. DOI: 10.1038/s41416-022-01758-x 16. Pencina MJ, D’Agostino RB. **Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation**. *Stat Med* (2004.0) **23** 2109-23. DOI: 10.1002/sim.1802 17. Lee FEH, Mark KL. **Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors**. *Stat Med* (1996.0) **15** 361-87. DOI: 10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4 18. Demler OV, Paynter NP, Cook NR. **Tests of calibration and goodness of fit in the survival setting**. *Stat Med* (2015.0) **34** 1659-80. DOI: 10.1002/sim.6428 19. Yu B, Silva CT. **VisFlow - web-based visualization framework for tabular data with a subset flow model**. *IEEE Trans Vis Comput Graph* (2017.0) **23** 251-60. DOI: 10.1109/TVCG.2016.2598497 20. Greenland P, Smith SC, Grundy SM. **Improving coronary heart disease risk assessment in asymptomatic people: role of traditional risk factors and noninvasive cardiovascular tests**. *Circulation* (2001.0) **104** 1863-7. DOI: 10.1161/hc4201.097189 21. Lloyd-Jones DM, Braun LT, Ndumele CE, Smith SC, Sperling LS, Virani SS. **Use of risk assessment tools to guide decision-making in the primary prevention of atherosclerotic cardiovascular disease: a special report from the American Heart Association and American College of Cardiology**. *Circulation* (2019.0) **139** e1162-77. DOI: 10.1161/CIR.0000000000000638 22. Liu J. **Predictive value for the chinese population of the Framingham CHD risk assessment tool compared with the chinese multi-provincial cohort study**. *JAMA* (2004.0) **291** 2591-9. DOI: 10.1001/jama.291.21.2591 23. Yang X, Li J, Hu D, Chen J, Li Y, Huang J. **Predicting the 10-year risks of atherosclerotic cardiovascular disease in chinese population: the China-PAR project (prediction for ASCVD risk in China)**. *Circulation* (2016.0) **134** 1430-40. DOI: 10.1161/CIRCULATIONAHA.116.022367 24. Yang X, So W-Y, Kong APS, Ho C-S, Lam CWK, Stevens RJ. **Development and validation of stroke risk equation for Hong Kong Chinese patients with type 2 diabetes**. *Diabetes Care* (2007.0) **30** 65-70. DOI: 10.2337/dc06-1273 25. Li TC, Wang HC, Li CI, Liu CS, Lin WY, Lin CH. **Establishment and validation of a prediction model for ischemic stroke risks in patients with type 2 diabetes**. *Diabetes Res Clin Pract* (2018.0) **138** 220-8. DOI: 10.1016/j.diabres.2018.01.034 26. Ramaswami R, Bayer R, Galea S. **Precision medicine from a public health perspective**. *Annu Rev Public Health* (2018.0) **39** 153-68. DOI: 10.1146/annurev-publhealth-040617-014158 27. König IR, Fuchs O, Hansen G, von Mutius E, Kopp MV. **What is precision medicine?**. *Eur Respir J* (2017.0) **50** 1700391. DOI: 10.1183/13993003.00391-2017 28. Paddock E, Looker HC, Piaggi P, Knowler WC, Krakoff J, Chang DC. **One-hour plasma glucose compared with two-hour plasma glucose in relation to diabetic retinopathy in american Indians**. *Diabetes Care* (2018.0) **41** 1212-7. DOI: 10.2337/dc17-1900 29. Pareek M, Bhatt DL, Nielsen ML, Jagannathan R, Eriksson K-F, Nilsson PM. **Enhanced predictive capability of a 1-hour oral glucose tolerance test: a prospective population-based cohort study**. *Diabetes Care* (2018.0) **41** 171-7. DOI: 10.2337/dc17-1351 30. Rong S, Li B, Chen L, Sun Y, Du Y, Liu B. **Association of low-density lipoprotein cholesterol levels with more than 20‐year risk of cardiovascular and all‐cause mortality in the general population**. *J Am Heart Assoc* (2022.0) **11** e023690. DOI: 10.1161/JAHA.121.023690 31. Abdullah SM, Defina LF, Leonard D, Barlow CE, Radford NB, Willis BL. **Long-term association of low-density lipoprotein cholesterol with cardiovascular mortality in individuals at low 10-year risk of atherosclerotic cardiovascular disease: results from the cooper center longitudinal study**. *Circulation* (2018.0) **138** 2315-25. DOI: 10.1161/CIRCULATIONAHA.118.034273
--- title: Qualitative analysis of stakeholder perspectives on engaging Latinx patients in kidney-related research authors: - Flor Alvarado - Cynthia Delgado - Susanne B. Nicholas - Allison Jaure - Lilia Cervantes journal: BMC Nephrology year: 2023 pmcid: PMC10061843 doi: 10.1186/s12882-023-03128-y license: CC BY 4.0 --- # Qualitative analysis of stakeholder perspectives on engaging Latinx patients in kidney-related research ## Abstract ### Background Latinx individuals are disproportionally burdened by kidney diseases compared to non-Latinx White individuals and are underrepresented in kidney-related research. We aimed to describe stakeholder perspectives on Latinx patient engagement in kidney-related research. ### Methods We conducted a thematic analysis of two online moderated discussions and an interactive online survey with open-text responses involving participants (i.e. stakeholders), with personal and/or professional experiences with Latinx patients with kidney diseases and their families/caregivers. ### Results Among the eight stakeholders (Female:$75\%$; Latinx ethnicity:$88\%$), there were three physicians, one nurse, one patient with kidney disease who received a kidney transplant, one policy maker, one Doctor of Philosophy, and one executive director of a non-profit health organization. We identified five themes. The majority of themes and their respective subthemes (in parentheses) reflected barriers to engagement: Lack of personal relevance (unable to relate to research staff and marketing resources, and unclear benefit of research to self, family, and community); fear and vulnerability (immigration concerns, stigma with seeking care, skepticism of Western medicine); logistical and financial barriers (limited opportunities to enroll in clinical trials, out-of-pocket costs, transportation issues); and distrust and asymmetry of power (related to limited English proficiency or health literacy, and provider bias). The last theme centered on stimulating interest and establishing trust in the research process. ### Conclusions To overcome barriers to engagement in kidney-related research and establish trust among potential Latinx research participants, stakeholders recommended employing cultural responsiveness and community-based strategies. These strategies can help identify local health priorities, enhance research recruitment and retention strategies, and establish partnerships that continue to elevate research endeavors aiming to enhance the health of Latinx individuals with kidney diseases. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12882-023-03128-y. ## Introduction Over 60 million individuals comprise the Latinx (i.e., Hispanic, a non-gender-based term for Latino/Latina) population, making it the largest ethnic minority group in the United States (U.S.) [1]. This fast-growing group includes an admixture of individuals from varying races and ancestries, with diverse cultural beliefs, social determinants of health, and levels of acculturation [2–4]. The Latinx population is disproportionately burdened by social challenges contributing to persistent health disparities [4–7]. In terms of kidney-related disparities, U.S. Latinx individuals have 1.3 times the risk of developing kidney failure relative to White individuals [8], and are less likely to receive pre-kidney failure nephrology care, initiate kidney replacement therapy with home dialysis, or undergo transplantation. Despite their disproportionate burden of kidney disease, the Latinx population is underrepresented in clinical research [9]. Garnering greater Latinx participation (and that of other socially marginalized groups) in clinical research may lead to stronger and more generalizable inferences, and deepen the understanding of therapeutic variation between racial and ethnic subgroups [10]. Previous research has identified participation barriers for Latinx patients; however, there may be barriers that are unique to Latinx patients living with chronic kidney disease (CKD) [11–17]. To increase Latinx participation in kidney research, it is critical to gather perspectives from Latinx patients with kidney disease, their families/caregivers and medical providers, kidney disparities researchers, and other relevant stakeholders. Herein, we discuss a unique opportunity to explore the perspectives of stakeholders with personal and professional experiences with Latinx patients with kidney diseases. In this thematic analysis, we aimed to describe stakeholders’ perspectives on engaging Latinx patients in kidney-related research. ## Setting, participants, and data collection We conducted a thematic analysis of data obtained from the transcripts of two online 2-hour moderated discussions and an interactive online survey with open text responses conducted by Travere Therapeutics (© 2023 Travere Therapeutics, Inc.), a biopharmaceutical company whose mission is to identify, develop, and deliver therapies to individuals with rare diseases [18]. The purpose of the internal research activity, organized by Travere, was to gain insights into engaging with Latinx communities and patients with kidney diseases, identify ways to communicate and educate about clinical trials and potential treatment options, and discuss best practices and opportunities to increase awareness of kidney diseases. The invited advisory board (herein referred to as stakeholders) were individuals with personal and/or professional experiences with Latinx patients with kidney diseases. Recruited stakeholders included representatives of patient advocacy organizations working with patients with kidney diseases, key opinion leaders involved in diversity, equity and inclusion activities, individuals working with healthcare organizations or academic institutions that provide care or outreach to Latinx communities, or were Latinx patients with a kidney disease. Purposive sampling was used to recruit participants, specifically, Travere representatives attempted to contact participants by phone calls and emails. Potential participants included individuals with a previous working relationship with Travere, and those identified via an internet search who met the recruitment criteria. Travere collected participant information including ethnicity, profession, state of residence, and sex. Travere’s engagement with stakeholders occurred via 1) an initial online 2-hour moderated discussion, 2) a two-week interactive survey via an online engagement platform, and 3) a closing online 2-hour moderated discussion (Supplemental Tables 1 and 2). During the initial online moderated discussion, Travere representatives discussed the company’s goals to improve kidney disease outcomes and engagement with Latinx patients and discussed the planned activities for the investigation. An online engagement platform Within3 (© 2008–2023 Within3), was used for the 2-week interactive online survey with open-text responses. The questions for the online survey were developed by Travere based on literature review and gaps of knowledge perceived by the organization representatives and that of patient advocacy organizations working with racially and ethnically diverse patients. The questions were designed to elicit stakeholder perspectives on engaging Latinx patients in kidney-related research. There were two objectives of the online survey. The first was to understand the unique challenges and needs of the Latinx population with kidney disease, as well as the role of family. Communications discussing this objective occurred via Within3 over the first week of the activity. Second, stakeholders discussed issues related to Latinx participation in kidney-related research; this occurred during week two of the activity. At the beginning of each week, moderators posted questions to stakeholders related to the applicable objective, and stakeholders provided answers within the first three days of the week. All stakeholders were asked to answer every survey question. Moderators monitored the discussions and posed further questions to stimulate further dialogue. Following the two weeks of communications via Within3, stakeholders took part in a closing online moderated discussion where time was provided to review results, and consider potential solutions and collaborative opportunities. Travere Therapeutics sponsored the activity for internal research purposes and sponsored publication costs but did not play a role in the thematic analysis or the final decision to publish this manuscript. All study protocols were granted an exemption from requiring ethics approval by the Colorado Multiple Institutional Review Board and participant informed consent requirements were waived. This study followed the Consolidated Criteria for Reporting Qualitative Research (COREQ) reporting guideline [19] (Supplemental Table 3). ## Analysis Transcripts were imported into HyperRESEARCH (version 4.0.1 ResearchWare Inc. Randolph MA). Using thematic analysis, A.T. read transcripts and inductively identified preliminary concepts, and grouped similar concepts into initial themes and subthemes. These were reviewed and discussed with F.A. and L.C. A.T. coded the transcripts and identified patterns within the data. Consensus on themes and subthemes occurred following review of the thematic analysis (A.T., C.D., F.A., L.A., S.B.N.) The research team was composed of female scientists (with expertise in translational, patient-centered outcomes, and qualitative research), policy advocates, and physicians. ## Results The eight stakeholders were comprised of three physicians (one nephrologist and two internal medicine physicians), one registered nurse, one patient with kidney disease who received a transplant, one policy decision-maker (Juris Doctor), one Doctor of Philosophy-trained health disparities researcher, and one executive director of a non-profit health organization. Seven out of the 8 stakeholders self-identified as Latinx at least once during the study; information about ethnicity was not available for one participant (Table 1).Table 1Participant characteristics, $$n = 8$$Characteristicn (%)Female6 [75]Age: ≥ 18 years of age8 [100]Latinx ethnicity a7 (87.5)Region of Residence in the US West1 (12.5) Midwest0 [0] Northeast3 (37.5) Southeast1 (12.5) Southwest3 (37.5)Professionb Nephrologist1(12.5) Internal medicine physician2 [25] Registered nurse1(12.5) Policymaker1 (12.5) Director of a Latinx health center 1 (12.5) Disparities researcher4 [50] Marketing and Communications Director1 (12.5)Patient with kidney disease1 (12.5)a Ethnicity not available for one participant bParticipants could have overlapping roles We identified five themes: 1) lack of personal relevance, 2) fear and vulnerability, 3) logistical and financial barriers, 4) distrust and asymmetry of power, and 5) stimulating interest and establishing trust in the research process. The first four themes encompassed barriers that stakeholders perceived hindered Latinx participation in kidney-related research, whereas the fifth theme conveyed stakeholder-recommended strategies to overcome barriers. Respective subthemes are described in the following sections and in Fig. 1 with selected supporting quotations provided in Table 2.Fig. 1Thematic schemaTable 2Selected supporting quotationsThemeSelected illustrative quotationsLack of personal relevance Unable to relate to research staff and marketing resources“If the person in the ad does not appear relatable—you assume you must not qualify. You have to see something to believe it includes you. ”“Opportunities to hear from clinical trial participants that look like them and have similar experiences in a culturally appropriate way might help address the lack of knowledge and create more receptivity. ”“Clinical trials need to incorporate more Latino investigators, researchers and other staff so they can build trust, be Latino-centered and help increase participation of more Latinos. ”“It starts from the inside out. What does the current staff look like? Is there any relatability? Not just race. And [it is] okay if there isn't but then you have to lean more into the communities where you want to help most and find people that the community trusts.” Unclear benefit of research to self, family, and community“I honestly [think] there is not enough information shared in the general public, let alone within Latino/Hispanic communities regarding Clinical trials. ”“They are hesitant, mainly due to the fact of not knowing if they will receive a placebo or the real test. ”“Talking about patients who are undocumented, and just thinking ahead, if you do get approval for your investigational product, sometimes it's even hard to get the medications through the patient assistance programs, because sometimes in some companies, if they can't document a legal status, a lot of times they won't qualify for their patient assistance programs to get them free medication. ”“Parents and caregivers, may be hesitant to enroll their children in clinical trials if they perceive there will not be an immediate benefit to the child. ”“Latinos would be very receptive to participating in clinical trials because they always want to help their families and communities. ”Fear and vulnerability Concern about deportation and legal recourses“It needs to be explicitly stated that a patient's information will not be shared because many fear deportation when joining research. ”“Patients who are undocumented may forego seeking medical care due to fear of deportation. ”“[One patient] stopped coming to see me in the office because she was scared to leave her house for fear of being picked up and getting deported.” Stigma associated with seeking healthcare“The biggest challenge for Latinos/*Hispanics is* a strong stigma to seek care. ”“In the community I serve, there is a lot of stigma shown as fear of "getting worse" and not being able to pay for follow up care. ”“Beyond machismo, it's fear. People say 'If there's nothing wrong with me, why do I want to go and find out? My relative, so and so was fine until they went to the doctor. And now they have five different problems that they [did not] know before.' So it goes beyond this idea of machismo, but also just fear in general, that something will be found. ”“If God wants me to have a problem, then I'll deal with it, then. I’m not going to deal with it until it's almost like destiny, but it's really more like fatalism.” Skepticism about Western medicine“Preference for non-Western medicine is also something that comes up as a reason (reliance on natural remedies).”“Of course, turning to friends and family members is also common among some Latinos seeking natural remedies from their most trusted circle of family and friends. ”Logistical and financial barriers Limited opportunities to enroll in clinical trials“The primary care doctors … they're so overwhelmed. They're more worried about [getting] them to see the nephrologist… Do they have coverage? Can they get an appointment in time? A lot of times our communities are so underserved, that research is the icing on the cake. We're barely trying to get the plain vanilla cake to begin with.” Prohibitive out-of-pocket costs“For many Latinos/Hispanics it is not financially feasible for them to join a study… they maybe cannot take a day off from work to have blood work done or complete study visits, it may be too expensive for them to travel to a study visit (e.g. bus tokens, cost of gasoline, etc.), they may not have childcare and may need to pay for this out of pocket, etc. It is critical that these patients be well compensated. ”“They would consider potential costs associated with the participation in the trial. ”“Financial barriers for Latinos with low socio-economic status is a real impediment, in additional to the other social determinants of health.” Difficulties with transportation“Another common challenge (among older participants) is transportation. To circumvent this challenge we make in-home appointments or suggest public (yet private places) such as local public library study rooms. However, with clinical trials collecting biospecimen the library is not as feasible. We sometimes do hire Ubers or have institutional transportation vehicles bring patients to/from home. ”“It's important for the sites to communicate about reimbursement of transportation costs (or provide vouchers)”“In terms of economic concerns we try to mitigate these by providing transportation (we have a designated staff member who picks up the patient and takes them back home after their visits), and by providing a stipend that is appropriate to compensate them for their time. ”Distrust and asymmetry of power Intimidation due to limited English proficiency and low health literacy“Medical terms need to be broken down, but not to sound scarier. ”“When you are [in] clinic and you see people looking through the forms, totally overwhelmed. A few bilingual staff members [are there] to help translate a bare minimum. ”“I have seen patients go more out of their way to seek Spanish-speaking providers in areas where Spanish-speaking providers are less readily available. ”“Addressing the lack of knowledge about clinical trials by developing bilingual educational materials in a manner that is culturally appropriate, especially for Latinos with lower educational attainment might eliminate this barrier.” Provider bias and assumptions of non-adherence“Latino patients reportedly have poor medication adherence, which is exacerbated by lower levels of health literacy, lower socio-economic status, as well as acculturation. ”“Physicians' attitudes/bias about Hispanics not having the ability to follow through on the management protocols. ”Stimulating interest and establishing trust in the research process Establish community partnerships and buy-in“You need to do things for the community like FREE Kidney Disease screenings, FREE cooking classes, create a cheat sheet (English/Spanish) that advises people what to look out for that makes them high risk, how to advocate for their own health. ”“When participants see that the venue personnel communicate positively about recruiters they are more open to having dialogue with the recruiters. ”“Community trust is key in research. I frequently see how study teams struggle to recruit participants because they want to send an email and get a positive response, but that's often not possible. It is important to incorporate community voices at the systems level to ensure people see us and we see them. Some common practices include establishing advisory boards, where researchers present their projects and consult with patients and present their findings, engaging in community programming, and developing translational research programming. ”“Community centers themselves (e.g. adult education centers) have been able to provide information on other community resources (e.g. community clinics) that patients may leverage for health information and health care. Churches and religious centers have also been able to provide health information”“Securing "buy-in" with community clinic staff, and providers has greatly assisted my efforts in recruitment. Other venues such as public libraries, and local division of motor vehicles have been opened to us tabling and sharing information on available clinical trials.” Appeal to cultural priorities and values“Cultural targeting of information/education that speaks to music, food, family, and in some cases religion will pay dividends when it comes to getting potential participants interested in participating in clinical trials. ”“We also make sure that our health communication pieces whether written or video is culturally tailored. ”“Relevant to providing information or for recruitment efforts via morning show we use "Despierta America" to reach older audiences“It may be a multilayer approach including grassroots organizations, leaders, and community members. Conducting an assessment about how much they know about the topic and answering any general questions, following up with discussions and forums about their needs and fears, and facilitating channels to resolve systemic concerns. ”“Another strategy is partnering with patient-oriented organizations to get the information to the patients. Again having research staff who is language/culture concordant is key.” Empower decision-making“Setting up a chat bot or frequently-asked-questions site with commonly asked questions. You could monitor and verify or flag posts from patient community. Provide multiple options to communicate (email/call/schedule [appointment]/text) Education and really understanding your diagnosis is powerful. ”“When you were talking about videos and telenovelas and this and that, I thought that would be a great way to try to explain [the objective of the] clinical trial to a patient … we’re trying to get informed consent. Because a lot of times we basically give them a stack of papers and we go through it with them… Kind of goes over [their] head, and you wonder, does that really inform them or not?”“Patient advocacy groups are indeed a trusted source of health information. In my experience I have seen that membership into these groups are often suggested after diagnoses. However, once patients join, information shared within these groups is highly trusted among many members. ”“*Provide a* list of ways they can be involved and allow them to volunteer to assist with patient recruitment.” Respect the centrality of family“The family wants to be involved and wants to participate in decision-making including the management of the illness. In my experience, when the family is involved in decision-making and understands the illness/management, the patient tends to be more adherent to medications because their families are involved and supporting them. ”“Latinos consider family an important pillar to deal with problems (familism). In healthcare, it is important to follow a patient and family engagement approach that can discuss any issues and answers any questions from the patients and caregivers. This is particularly sensitive when patients are children, in which case parents are even more critical about how they care their children. I insist in the importance of providing culturally appropriate information in a timely manner. Talking with patients and caregivers, all the time and making them part of the patient's care is essential to ensure quality of care. ”“Many Latinos seek advice and encouragement from extended family members. So there needs to be a concerted effort to engage family members in helping treat and manage chronic kidney disease. Women/mothers/wives especially play a big role in decision making.” Build familiarity and confidence in technology“Newly arrived immigrants have not had as in-depth access to technology and are thus disadvantaged in terms of health communications, or telehealth that leverage newer technologies. ”“A critical programming need is telehealth/ mobile device/ electronic health record—patient portal training for medical providers and patients/participants. ”“Availability of a mobile training team that can make in home visits would definitely facilitate patient comfort with digital health technology / telehealth devices they have been provided. This language-specific training should be made available to patients and caregivers. ”“We do of course see that most older patients are likely to need assistance navigating the tablet.” ## Unable to relate to research staff and marketing resources Stakeholders stated that Latinx patients may be reluctant to engage in research because they do not relate to those who appear in study advertisements. They suggested that patients “have to see something to believe it includes [them].” Stakeholders remarked there was a lack of Latino researchers, staff, and advocates who “look like the patient,” and that study communications were not always in the patient’s or family’s preferred language. “ Take time to hire diverse staff who can help infuse cultural nuances that will be lost in translation.” Stakeholders believed these issues should be addressed since obtaining study information “from people [who] look like them, speak like them,” could help establish trust, and may thereby “create more receptivity” to participating in trials and other forms of clinical research. ## Unclear benefit of research to self, family, and community Stakeholders believed there were “not enough educational resources on the power of clinical trials.” The need for simple, culture- and language-concordant educational resources about the potential benefits of participation was advocated. One stakeholder (a kidney transplant recipient) remarked, “It wasn't until I got involved with [fundraising organization] that I really started to understand the importance of research and clinical trials.” Stakeholders were concerned that Latinx patients may not perceive a personal or direct benefit of engaging in trials or other forms of clinical research. One stakeholder (medical provider caring for patients with kidney disease) added: “Our patients are very receptive to participating because they can see how much more attention patients get in a clinical trial (this is very evident to the in-center hemodialysis patients who can see their peers participating in trials in the center).” Regarding the structure and processes, of clinical trials, not knowing whether patients would be placed in the intervention arm or control arm of a clinical trial contributes to hesitancy. Moreover, one stakeholder remarked that while investigational products were approved based on trial evidence, undocumented patients would not be able to access approved medications. As such, their participation may not be considered to directly benefit their own family and the larger community. Stakeholders emphasized informing patients of the “disparities and increased burden” of diseases affecting the Latinx community. “ The average Latino/a may not know how diversity in clinical trials can improve patient health outcomes for themselves, their families and/or their communities.” ## Concern about deportation and legal recourses Stakeholders believed fear of deportation was a major concern. They explained that undocumented patients were often reluctant to seek healthcare as they were terrified of being reported to authorities and that there was a “lack of assurance from clinical trial recruitment staff that immigration status [would be kept confidential].” ## Stigma associated with seeking health care Through previous personal and professional experiences with Latinx family members, friends, and patient individuals, stakeholders recognized a societal and cultural pressure to “always appear healthy,” and believed many Latinx individuals avoided being “diagnosed,” or “sick or weak,” and feared that receiving a diagnosis would threaten their source of income. Stakeholders described how some individuals feared that by seeking care, their health would deteriorate, and they would not be able to afford follow-up. Stakeholders believed the “strong stigma” attached with seeking care was inadvertently transferred to the context of trial participation. ## Skepticism about western medicine Stakeholders suggested that Latinx individuals may be averse to enrolling in trials evaluating pharmacological interventions as patients had a “preference for non-western medicine” and wanted to rely on natural remedies. ## Limited opportunities to enroll in clinical trials Stakeholders believed that many medical providers serving Latinx communities felt “overwhelmed” having to address social challenges experienced by their patients and were more focused on ensuring appropriate medical care and follow-up over enrolling Latinx patients in research. These logistical barriers hinder Latinx patients’ ability to even enter clinical research studies. ## Prohibitive out-of-pocket costs Participating in a trial was expected to impose a financial burden on Latinx individuals. Stakeholders were concerned that the costs of taking time off work and childcare would be prohibitive for Latinx individuals of low-socioeconomic status who often were uninsured. Stakeholders advocated for compensation to address the financial obstacles of study participation. “ Ramping up the financial incentives for low-income potential enrollees may also address part of the obstacle to participating.” ## Difficulties with transportation The cost and challenges of arranging transportation were identified as a major impediment to participating in trials. “ It may be too expensive for them to travel to a study visit.” Stakeholders suggested arranging transportation or providing vouchers, scheduling “in-home appointments” and using easily accessible community venues to address transportation-related barriers. One stakeholder commented, “I can't stress enough how important the transportation piece is. The transportation is not as important for [patients receiving in-center hemodialysis] because they're already coming to the dialysis center, but for the non-dialysis, chronic kidney disease patients, sometimes they have to travel 35–40 miles.” ## Intimidation due to limited English proficiency and low health literacy Stakeholders remarked that Latinx individuals may feel “reluctant to admit when they don’t understand.” When having to fill out forms in clinical settings, for example, bilingual staff members may be able to “translate a bare minimum,” but even in a group setting, patients may feel “intimidated to admit they don't understand.” In the context of clinical care, Latinx individuals may “play down their illness, pain,” which could inadvertently delay referrals to specialists or relevant clinical trials for an underlying disease process. ## Provider bias and assumptions of non-adherence Some stakeholders believed that physicians may not discuss research opportunities with Latinx patients as they assumed that Latinx patients had “poor medical adherence” exacerbated by poor health literacy and low socioeconomic status. This biased perspective may make providers less inclined to enroll Latinx patients in studies. ## Establish community partnerships and buy-in To foster interest in trials, stakeholders suggested that research teams harness community events and settings, and that they deliver information through trusted community members. “ You have to lean more into the communities where you want to help most and find people that the community trusts.” They explained that taking action to help the community (e.g. free screening for kidney disease) could pique the interest in research among Latinx patients and families. Prioritizing visibility of community-centeredness was deemed critical to establish trust. ## Appeal to cultural priorities and values “Cultural targeting” of information and education that resonated with Latinx communities was underlined as a vital strategy to promote engagement. This could include the integration of community priorities such as family, food, music, and religion, and sharing information via news and media channels that were familiar and trusted by the target community. Specifically, framing participation in research studies as an opportunity to “help their families and communities” may further resonate with Latinx individuals. Stakeholders also recommended obtaining input early on from community members or “Latino organizations” to help ensure cultural appropriateness in research designs and outreach strategies. ## Empower decision-making Stakeholders suggested that Latinx patients and families should “feel completely comfortable with their decision to consent.” They believed it was important to present information in a clear and engaging manner (e.g. using videos rather than “a stack of papers”), provide sufficient time for decision-making, and opportunities to ask questions using multiple platforms, including by email, phone call, or during face-to-face appointments. ## Respect the centrality of family Stakeholders raised the concept of “familismo” and explained that for many Latinx individuals, “family comes before self” and that the family “took on the burden” of the patient’s illness by providing emotional and social support and participating in decision-making. Latinx patients would “seek advice and encouragement from extended family members.” Family members were recognized to have the “power to influence the perception and behavior of the patient.” Family members also had a role in “bridging” cultural aspects in managing the illness, for example, “younger generations being information brokers for older generations.” Moreover, family support could also encourage adherence to treatment and thereby trial protocols. ## Build familiarity and confidence in technology The increasing use of technology in trial recruitment and follow-up was acknowledged and thus stakeholders suggested providing access to, and training, in the use of technology. For example, they suggested that a mobile training team could conduct home visits to conduct “language-specific training” with patients in the use of health technology and telehealth devices (e.g. blood pressure monitors). ## Discussion In this thematic analysis we identified five themes relating to Latinx engagement in clinical research. The first four themes conveyed barriers to engagement: lack of personal relevance, fear and vulnerability, financial and logistical barriers, and distrust and/or asymmetry of power. We also described stakeholders’ recommendations to overcome these barriers, centering around a fifth theme of stimulating interest and establishing trust in the research process. A unique aspect of our study is that we provide perspectives from stakeholders with personal and professional experience with Latinx patients with kidney disease. Previous studies exploring barriers and facilitators to research participation among Latinx patients have largely involved persons with non-kidney-related conditions [11, 20–23]. Previously, one international qualitative study explored patient/caregiver perspectives regarding their involvement in CKD research, however, participants were English-speaking, mainly of White race, and did not include Latinx participants [24]. Among strategies to build trust and enhance Latinx participation in clinical research, similar to previous recommendations, stakeholders advocated for cultural responsiveness (i.e. recognizing and respecting cultural differences and attempting to accommodate those differences) [20, 25]; this could include asking Latinx participants about their language preferences (and providing appropriate language access services, if needed), hiring research staff who are bilingual and bicultural, and/or partnering with trusted community members that may provide relevant insight into the community’s needs, values and priorities [7, 21, 26, 27]. More recently, culture- and language- concordant peer-navigators interventions have been explored as a means to improve patient-centered and clinical outcomes in kidney disease [28]. Similar culturally responsive peer navigator systems could be adapted for study recruitment and implementation purposes. Consistent with previous literature, stakeholders underscored the influence of familismo (familism), the “strong sense of importance and connectedness in family relationships and obligations” [29]. Family members may act as protective gatekeepers or information brokers, ultimately influencing health beliefs and behaviors such as medication adherence, and lifestyle changes [21, 29, 30]. Family members may also impact research participants’ willingness to engage in research studies, thus family/caregiver involvement should be encouraged early-on. To further stimulate interest and trust in the research process, it is critical to involve the public (i.e. the community) in the design, conduct, and dissemination of research. Several research approaches and frameworks have been developed to facilitate partnership and collaboration between researchers and stakeholders including community-based participatory research (CBPR), patient and public involvement (PPI), the Patient-Centered Outcomes Research Institute (PCORI) dissemination and implementation framework, and the Public involvement in research: values and principles Framework (INVOLVE) [21, 31–34]. CBPR, specifically, is rooted in the “active involvement of community members, organization representatives and researchers in all aspects of the research process,” wherein partners contribute their expertise to improve the understanding of the social, structural, or physical inequities impacting the community [35]. Stakeholders expressed that research teams should at the onset, plan for adequate time and financial resources to establish and maintain community partnerships [21]. To sustain partnerships, stakeholders also recommended that research teams maintain personal communication with community partners and demonstrate reciprocity (by volunteering or participating in community events); this is consistent with previous recommendations in the literature [21, 36]. Importantly, it is well documented that CBPR has informed many successful interventions among Latinx research participants with varying chronic diseases [21, 29, 36–40]. Consistent with previous studies, our findings highlight several psychosocial and structural barriers experienced by Latinx patients. Psychosocial barriers include mistrust in the research process, fear of discrimination, and confidentiality, particularly as it relates to immigration status [11–14, 21]. Strategies to help overcome these barriers include providing Latinx patients and their family/caregivers with information describing how the research team will maintain confidentiality, and establishing partnerships with community-based organizations to increase public trust in research processes [41]. Structural barriers include financial concerns, time constraints due to work or caregiving responsibilities, lack of transportation, and communication issues [7, 12, 15, 16]. Many of these structural barriers are remediable. Research teams may provide financial compensation for time off work, childcare needs, etc. Additional compensation to cover travel expenses could include vouchers, bus tokens, or independent/private vehicles arranged by the research team. Other considerations to mitigate transportation or participant-scheduling issues include choosing sites for data collection and interviews near participants’ home or workplace, easily accessible public locations such as libraries, or arranging in-home visits; this might also allow scheduling appointments outside of working hours. With the recent uptake of telehealth use and advances including smartphones, investigators could incorporate virtual video appointments for baseline and follow-up survey/data collection (in addition to the standard practice of telephone follow-ups), or even invest in virtual peer navigator systems to assist participants. Leveraging mobile internet and smartphone use is particularly relevant since most Latinx individuals ($94\%$) report using a mobile device to access the internet [42]. ## Limitations This thematic analysis has several limitations. Questions posed in the online moderated discussions and the interactive online-survey were tailored for internal investigational purposes by Travere Therapeutics, participation was limited to eight stakeholders of which only one was a patient with kidney disease [24], and important participant characteristics such as self-reported race, gender, sexual orientation, or preferred language were not queried. Social desirability bias may have led to censorship of negative opinions about Latinx engagement in research, particularly since stakeholders were not blind to other participants’ identification or credentials. Further, we were unable to further probe study participants regarding their responses. Nevertheless, the methods provided a rich depiction of the stakeholders’ perspectives on the barriers hindering Latinx engagement in kidney-related research, and strategies to overcome them. Future studies should incorporate more patient perspectives on research participation and should include Latinx patients of varying stages of CKD; this will help researchers address multi-level social risk factors and other challenges. ## Conclusion Numerous challenges uniquely impacting Latinx patients with kidney disease limit their ability to participate in clinical research. As a large, rapidly-growing and heterogenous population, it becomes near impossible to delineate all possible strategies to enhance engagement among Latinx individuals or to know which strategies are more relevant for a specific community. Instead, the leading approach is to incorporate community engagement and cultural responsiveness at the onset. Doing so will help investigators understand the health priorities of the community, build trust, enhance research recruitment and retention strategies, and establish long-lasting partnerships that will continue to guide and elevate research endeavors aiming to enhance the health of all Latinx communities with kidney disease. ## Supplementary Information Additional file 1: Supplemental Table 1. Meeting Outline for Introductory and Closing Webinars. Supplemental Table 2. Virtual engagement platform questions. Supplemental Table 3. Consolidated Criteria for reporting qualitative studies (COREQ): 32-Item Checklist. ## References 1. 1.United States Census Bureau. 2019 Population Estimates by Age, Sex, Race and Hispanic Origin. https://www.census.gov/newsroom/press-kits/2020/population-estimates-detailed.html. Accessed 24 Jan 2022. 2. Vega WA, Rodriguez MA, Gruskin E. **Health disparities in the Latino population**. *Epidemiol Rev* (2009.0) **31** 99-112. DOI: 10.1093/epirev/mxp008 3. Enid Zambrana R, Amaro G, Butler C, DuPont-Reyes M, Parra-Medina D. **Analysis of Latina/o sociodemographic and health data sets in the United States from 1960 to 2019: findings suggest improvements to future data collection efforts**. *Health Educ Behav* (2021.0) **48** 320-331. DOI: 10.1177/10901981211011047 4. Desai N, Lora CM, Lash JP, Ricardo AC. **CKD and ESRD in US Hispanics**. *Am J Kidney Dis* (2019.0) **73** 102-111. DOI: 10.1053/j.ajkd.2018.02.354 5. Cervantes L, Tuot D, Raghavan R. **Association of emergency-only vs standard hemodialysis with mortality and health care use among undocumented immigrants with end-stage renal disease**. *JAMA Intern Med* (2018.0) **178** 188-195. DOI: 10.1001/jamainternmed.2017.7039 6. Fischer MJ, Go AS, Lora CM. **CKD in Hispanics: baseline characteristics from the CRIC (Chronic Renal Insufficiency Cohort) and Hispanic-CRIC studies**. *Am J Kidney Dis* (2011.0) **58** 214-227. DOI: 10.1053/j.ajkd.2011.05.010 7. Cervantes L, Rizzolo K, Carr AL. **Social and cultural challenges in caring for Latinx individuals with kidney failure in urban settings**. *JAMA Netw Open* (2021.0) **4** e2125838. DOI: 10.1001/jamanetworkopen.2021.25838 8. 8.System USRD. 2020 USRDS Annual Data Report: Epidemiology of kidney disease in the United States. , 2020. https://adr.usrds.org/2020. 9. McGill N. **As Hispanics lag in clinical trials, health researchers take action: outreach expands**. *Nation Health* (2013.0) **43** 1 10. Clark LT, Watkins L, Piña IL. **Increasing diversity in clinical trials: overcoming critical barriers**. *Curr Problems Cardiol* (2019.0) **44** 148-172. DOI: 10.1016/j.cpcardiol.2018.11.002 11. García AA, Zuñiga JA, Lagon C. **A personal touch: the most important strategy for recruiting Latino research participants**. *J Transcult Nurs* (2016.0) **28** 342-347. DOI: 10.1177/1043659616644958 12. Fischer SM, Kline DM, Min SJ, Okuyama S, Fink RM. **Apoyo con Cariño: strategies to promote recruiting, enrolling, and retaining Latinos in a cancer clinical trial**. *J Natl Compr Canc Netw* (2017.0) **15** 1392-1399. DOI: 10.6004/jnccn.2017.7005 13. Derose KP, Williams MV, Branch CA. **A community-partnered approach to developing church-based interventions to reduce health disparities among African-Americans and Latinos**. *J Racial Ethn Health Disparities* (2019.0) **6** 254-264. DOI: 10.1007/s40615-018-0520-z 14. Cunningham-Erves J, Barajas C, Mayo-Gamble TL. **Formative research to design a culturally-appropriate cancer clinical trial education program to increase participation of African American and Latino communities**. *BMC Public Health.* (2020.0) **20** 840. DOI: 10.1186/s12889-020-08939-4 15. Gelman CR. **Learning from recruitment challenges: barriers to diagnosis, treatment, and research participation for Latinos with symptoms of Alzheimer's disease**. *J Gerontol Soc Work* (2010.0) **53** 94-113. DOI: 10.1080/01634370903361847 16. Ali SH, Islam NS, Commodore-Mensah Y, Yi SS. **Implementing hypertension management interventions in immigrant communities in the US: a narrative review of recent developments and suggestions for programmatic efforts**. *Curr Hyperten Rep* (2021.0) **23** 5. DOI: 10.1007/s11906-020-01121-6 17. George S, Duran N, Norris K. **A systematic review of barriers and facilitators to minority research participation among African Americans, Latinos, Asian Americans, and Pacific Islanders**. *Am J Public Health.* (2013.0) **104** e16-e31. DOI: 10.2105/AJPH.2013.301706 18. 18.Travere Therapeutics. https://travere.com/. Accessed 7 Feb 2023. 19. Tong A, Sainsbury P, Craig J. **Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups**. *Int J Qual Health Care* (2007.0) **19** 349-357. DOI: 10.1093/intqhc/mzm042 20. Hildebrand JA, Billimek J, Olshansky EF, Sorkin DH, Lee JA, Evangelista LS. **Facilitators and barriers to research participation: perspectives of Latinos with type 2 diabetes**. *Eur J Cardiovasc Nurs* (2018.0) **17** 737-741. DOI: 10.1177/1474515118780895 21. Crist JD, Ruiz MR, Torres-Urquidy OH, Pasvogel A, Hepworth JT. **Recruiting hospitalized Mexican American elder adults and caregivers: challenges and strategies**. *Res Gerontol Nurs* (2013.0) **6** 22-28. DOI: 10.3928/19404921-20121205-01 22. Marquez DX, Perez A, Johnson JK. **Increasing engagement of Hispanics/Latinos in clinical trials on Alzheimer's disease and related dementias**. *Alzheimer's Dementia: Transl Res Clin Interven* (2022.0) **8** e12331. DOI: 10.1002/trc2.12331 23. Arevalo M, Heredia NI, Krasny S. **Mexican-American perspectives on participation in clinical trials: a qualitative study**. *Contemp Clin Trials Commun* (2016.0) **4** 52-57. DOI: 10.1016/j.conctc.2016.06.009 24. Gutman T, Kelly A, Scholes-Robertson N, Craig JC, Jesudason S, Tong A. **Patient and caregiver experiences and attitudes about their involvement in research in chronic kidney disease**. *Clin J Am Soc Nephrol* (2022.0) **17** 215. DOI: 10.2215/CJN.05960521 25. Minnican C, O’Toole G. **Exploring the incidence of culturally responsive communication in Australian healthcare: the first rapid review on this concept**. *BMC Health Serv Res* (2020.0) **20** 20. DOI: 10.1186/s12913-019-4859-6 26. González HM, Vega WA, Tarraf W. **Health care quality perceptions among foreign-born Latinos and the importance of speaking the same language**. *J Am Board Fam Med* (2010.0) **23** 745-752. DOI: 10.3122/jabfm.2010.06.090264 27. García AA, Zuñiga JA, Lagon C. **A personal touch: the most important strategy for recruiting Latino research participants**. *J Transcult Nurs* (2017.0) **28** 342-347. DOI: 10.1177/1043659616644958 28. Cervantes L, Hasnain-Wynia R, Steiner JF, Chonchol M, Fischer S. **Patient navigation: addressing social challenges in dialysis patients**. *Am J Kidney Dis* (2020.0) **76** 121-129. DOI: 10.1053/j.ajkd.2019.06.007 29. Doty JL, Brady SS, MonardezPopelka J. **Designing a mobile app to enhance parenting skills of Latinx parents: a community-based participatory approach**. *JMIR Form Res.* (2020.0) **4** e12618. DOI: 10.2196/12618 30. Cervantes L, Jones J, Linas S, Fischer S. **Qualitative interviews exploring palliative care perspectives of Latinos on dialysis**. *Clin J Am Soc Nephrol* (2017.0) **12** 788-798. DOI: 10.2215/cjn.10260916 31. Cook N, Siddiqi N, Twiddy M, Kenyon R. **Patient and public involvement in health research in low and middle-income countries: a systematic review**. *BMJ Open* (2019.0) **9** e026514-e026514. DOI: 10.1136/bmjopen-2018-026514 32. Russell J, Fudge N, Greenhalgh T. **The impact of public involvement in health research: what are we measuring? Why are we measuring it? Should we stop measuring it?**. *Res Involve Engage* (2020.0) **6** 63. DOI: 10.1186/s40900-020-00239-w 33. 33.INVOLVE (2015) Public involvement in research: values and principles framework, INVOLVE: Eastleigh. https://www.invo.org.uk/wp-content/uploads/2017/08/Values-Principles-framework-Jan2016.pdf. Accessed 10 Feb 2023. 34. 34.The Patient-Centered Outcomes Research Institute (PCORI). PCORI Dissemination and Implementation Framework. Februart 2015. https://www.pcori.org/sites/default/files/PCORI-DI-Framework-February-2015.pdf. Accessed 10 Feb 2023. 35. Israel BA, Schulz AJ, Parker EA, Becker AB. **Community-based participatory research: policy recommendations for promoting a partnership approach in health research**. *Educ Health (Abingdon)* (2001.0) **14** 182-197. DOI: 10.1080/13576280110051055 36. Crist J, Escandon S. **Identifying and recruiting Mexican American partners and sustaining community partnerships**. *J Transcult Nurs : J Transcult Nurs Soc / Transcult Nurs Soc* (2003.0) **14** 266-71. DOI: 10.1177/1043659603014003013 37. 37.Barceló NE, Lopez A, Tang L, et al. Community Engagement and Planning versus Resources for Services for Implementing Depression Quality Improvement: Exploratory Analysis for Black and Latino Adults. Ethnicity & disease. 2019;29(2):277-286. 10.18865/ed.29.2.277. http://europepmc.org/abstract/MED/3105731310.18865/ed.29.2.277https://europepmc.org/articles/PMC6478049https://europepmc.org/articles/PMC6478049?pdf=render. Accessed 2019. 38. 38.Billimek J, Guzman H, Angulo MA. Effectiveness and feasibility of a software tool to help patients communicate with doctors about problems they face with their medication regimen (EMPATHy): study protocol for a randomized controlled trial. Trials. 2015;16:145. 10.1186/s13063-015-0672-7http://europepmc.org/abstract/MED/2587334910.1186/s13063-015-0672-7https://europepmc.org/articles/PMC4409752https://europepmc.org/articles/PMC4409752?pdf=render. Accessed 2015/04//. 39. Briant KJ, Sanchez JI, Ibarra G. **Using a culturally tailored intervention to increase colorectal cancer knowledge and screening among hispanics in a rural community**. *Cancer Epidemiol Biomark Prev* (2018.0) **27** 1283-1288. DOI: 10.1158/1055-9965.EPI-17-1092 40. Butler AM, Hilliard ME, Comer-HaGans D. **Review of community-engaged research in pediatric diabetes**. *Curr DiabRep* (2018.0) **18** 56-56. DOI: 10.1007/s11892-018-1029-x 41. Cervantes L, Martin M, Frank MG. **Experiences of Latinx individuals Hospitalized for COVID-19: a qualitative study**. *JAMA Netw Open* (2021.0) **4** e210684-e210684. DOI: 10.1001/jamanetworkopen.2021.0684 42. 42.Brown A, López G, Lopez MH. Digital Divide Narrows for Latinos as More Spanish Speakers and Immigrants Go Online. Pew Research Center. Updated July 20, 2016. https://www.pewresearch.org/hispanic/wp-content/uploads/sites/5/2016/07/PH_2016.07.21_Broadbank_Final.pdf. Accessed 24 Mar 2022.
--- title: Clinical values of serum Semaphorin 4D (Sema4D) in medication‑related osteonecrosis of the jaw authors: - Hong Mu - Ying Pang - Lili Liu - Jingbo Liu - Chunsheng Liu journal: European Journal of Medical Research year: 2023 pmcid: PMC10061851 doi: 10.1186/s40001-023-01095-6 license: CC BY 4.0 --- # Clinical values of serum Semaphorin 4D (Sema4D) in medication‑related osteonecrosis of the jaw ## Abstract ### Background Bisphosphonates (BPs) are widely used in clinical practice to prevent and treat bone metabolism-related diseases. Medication-related osteonecrosis of the jaw (MRONJ) is one of the major sequelae of BPs use. Early prediction and intervention of MRONJ are of great significance. ### Methods Ninety-seven patients currently on treatment with BPs or with a history of BPs usage and 45 healthy volunteers undergoing dentoalveolar surgery were included in this study. Participants' serum Semaphorin 4D (Sema4D) levels were measured and analyzed before participants underwent surgery (T0) and after a 12-month follow-up (T1). Kruskal–Wallis test and ROC analysis were used to examine the predictive effect of Sema4D on MRONJ. ### Results Sema4D levels in serum of patients corresponding to confirmed MRONJ were significantly lower at both T0 and T1 time points compared to non-MRONJ and healthy controls. Sema4D has a statistically predictive effect on the occurrence and diagnosis of MRONJ. Serum Sema4D levels were significantly reduced in MRONJ class 3 patients. MRONJ patients who received intravenous BPs had significantly lower Sema4D levels than those who received oral BPs. ### Conclusion Serum Sema4D level has predictive value for the onset of MRONJ in BPs users within 12 weeks after dentoalveolar surgery. ## Background Bisphosphonates (BPs) are stable analogs of pyrophosphate [1]. BPs are widely used in clinical practice for the prevention and treatment of bone metabolism-related diseases such as osteoporosis, multiple myeloma, osteitis deformans, bone metastases from malignant tumors, and tumor-derived hypercalcemia [2]. BPs inhibit the destruction of bone and control the bone metastasis of malignant tumors [3]. However, the deposition of BPs in bone cannot be completely metabolized [4]. Osteonecrosis of the jaw associated with BPs can occur many years later due to trauma, tooth extraction, etc. [ 5]. Therefore, the number of patients with BPs-related osteonecrosis of the jaw and patients with potential osteonecrosis risk is huge [6]. Medication-related osteonecrosis of the jaw (MRONJ) is a rare clinical disease that mainly occurs in patients with osteoporosis, bone metastases and other bone-destructive diseases who receive bisphosphonate therapy [7, 8]. The pathogenesis of MRONJ remains unclear [9]. Accumulating evidence has demonstrated that MRONJ may be associated with imbalances in bone remodeling, inhibition of angiogenesis, inflammatory response to infection, and soft tissue toxicity [9, 10]. Most of the current research shows that the treatment plan of MRONJ should comprehensively consider the disease stage and the patient's systemic condition [11]. Perioperative use of antibacterial mouthwash and systemic antibiotic therapy are effective measures to prevent the occurrence of MRONJ after tooth extraction [12]. Semaphorins are a newly discovered family of proteins with common domains that are widely present in organisms [13]. They have attracted widespread attention due to their bidirectional regulation of osteoclasts and osteoblasts [14]. It has been reported that some sema family proteins and their receptors are involved in the regulation of bone remodeling [15]. Semaphorin 4D (Sema4D) directly or indirectly affects the expression, differentiation and migration of osteoblasts and osteoclasts, thereby inhibiting or promoting the process of bone remodeling [16]. Sema4D expression is increased in canonical receptor activator of nuclear kappa-B (RANKL)-mediated osteoclast differentiation [17]. It was found that Sema4D protein expression was significantly reduced in MRONJ model tissues by immunohistochemical staining in animal models [13]. In order to further verify the clinical value of Sema4D in MRONJ and provide a basis for early warning and diagnosis of MRONJ, we study the clinical significance of Sema4D in the early diagnosis of MRONJ in this work. ## Study design A total of 153 patients who had currently on treatment with oral bisphosphonates for more than 2 years or had received at least two intravenous bisphosphonate injections were preparing for dentoalveolar surgery. Eight patients declined to participate in the study, 28 patients were experiencing MRONJ, 13 patients had previous MRONJ, and 7 patients were excluded for other reasons. Therefore, 97 patients at risk for MRONJ were included. After 12 weeks of follow-up, 55 patients had no confirmed MRONJ to the follow-up endpoint, and 42 patients had confirmed MRONJ. The time point at which patients at risk for MRONJ were included in the study was defined as T0. The time point of the study endpoint after the end of 12-month follow-up was defined as T1. In addition, we selected 45 healthy patients to detect serum Sema4D levels at T0 and T1 time points (Fig. 1). The study was approved by the ethics committee of Cangzhou Central Hospital (#2021-189-02(Z)), and the participants signed written informed consent. Fig. 1Study design ## MRONJ diagnosis Diagnostic criteria for MRONJ: current or previous history of bisphosphonate therapy; osteonecrosis of the jaw for more than 8 weeks without improvement; no history of radiotherapy to the head and neck. Patients meeting the above three conditions at the same time can be diagnosed as MRONJ. ## Staging of MRONJ MRONJ is divided into risk period, stage 0, stage 1, stage 2 and stage 3 clinically. Patients at risk of MRONJ were asymptomatic and without osteonecrosis. Patients with MRONJ stage 0 mainly present with no osteonecrosis or bone exposure, and only non-specific symptoms. Patients with MRONJ stage 1 mainly present with osteonecrosis or bone exposure, without clinical symptoms and signs of infection. Patients with MRONJ stage 2 had osteonecrosis or bone exposure with focal infection. Patients with MRONJ stage 3 have osteonecrosis or exposed bone with painful infection. In addition, stage 3 MRONJ should also include one or more of the following manifestations: pathological fracture, extraoral fistula, and jaw with lesions beyond the alveolar bone. ## Participants All participants in this study received elective dentoalveolar surgery in our hospital, including frenulum correction, alveolar bone revision, and oro-antral fistula repair. Inclusion criteria: [1] Patients who underwent alveolar surgery in our hospital; [2] Patients who received oral BPs for at least 2 years or at least two intravenous injections of BPs; [3] No head and neck radiation therapy; [4] Patients without jaw metastatic disease. Exclusion criteria: [1] Patients who already suffered from MRONJ at the time of recruitment. [ 2] Patients with current evidence of MRONJ (exposed bone or bone that can be explored through the formation of intraoral or extraoral fistulas in the maxillofacial region); [3] Patients with serious cardiovascular and cerebrovascular and systemic diseases, allergies, and mental illnesses. The 97 patients at risk for MRONJ were included in this study. After 12 weeks of follow-up, 55 patients had no confirmed MRONJ to the follow-up endpoint, and 42 patients had confirmed MRONJ. Therefore, they were divided into non-MRONJ group ($$n = 55$$) and MRONJ group ($$n = 42$$). In addition, we also recruited 45 healthy people without MRONJ risk as a healthy control group ($$n = 45$$). ## Intervention In order to detect the concentration of Sema4D in the serum of different patients in different periods, we collected 5 mL of fasting peripheral venous blood from each patient at T0 and T1 time points respectively. Patients were asked to fast for eight hours before the blood collection, which took place from 6:00 am to 9:00 am. The blood collection process was completed by experienced nurses using vacuum blood collection tubes according to the hospital's blood collection procedures. Each patient was required to have two tubes of blood collected at each blood draw for research and storage. Blood collected was centrifuged at 3,000 g for 10 min to obtain patient serum immediately on the same day and stored at −80 °C. Sema4D concentrations were assessed using an enzyme-labeled immunosorbent assay (ELISA) kit (Xinle Co., Ltd., Shanghai, China) according to the instructions. ## Statistical analysis SPSS 19.0 software was used to perform χ2 test for categorical variables and t test for continuous variables. The Mann–Whitney test or Kruskal–Wallis test was used to analyze Sema4D levels in serum of MRONJ patients at different time points. Statistically significant differences were indicated by *$p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001.$ ## Demographic and clinical characteristics of patients The demographic and clinical characteristics of 97 patients who received dentoalveolar surgery were shown in Table 1. A total of 42 of the 97 patients were diagnosed with MRONJ, and the other 55 served as non-MRONJ controls. There were no significant differences in their basic characteristics, including gender, age, duration of BPs use, types of BPs used, and types of surgery. The two groups of patients had osteoporosis and bone metastasis before the use of BPs, and there was no significant difference in the number of patients. In patients in the MRONJ group, the sites of osteonecrosis included the mandible ($66.7\%$), the maxilla ($26.2\%$), and the mandible and maxilla ($7.1\%$). MRONJ grades include stage I ($35.7\%$), stage II ($45.2\%$) and stage III ($19.1\%$).Table 1Demographic and clinical characteristics of patients received dentoalveolar surgery ($$n = 97$$)CharacteristicsStudy grouppNon-MRONJ ($$n = 55$$)MRONJ ($$n = 42$$)Gender Male21 ($38.2\%$)14 ($33.3\%$)0.674 Female34 ($61.8\%$)28 ($66.7\%$)Age (years)65.4 ± 9.668.7 ± 10.70.108Duration of bisphosphonate exposure (months)44.2 ± 27.851.1 ± 32.50.223Indication for bisphosphonate treatment Osteoporosis36 ($65.5\%$)30 ($71.4\%$)0.661 Bone metastasis19 ($34.5\%$)12 ($28.6\%$)Administration route IV20 ($36.4\%$)22 ($52.4\%$)0.149 PO35 ($63.6\%$)20 ($47.6\%$)Bisphosphonates use Alendronate12 ($21.8\%$)8 ($19\%$)0.212 Pamidronate3 ($5.5\%$)1 ($2.4\%$) Ibandronate16 ($29.1\%$)16 ($38.1\%$) Zoledronate22 ($40\%$)11 ($26.2\%$) Risedronate2 ($3.6\%$)6 ($14.3\%$)Dentoalveolar surgery Tooth extraction36 ($65.5\%$)25 ($59.5\%$)0.765 Dental prosthesis11 ($20\%$)11 ($26.2\%$) Root cannel procedure8 ($14.5\%$)6 ($14.3\%$)Location Mandible–28 ($66.7\%$)– Maxilla11 ($26.2\%$) Mandible and maxilla3 ($7.1\%$)Stage I–15 ($35.7\%$)– II19 ($45.2\%$) III8 ($19.1\%$)Values were expressed as n (percentage, %) or mean ± SD. p values for each group were derived from Mann–Whitney test. Chi-square test or Fisher’s exact test was used for assessing distribution of observations or phenomena between two groups ## Serum Sema4D among different groups To further analyze the relationship between the onset of MRONJ and the level of Sema4D in serum, we compared serum Sema4D among healthy control, non-MRONJ and MRONJ at enrollment (T0, Fig. 2A) and diagnosis (T1, Fig. 2B) using Kruskal–Wallis test. As shown in Fig. 2A, the serum Sema4D of patients in the non-MRONJ group was significantly lower than that in the healthy group ($p \leq 0.05$). In addition, the Sema4D levels of patients in the MRONJ group were also significantly lower than those in the non-MRONJ group ($p \leq 0.01$). As shown in Fig. 2B, the Sema4D level of patients in MRONJ group was significantly lower than that in non-MRONJ group patients after diagnosis in MRONJ group patients, and the difference between the two groups was more significant ($p \leq 0.001$).Fig. 2Comparison of serum Sema4D among healthy control, non-MRONJ and MRONJ at enrollment (T0, A) and diagnosis (T1, B.) Data were presented with median (IQR). * $p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001.$ Kruskal–Wallis test followed by Dunn's multiple comparisons test ## ROC analysis of serum Sema4D for the prediction and diagnosis of MRONJ To further analyze the predictive ability of Sema4D for the onset of MRONJ, we used ROC analysis to analyze serum Sema4D for the prediction (Fig. 3A) and diagnosis (Fig. 3B) of MRONJ among patients with the risk of osteonecrosis of the jaw at T0 and T1. For Sema4D at T0, the cut off value was 11.83 ng/ml, the sensitivity was $78.57\%$, the specificity was $65.45\%$ and the AUC was 0.72 ($p \leq 0.001$, Fig. 3A). For Sema4D at T1, the cut off value was 6.57 ng/ml, the sensitivity was $71.43\%$, the specificity was $85.45\%$ and the AUC was 0.85 ($p \leq 0.001$, Fig. 3B).Fig. 3ROC analysis of serum Sema4D for the prediction A and diagnosis B of MRONJ among patients with the risk of osteonecrosis of the jaw ## Serum Sema4D in MRONJ patients with different stages and different routes of BPs administrations To further analyze the relationship between Sema4D and MRONJ, we compared serum Sema4D in MRONJ patients with different stages (Fig. 4A) and different routes of bisphosphonate administrations (Fig. 4B). There were significant differences in serum Sema4D levels in patients with grade III MRONJ with increasing disease severity, and there was no significant difference between stage I and II (Fig. 4A). On the other hand, the serum Sema4D level of the intravenous BPs was significantly lower than that of the oral BPs (Fig. 4B).Fig. 4Comparison of serum Sema4D in MRONJ patients with different stage (A) and different routes of bisphosphonate administrations (B). Data were presented with median (IQR). * $p \leq 0.05.$ Mann–Whitney test and Kruskal–Wallis test ## Discussion BPs have been discovered since the 1970s and have been widely used in clinic [18]. BPs were mainly used in clinic to treat osteoporosis and Paget's disease and control the bone metastasis of malignant tumors by inhibiting the function of osteoclasts [19]. BPs mainly affect the formation and activation of osteoclasts by down-regulating the transduction of RhoA signaling channel, and further affecting the cytoskeleton and other structures and the migration of intracellular vesicles [20]. Third-generation BPs, including zoledronic acid and ibandronate, are more resistant to bone resorption [21]. The incidence of adverse reactions of third-generation BPs was significantly lower than that of first- and second-generation BPs [22]. Therefore, now the third generation BPs have been widely used in clinical treatment. Clinical studies have shown that BPs have a very good therapeutic effect on the treatment of bone resorption diseases and the control of bone metastasis [23, 24]. However, it has been found in long-term applications that even the improved third-generation drugs still have a certain degree of adverse reactions and side effects, such as acute phase reactions (gastrointestinal reactions, skin symptoms, etc.) that can appear in a short period of time [22]. More importantly, long-term use of BPs can lead to severe skeletal sequelae, mainly including MRONJ, atypical femoral fractures, and skeletal muscle pain, which seriously affect the long-term quality of life of patients [25]. MRONJ is a rare benign lesion of the jaw in oral and maxillofacial surgery, which is more common in patients with malignant tumors treated with long-term intravenous administration of zoledronic acid [26]. Oral soft tissue in patients with MRONJ is difficult to heal after alveolar surgery. These patients develop osteomyelitis of the jaw several weeks later, with the formation of sequestrum, fistulas, etc. In 2014, the American Association of Oral and Maxillofacial Surgeons (AAOMS) recommended that MRONJ be expanded into medicine-related osteonecrosis of the jaw (MRONJ) [27]. AAOMS believes that in addition to BPs, other antiresorptive drugs such as denosumab, antiangiogenic drugs such as bevacizumab, and sunitinib may also cause osteonecrosis of the jaw [28]. Epidemiological statistics show that the incidence of MRONJ is significantly correlated with the patient's primary disease, the drugs used, the time of administration, the way of administration, and the dosage of medication [29]. For patients with postoperative chemotherapy for malignant tumors, regular intravenous use of zoledronic acid and alveolar surgery, the longer the medication time, the higher the incidence of MRONJ. No effective clinical treatment for MRONJ has yet been found [30]. Therefore, a series of preventive measures such as preventive dental care and timely intervention for early MRONJ symptoms are more important for patients who may develop MRONJ. In this study, we further verified the clinical value of Sema4D in the early prediction of MRONJ, and provided the basis for the early warning and diagnosis of MRONJ. Since alveolar surgery is a known predisposing factor for MRONJ, we included in this study 97 patients at risk for MRONJ who underwent alveolar surgery at our hospital. After 12 weeks of follow-up, 42 patients were diagnosed with MRONJ, implying that the probability of alveolar surgery leading to the onset of MRONJ with major medication history of BPs is extremely high. We believe that this is because long-term medication of BPs and alveolar surgery are important risk factors for the occurrence of MRONJ, and their combined effects lead to a high incidence of MRONJ in our study population. We measured serum Sema4D levels in all participants at two time points before surgery (T0) and after the end of follow-up (T1). We found that the level of Sema4D in serum of patients with confirmed MRONJ was significantly lower than that of non-MRONJ and healthy controls at two different time points T0 and T1, suggesting that Sema4D may have an inhibitory effect on the occurrence and development of MRONJ. We further performed ROC analysis on Sema4D in both non-MRONJ and MRONJ groups at T0 and T1 time points. We further performed ROC analysis on the predictive effect of participants' serum Sema4D levels on MRONJ diagnosis. Our results show that Sema4D has a significant predictive value for the occurrence of MRONJ. As a newly discovered family of proteins with the same domain, Semaphorins have bidirectional regulatory effects on osteoclasts and osteoblasts [31]. Factors in the Sema family that have been identified to play a role in bone homeostasis include Sema3A, Sema3E, Sema4D and so on [32]. Sema4D is clearly known to be expressed in osteoclasts. It can act on osteoblasts and can also counteract on osteoclasts [33]. Previous studies have shown that BPs and Sema4D act in opposite ways in many ways. BPs directly inhibited the activity and function of osteoclasts by inhibiting the RhoA and RANKL pathways, thereby reducing jaw bone resorption and increasing bone mineral density [34]. While Sema4D is expressed in osteoclasts, its receptor plexin B1 is expressed in osteoblasts [35]. Sema4D affects the migration of osteoclasts and the migration and functional activation of osteoblasts by activating the RhoA/ROCK pathway [36]. Therefore, it is possible to increase the concentration of sema4D in a cellular environment inhibited by BPs to increase the inhibitory effect of osteoblasts, thereby reducing bone formation. In this study, we also report the mutual exclusion of MRONJ and Sema4D, which is consistent with other findings. We demonstrate that serum Sema4D levels were significantly lower in patients with confirmed MRONJ compared to both non-MRONJ and healthy controls. Furthermore, we found that serum Sema4D levels were significantly lower in patients with grade III MRONJ relative to patients with grade I, II MRONJ. In addition, we reported that MRONJ patients who received intravenous BPs had significantly lower Sema4D levels than those who received oral BPs. This finding is in line with previous studies that patients with intravenous BPs had a higher prevalence of MRONJ than those with oral BPs. Our study to a certain extent indicates that the regulatory effect of Sema4D on bone homeostasis can be affected by BPs, which is related to the occurrence and development of MRONJ. However, the research at this stage is relatively basic, and the clear relationship between Sema4D in the occurrence and development of MRONJ has not been proved. We will continue to focus on this issue in future research. ## Conclusion In conclusion, we mainly investigated the relationship between serum Sema4D levels and the onset of MRONJ within 12 weeks after dentoalveolar surgery in patients with a history of BPs use in this study. We reported that Sema4D levels were significantly reduced in the serum of patients with confirmed MRONJ. Sema4D has a statistically predictive effect on the occurrence and diagnosis of MRONJ. We hope that our study will provide a rationale for early prevention and intervention of MRONJ. ## References 1. Yuan F, Peng W, Yang C, Zheng J. **Teriparatide versus bisphosphonates for treatment of postmenopausal osteoporosis: a meta-analysis**. *Int J Surg* (2019) **66** 1-11. DOI: 10.1016/j.ijsu.2019.03.004 2. Black DM, Rosen CJ. **Clinical practice postmenopausal osteoporosis**. *N Engl J Med* (2016) **374** 254-262. DOI: 10.1056/NEJMcp1513724 3. Ensrud KE. **Bisphosphonates for postmenopausal osteoporosis**. *JAMA* (2021) **325** 96. DOI: 10.1001/jama.2020.2923 4. Tella SH, Gallagher JC. **Prevention and treatment of postmenopausal osteoporosis**. *J Steroid Biochem Mol Biol* (2014) **142** 155-170. DOI: 10.1016/j.jsbmb.2013.09.008 5. Favus MJ. **Bisphosphonates for osteoporosis**. *N Engl J Med* (2010) **363** 2027-2035. DOI: 10.1056/NEJMct1004903 6. Reid IR. **A broader strategy for osteoporosis interventions**. *Nat Rev Endocrinol* (2020) **16** 333-339. DOI: 10.1038/s41574-020-0339-7 7. Kim J, Lee DH, Dziak R, Ciancio S. **Bisphosphonate-related osteonecrosis of the jaw: current clinical significance and treatment strategy review**. *Am J Dent* (2020) **33** 115-128. PMID: 32470236 8. Ferreira LH, Mendonca KD, de Souza JC, Dos Reis DCS, do Guedes CCFV, de Filice LSC. **Bisphosphonate-associated osteonecrosis of the jaw**. *Minerva Dent Oral Sci* (2021) **70** 49-57. PMID: 32960522 9. Chien HI, Chen LW, Liu WC, Lin CT, Ho YY, Tsai WH. **Bisphosphonate-related osteonecrosis of the jaw**. *Ann Plast Surg* (2021) **86** S78-S83. DOI: 10.1097/SAP.0000000000002650 10. Shibahara T. **Antiresorptive Agent-Related Osteonecrosis of the Jaw (ARONJ): a twist of fate in the bone**. *Tohoku J Exp Med* (2019) **247** 75-86. DOI: 10.1620/tjem.247.75 11. Otto S, Pautke C, Van den Wyngaert T, Niepel D, Schiodt M. **Medication-related osteonecrosis of the jaw: prevention, diagnosis and management in patients with cancer and bone metastases**. *Cancer Treat Rev* (2018) **69** 177-187. DOI: 10.1016/j.ctrv.2018.06.007 12. Kawahara M, Kuroshima S, Sawase T. **Clinical considerations for medication-related osteonecrosis of the jaw: a comprehensive literature review**. *Int J Implant Dent* (2021) **7** 47. DOI: 10.1186/s40729-021-00323-0 13. Alto LT, Terman JR. **Semaphorins and their Signaling Mechanisms**. *Methods Mol Biol* (2017) **1493** 1-25. DOI: 10.1007/978-1-4939-6448-2_1 14. Zhang CL, Hong CD, Wang HL, Chen AQ, Zhou YF, Wan Y. **The role of semaphorins in small vessels of the eye and brain**. *Pharmacol Res* (2020) **160** 105044. DOI: 10.1016/j.phrs.2020.105044 15. Solmaz M, Lane A, Gonen B, Akmamedova O, Gunes MH, Komurov K. **Graphical data mining of cancer mechanisms with SEMA**. *Bioinformatics* (2019) **35** 4413-4418. DOI: 10.1093/bioinformatics/btz303 16. Zhang Y, Liu B, Ma Y, Jin B. **Sema 4D/CD100-plexin B is a multifunctional counter-receptor**. *Cell Mol Immunol* (2013) **10** 97-98. DOI: 10.1038/cmi.2012.65 17. Hernandez-Fleming M, Rohrbach EW, Bashaw GJ. **Sema-1a reverse signaling promotes midline crossing in response to secreted semaphorins**. *Cell Rep* (2017) **18** 174-184. DOI: 10.1016/j.celrep.2016.12.027 18. Ralston SH. **Bisphosphonates in the management of Paget's disease**. *Bone* (2020) **138** 115465. DOI: 10.1016/j.bone.2020.115465 19. Takeuchi Y. **Innovation of bisphosphonates for improvement of adherence**. *Clin Calcium* (2017) **27** 197-202. PMID: 28123121 20. Baroncelli GI, Bertelloni S. **The use of bisphosphonates in pediatrics**. *Horm Res Paediatr* (2014) **82** 290-302. DOI: 10.1159/000365889 21. Cremers S, Papapoulos S. **Pharmacology of bisphosphonates**. *Bone* (2011) **49** 42-49. DOI: 10.1016/j.bone.2011.01.014 22. Molvik H, Khan W. **Bisphosphonates and their influence on fracture healing: a systematic review**. *Osteoporos Int* (2015) **26** 1251-1260. DOI: 10.1007/s00198-014-3007-8 23. Lili Liu HM, Pang Ying. **Caffeic acid treatment augments the cell proliferation, differentiation, and calcium mineralization in the human osteoblast-Like MG-63 cells**. *Pharmacogn Mag* (2021) **17** 6 24. Hong Mu YP, Liu Lili, Li F, Wang J. **Citral promotes the cell proliferation, differentiation, and calcium mineralization in human osteoblast-like MG-63 Cells**. *Pharmacogn Mag* (2021) **17** 5 25. AlRahabi MK, Ghabbani HM. **Clinical impact of bisphosphonates in root canal therapy**. *Saudi Med J* (2018) **39** 232-238. DOI: 10.15537/smj.2018.3.20923 26. Shannon J, Shannon J, Modelevsky S, Grippo AA. **Bisphosphonates and osteonecrosis of the jaw**. *J Am Geriatr Soc* (2011) **59** 2350-2355. DOI: 10.1111/j.1532-5415.2011.03713.x 27. Kanwar N, Bakr MM, Meer M, Siddiqi A. **Emerging therapies with potential risks of medicine-related osteonecrosis of the jaw: a review of the literature**. *Br Dent J* (2020) **228** 886-892. DOI: 10.1038/s41415-020-1642-3 28. Gurav S, Dholam KP, Singh GP. **Treatment of refractory medicine related osteonecrosis of jaw with piezosurgical debridement and autologous platelet rich fibrin: feasibility study**. *J Craniofac Surg* (2021). DOI: 10.1097/SCS.0000000000007981 29. Mathijssen NM, Buma P, Hannink G. **Combining bisphosphonates with allograft bone for implant fixation**. *Cell Tissue Bank* (2014) **15** 329-336. DOI: 10.1007/s10561-013-9416-7 30. Cohen SB. **An update on bisphosphonates**. *Curr Rheumatol Rep* (2004) **6** 59-65. DOI: 10.1007/s11926-004-0084-2 31. Yazdani U, Terman JR. **The semaphorins**. *Genome Biol* (2006) **7** 211. DOI: 10.1186/gb-2006-7-3-211 32. Limoni G, Niquille M. **Semaphorins and Plexins in central nervous system patterning: the key to it all?**. *Curr Opin Neurobiol* (2021) **66** 224-232. DOI: 10.1016/j.conb.2020.12.014 33. Wu JH, Li YN, Chen AQ, Hong CD, Zhang CL, Wang HL. **Inhibition of Sema4D/PlexinB1 signaling alleviates vascular dysfunction in diabetic retinopathy**. *EMBO Mol Med* (2020) **12** e10154. DOI: 10.15252/emmm.201810154 34. Anastasilakis AD, Polyzos SA, Makras P, Gkiomisi A, Sakellariou G, Savvidis M. **Circulating semaphorin-4D and plexin-B1 levels in postmenopausal women with low bone mass: the 3-month effect of zoledronic acid, denosumab or teriparatide treatment**. *Expert Opin Ther Targets* (2015) **19** 299-306. DOI: 10.1517/14728222.2014.983078 35. Movila A, Mawardi H, Nishimura K, Kiyama T, Egashira K, Kim JY. **Possible pathogenic engagement of soluble Semaphorin 4D produced by gammadeltaT cells in medication-related osteonecrosis of the jaw (MRONJ)**. *Biochem Biophys Res Commun* (2016) **480** 42-47. DOI: 10.1016/j.bbrc.2016.10.012 36. Zhang Y, Wei L, Miron RJ, Zhang Q, Bian Z. **Prevention of alveolar bone loss in an osteoporotic animal model via interference of semaphorin 4d**. *J Dent Res* (2014) **93** 1095-1100. DOI: 10.1177/0022034514552676
--- title: Mitofusin2 expression is associated with podocyte injury in IgA nephropathy authors: - Xuanli Tang - Yuan Yuan - Xiaoli Liang - Xue Jiang journal: European Journal of Medical Research year: 2023 pmcid: PMC10061881 doi: 10.1186/s40001-023-01107-5 license: CC BY 4.0 --- # Mitofusin2 expression is associated with podocyte injury in IgA nephropathy ## Abstract ### Background Podocyte injury is associated with IgA nephropathy (IgAN) prognosis. Mitochondrial dysfunction is a major contributor to podocyte injury and death. Mitofusin2 (Mfn2) plays an important role in regulating the morphology and function of mitochondria. This study aimed to investigate the potential of Mfn2 as a biomarker to evaluate the degree of podocyte injury. ### Methods This single-center, retrospective study enrolled 114 patients with biopsy-proven IgAN. Immunofluorescence and TUNEL staining were applied, and clinical and pathological features were compared between patients with different patterns of Mfn2 expression. ### Results In IgAN, Mfn2 is mainly expressed in podocytes and significantly associated with nephrin, TUNEL, and Parkin staining. Among the 114 IgAN patients, 28 ($24.56\%$) did not exhibit Mfn2 expression in podocytes. The patients in the Mfn2-negative group had lower serum albumin (34.43 ± 4.64 g/L vs. 36.48 ± 3.52 g/L, $$P \leq 0.015$$) and estimated glomerular filtration rate (eGFR) (76.59 ± 35.38 mL/min vs. 92.13 ± 25.35 mL/min, $$P \leq 0.013$$), higher 24 h proteinuria (2.48 ± 2.72 g/d vs. 1.27 ± 1.31 g/d, $$P \leq 0.002$$), serum creatinine (Scr) (107.39 ± 57.97 μmol/L vs. 84.70 ± 34.95 μmol/L, $$P \leq 0.015$$), blood urea nitrogen (BUN) (7.36 ± 4.45 mmol/L vs. 5.68 ± 2.14 mmol/L, $$P \leq 0.008$$), and higher S/T scores ($92.86\%$ vs. $70.93\%$ and $42.85\%$ vs. $15.12\%$, respectively, $P \leq 0.05$). In the Mfn2-negative group, the mitochondria were punctate and round ridges disappeared, and a lower length-to-width ratio and much higher M/A ratio were observed. Correlation analysis showed that the intensity of Mfn2 was negatively correlated with Scr (r = − 0.232, $$P \leq 0.013$$), 24 h proteinuria (r = − 0.541, $$P \leq 0.001$$), and the degree of podocyte effacement (r = − 0.323, $$P \leq 0.001$$), and positively correlated with eGFR ($r = 0.213$, $$P \leq 0.025$$). Logistic regression analysis showed that the Mfn2-negative group had a higher risk of severe podocyte effacement (≥ $50\%$) (OR = 3.061, $$P \leq 0.019$$). ### Conclusion Mfn2 was negatively correlated with proteinuria and renal function. A lack of Mfn2 in podocytes indicates severe podocyte injury and a high degree of podocyte effacement. ## Introduction Immunoglobulin A (IgA) nephropathy (IgAN) is the most common primary glomerulonephritis in the world [1]. As the leading cause of end-stage renal disease (ESRD), approximately 10–$60\%$ of patients with IgAN progress to kidney failure within 10–20 years [1, 2]. Mesangial cell proliferation and IgA1-predominant mesangial deposits are pathological hallmarks of IgAN. Previous studies have suggested that podocyte injury also occurs in IgAN and is associated with the pathogenesis of Gd-IgA1. Podocyte injury is usually associated with significant proteinuria, manifested as foot process effacement, which is considered to be a key factor leading to progression and poor prognosis in IgAN [3–5]. Podocytes are terminally differentiated and have a poor proliferative capacity. Mitochondrial dysfunction is a major contributor to podocyte injury and death [6, 7]. Various mitochondrial dysfunction pathways have been identified as the main molecular mechanisms of podocyte injury, such as elevated mitochondrial ROS production [8], imbalanced mitochondrial dynamics [9], and decreased mitochondrial biogenesis [10, 11]. Mitofusin2 (Mfn2) was initially identified as a dynamin-like protein involved in fusion of the outer mitochondrial membrane (OMM) that participates in mitochondrial fusion and contributes to the maintenance of the mitochondrial network [12]. Moreover, Mfn2 is involved in the clearance of damaged mitochondria, serves as a mitochondrial receptor for Parkin (E3 ubiquitin ligase), and facilitates the recruitment of Parkin to the impaired mitochondria, which participates in mitophagy [13–15]. Our previous study showed that Mfn2 deficiency participates in podocyte injury in a focal segmental glomerulosclerosis (FSGS) animal model by inhibiting Pink1/Parkin-associated mitophagy. In diabetic kidney disease (DKD), Cao et al. [ 16] reported that Mfn2 regulates the morphology and functions of mitochondria-associated ER membranes (MAMs) and mitochondria by inhibiting the PERK pathway and exerts anti-apoptotic effects on podocytes. Whether Mfn2 participates in podocyte injury and is related to clinical and pathological characteristics in IgA nephropathy has not been reported to date. Therefore, this study aimed to explore the relationship between Mfn2 expression and podocyte injury and further elucidate its potential predictive value for IgAN prognosis. ## Patients Patients aged ≥ 18 years with biopsy-proven IgAN in the Hangzhou Hospital of Traditional Chinese Medicine between April 2022 and August 2022 were enrolled, and secondary causes of IgAN, such as liver or inflammatory bowel diseases, other autoimmune disorders, infections, and Henoch–Schönlein purpura, were excluded. Clinical data, including sex, age, proteinuria, serum creatinine (Scr), blood urea nitrogen (BUN), serum albumin (ALB), blood pressure, and serum IgA, were collected at the time of biopsy. ## Histopathology Renal histological lesions were graded based on the MEST-C score [17]. M0/M1 was defined as ≤ / > $50\%$ of glomeruli exhibiting mesangial hypercellularity, E0/E1 as the absence/presence of endocapillary hypercellularity, S0/S1 as the absence/presence of segmental glomerulosclerosis, T0/T1/T2 as tubular atrophy/interstitial fibrosis ≤ 25–$50\%$ > $50\%$, and C0/C1/C2 as absence/ < $25\%$/ ≥ $25\%$ of crescent lesions. The immunofluorescence samples were stained with fluorescein isothiocyanate (FITC)-conjugated antibodies specific for human IgG, IgM, IgA, C3, C4, and C1q (1:50, DAKO, Glostrup,Denmark). The degree of immunofluorescence was scored on a scale of 0–4 (score 0, negative; score 1, +; score 2, + +; score 3, + + +; score 4, + + + +). ## Immunofluorescence staining Frozen tissues were embedded in OCT, cut into 5 μm sections, and then stored at – 20 ℃. Rabbit anti-human Mfn2 antibody (1:100; Cat No. M6319, Sigma-Aldrich), rabbit anti-human nephrin monoclonal antibody (1:100; ab50339, Abcam), rat anti-human collagen IV alpha 5 monoclonal antibody (1:100; C-452, Cosmo Bio), rabbit anti-human Parkin antibody(1:100; 14060-1-AP, Proteintech) were reacted with renal tissue at 4 °C overnight. AF488-conjugated donkey anti-rabbit IgG antibody (1:500; A-21206, Invitrogen), and FITC conjugated donkey anti-rat IgG antibody (1:500, A-18740, Invitrogen) were incubated for half an hour at 37 ℃. Sections were observed using a fluorescence microscope (Nikon 80i; Nikon, Tokyo, Japan). ## Electron microscopy The renal biopsy specimens were fixed with osmic acid and glutaraldehyde, dehydrated, and embedded in EPON™ resin. Sections with a thickness of 1 mm were cut and stained with uranyl acetate and lead citrate. Thin sections were examined using a JEOL-1400 electron microscope (JEOL, Tokyo, Japan). The degree of podocyte effacement was graded on a scale of 1–5 with 1 = podocyte effacement < $25\%$, 2 = 25–$50\%$, 3 = 50–$75\%$, 4 = 75–$95\%$, and 5 = ≥ $95\%$. The number of mitochondria (M) and the area of podocytes (A) were assessed using ImageJ software. The ration M/A was used to evaluate the number of mitochondria per area and the length-to-width ratio of mitochondria was used to evaluate mitochondrial morphology. All pathological parameters were assessed and measured by two independent pathologists. ## Apoptosis assay Tissue sections were stained with an In Situ Cell Death Detection Kit, Fluorescein, (Roche Cat No. 11684795910), and the slices were exposed to freshly prepared permeabilization solution for 2 min on ice ($0.1\%$ Triton X-100, $0.1\%$ sodium citrate). After washing with PBS, the samples were resuspended in 50 µL of TUNEL (terminal deoxynucleotidyl transferase-mediated nick end-labeling (TUNEL) reaction mixture and incubated for 60 min in a dark, humidified environment. The samples were then washed with PBS, and the slides were examined using a fluorescence microscope (Nikon 80i; Nikon, Tokyo, Japan). ## Statistical analysis Statistical analyses were performed using SPSS (version 23.0; SPSS Inc., Chicago, IL, USA) and GraphPad Prism (version 5.0; GraphPad Software, Inc., La Jolla, CA, USA). Fisher's exact test or the χ2 test was used to compare qualitative data, and the Wilcoxon rank-sum test was used for continuous variables. Spearman’s correlation test was used to assess the strength of the association between Mfn2 levels and clinical or pathological variables. Logistic regression analysis was performed to ascertain the relationship between Mfn2 expression and podocyte effacement. Statistical significance was set at $P \leq 0.05.$ ## Patient characteristics at the time of renal biopsy One hundred and fourteen patients who underwent renal biopsy between April 2022 and August 2022 were enrolled, including 57 males ($50\%$) and 57 females ($50\%$). The mean age was 39.4 ± 12.1 years. Thirty-eight patients had hypertension, but no diabetes was found at the time of renal biopsy. Sixty-four patients had stage 1 chronic kidney disease (CKD1), 26 had CKD2, 22 had CKD3, and only 2 had CKD4 [18]. ## Mfn2 staining Mfn2 was detected in glomeruli at different intensities, and a staining threshold was used to divide the patients into two groups. The positive group was defined as Mfn2 expressed globally or segmentally in the glomeruli, and the negative group was defined as no or only traces of Mfn2 staining in glomeruli (Fig. 1A).Fig. 1Mfn2 expression in patients with IgAN. A Representative images of immunohistochemical staining of Mfn2 in glomeruli per group (original magnification × 400). Mfn2 expressed globally or segmentally in the glomeruli were divided into positive group, no/trace Mfn2 staining in glomeruli was defined as negative group. B The location and form of Mfn2 expression in glomeruli. Mfn2 was co-stained with nephrin and Col-IV α5. Mfn2 deposition in two different types, granular deposition or deposition along the GBM at the same time (original magnification × 400). C Representative images of immunohistochemical staining of Mfn2 in DKD, MN and normal control (original magnification × 400) The location of Mfn2 expression was determined by double-immunofluorescence staining with anti-Col IV α5 or anti-nephrin antibody. The results show that Mfn2 was located outside the glomerular basement membrane (GBM) and co-localized with nephrin, indicating that Mfn2 is expressed in podocytes (Fig. 1B). We also found that Mfn2 deposition occurred in two different ways: granular deposition or deposition along the GBM at the same time (Fig. 1B) We also detected Mfn2 expression in five DKD and five membranous nephropathy (MN) patients as disease controls, and five precancerous tissues from kidney tumors served as normal controls. As shown in Fig. 1C, in the normal control group, we detected diffuse Mfn2 deposition in the glomeruli. While all five of the DKD patients did not have detectable Mfn2 deposition. In MN, all five MN patients had segmental or traces of Mfn2 staining in glomeruli, and there were no significant differences in the deposition site and form of Mfn2 in MN compared with IgAN. We further compared the correlation between Mfn2 and nephrin expression. The results show that the fluorescence intensity of Mfn2 decreased in parallel with the intensity of nephrin expression, indicating that there was probably a correlation between Mfn2 and nephrin expression (Fig. 2A).Fig. 2The association between Mfn2 expression and podocyte apoptosis and mitophagy. A Representative images of immunofluorescent staining of Mfn2 and nephrin in glomeruli per group (original magnification × 400). B Representative images of TUNEL staining in different group, Red arrows marked TUNEL positive cells (original magnification × 400). C Representative images of Mfn2 and Parkin staining in different group (original magnification × 400) Meanwhile, TUNEL staining showed that more positive cells were observed in the glomeruli in the Mfn2-negative group than in the positive group, indicating that podocytes without Mfn2 expression may be in the process of undergoing apoptosis (Fig. 2B). ## The correlation between Mfn2 expression and clinicopathological features As shown in Table 1, 28 patients ($24.56\%$) were assigned to the negative group, whereas 86 patients ($75.44\%$) were assigned to the positive group. There were no significant differences in terms of age, gender, or blood pressure between the two groups, although the patients in the Mfn2-negative group had lower serum albumin (34.43 ± 4.64 g/L vs. 36.48 ± 3.52 g/L, $$P \leq 0.015$$) and eGFR levels (76.59 ± 35.38 mL/min vs. 92.13 ± 25.35 mL/min, $$P \leq 0.013$$) than the positive group. Additionally, the 24 h proteinuria level (2.48 ± 2.72 g/d vs. 1.27 ± 1.31 g/d, $$P \leq 0.002$$), Scr (107.39 ± 57.97 μmol/L vs. 84.70 ± 34.95 μmol/L, $$P \leq 0.015$$), and BUN (7.36 ± 4.45 mmol/L vs. 5.68 ± 2.14 mmol/L, $$P \leq 0.008$$) were much higher in the Mfn2-negative group than the positive group (shown in Table 1).Table 1Baseline data of patients with IgAN in the Mfn2-negative group and Mfn2-positive groupMfn2 (+)($$n = 86$$)Mfn2[-]($$n = 28$$)P valueDiastolic blood pressure (mmHg)79.30 ± 11.6380.89 ± 14.060.552Systolic blood pressure (mmHg)125.95 ± 16.77127.04 ± 13.300.757Age (Year)40.20 ± 11.7137.00 ± 13.190.226Sex(male/female)$\frac{44}{4213}$/150.663Hypertension(%)30 ($34.88\%$)9 ($34.62\%$)0.825Scr (μmol/L)84.70 ± 34.95107.39 ± 57.970.015BUN (mmol/L)5.68 ± 2.147.36 ± 4.450.008UA (μmol/L)374.26 ± 112.15389.46 ± 104.380.528Alb (g/L)36.48 ± 3.5234.43 ± 4.640.01524hPro (g/d)1.27 ± 1.312.48 ± 2.720.002GFR (mL/min)92.13 ± 25.3576.59 ± 35.380.013Serum IgA325.24 ± 106.58303.82 ± 80.100.332 The renal pathological parameters showed higher S/T scores ($92.86\%$ vs. $70.93\%$ and $42.85\%$ vs. $15.12\%$, respectively, $P \leq 0.05$) and C1q deposits ($46.4\%$ vs. $25.6\%$, $$P \leq 0.05$$) in the Mfn2-negative group than in the positive group. Furthermore, a higher degree of podocyte effacement was observed in the negative group (2.78 ± 1.24 vs. 2.10 ± 0.81, $$P \leq 0.02$$) than in the positive group by electron microscopy (Table 2).Table 2Renal pathological parameters of patients with IgAN in the Mfn2-negative group and Mfn2-positive groupMfn2 (+)($$n = 86$$)Mfn2(−)($$n = 28$$)PM [n (%)]M0 0 ($0\%$)M1 86 ($100\%$)M0 1 (3.57)M1 26($96.43\%$)0.25E[n (%)]E0 45 ($52.32\%$)E1 41 ($47.67\%$)E0 15 ($53.57\%$)E1 13 ($46.43\%$)0.58S [n (%)]S0 25 (29.07)S1 61 ($70.93\%$)S0 2 ($7.14\%$)S1 26 ($92.86\%$)0.04T [n (%)]T0 73 ($84.88\%$)T1 13 ($15.12\%$)T2 0 ($0\%$)T0 14 ($50\%$)T1 12 ($42.85\%$)T2 2 ($7.14\%$)0.01C [n (%)]C0 38 ($44.19\%$)C1 47 ($54.65\%$)C2 1 ($1.16\%$)C0 10 ($35.71\%$)C1 18 ($64.29\%$)C2 0 ($0\%$)0.58Intensity of IgA deposition2.98 ± 0.322.89 ± 0.420.21Intensity of IgG deposition0.72 ± 0.650.71 ± 0.620.96Intensity of IgM deposition1.14 ± 0.331.37 ± 0.630.07Intensity of C3 deposition2.24 ± 0.592.21 ± 0.730.88C4 deposition [n (%)]6 ($7.1\%$)3 ($10.7\%$)0.68C1q deposition [n (%)]22 ($25.6\%$)13 ($46.4\%$)0.05M/A0.112 ± 0.4830.156 ± 0.670.001Length to width ratio2.03 ± 0.931.23 ± 0.240.0001Degree of podocyte fusion2.10 ± 0.812.78 ± 1.240.02 Accumulating evidence has revealed that PTEN-induced putative kinase 1 (PINK1) and Parkin participate in mitophagy during cardiac and kidney injury [14, 15]. PINK1-activated Parkin translocates to mitochondria with low membrane potential to initiate the autophagic degradation of damaged mitochondria [19] *As a* receptor of Parkin, Mfn2 is involved in the regulation of PINK1/Parkin-regulated mitophagy [14]. Therefore, we compared Parkin expression between the two groups, as shown in Fig. 2C. Parkin expression in glomeruli paralleled Mfn2 expression, with Parkin expression at the same time in the Mfn2-positive group, while there was no Parkin staining in the Mfn2-negative group. In addition to the detection of autophagy-related proteins, we also directly observed morphological and structural changes in mitochondria using TEM. Comparison of the number of mitochondria per unit area of podocyte (M/A) between the two groups showed that the M/A ratio was significantly higher in the Mfn2-negative group than in the positive group (0.156 ± 0.67 vs. 0.112 ± 0.483, $$P \leq 0.001$$), indicating that more mitochondria were present in the Mfn2-negative group than in the positive group (Table 2). The morphology of mitochondria was evaluated by comparing the length-to-width ratio, and the results showed that most of the mitochondria in the negative group exhibited a punctate or round shape with the disappearance of ridges, whereas they had an oval shape with normal ridges in the Mfn2-positive group (Fig. 3). Furthermore, we found that the length-to-width ratio of mitochondria was much lower in the Mfn2-negative group than that in the positive group, indicating that impaired mitochondria were common in the Mfn2-negative group (Table 2, Fig. 3).Fig. 3Mitochondria changes in patients with IgAN. The Transmission Electron Microscopy (TEM) images of Mitochondrial morphology and number in Mfn2 negative and positive group. ( A The morphology of mitochondria in Mfn2 positive and negative group. The red arrows marked normal mitochondria Swollen and injured mitochondria marked by stars (original magnification × 20,000) B Representative images of mitochondria in podocyte with Mfn2 negative and positive group. In negative Mfn2 showed severe podocyte effacement marked by bule arrow (original magnification × 5000) ## Correlation between Mfn2 quantitative fluorescence intensity and clinical data The Mfn2 staining intensity in glomeruli was quantified using ImageJ software. The fluorescence intensity was 85.49 ± 16.05 in the Mfn2-positive group versus 50.57 ± 8.89 in the Mfn2-negative group, which was significantly different ($$P \leq 0.003$$). Correlation analysis showed that the intensity of Mfn2 was negatively correlated with the levels of Scr (r = − 0.232, $$P \leq 0.013$$), 24 h proteinuria (r = − 0.541, $$P \leq 0.001$$), and the degree of podocyte effacement (r = − 0.323, $$P \leq 0.001$$), whereas it was positively correlated with eGFR ($r = 0.213$, $$P \leq 0.025$$) (Fig. 4).Fig. 4Line correlation between quantitative fluorescence intensity of Mfn2 and clinical data. Expression levels of Mfn2 in glomeruli negative correlated with Scr and proteinuria, positive correlated with GFR Logistic regression analysis showed that the Mfn2-negative group had a significantly higher risk of severe podocyte effacement (≥ $50\%$) than that of the positive group (OR = 3.061, $$P \leq 0.019$$), which suggests Mfn2 negativity indicates a higher possibility of podocyte injury. ## Discussion In our study, we found that nearly $75.44\%$ of patients with IgAN had Mfn2 expression in the kidney. Through immunofluorescence co-staining of Mfn2 with Col IV α5 and nephrin, we found that Mfn2 was mainly expressed in podocytes, and the intensity of Mfn2 decreased consistently with nephrin expression. TUNEL staining revealed an increase in podocyte apoptosis in the Mfn2-negative group. These findings indicate that Mfn2 expression is associated with podocyte injury in IgAN patients. In patients with DKD, Cao et al. similarly found a dramatic reduction in Mfn2 expression in DKD patients compared to the expression in healthy individuals, along with increased podocyte apoptosis, as detected by TUNEL and decreased synaptopodin expression [16]. As a mitochondrial membrane protein, Mfn2 participates in mitochondrial fusion and contributes to the maintenance and functioning of the mitochondrial network [20]. Additionally, Mfn2 is also involved in the regulation of mitophagy [13]. Mfn2 deletion has been reported to suppress mitophagy in mouse embryonic fibroblasts, cardiomyocytes, and macrophages. [ 13–15] Cao et al. reported that high glucose (HG)-induced podocyte mitochondrial dysfunction, MAMs reduction, and increased apoptosis in vitro were accompanied by downregulation of Mfn2 [15]. Jiang et al. found that palmitic acid (PA)-induced podocyte injury was accompanied by downregulation of Mfn2 and inhibition of mitophagy [21]. Our previous in vitro and animal model studies have indicated that overexpression of Mfn2 can inhibit puromycin aminonucleoside (PAN)-induced podocyte apoptosis. In the present study, we made similar findings: patients lacking Mfn2 expression had more serious podocyte injury and mitophagy. Electron microscopy showed that the degree of podocyte effacement was more pronounced in the Mfn2-negative group. By observing the number and the morphological structure of mitochondria in podocytes, we discovered that, compared with the positive group, the number of mitochondria per unit area was higher in the Mfn2-negative group, while the length-to-width ratio of mitochondria was smaller, indicating that there were more damaged mitochondria in podocytes in the Mfn2-negative group. Lack of Mfn2 inhibits mitophagy, which results in the accumulation of damaged mitochondria and excessive ROS production in cells, ultimately promoting podocyte apoptosis, which causes podocyte effacement and impairs the selective filtration of GBM, thereby leading to proteinuria. Podocyte injury plays an important role in IgAN. Podocyte dedifferentiation is associated with IgAN [3, 22, 23]. Podocytopenia (loss of podocytes) has been reported to correlate with proteinuria and renal outcomes in IgAN patients [24, 25]. Moreover, mitochondria play a key role in maintaining the function and structure of podocytes. In our study, we found that $75\%$ of IgAN were Mfn2-positive and $24\%$ were negative. The negative patients had higher levels of proteinuria and Scr, higher S/T scores, and a higher degree of podocyte effacement and M/A value, indicating that Mfn2 expression negatively correlates with the severity of IgAN. Furthermore, TUNEL staining demonstrated that negative Mfn2 expression was associated with severe podocyte apoptosis, and the expression of Mfn2 was an independent risk factor for the severity of podocyte effacement, thus indicating that a reduced Mfn2 results in inhibition of mitophagy and the accumulation of damaged mitochondria and excessive ROS production in the cytoplasm. Ultimately, it promotes podocyte apoptosis and exacerbates IgAN progression. ## Conclusion In IgAN, Mfn2 is mainly expressed in podocytes and is negatively correlated with proteinuria and renal function. A lack of Mfn2 in podocytes indicates severe podocyte injury and a high degree of podocyte effacement. Thus, Mfn2 could be a useful indicator of the severity and poor prognosis of IgAN. This article still has several shortcomings. Firstly, it is a single-center study with a limited sample size. Additionally, due to a short-term follow-up, the treatment and outcome data have not been collected. Secondly, this article does not include a study of other mitochondrial morphology-related proteins such as mitofusin 1 (Mfn1), optic atrophy 1 (OPA1), and dynamin-related protein 1 (Drp1). Further studies are necessary to enhance the understanding of the correlation between Mfn2 and the outcome in IgAN patients, as well as to investigate other mitochondrial fusion and fission proteins. ## References 1. Wyatt RJ, Julian BA. **IgA nephropathy**. *N Eng l J Med* (2013) **368** 2402-2414. DOI: 10.1056/NEJMra1206793 2. Le W, Liang S, Hu Y. **Long-term renal survival and related risk factors in patients with IgA nephropathy: results from a cohort of 1155 cases in a Chinese adult population**. *Nephrol Dial Transplant* (2012) **27** 1479-1485. DOI: 10.1093/ndt/gfr527 3. Hill GS, Karoui KE, Karras A, Mandet C, Van Huyen JPD, Nochy D, Bruneval P. **Focal segmental glomerulosclerosis plays a major role in the progression of IgA nephropathy. I. Immunohistochemical studies**. *Kidney Int* (2011) **79** 635-642. DOI: 10.1038/ki.2010.466 4. El Karoui K, Hill GS, Karras A, Moulonguet L, Caudwell V, Loupy A, Bruneval P, Jacquot C, Nochy D. **Focal segmental glomerulosclerosis plays a major role in the progression of IgA nephropathy. II. Light microscopic and clinical studies**. *Kidney Int* (2011) **79** 643-654. DOI: 10.1038/ki.2010.460 5. Cook HT. **Focal segmental glomerulosclerosis in IgA nephropathy: a result of primary podocyte injury?**. *Kidney Int* (2011) **79** 581-583. DOI: 10.1038/ki.2010.521 6. Carney EF. **Glomerular disease: autophagy failure and mitochondrial dysfunction in FSGS**. *Nat Rev Nephrol* (2015) **11** 66. PMID: 25488857 7. Arif E, Solanki AK, Srivastava P, Rahman B, Fitzgibbon WR, Deng P. **Mitochondrial biogenesis induced by the β2-adrenergic receptor agonist formoterol accelerates podocyte recovery from glomerular injury**. *Kidney Int* (2019) **96** 656-673. DOI: 10.1016/j.kint.2019.03.023 8. Jha JC, Thallas-Bonke V, Banal C, Gray SP, Chow BSM, Ramm G. **Podocyte-specific Nox4 deletion affords renoprotection in a mouse model of diabetic nephropathy**. *Diabetologia* (2016) **59** 379-389. DOI: 10.1007/s00125-015-3796-0 9. Ayanga BA, Badal SS, Wang Y, Galvan DL, Chang BH, Schumacker PT. **Dynamin-related protein 1 deficiency improves mitochondrial fitness and protects against progression of diabetic nephropathy**. *JASN* (2016) **27** 2733-2747. DOI: 10.1681/ASN.2015101096 10. Bhargava P, Schnellmann RG. **Mitochondrial energetics in the kidney**. *Nat Rev Nephrol* (2017) **13** 629-646. DOI: 10.1038/nrneph.2017.107 11. Qin X, Jiang M, Zhao Y, Gong J, Su H, Yuan F. **Berberine protects against diabetic kidney disease via promoting PGC-1α-regulated mitochondrial energy homeostasis**. *Br J Pharmacol* (2020) **177** 3646-3661. DOI: 10.1111/bph.1493 12. Cao YL, Meng S, Chen Y, Feng JX, Gu DD, Yu B. **MFN1 structures reveal nucleotide-triggered dimerization critical for mitochondrial fusion**. *Nature* (2017) **542** 372-376. DOI: 10.1038/nature21077 13. Chen Y, Dorn GW. **PINK1-phosphorylated mitofusin 2 is a parkin receptor for culling damaged mitochondria**. *Science* (2013) **340** 471-475. DOI: 10.1126/science.1231031 14. Xiong W, Ma Z, An D, Liu Z, Cai W, Bai Y, Zhan Q, Lai W, Zeng Q, Ren H, Xu D. **Mitofusin 2 participates in mitophagy and mitochondrial fusion against angiotensin II-induced cardiomyocyte injury**. *Front Physiol* (2019) **10** 411. DOI: 10.3389/fphys.2019.00411 15. Bhatia D, Chung KP, Nakahira K, Patino E, Rice MC, Torres LK, Muthukumar T, Choi AM, Akchurin OM, Choi ME. **Mitophagy dependent macrophage reprogramming protects against kidney fibrosis**. *JCI Insight* (2019) **4** e132826. DOI: 10.1172/jci.insight.132826 16. Cao Y, Chen Z, Hu J, Feng J, Zhu Z, Fan Y, Lin Q, Ding G. **Mfn2 regulates high glucose-induced mams dysfunction and apoptosis in podocytes via PERK pathway**. *Front Cell Dev Biol* (2021) **9** 769213. DOI: 10.3389/fcell.2021.769213 17. Trimarchi H, Barratt J, Cattran DC. **Oxford classification of IgA nephropathy 2016: an update from the IgA nephropathy classification working group**. *Kidney Int* (2017) **91** 1014-1021. DOI: 10.1016/j.kint.2017.02.003 18. Rutkowski M, Mann W, Derose S. **Implementing KDOQI CKD definition and staging guidelines in Southern California Kaiser Permanente**. *Am J Kidney Dis* (2009) **53** S86-99. DOI: 10.1053/j.ajkd.2008.07.052 19. Matsuda N, Sato S, Shiba K, Okatsu K, Saisho K, Gautier CA. **PINK1 stabilized by mitochondrial depolarization recruits Parkin to damaged mitochondria and activates latent Parkin for mitophagy**. *J Cell Biol* (2010) **189** 211-221. DOI: 10.1083/jcb.200910140 20. Santel A, Fuller MT. **Control of mitochondrial morphology by a human mitofusin**. *J Cell Sci* (2001) **114** 867-874. DOI: 10.1242/jcs.114.5.867 21. Jiang XS, Chen XM, Hua W, He JL, Liu T, Li XJ, Wan JM, Gan H, Du XG. **PINK1/Parkin mediated mitophagy ameliorates palmitic acid-induced apoptosis through reducing mitochondrial ROS production in podocytes**. *Biochem Biophys Res Commun* (2020) **525** 954-961. DOI: 10.1016/j.bbrc.2020.02.170 22. Lai KN, Leung JC, Chan LY, Saleem MA, Mathieson PW, Tam KY, Xiao J, Lai FM. **TangSC: Podocyte injury induced by mesangial derived cytokines in IgA nephropathy**. *Nephrol Dial Transplant* (2009) **24** 62-72. DOI: 10.1093/ndt/gfn441 23. Kang YS, Li Y, Dai C, Kiss LP, Wu C, Liu Y. **Inhibition of integrin-linked kinase blocks podocyte epithelial-mesenchymal transition and ameliorates proteinuria**. *Kidney Int* (2010) **78** 363-373. DOI: 10.1038/ki.2010.137 24. Lemley KV, Lafayette RA, Safai M, Derby G, Blouch K, Squarer A, Myers BD. **Podocytopenia and disease severity in IgA nephropathy**. *Kidney Int* (2002) **61** 1475-1485. DOI: 10.1046/j.1523-1755.2002.00269.x 25. Xu L, Yang HC, Hao CM, Lin ST, Gu Y, Ma J. **Podocyte number predicts progression of proteinuria in IgA nephropathy**. *Mod Pathol* (2010) **23** 1241-1250. DOI: 10.1038/modpathol.2010.110
--- title: Identification of potential serum biomarkers for congenital heart disease children with pulmonary arterial hypertension by metabonomics authors: - Nan Jin - Mengjie Yu - Xiaoyue Du - Zhiguo Wu - Changlin Zhai - Haihua Pan - Jinping Gu - Baogang Xie journal: BMC Cardiovascular Disorders year: 2023 pmcid: PMC10061882 doi: 10.1186/s12872-023-03171-5 license: CC BY 4.0 --- # Identification of potential serum biomarkers for congenital heart disease children with pulmonary arterial hypertension by metabonomics ## Abstract ### Background Pulmonary arterial hypertension is a common complication in patients with congenital heart disease. In the absence of early diagnosis and treatment, pediatric patients with PAH has a poor survival rate. Here, we explore serum biomarkers for distinguishing children with pulmonary arterial hypertension associated with congenital heart disease (PAH-CHD) from CHD. ### Methods Samples were analyzed by nuclear magnetic resonance spectroscopy-based metabolomics and 22 metabolites were further quantified by ultra-high-performance liquid chromatography–tandem mass spectroscopy. ### Results Serum levels of betaine, choline, S-Adenosyl methionine (SAM), acetylcholine, xanthosine, guanosine, inosine and guanine were significantly altered between CHD and PAH-CHD. Logistic regression analysis showed that combination of serum SAM, guanine and N-terminal pro-brain natriuretic peptide (NT-proBNP), yielded the predictive accuracy of 157 cases was $92.70\%$ with area under the curve of the receiver operating characteristic curve value of 0.9455. ### Conclusion We demonstrated that a panel of serum SAM, guanine and NT-proBNP is potential serum biomarkers for screening PAH-CHD from CHD. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12872-023-03171-5. ## Introduction Congenital heart disease (CHD) is a common cardiovascular disease in pediatrics with a complex etiology associated with multiple factors. If CHD is not treated in a timely manner, the decrease in cardiac output, the increase in cardiac load, and the increase in pulmonary blood flow lead to an increase in mean pulmonary arterial pressure (MPAP), resulting in pulmonary arterial hypertension (PAH) and the loss of surgical opportunities in advanced irreversible PAH [1, 2]. Therefore, it’s critical to get a diagnosis and treatment as soon as possible in order to lower mortality rate and enhance quality of life for PAH-CHD patients. Right cardiac catheterization is the gold standard for the diagnosis of PAH, but it is an invasive interventional technique [3]. Thoracic echocardiography is a non-invasive method for the diagnosis of PAH, but this method is limited by the detection window of the trachea, and the display of the extracardiac structures is not clear [4, 5]. Biomarkers can be detected from blood, urine, feces and other body fluids, and the detection is convenient, low cost and non-invasive, which provides a new strategy for early diagnosis and treatment in the field of CHD-PAH. In recent years, studies have shown that brain natriuretic peptide (BNP), N-terminal pro- brain natriuretic peptide (NT-proBNP), asymmetric dimethylarginine (ADMA), and vascular endothelial growth factor (VEGF) may be potential diagnostic biomarkers for PAH-CHD, but the specificity of these biomarkers is still controversial [6, 7]. Therefore, it is of great clinical significance to screen suitable non- invasive biomarkers for early diagnosis of PAH-CHD from CHD in infants and young children. With the development of high throughput analytical technology, omics-based analyses of DNA, RNA, proteins and metabolites are increasingly being applied to screen the diagnostic biomarkers [8]. As the final products of gene expression and protein alteration, metabolites not only reflect the changes in some functions of the body, but also reflect the overall metabolic changes of the body [9, 10]. Some biomarkers for the diagnosis and prognosis of cardiovascular diseases have been screened and identified by metabolomics [11–13]. Our previous study showed that the combination of four metabolites, namely, betaine, taurine, glutamine, and phenylalanine, can be used as potential serum diagnostic biomarkers in the children with CHD from the healthy controls by nontargeted proton nuclear magnetic resonance spectroscopy (1HNMR)-based and targeted ultra-high-performance liquid chromatography–tandem mass spectroscopy (UPLC–MS/MS)-based metabolomics [14]. However, these technologies have not been employed to screen and identify biomarkers of PAH-CHD in children. In this study, the serum of CHD, PAH-CHD and the healthy control (HC) children were investigated. Firstly, the 1HNMR based metabolomics were used to screen and identify the differential metabolites of CHD and PAH-CHD. Then, 22 serum metabolites were further quantified by UPLC-MS/MS. The quantitative data and clinical biochemical index were screened by binary logistic regression analysis and diagnostic efficacy of PAH-CHD and CHD by the metabolites combination was evaluated by ROC. The experimental flowchart of this study is shown in Fig. 1. Fig. 1Experimental flowchart for screening the potential serum diagnostic biomarkers of PAH-CHD from CHD. ## Reagents and chemicals Acetonitrile and methanol of LC-MS grade was purchased from Thermo Fisher Scientific (Shanghai, China). Phosphate buffer salts (NaH2PO4 and K2HPO4), D2O were purchased from Tianjin Weiyi Chemical Technology Co., Ltd (Tianjin, China). The following compounds were obtained from Sigma-Aldrich: ammonium formate and formic acid of LC-MS grade, 3-(Trimethylsilyl) propionic-2,2,3,3-d4 acid sodium salt(TSP), betaine, chloride, taurine, glutamine, glutamate, S-adenosvlmeIhionine (SAM), valine, leucine, methionine, tyrosine, xanthine, xanthosine, uric acid, guanosine, adenine, hypoxanthine, inosine, guanine of analytical grade (> $99\%$). ## Sample collection Serum samples were collected from Jiangxi Children’s Hospital and the Second Affiliated Hospital of Nanchang University. All venous blood was collected in the morning of the second day after admission with fasting status before corrective surgery. The serum samples were kept under 4 °C temperature before stored at -80 °C within 6 h after serum isolation [15]. A total of 329 subjects were enrolled in this study, including 132 in group of CHD, 97 in group of PAH-CHD and 100 in group of HC. Written informed consent for participation was obtained and the study was reviewed and approved by The Ethics Committee of Jiangxi Children’s Hospital (No. Jxsetyy-yxky-20,200,101) in accordance with the Declaration of Helsinki. ## Sample preparation for 1HNMR analysis The serum samples were thawed at room temperature and 200.0 µL of it was added to 800.0 µL methanol. After mixing, the samples were centrifugated at 12,000 g for 5 min. We took the 800.0 µL supernatant and dried at SpeedVac system (Hersey Instrument Co., Ltd, China) at the temperature of 45 ℃. We added 450.0 µL distilled water, 50.0 µL Phosphoric acid buffer solution(pH = 7.4)and 50.0 µL 5.0 mmol/L TSP in D2O solution to the residues. After mixing, samples were centrifuged at 12,000 g for 5 min, and 500.0 µL of supernatant was removed into the 5 mm NMR tube. The samples were stored at 4 ℃ before analysis. The 1HNMR data were collected by Bruker AvanceII-600 MHz spectrometer (Germany) at 298 K temperature with Noesy pulse sequence according to previous report [16]. The water peak was inhibited by the presaturation method, and the 1D proton spectra were obtained from 64 scans over a spectral width of 14 ppm. Phase correction and baseline correction of all 1HNMR spectra were performed using Topspin 2.1 (Bruker, Biospin, GmbH, Rheinstetten, Germany), and the TSP chemical shift was calibrated to 0.00 ppm. In order to eliminate the influence of water peak and residual methanol peak, 4.6–5.4 ppm and 3.36-3.37ppm region data were deleted. ## Multivariate statistical analysis of 1HNMR data The data of each spectral was normalized to the total spectral intensity over the entire spectrum. We imported the normalized data into SIMCA-P (version 14.0, Umetrics, Umea, Sweden), and completing multivariate statistical analysis such as principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) [17]by using unit variance (UV) scaling preprocessing. The variables that cause the sample classification are obtained by the value of variable importance projection (VIP) [18]. The metabolites with VIP > 1.0 were identified by comparing the chemical shifts and coupling modes of the metabolites in 1HNMR spectra with the parameters in the literature and publicly accessible databases (http://www.bmrb.wisc.edu, http://www.hmdb.ca) and some standards were purchased for further identification of metabolites according to the method described in previous paper [19]. ## Quantitative analysis of metabolites for classification of CHD and PAH-CHD by UPLC-MS/MS The serum samples were thawed at room temperature, and 50.0 µL serum was added to 200. 0µL methanol, 25.0 µL 2-Aminoheptanedioic acid as internal standard (IS) dissolved in $80\%$ methanol. The solution was further centrifuged at 14,000 g for 10 min, and 200.0 µL supernatant was put in the automatic sample bottle. UPLC-MS/MS was performed by the ACQUITY UPLC system coupled with Xevo G2-XS QTof (Waters, USA). Chromatographic condition: ACOQUITY UPLC® BEH HILIC chromatographic separation column (2.1 × 100 mm, 1.7 μm; Waters, USA), 10.0 mmol/L ammonium formate + $0.01\%$ formic acid aqueous solution (A) and acetonitrile (B), flow rate of 0.25 mL/min, injection volume of 3.0µL. The separation time was 5 min, and the following gradient elution was used: 0- 1.5 min, $20\%$ A; 1.5-3.5 min, 20-$80\%$ A; 3.5-5 min $80\%$ A. The temperature of the chromatographic column was set to 40℃, and the temperature of the automatic injector was set to 8℃. Mass spectrum condition: The metabolites were quantitatively analyzed by MS/MS under multiple reaction monitoring mode (MRM) with the positive electrospray ionization. The electrospray capillary voltage was set to 3.0 kV, and nitrogen was used as a dry gas for solvent evaporation, with a flow rate of 50 L/h. The temperature of the ion source was 100℃, and the Masslynx4.1 (Waters, USA) was used to control the instrument and data procession. ## Statistical analysis Quantitative data were analyzed using IBM SPSS software package (22.0, IBM Corp). The K-S test was employed to determine the normality of distribution of the data. Normally distributed data were presented by mean and their standard deviation (SD). Abnormally distributed data were described using median and interquartile range. The 1HNMR peaks identified were integrated and the values of the peaks in CHD and PAH-CHD groups were compared with independent sample T test. Kruskal–Wallis test was used by comparing abnormally distributed quantitative data. Receiver operating characteristics (ROC) curve was employed to assess the predictive value of metabolite to predict PAH-CHD. ## Screening of optimal diagnostic marker combinations by binary logistic regression analysis UPLC-MS/MS quantitative data and clinical biochemical data were integrated in a dataset. The binary logistic regression analysis was carried out with these variables. The model adopted the maximum likelihood estimation forward method to carry out stepwise regression analysis. The standard of the selected variables was $p \leq 0.05.$ The criterion for eliminating the variables was $p \leq 0.10.$ The optimal metabolite combination of the highest diagnostic efficiency was screened, and the diagnostic performance of the model was evaluated using ROC. ## General information of patients According to the Sixth Pulmonary Hypertension World Symposium 2018, PAH is determined by the mean pulmonary artery pressure above 20 at rest, the group was divided based on measurement of pulmonary pressure in cardiac catheterization. 329 children including 132 cases in the CHD group, 97 in the PAH-CHD group and 100 in the HC group were enrolled in this study. The inclusion criteria are children with CHD including ventricular septal defect, atrial septal defect, Patent Ductus Arteriosus, and patent foramen ovale were enrolled. The patients with Tetralogy of Fallot were excluded. The exclusion criteria are the patients with chronic respiratory disease, acute heart failure, pulmonary venous hypertension, chronic liver or renal disease. The clinical information regarding patients is provided in Table 1. As shown in Table 1, some biochemical indicators such as urea nitrogen (BUN) and NT-proBNP were significantly altered when comparing PAH-CHD with CHD group. Table 1Baseline characteristics of overall participantsCHDPAH-CHDHCP-valueCHD vs. PAH-CHDNumber (male/female)132 ($\frac{63}{69}$)97 ($\frac{47}{50}$)100($\frac{48}{52}$)-Age (years)3.35 ± 2.052.83 ± 3.033.38 ± 3.100.1261Weight(kg)14.2 ± 5.013.4 ± 8.50Not available0.4467Height (cm)94.3 ± 14.380.9 ± 14.3Not available0.7692WBC (109/L)9.71 ± 0.2710.58 ± 0.4410.32 ± 0.470.0973LYMPH% (%)55.56 ± 1.4256.08 ± 56.0856.69 ± 1.640.8190LYMPH(109/L)5.37 ± 0.195.88 ± 0.315.94 ± 0.350.1646LDH(U/L)333.68 ± 5.85353.58 ± 13.52201.12 ± 15.320.1805CK(U/L)134.05 ± 5.80115.49 ± 10.01125.68 ± 8.900.1116Mb(µg /L)15.02 ± 0.6814.53 ± 1.0516.18 ± 2.360.6869BUN(mmol/L)4.45 ± 0.143.44 ± 0.173.79 ± 0.17< 0.0001CR(µmol/L)25.49 ± 0.6424.59 ± 0.9025.25 ± 0.720.4037UA(µmol/L)254.25 ± 6.92259.11 ± 12.82257.64 ± 8.910.7396CRP(mg/L)1.71 ± 0.400.84 ± 0.221.3 ± 0.640.0573SAA(mg/L)20.59 ± 5.5717.10 ± 4.906.87 ± 2.380.6565NT-proBNP(pg/mL)247.97 ± 17.721787.07 ± 202.68Not available< 0.0001Note: WBC: White Blood Cell; LYMPH%: the percentage of lymphocytes; LYMPH: the count of lymphocytes; LDH: Lactate Dehydrogenase; CK: Creatine Kinase; Mb: Myoglobin; BUN: Urea Nitrogen; CR: Creatinine; UA: Uric Acid; CRP: C-reactive Protein; SAA: Serum Amyloid A; NT-proBNP: N terminal pro B type natriuretic peptide ## PCA and OPLS-DA analysis of 1HNMR data The normalized data of 1HNMR were imported into SIMCA-P software for multivariate analysis. The PCA and OPLS-DA scores plot were shown in Fig. 2A and B. The quality of OPLS-DA model is usually evaluated by R2Y (cum) and Q2 (cum) two parameters. R2Y (cum) represents the explanatory power of the model, and Q2 (cum) represents the predictive power of the model. *In* general, when the values of R2Y (cum) and Q2 (cum) are more than 0.5, the model is better [20]. In this study, the three parameters of the OPLS-DA were (R2X = 0.407, R2Y = 0.54, Q2 = 0.315), which showed that the model was feasible. At the same time, we tested the reproducibility of the model and determined whether the model was overfitting by seven-fold cross validation and permutation test (200 times). The intercept value of the R2 and Q2 regression lines and the axis were used as the criterion to measure whether the model was over fitting. When the intercept of Q2 is negative value, the model is effective [21]. Figure 2 C showed that R2 = 0.153, Q2=-0.197, indicating that the model was effective. Our results indicated that OPLS- DA analysis could be used to detect the metabolic differences between the two groups of CHD and PAH-CHD. Fig. 2Result of PCA, OPLS-DA generated by data from 1HNMR spectra and permutation test plots (200 permutations) of the OPLS-DA model. ( A) PCA scores plot; (B) OPLS-DA scores plot; (C) Permutation test plots (200 permutations) of the OPLS-DA model ## Metabolites contributing to the classification of CHD from PAH-CHD The metabolites with variable importance (VIP) values greater than l.0 based on OPLS-DA model were supposed to the potential biomarkers for contributing to the classification of CHD from PAH-CHD [16, 22]. In this study, 12 metabolites of VIP > 1.0 were identified, as shown in Table 2 and Supplementary Figure s1. After the independent sample T test, the levels of 8 metabolites including leucine, valine, glutamate, glutamine, betaine, taurine, phenylalanine and xanthine were observed to be significantly changed ($P \leq 0.05$) in the PAH-CHD group. The biological pathways involved in the metabolism of these metabolites and their biological roles were determined by enrichment analysis using Metaboanalyst [23]. Results showed that 5 metabolic pathways including aminoacyl-tRNA biosynthesis, alanine, aspartate and glutamate metabolism, valine, leucine and isoleucine biosynthesis, purine metabolism, glycine, serine and threonine metabolism were altered in PAH-CHD group comparing with the CHD group (Fig. 3). Table 2Information of metabolites for classification of CHD and PAH-CHD by 1HNMR dataMetabolitesδ1HIntegral intervalRelative levels in serum(mean ± SE)VIPP-valueCHD vs PAH-CHDCHD($$n = 40$$)PAH-CHD($$n = 32$$)HC($$n = 42$$)1Leucine0.95(t)0.95–0.980.0093 ± 0.00070.0070 ± 0.00050.0103 ± 0.00061.16100.00662Valine1.02(d)1.005–1.0250.0732 ± 0.00420.0518 ± 0.00480.0901 ± 0.00351.39500.00123Isoleucine1.03(d)1.03–1.0550.0232 ± 0.00140.0270 ± 0.00200.0325 ± 0.00121.12370.12354Alanine1.48(d)1.47–1.510.0014 ± 0.00010.0013 ± 0.00010.0018 ± 0.00011.13100.50825Glutamate2.34(m)2.33–2.3750.0351 ± 0.00120.0297 ± 0.00200.0293 ± 0.00101.13890.02616Glutamine2.44(m)2.438–2.480.0762 ± 0.00250.0645 ± 0.00440.0662 ± 0.00221.03940.02427Choline3.19(s)3.2-3.2150.0081 ± 0.00040.0088 ± 0.00050.0058 ± 0.00031.32030.28398Betaine3.26(s)3.261–3.2780.0220 ± 0.00150.0559 ± 0.00520.0197 ± 0.00091.6773< 0.00019Taurine3.42(t)3.405–3.440.0156 ± 0.00070.0125 ± 0.00100.0135 ± 0.00071.35620.008810Phenylalanine7.42(m)7.41–7.440.0557 ± 0.00390.0710 ± 0.00610.0835 ± 0.00301.44250.038111Xanthine7.80(s)7.72–7.770.0117 ± 0.00080.0172 ± 0.00100.0100 ± 0.00041.12330.000112Hypoxanthine8.20(d)8.19–8.220.3153 ± 0.00610.3040 ± 0.01250.3148 ± 0.01111.13540.4204 Fig. 3Significantly altered metabolic pathways between CHD and PAH-CHD patientsAll matched pathways were shown according to p values from the pathway enrichment analysis (y-axis) and pathway impact values from pathway topology analysis (x-axis) [23], with the most impacted pathways colored in red ## Quantitative analysis of metabolites by UPLC-MS/MS Serum levels of 10 amino acids, 4 metabolite in choline metabolism pathway including choline, betaine, acety-choline, SAM, and 8 metabolites in purine metabolism pathway were further determined by established UPLC-MS/MS method with hydrophilic chromatography column in 92 cases of CHD, 65 PAH-CHD and 58 HC cases. The analysis method parameters were shown in the supplementary table s1 and table s2. As shown in Fig. 4 and supplementary table s3, compared with the CHD group, 8 metabolites including betaine, choline, S-Adenosyl methionine (SAM), acetylcholine, xanthosine, guanosine, inosine and guanine were significantly altered in PAH-CHD patients comparing with CHD (independent sample T test, $P \leq 0.05$). Fig. 4Significantly altered serum metabolites between PAH-CHD and CHD patients ## Binary logistic regression analysis for screening the best combination of diagnostic markers The 8 metabolites which significantly altered in PAH-CHD, together with urea nitrogen (BUN) and N terminal pro B type natriuretic peptide (NT-proBNP) as a total of 10 variables were employed for binary logistic regression analysis. After the stepwise regression analysis, a total of 3 variables, SAM, guanine and NT-proBNP, were selected for the established logistic model. The model equation was Logit (p) = -5.394 + 0.057 × 1 + 0.020 × 2 + 0.008 × 3. The serum levels of SAM, guanine and NT-proBNP of the patients were X1, X2 and X3, respectively. The values of the area under the curve (AUROC) of ROC curve were used to evaluate the diagnostic efficiency. The greater the AUC value, the more reliable the diagnostic effect is [24, 25]. As shown in Table 3, the AUROC value of each variable was obtained, we can see that most of metabolites were not good for diagnosis of CHD from PAH- CHD (AUROC < 0.77) except NT-proBNP (AUROC = 0.8812). The Logistic equation established with 3 variables of SAM, guanine and NT-proBNP showed $92.70\%$ accuracy for the average prediction of the above 92 CHD and 65 PAH-CHD serum samples, and 0.9455 for AUROC values. The sensitivity and specificity were 0.8333 and 0.9873 respectively. Table 3The results of ROC analysis using the quantified dataMetaboliteAUC$95\%$ConfidenceintervalSensitivitySpecificityCut-offvalue1Betaine0.67540.5421–0.80870.92590.325020.09952Choline0.54980.3899–0.70960.29630.850018.97843SAM0.73140.5934–0.86950.40740.92504.65424Acetylcholine0.61550.4630–0.76790.33330.87500.64235Xanthosine0.58260.4308–0.73440.48150.92500.14936Guanosine0.59230.4407–0.74380.51850.90000.24957Inosine0.69660.5551–0.83820.48150.80004.49078Guanine0.75940.6364–0.88240.51850.875042.95419BUN0.73560.5750–0.89631.00000.03451.580010NT-proBNP0.88120.8088–0.95350.86400.8480492.2000 ## Discussion Firstly, we screened and identified the differential metabolites in 40 cases of CHD, 32 cases of PAH-CHD and 42 HC subjects by 1HNMR based metabolomic method. We found that the major metabolic pathways affected were amino acid metabolism, choline metabolism and purine metabolism. However, due to the limited sensitivity of 1HNMR, it is difficult for the identification and absolute quantitative analysis. Therefore, we established an UPLC-MS/MS method to quantify serum levels of 22 metabolites in 92 CHD patients, 65 PAH-CHD patients and 58 HC subjects. Our results showed that compared with the CHD group, serum contents of 8 metabolites, such as betaine, choline, SAM, acetylcholine, xanthosine, guanosine, inosine and guanine, were significantly altered($P \leq 0.05$) in PAH-CHD group. Zeneng Wang et al. reported that the increased levels of blood choline and betaine were associated with increased risk of heart disease [26, 27]. Our results showed that the contents of betaine, choline and acetylcholine increased in the CHD group as compared with the HC group. A metabolomic study showed that elevation of blood serotonin, taurine, creatine, sarcosine, and 2-oxobutanoate, and decrease of vanillylmandelic acid, 3,4-dihydroxymandelate, 15-keto-prostaglandin F2α, fructose 6-phosphate, l-glutamine, dehydroascorbate, hydroxypyruvate, threonine, l-cystine, and 1-aminocyclopropane-1-carboxylate in CHD-PAH group compared with the CHD group. Nevertheless, absolute quatificaion of these metabolites were not obtained in this study [28]. Here, we confirmed the increased contents of serum betaine, choline, acetylcholine and SAM in the PAH-CHD group by UPLC-MS/MS, which was similar to that reported in literature [29]. Our results indicated that during the process of CHD to PAH-CHD, the content of betaine, choline, acetylcholine and SAM increased continuously, suggesting that these 4 metabolites might be associated with the occurrence and development of CHD disease. In addition, A Edlund et al. found that in patients with ischemic heart disease, metabolites in purine metabolism were largely released in the myocardium during myocardial ischemia, suggesting that ischemic heart disease might be associated with purine metabolism [30]. Recent study showed that purine metabolism were significantly disturbed in the PAH associated with the congenital Left-to-Right shunt (PAH-LTRS) cohort [31]. Our experimental results showed that the purine metabolites in the CHD group and the PAH-CHD group were significantly changed from those in the HC group. Compared with the CHD group, the contents of xanthosine, guanosine, inosine and guanine in the PAH-CHD group increased significantly ($P \leq 0.05$). Wei Sheng et al. reported a significant reduction in methylation levels in children with tetralogy of Fallot (TOF) and suggested that low methylation levels might increase the risk of TOF in children [32]. Some studies showed that betaine, choline, acetylcholine, SAM and xanthosine, guanosine, inosine and guanine were the metabolites related to the carbon metabolism, which are the common methyl donor for the methylation reaction [33–35]. Therefore, we speculated that methylation might be associated with the development of CHD. The accumulation of betaine, choline, acetylcholine, SAM, xanthosine, guanosine, inosine and guanine in children with PAH-CHD might be caused by the low methylation reaction of PAH-CHD children. In summary, the changes in metabolites in PAH-CHD and whether these alteration are related to the development of PAH-CHD are rarely reported, and we will further investigate the mechanism. PAH is a common complication in late stage of CHD which has high morbidity and mortality, poor prognosis. Therefore, it is important to screen novel non-interventional biomarkers for diagnosis of PAH-CHD. George Giannakoulas et al. reviewed 26 studies related to PAH-CHD systematically, and found that compared with healthy controls, PAH-CHD patients had higher B type natriuretic peptide (BNP) and NT-proBNP, and they suggested that BNP might be simple and effective markers for the prognosis and timing of treatment intervention of PAH-CHD [6]. David M. et al. found that circulating endothelial cells could be a valuable tool to define therapeutic strategies in PAH-CHD patients. In addition, studies showed that sensitive cardiac troponins, connective tissue growth factor and growth differentiation factor-15 might be the diagnostic marker of PAH-CHD [36–38]. Compared with the difficulty and high cost of quantifying specific proteins and peptides, it is much easier to quantify serum small molecular metabolites. Literatures reported that NT-ProBNP may be a potential biomarker of pulmonary hypertension [6, 39]. Consistent with the literature, we found that the NT-proBNP content in the PAH-CHD group was significantly increased (about 7 times, Table 1) compared with the CHD group. After binary logistic regression analysis, combinations of SAM, guanine and NT-proBNP were selected for the best diagnostic efficiency. Our results showed that the average prediction accuracy of 92 cases of CHD and 65 cases of PAH-CHD serum samples was $92.70\%$, the AUROC was 0.9455, and the sensitivity and specificity were 0.8333 and 0.9873 respectively. Therefore, serum SAM, guanine and NT-proBNP are expected to be potential biomarker combination for the differential diagnosis of PAH-CHD. However, a study on large number of clinical samples, metabolite flux and biomarker stability for serum collection should be conducted to further validate the results of this study. ## Conclusion Screening novel serum biomarkers is of great clinical significance in improving the diagnosis of PAH-CHD. 1HNMR based metabolomics showed that the metabolic pathways of nitrogen, amino acids and purines were significantly altered in serum of PAH-CHD patients. Therefore, 22 metabolites were further quantified by UPLC-MS/MS. After binary logistic regression analysis, a biomarker panel consisting of SAM, guanine and NT-proBNP showed the best diagnostic efficiency. The average prediction accuracy of 92 cases of CHD and 65 PAH-CHD serum samples was $92.70\%$ with AUROC of 0.9455. We therefore believe this 3-marker panel has the potential to be used in clinical practice for the early diagnosis and screening of PAH-CHD. ## Electronic supplementary material Below is the link to the electronic supplementary material. Table s1: The regression equation, limits of detection (LOD) and quantitation (LOQ).Table s2: MRM quantitative parameters of metabolites quantified by UPLC-MS/MS.Table s3: The content of serum metabolites quantified by UPLC-MS/MS.Figure s1: VIP-coded loadings plot. The color scales (VIP values) show variable importance in the OPLS-DA projection generated by the serum 1H NMR data. 1, Leucine; 2, Valine; 3, Isoleucine; 4, Alanine; 5, Glutamate; 6, Glutamine; 7, Choline; 8, Betaine; 9, Taurine; 10, Phenylalanine; 11, Xanthine; 12, Hypoxanthine. ## References 1. Amoozgar H, Banafi P, Mohammadi H, Edraki MR, Mehdizadegan N, Ajami G, Borzouee M, Keshaarz K, Moradi P, Dehghani E. **Management of Persistent Pulmonary Hypertension after correction of congenital heart defect with autologous marrow-derived mononuclear stem cell injection into the Pulmonary artery: a pilot study**. *Pediatr Cardiol* (2020.0) **41** 398-406. DOI: 10.1007/s00246-019-02273-2 2. Rosenzweig EB, Krishnan U. **Congenital heart Disease-Associated Pulmonary Hypertension**. *Clin Chest Med* (2021.0) **42** 9-18. DOI: 10.1016/j.ccm.2020.11.005 3. Hansmann G, Apitz C, Abdul-Khaliq H, Alastalo T-P, Beerbaum P, Bonnet D, Dubowy K-O, Gorenflo M, Hager A, Hilgendorff A. **Executive summary. Expert consensus statement on the diagnosis and treatment of paediatric pulmonary hypertension. The European Paediatric Pulmonary Vascular Disease Network, endorsed by ISHLT and DGPK**. *Heart* (2016.0) **102** 86-100. DOI: 10.1136/heartjnl-2015-309132 4. Emma P, Tulloh Robert MR. **Pulmonary hypertension in congenital heart disease**. *Future Cardiol* (2018.0) **14** fca-2017 5. Malik SB, Chen N, Parker RA, Hsu JY. **Transthoracic Echocardiography: Pitfalls and Limitations as delineated at Cardiac CT and MR Imaging(1)**. *Radiographics* (2017.0) **37** 383-406. DOI: 10.1148/rg.2017160105 6. Giannakoulas G, Mouratoglou S-A, Gatzoulis MA, Karvounis H. **Blood biomarkers and their potential role in pulmonary arterial hypertension associated with congenital heart disease. A systematic review**. *Int J Cardiol* (2014.0) **174** 618-23. DOI: 10.1016/j.ijcard.2014.04.156 7. Li X-y, Zheng Y, Long Y, Zhang X, Zhang L, Tian D, Zhou D, Lv Q-z. **Effect of iloprost on biomarkers in patients with congenital heart disease-pulmonary arterial hypertension**. *Clin Exp Pharmacol Physiol* (2017.0) **44** 914-23. DOI: 10.1111/1440-1681.12796 8. Piran S, Liu P, Morales A, Hershberger RE. **Where Genome meets Phenome: Rationale for integrating genetic and protein biomarkers in the diagnosis and management of dilated cardiomyopathy and heart failure**. *J Am Coll Cardiol* (2012.0) **60** 283-9. DOI: 10.1016/j.jacc.2012.05.005 9. Allen J, Davey HM, Broadhurst D, Heald JK, Rowland JJ, Oliver SG, Kell DB. **High-throughput classification of yeast mutants for functional genomics using metabolic footprinting**. *Nat Biotechnol* (2003.0) **21** 692-6. DOI: 10.1038/nbt823 10. Nicholson JK, Wilson ID. **Understanding ‘global’ systems biology: Metabonomics and the continuum of metabolism**. *Nat Rev Drug Discovery* (2003.0) **2** 668-76. DOI: 10.1038/nrd1157 11. 11.Guasch-Ferre M, Hu FB, Ruiz-Canela M, Bullo M, Toledo E, Wang DD, Corella D, Gomez-Gracia E, Fiol M, Estruch R et al. Plasma Metabolites From Choline Pathway and Risk of Cardiovascular Disease in the PREDIMED (Prevention With Mediterranean Diet) Study. Journal of the American Heart Association 2017, 6(11). 12. Kappel BA, Lehrke M, Schutt K, Artati A, Adamski J, Lebherz C, Marx N. **Effect of Empagliflozin on the metabolic signature of patients with type 2 diabetes Mellitus and Cardiovascular Disease**. *Circulation* (2017.0) **136** 969-72. DOI: 10.1161/CIRCULATIONAHA.117.029166 13. 13.Ruiz-Canela M, Hruby A, Clish CB, Liang L, Martinez-Gonzalez MA, Hu FB. Comprehensive Metabolomic Profiling and Incident Cardiovascular Disease: A Systematic Review. Journal of the American Heart Association 2017, 6(10). 14. 14.Yu M, Sun S, Yu J, Du F, Zhang S, Yang W, Xiao J, Xie B. Discovery and Validation of Potential Serum Biomarkers for Pediatric Patients with Congenital Heart Diseases by Metabolomics. J Proteome Res 2018, 17(10):3517–3525. 15. Yin P, Peter A, Franken H, Zhao X, Neukamm SS, Rosenbaum L, Lucio M, Zell A, Haering H-U, Xu G. **Preanalytical Aspects and Sample Quality Assessment in Metabolomics Studies of Human Blood**. *Clin Chem* (2013.0) **59** 833-45. DOI: 10.1373/clinchem.2012.199257 16. Xie B, Liu A, Zhan X, Ye X, Wei J. **Alteration of gut Bacteria and Metabolomes after Glucaro-1,4-lactone treatment contributes to the Prevention of Hypercholesterolemia**. *J Agric Food Chem* (2014.0) **62** 7444-51. DOI: 10.1021/jf501744d 17. Mickiewicz B, Vogel HJ, Wong HR, Winston BW. **Metabolomics as a Novel Approach for early diagnosis of Pediatric Septic Shock and its mortality**. *Am J Respir Crit Care Med* (2013.0) **187** 967-76. DOI: 10.1164/rccm.201209-1726OC 18. 18.Amathieu R, Triba MN, Nahon P, Bouchemal N, Kamoun W, Haouache H, Trinchet J-C, Savarin P, Le Moyec L, Dhonneur G. Serum 1H-NMR Metabolomic Fingerprints of Acute-On-Chronic Liver Failure in Intensive Care Unit Patients with Alcoholic Cirrhosis. Plos One 2014, 9(2). 19. Yu M, Xiang T, Wu X, Zhang S, Yang W, Zhang Y, Chen Q, Sun S, Xie B. **Diagnosis of acute pediatric appendicitis from children with inflammatory diseases by combination of metabolic markers and inflammatory response variables**. *Clin Chem Lab Med* (2018.0) **56** 1001-10. DOI: 10.1515/cclm-2017-0858 20. Yin P, Wan D, Zhao C, Chen J, Zhao X, Wang W, Lu X, Yang S, Gu J, Xu G. **A metabonomic study of hepatitis B-induced liver cirrhosis and hepatocellular carcinoma by using RP-LC and HILIC coupled with mass spectrometry**. *Mol Biosyst* (2009.0) **5** 868-76. DOI: 10.1039/b820224a 21. Zheng P, Gao HC, Li Q, Shao WH, Zhang ML, Cheng K, Yang DY, Fan SH, Chen L, Fang L. **Plasma metabonomics as a Novel Diagnostic Approach for Major Depressive Disorder**. *J Proteome Res* (2012.0) **11** 1741-8. DOI: 10.1021/pr2010082 22. Mamtimin B, Xia G, Mijit M, Hizbulla M, Kurbantay N, You L, Upur H. **Metabolic differentiation and classification of abnormal Savda Munziq’s pharmacodynamic role on rat models with different diseases by nuclear magnetic resonance-based metabonomics**. *Pharmacognosy Magazine* (2015.0) **11** 698-706. DOI: 10.4103/0973-1296.165551 23. Xia J, Wishart DS. **Web-based inference of biological patterns, functions and pathways from metabolomic data using MetaboAnalyst**. *Nat Protoc* (2011.0) **6** 743-60. DOI: 10.1038/nprot.2011.319 24. Baker AM, Hsu FC, Gayzik FS. **A method to measure predictive ability of an injury risk curve using an observation-adjusted area under the receiver operating characteristic curve**. *J Biomech* (2018.0) **72** 23-8. DOI: 10.1016/j.jbiomech.2018.02.018 25. Schisterman EF, Faraggi D, Reiser B, Trevisan M. **Statistical inference for the area under the receiver operating characteristic curve in the presence of random measurement error**. *Am J Epidemiol* (2001.0) **154** 174-9. DOI: 10.1093/aje/154.2.174 26. Wang Z, Klipfell E, Bennett BJ, Koeth R, Levison BS, Dugar B, Feldstein AE, Britt EB, Fu X, Chung Y-M. **Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease**. *Nature* (2011.0) **472** 57-U82. DOI: 10.1038/nature09922 27. Wang Z, Tang WHW, Buffa JA, Fu X, Britt EB, Koeth RA, Levison BS, Fan Y, Wu Y, Hazen SL. **Prognostic value of choline and betaine depends on intestinal microbiota-generated metabolite trimethylamine-N-oxide**. *Eur Heart J* (2014.0) **35** 904-10. DOI: 10.1093/eurheartj/ehu002 28. Barnes PJ. **COPD 2020: new directions needed**. *Am J Physiology-Lung Cell Mol Physiol* (2020.0) **319** L884-6. DOI: 10.1152/ajplung.00473.2020 29. Alsayed R, Al Quobaili F, Srour S, Geisel J, Obeid R. **Elevated dimethylglycine in blood of children with congenital heart defects and their mothers**. *Metabolism-Clinical and Experimental* (2013.0) **62** 1074-80. DOI: 10.1016/j.metabol.2013.01.024 30. Edlund A, Berglund B, van Dorne D, Kaijser L, Nowak J, Patrono C, Sollevi A, Wennmalm A. **Coronary flow regulation in patients with ischemic heart disease: release of purines and prostacyclin and the effect of inhibitors of prostaglandin formation**. *Circulation* (1985.0) **71** 1113-20. DOI: 10.1161/01.CIR.71.6.1113 31. 31.Rosin DL, Hall JP, Zheng SQ, Huang LP, Campos-Bilderback S, Sandoval R, Bree A, Beaumont K, Miller E, Larsen J et al. Human Recombinant Alkaline Phosphatase (Ilofotase Alfa) Protects Against Kidney Ischemia-Reperfusion Injury in Mice and Rats Through Adenosine Receptors. Frontiers in Medicine 2022,9. 32. 32.Sheng W, Wang H, Ma X, Qian Y, Zhang P, Wu Y, Zheng F, Chen L, Huang G, Ma D. LINE-1 methylation status and its association with tetralogy of fallot in infants. Bmc Medical Genomics 2012, 5. 33. Chiang PK, Gordon RK, Tal J, Zeng GC, Doctor BP, Pardhasaradhi K, McCann PP. **S-adenosylmethionine and methylation**. *Faseb J* (1996.0) **10** 471-80. DOI: 10.1096/fasebj.10.4.8647346 34. 34.Kalhan, Satish C. One carbon metabolism in pregnancy: Impact on maternal, fetal and neonatal health. Molecular & Cellular Endocrinology 2016:48–60. 35. Lu SC. **S-Adenosylmethionine**. *Int J Biochem Cell Biol* (2000.0) **32** 391-5. DOI: 10.1016/S1357-2725(99)00139-9 36. Kayali S, Ertugrul I, Yoldas T, Kaya O, Karademir S. **Sensitive Cardiac Troponins: could they be new biomarkers in Pediatric Pulmonary Hypertension due to congenital heart disease?**. *Pediatr Cardiol* (2018.0) **39** 1-8. DOI: 10.1007/s00246-018-1811-1 37. 37.Li G, Li Y, Tan XQ, Jia P, Zhao J, Liu D, Wang T, Liu B. Plasma Growth Differentiation Factor-15 is a Potential Biomarker for Pediatric Pulmonary Arterial Hypertension Associated with Congenital Heart Disease. Pediatric Cardiology 2017. 38. Li G, Tang L, Jia P, Zhao J, Liu D, Liu B. **Elevated plasma connective tissue growth factor levels in children with pulmonary arterial hypertension Associated with congenital heart disease**. *Pediatr Cardiol* (2016.0) **37** 714-21. DOI: 10.1007/s00246-015-1335-x 39. Berghaus TM, Kutsch J, Faul C, Von Scheidt W, Schwaiblmair M. **The association of N-terminal pro-brain-type natriuretic peptide with hemodynamics and functional capacity in therapy-naive precapillary pulmonary hypertension: results from a cohort study**. *BMC Pulm Med* (2017.0) **17** 167. DOI: 10.1186/s12890-017-0521-4
--- title: Rural-urban differentials of prevalence and lifestyle determinants of pre-diabetes and diabetes among the elderly in southwest China authors: - Yi Zhao - Hui-fang Li - Xia Wu - Guo-hui Li - Allison Rabkin Golden - Le Cai journal: BMC Public Health year: 2023 pmcid: PMC10061888 doi: 10.1186/s12889-023-15527-9 license: CC BY 4.0 --- # Rural-urban differentials of prevalence and lifestyle determinants of pre-diabetes and diabetes among the elderly in southwest China ## Abstract ### Background Diabetes has become a major public health problem in China. A better understanding of diabetes determinants and urban-rural differences is essential to crafting targeted diabetes prevention measures for the elderly living in both urban and rural areas. This study aimed to compare rural-urban differentials in prevalence and lifestyle determinants of pre-diabetes and diabetes among the elderly in southwest China. ### Methods A cross-sectional health interview and examination survey was conducted among individuals aged ≥ 60 years in both a rural and urban area of China. Anthropometric measurements, including height, weight, and waist circumference, as well as blood pressure and fasting blood glucose measurements were taken. Associated risk factors for pre-diabetes and diabetes were evaluated using multivariate logistic regression analysis. ### Results In total, 1,624 urban residents and 1,601 rural residents consented to participate in the study. The urban prevalence of pre-diabetes and diabetes ($46.8\%$ and $24.7\%$, respectively), was higher than the rural prevalence ($23.4\%$ and $11.0\%$, respectively, $P \leq 0.01$). Urban elderly participants had markedly higher prevalence of obesity, central obesity, and physical inactivity than their rural counterparts ($15.3\%$, $76.0\%$, and $9.2\%$ vs. $4.6\%$, $45.6\%$, and $6.1\%$, $P \leq 0.01$). In contrast, rural elderly adults had higher prevalence of smoking than urban ones ($23.2\%$ vs. $17.2\%$, $P \leq 0.01$). Obese (OR 1.71, $95\%$ CI 1.27–2.30 vs. OR 1.73, $95\%$ CI 1.30–3.28) and centrally obese participants (OR 1.59, $95\%$ CI 1.18–2.15 vs. OR 1.83, $95\%$ CI 1.32–2.54) were more likely to suffer from diabetes in both urban and rural regions. Furthermore, urban current smokers had a higher probability of suffering from diabetes (OR 1.58, $95\%$ CI 1.11–2.25), while hypertension was positively associated with the prevalence of diabetes in the rural area (OR 2.13, $95\%$ CI 1.54–2.95). Obese participants in the rural area were more likely to suffer from pre-diabetes (OR 2.50, $95\%$ CI 1.53–4.08), while physical inactivity was positively associated with prevalence of pre-diabetes in the urban area (OR 1.95, $95\%$ CI 1.37–2.80). ### Conclusion Pre-diabetes and diabetes are more prevalent among urban older adults than their rural counterparts in southwest China. The identified rural-urban differentials of lifestyle factors have significant impacts on prevalence of pre-diabetes and diabetes. Thus, tailored lifestyle interventions are needed to improve diabetes prevention and management among the elderly in southwest China. ## Background Diabetes is a growing public health challenge globally, with type 2 diabetes contributing up to $90\%$ of all diabetes cases [1]. The International Diabetes Federation estimates the global diabetes prevalence in 2021 was $10.5\%$ (536.6 million people), and will rise to $12.2\%$ (783.2 million people) in 2045 [2]. The prevalence of pre-diabetes, the preceding condition of diabetes defined as blood glucose levels that are higher than normal, but lower than diabetes thresholds [3], is also rising worldwide [4]. As those with pre-diabetes are at a high risk for developing diabetes, the rapid increase in the prevalence of diabetes and pre-diabetes is translating into a growing diabetes prevention and treatment challenge. Over the last several decades China’s population has steadily aged. In China’s most recent census, 260 million people were 60 years and older, accounting for $18.7\%$ of the total population [5]. Older adults are at higher risk for diabetes and the prevalence of diabetes increases with age [6, 7]. Over the past 30 years, the prevalence of diabetes in China has increased 17-fold, from 0.67 to $11.6\%$ due to socioeconomic development, the aging of the population, lifestyle changes, obesity, and increasing urbanization [8, 9]. Diabetes is more prevalent in the urban Chinese population versus rural population [7, 10]. At the same time, the prevalence of pre-diabetes among the elderly aged over 60 years has also increased significantly in China, rising from $24.5\%$ in 2008 [7] to $45.8\%$ in 2013 [11]. A better understanding of diabetes determinants and urban-rural differences is essential to crafting targeted diabetes prevention measures for the elderly living in both urban and rural areas. Previous research established several factors associated with increased risk of diabetes, including aging [6, 7, 12], obesity [7, 12, 13], living environment [6, 7], physical inactivity [13, 14], hypertension [13, 15] and socioeconomic status [16]. However, there is limited research on the rural-urban differentials of the prevalence and determinants of diabetes in both China [17] and other countries [18, 19]. Moreover, few studies have focused on health disparities between the urban and rural elderly populations. Due to uneven economic development, education levels, and living environments across urban and rural regions in China, health disparities between the urban and rural elderly are rapidly growing [20]. Thus, this study aimed to address this growing challenge by uncovering the rural-urban differentials in prevalence and determinants of pre-diabetes and diabetes among the elderly in southwest China. ## Data sources and study population Yunnan Province, one of China’s poorest provinces, is located in southwest China, bordering Myanmar, Laos, and Vietnam. It has 129 counties and a total population of 47.2 million people (as of 2020). Kunming is the capital of Yunnan, and has 7 urban districts, 7 counties, and a recorded population of 8.46 million (as of 2020). The present study employed a cross-sectional health interview and examination survey conducted from July 2019 to October 2019 in one rural area and one urban area of Kunming. The rural and urban areas were selected using a four-stage, stratified, random sampling method. In the first stage of selection, Kunming was divided into two strata: rural regions and urban regions, with one area then randomly chosen from each stratum. In the second stage, each selected area was then further classified into three categories according to wealth distribution (per capita GDP): low, medium, and high. From each of these three categories, one street or one township was then randomly selected, for a total of 6 streets and 6 townships. In the third stage, three communities and three villages were randomly selected from the selected streets and townships using probability proportion to size sampling method (PPS sampling). In the fourth and final stage, eligible participants aged 60 years and over were selected to be invited to participate within each chosen community or village, using simple random sampling. Inclusion criteria were [1] participants aged ≥ 60 years; and [2] participants residing in the selected community or village ≥ 5 years and willing to participate in the study. Exclusion criteria were [1] participants aged < 60 years; [2] those with cognitive dysfunction or inability to communicate with the interviewers; and [3] those refused to provide informed consent to participate in the study. ## Data collection and measurement Kunming Medical University medical students were selected and trained as interviewers for data collection. These trained interviewers administered a pre-tested and structured questionnaire via a face-to-face interview to collect data on demographic characteristics and lifestyle factors for all participants. Anthropometric measurements, fasting blood glucose (FBG), and blood pressure (BP) tests were also collected and recorded. Physicians at the First Affiliated Hospital of Kunming Medical University performed the FBG tests. FBG was measured the morning after participants completed a minimum of 8 h of overnight fasting. Participants who reported that they had not fasted at least 8 h were invited to receive an additional FBG test on another day. Using a mercury sphygmomanometer, BP was measured three times for all participants, in accordance with American Heart Association recommendations [21]. After participants rested for five minutes in a seated position, the three readings were averaged and the final averaged BP value was recorded. Anthropometric measurements, including weight, height, and waist circumference, were measured using standardized equipment and procedures as described in the World Health Organization (WHO) STEPS manual [22]. Body mass index (BMI) was also recorded and calculated as weight (kg) divided by squared height (m2). ## Ethical approval The Ethics Committee of Kunming Medical University approved this study prior to the commencement of the research. ## Definitions Diabetes was defined as FBG ≥ 7.0 mmol/l (126 mg/dl), a reported use of antidiabetic medications within the two weeks prior to the study, or a reported previous diagnosis of diabetes by a healthcare professional. Pre-diabetes was defined as FBG ≥ 6.1 mmol/l and < 7.0 mmol/l. Hypertension was defined as an average systolic blood pressure ≥ 140 mmHg, and/or an average diastolic blood pressure ≥ 90 mmHg, or self-reported use of an antihypertensive drug in the past 2 weeks [23]. A previous diagnosis of hypertension by a health professional was also considered as hypertension in this study. Obesity was defined as a BMI of 28.0 kg/m2 or higher [24]. Central obesity was defined as waist circumference at or above 90 cm in men, and at or above 80 cm in women, based on WHO recommendations for Asian adults [24]. Current smoker was defined as having smoked more than 100 cigarettes or at least 150 g of tobacco in one’s lifetime, and smoked any form of tobacco product on a daily basis at the time of the survey. Current drinker was defined as drinking alcohol regularly on 12 days or more during the 12 months preceding the study. Physical inactivity was defined as failure to do at least 150 min of moderate-intensity physical activity throughout the week, or failure to do at least 75 min of vigorous-intensity physical activity throughout the week [25]. ## Statistical analysis Continuous variables were expressed as mean ± standard deviation (X±S), while categorical variables were described as counts and percentages. A chi-squared test was used to compare categorical variables, while t-tests were used to analyze continuous measures. The overall prevalence of pre-diabetes and diabetes were adjusted for age and sex by direct standardization to the total population of the two study areas. Multivariate logistic regression analysis was used to test the association of prevalence of pre-diabetes and diabetes with other variables (age, sex, level of education, BMI, obesity, central obesity, hypertension, current drinkers, current smokers, and physical inactivity). Associations were expressed as odds ratios and $95\%$ confidence intervals (CI). P values < 0.05 were considered significant. All statistical analyses were conducted using SPSS version 22. ## Results The number of participants aged 60 years and over, invited to join in the survey was 1,700 in urban area, and1,700 in rural area. Of these, 1,624 urban residents and 1,601 rural residents consented to participate in the study, representing a response rate of $95.5\%$ and $94.2\%$, respectively. Table 1 displays the demographic characteristics of the study population. Proportion of each age group and gender among the participants did not differ between the urban and the rural regions ($P \leq 0.05$). However, urban participants had markedly higher levels of education as well as a higher prevalence of diagnosed hypertension by a health professional, obesity, central obesity, and physical inactivity than their rural counterparts ($P \leq 0.01$). Table 1Demographic and lifestyle characteristics and mean values of BP, FBG, and anthropometric measurements among the study populationCharacteristicRural areaUrban areaMale($$n = 741$$)Female($$n = 860$$)All($$n = 1601$$)Male($$n = 752$$)Female($$n = 872$$)All($$n = 1624$$)Age group (%)60–64 years152(20.5)208(24.2)360(22.5)151(20.1)212(24.3)363(22.4)65–69 years195(26.3)209(24.3)404(25.2)208(27.7)213(24.4)421(25.9)70–74 years153(20.6)201(23.4)354(22.1)154(20.5)205(23.5)359(22.1)75–79 years136(18.4)145(16.9)281(17.6)137(18.2)142(16.3)279(17.2)80–84 years73(9.9)69(8.0)142(8.9)69(9.2)68(7.8)137(8.4)85–89 years25(3.4)25(2.9)50(3.1)25(3.3)26(2.7)51(3.1)≥ 90 years7(0.9)3(0.3)10(0.6)8(1.1)9(0.7)14(0.9)Level of education (%)Primary (grade 1–6) or lower362(48.9)597(69.4)*959(59.9)11(1.5)**28(3.2)**39(2.4)**Middle (grade 7–9) or higher379(51.1)263(30.6)642(40.1)741(98.5)844(96.8)1585(97.6)Current smokers (%)365(49.3)6(0.7)371(23.2)274(36.4)**6(0.7)280(17.2)**Current drinkers (%)239(32.3)7(0.8)246(15.4)248(33.0)4(0.5)252(15.5)Obesity (%)28(3.8)46(5.3)74(4.6)125(16.6)**124(14.2)**249(15.3)**Central obesity (%)261(35.2)469(54.5)730(45.6)544(72.3)**691(79.2)**1235(76.0)**Hypertension (%)333(44.3)331(38.0)664(40.9)299(40.4)397(46.2)696(43.5)Diagnosed hypertension by a health professional (%)203(27.4)274 (31.9)477 (29.8)296 (39.4)345(39.6)641 (39.5)**Physical inactivity (%)44(5.9)54(6.3)98(6.1)66(8.8)*83(9.5)*149(9.2)*Height (cm, mean ± SD)162.3 ± 7.3151.5 ± 6.6156.5 ± 8.8164.9 ± 6.0*153.6 ± 6.2*158.8 ± 8.3*Weight (kg, mean ± SD)57.5 ± 9.851.1 ± 9.354.0 ± 10.068.4 ± 9.9**58.5 ± 8.2**63.0 ± 10.3**Waist circumference (cm, mean ± SD)81.3 ± 9.880.6 ± 9.880.9 ± 9.889.7 ± 8.1**86.1 ± 8.3**87.8 ± 8.4**BMI (kg/m2, mean ± SD)21.8 ± 3.522.2 ± 3.622.0 ± 3.625.1 ± 3.1**24.8 ± 3.6**25.0 ± 3.4**Systolic BP (mm Hg, mean ± SD)125.0 ± 19.3126.2 ± 19.2125.6 ± 19.3136.4 ± 17.5135.0 ± 16.3135.7 ± 16.9Diastolic BP (mm Hg, mean ± SD)78.8 ± 11.879.5 ± 11.279.2 ± 11.582.6 ± 11.279.0 ± 9.880.7 ± 10.6FBG (mmol/l, mean ± SD)5.9 ± 1.56.2 ± 2.26.1 ± 1.96.7 ± 2.6**6.7 ± 2.2**6.7 ± 2.4*** $P \leq 0.05$, ** $P \leq 0.01$BMI = body mass indexBP = blood pressureFBG = fasting blood glucoseSD = standard deviation Table 2 presents the age-standardized prevalence of diabetes and pre-diabetes by geographic region. The age-standardized prevalence of pre-diabetes and diabetes in the urban older adult study population ($46.8\%$ and $24.7\%$) was significantly higher than in the rural population ($23.4\%$ and $11.0\%$), and these higher rates were also found among subgroups stratified by gender, age, and education ($P \leq 0.01$). In both the urban and rural area, the prevalence of pre-diabetes decreased as age increased, while the prevalence of diabetes increased with age only in the urban region. Table 2Age-standardized prevalence of pre-diabetes and diabetes by sex, age, and level of education among rural and urban elderly people of Yunnan Province, ChinaCharacteristicPre-diabetesDiabetesRuraln (%)Urbann (%)Ruraln (%)Urbann (%)SexMale179 (24.0)341 (45.3)**71 (9.5)185 (24.7)**Female193 (22.1)417 (47.8)**106 (12.1)215 (24.5)Age group60–64 years92 (25.8)177 (48.8)**42 (11.9)80 (22.1)**65–74 years173 (22.7)375 (48.2)**84 (11.1)203 (25.7)**≥ 75 years107 (22.0)*206 (42.6)**51 (10.4)117 (28.5)**Level of education (%)Primary (grade 1–6) or lower222 (23.0)19 (48.7)**100 (10.4)7 (17.9)**Middle (grade 7–9) or higher150 (23.3)739 (46.6)**77 (12.0)393 (24.6)**All372 (23.4)758 (46.8)**177 (11.0)400 (24.7)*** $P \leq 0.05$, ** $P \leq 0.01$ Table 3 indicates the results of multivariate logistic regression analysis for prevalence of pre-diabetes and diabetes by demographic and lifestyle factors. Current smoker status (OR 1.58, $95\%$ CI 1.11–2.25), obesity (OR 1.71, $95\%$ CI 1.27–2.30) and central obesity (OR 1.59, $95\%$ CI 1.18–2.15) were positively associated with the probability of suffering from diabetes for urban older adults, whereas obesity (OR 1.73, $95\%$ CI 1.30–3.28), central obesity (OR 1.83, $95\%$ CI 1.32–2.54), and hypertension (OR 2.13, $95\%$ CI 1.54–2.95) were positively associated with the prevalence of diabetes for rural older adults. A positive association of obesity with pre-diabetes (OR 2.50, $95\%$ CI 1.53–4.08) was only found in the rural participants, while physical inactivity was only found to be positively associated with the prevalence of pre-diabetes (OR 1.95, $95\%$ CI 1.37–2.80) in the urban participants. Table 3Logistic regression of pre-diabetes and diabetes prevalence by demographic and lifestyle factors among rural and urban elderly people of Yunnan Province, ChinaVariablePre-diabetes (reference: no)Diabetes (reference: no)RuralUrbanRuralUrbanOdds ratio$95\%$ CIOdds ratio$95\%$ CIOdds ratio$95\%$ CIOdds ratio$95\%$ CISex(reference: male)Age group(reference: 60–64 years)0.95(0.70, 1.29)1.14(0.89, 1.45)1.25(0.83, 1.90)1.20(0.90, 1.60)65–74 years1.09(0.88, 1.34)1.10(0.88, 1.35)1.17(0.90, 1.57)1.16(0.89, 1.57)≥ 75 years1.05(0.88, 1.27)1.07(0.90, 1.38)1.12(0.89, 1.49)1.09(0.88, 1.45)Level of education(reference: primary (grade 1–6) or lower)0.97(0.75, 1.25)0.93(0.49,1.78)1.32(0.94, 1.87)1.54(0.67, 3.57)Current smoker(reference: no)1.07(0.75, 1.53)0.91(0.67, 1.24)1.07(0.64, 1.79)1.58**(1.11, 2.25)Current drinker(reference: no)1.37(1.00, 1.87)1.22(0.89, 1.67)0.99(0.57, 1.73)1.01(0.70, 1.45)Obesity(reference: no)2.50**(1.53, 4.08)1.00(0.72, 1.40)1.73**(1.30, 3.28)1.71**(1.27, 2.30)Central obesity1.22(0.93, 1.61)1.01(0.77, 1.33)1.83**(1.32, 2.54)1.59**(1.18, 2.15)(reference: no)Hypertension(reference: no)1.06(0.8, 1.36)0.91(0.74, 1.11)2.13**(1.54, 2.95)1.12(0.89, 1.42)Physical inactivity(reference: no)1.05(0.63, 1.72)1.95**(1.37, 2.80)0.81(0.44, 1.48)0.78(0.53, 1.14)** $p \leq 0.01$ ## Discussion This study uncovered significant urban-rural differences in prevalence and determinants of both pre-diabetes and diabetes among older adults in southwest China. Urban older adults experienced markedly higher prevalence of both pre-diabetes and diabetes than the rural elderly, while the associations between pre-diabetes and diabetes prevalence with lifestyle factors varied by region. The prevalence of pre-diabetes among urban participants ($46.8\%$) was higher than previously measured urban prevalence rates in China ($29.5\%$ in Dalian [26] and $18.32\%$ in Guangzhou [27]), as well as in other countries ($43.8\%$ in Ecuador [28] and $24.2\%$ in Vietnam [29]), highlighting the scale of the challenge of pre-diabetes in urban southwest China. In contrast, the prevalence of pre-diabetes among rural participants ($23.4\%$) was lower than measured among those of Ningbo in China ($30.97\%$) [30] as well as Daegu in Korea ($24.4\%$) [31]. The prevalence of diabetes in this study was higher than the prevalence of self-reported diabetes ($8.7\%$) in China among both rural and urban older adults [32]. Further, the prevalence of diabetes among urban older adults in this study was also higher than previously ($18.8\%$) [33]. However, some regions of China [12, 27] as well as Myanmar [18] have a higher diabetes prevalence in both urban and rural older adults than measured in the present study. These differences in diabetes prevalence may result from differing regional distribution of dietary patterns, economic levels, living environments, age groups, and ethnicity [34], as well as differing definitions of diabetes. Our findings indicate that a large population of urban southwest Chinese residents faces a high risk of progression of their pre-diabetes to diabetes, underscoring the need for interventions, such as lifestyle modifications and treatments, to prevent the development of diabetes among pre-diabetic older adults. In the present study, prevalence of pre-diabetes and diabetes in the urban older adult population was much higher than the rural population, consistent with findings from previous studies in China [6, 7] as well as studies conducted in other countries [18, 19]. This may result from the fact that urban older adults had a significantly higher prevalence of obesity and central obesity than their rural counterparts ($15.3\%$ and $76.0\%$, vs. $4.6\%$ and $45.6\%$), and that elderly people living in urban China tended to be less physically active during the day than rural elder adults ($9.2\%$ vs. $6.1\%$). The findings in this way highlight an urgent need to address urban lifestyle habits that create the conditions for obesity and sedentariness as well as the importance of surveillance of urban diabetes patterns in order to head off an emerging diabetes epidemic in urban southwest China. The present study also found that obesity and central obesity were positively associated with the probability of suffering from diabetes for both urban and rural older adults. The significant role of obesity and central obesity in contributing to the development of diabetes is well established in the literature [7, 15, 35]. However, the association of obesity with pre-diabetes in the present study was only found in the rural area. Further research is needed to uncover the roots of this inconsistent effect of obesity on pre-diabetes development between urban and rural areas. Our study additionally uncovered that while the prevalence of current smoking was higher in the rural region, current smoking status was positively associated with the probability of having diabetes among urban older adults but not among rural ones. This finding of an inconsistent effect of smoking on diabetes aligns with previous research [14, 18, 36, 37]: most studies have reported a positive association between smoking and diabetes, but some researchers have found a lower prevalence of diabetes among smokers than nonsmokers. The reasons behind this dichotomy are unclear and require further research. Among the urban elderly participants in this study, those lacking in daily physical activity were more likely to suffer from diabetes. It is well known that inadequate physical activity as well as irregular living habits contribute to the prevalence of obesity, which may lead to the development of metabolic diseases, including diabetes [38]. There is also robust evidence of the beneficial effects of physical exercise on preventing metabolic disease and cardiovascular disease [39]. Thus, regular physical exercise should be promoted to reduce the prevalence of diabetes among elderly residents of urban southwest China. Our study results showed that while the prevalence of hypertension was not significantly different between rural and urban older adults, the rate of hypertension, as diagnosed by a healthcare professional, among the urban elderly was obviously higher than the rural elderly, contributing to rural-urban health disparities in southwest China [40]. Moreover, rural hypertensive older adults were more likely to suffer from diabetes than urban ones. This may result from the fact that hypertensive patients in rural areas have developed unhealthy eating habits (with diets high in fat and sodium) [41], as hypertension is an independent risk factor for diabetes [42]. The study findings indicate that increasing access to healthcare and improving health literacy among the rural elderly population remains an important strategy to improve health outcomes in rural China. There are several limitations to the present study. First, the data analyzed were from a cross-sectional study, so causal relationships cannot be determined. Second, the present study might underestimate the true prevalence of pre-diabetes and diabetes as only FBG was used to diagnose diabetes and pre-diabetes, and hemoglobin A1C and postprandial blood glucose were not measured. Third, variables such as sleep quality and depressive disorder, considered potential risk factors for diabetes, were not measured in this study. Fourth, as the present study did not consider information regarding the proportion of diabetic patients who were migrants to include, the true rural-urban differentials of prevalence of diabetes might be biased. Finally, our research enrolled the elderly population in one district and one county of Yunnan Province, which may not fully represent the entire elderly population of southwest China. More data are needed to expand our findings. In conclusion, our study indicated that pre-diabetes and diabetes are more prevalent among urban older adults than rural older adults in southwest China. Further, our results uncovered rural-urban differentials of lifestyle factors that have significant impacts on prevalence of pre-diabetes and diabetes. Tailored lifestyle interventions are needed to improve diabetes prevention and management in southwest China into the future. ## References 1. Chatterjee S, Khunti K, Davies MJ. **Type 2 diabetes**. *Lancet (London England)* (2017.0) **389** 2239-51. DOI: 10.1016/S0140-6736(17)30058-2 2. Sun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, Stein C, Basit A, Chan JCN, Mbanya JC, Pavkov ME, Ramachandaran A, Wild SH, James S, Herman WH, Zhang P, Bommer C, Kuo S, Boyko EJ, Magliano DJ. **IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045**. *Diabetes Res Clin Pract* (2022.0) **183** 109119. DOI: 10.1016/j.diabres.2021.109119 3. Tabák AG, Herder C, Rathmann W, Brunner EJ, Kivimäki M. **Prediabetes: a high-risk state for diabetes development**. *Lancet (London England)* (2012.0) **379** 2279-90. DOI: 10.1016/S0140-6736(12)60283-9 4. Echouffo-Tcheugui JB, Selvin E. **Prediabetes and what it means: the Epidemiological evidence**. *Annu Rev Public Health* (2021.0) **42** 59-77. DOI: 10.1146/annurev-publhealth-090419-102644 5. 5.Interpretation of the Communique of the Seventh National Census. [http://www.stats.gov.cn/tjsj/tjgb/rkpcgb/qgrkpcgb/202106/t20210628_1818824.html Accessed 5 Aug 2022 6. Xu Y, Wang L, He J, Bi Y, Li M, Wang T, Wang L, Jiang Y, Dai M, Lu J, Xu M, Li Y, Hu N, Li J, Mi S, Chen CS, Li G, Mu Y, Zhao J, Kong L, Chen J, Lai S, Wang W, Zhao W, Ning G. **Prevalence and control of diabetes in chinese adults**. *JAMA* (2013.0) **310** 948-59. DOI: 10.1001/jama.2013.168118 7. Yang W, Lu J, Weng J, Jia W, Ji L, Xiao J, Shan Z, Liu J, Tian H, Ji Q, Zhu D, Ge J, Lin L, Chen L, Guo X, Zhao Z, Li Q, Zhou Z, Shan G, He J. **Prevalence of diabetes among men and women in China**. *N Engl J Med* (2010.0) **362** 1090-101. DOI: 10.1056/NEJMoa0908292 8. 8.Li MZ, Su L, Liang BY, Tan JJ, Chen Q, Long JX, Xie JJ, Wu GL, Yan Y, Guo XJ, Gu L. Trends in prevalence, awareness, treatment, and control of diabetes mellitus in mainland china from 1979 to 2012. International journal of endocrinology. 2013; 2013:753150. 9. 9.Shen X, Vaidya A, Wu S, Gao X, THE DIABETES, EPIDEMIC IN CHINA: AN INTEGRATED REVIEW OF NATIONAL SURVEYS. Endocrine practice: official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists. 2016; 22(9):1119–29. 10. Liu S, Wang W, Zhang J, He Y, Yao C, Zeng Z, Piao J, Howard BV, Fabsitz RR, Best L, Yang X, Lee ET. **Prevalence of diabetes and impaired fasting glucose in chinese adults, China National Nutrition and Health Survey, 2002**. *Prev Chronic Dis* (2011.0) **8** A13. PMID: 21159225 11. Wang L, Gao P, Zhang M, Huang Z, Zhang D, Deng Q, Li Y, Zhao Z, Qin X, Jin D, Zhou M, Tang X, Hu Y, Wang L. **Prevalence and ethnic pattern of diabetes and Prediabetes in China in 2013**. *JAMA* (2017.0) **317** 2515-23. DOI: 10.1001/jama.2017.7596 12. Wang Q, Zhang X, Fang L, Guan Q, Guan L, Li Q. **Prevalence, awareness, treatment and control of diabetes mellitus among middle-aged and elderly people in a rural chinese population: a cross-sectional study**. *PLoS ONE* (2018.0) **13** e0198343. DOI: 10.1371/journal.pone.0198343 13. Sun Y, Ni W, Yuan X, Chi H, Xu J. **Prevalence, treatment, control of type 2 diabetes and the risk factors among elderly people in Shenzhen: results from the urban chinese population**. *BMC Public Health* (2020.0) **20** 998. DOI: 10.1186/s12889-020-09045-1 14. Shi L, Shu XO, Li H, Cai H, Liu Q, Zheng W, Xiang YB, Villegas R. **Physical activity, smoking, and alcohol consumption in association with incidence of type 2 diabetes among middle-aged and elderly chinese men**. *PLoS ONE* (2013.0) **8** e77919. DOI: 10.1371/journal.pone.0077919 15. Ton TT, Tran ATN, Do IT, Nguyen H, Nguyen TTB, Nguyen MT, Ha VAB, Tran AQ, Hoang HK, Tran BT. **Trends in prediabetes and diabetes prevalence and associated risk factors in vietnamese adults**. *Epidemiol health* (2020.0) **42** e2020029. DOI: 10.4178/epih.e2020029 16. 16.Wang Z, Li X, Chen M. Socioeconomic Factors and Inequality in the Prevalence and Treatment of Diabetes among Middle-Aged and Elderly Adults in China. Journal of diabetes research. 2018; 2018:1471808. 17. Dong Y, Gao W, Nan H, Yu H, Li F, Duan W, Wang Y, Sun B, Qian R, Tuomilehto J, Qiao Q. **Prevalence of type 2 diabetes in urban and rural chinese populations in Qingdao, China**. *Diabet medicine: J Br Diabet Association* (2005.0) **22** 1427-33. DOI: 10.1111/j.1464-5491.2005.01658.x 18. Aung WP, Htet AS, Bjertness E, Stigum H, Chongsuvivatwong V, Kjøllesdal MKR. **Urban-rural differences in the prevalence of diabetes mellitus among 25–74 year-old adults of the Yangon Region, Myanmar: two cross-sectional studies**. *BMJ open* (2018.0) **8** e020406. DOI: 10.1136/bmjopen-2017-020406 19. Khorrami Z, Yarahmadi S, Etemad K, Khodakarim S, Kameli ME, Hazaveh ARM. **Urban-rural differences in the prevalence of self-reported diabetes and its risk factors: the WHO STEPS iranian noncommunicable disease risk factor surveillance in 2011**. *Iran J Med Sci* (2017.0) **42** 481-7. PMID: 29234181 20. 20.Zhang J, Li D, Gao J. Health Disparities between the Rural and Urban Elderly in China: A Cross-Sectional Study. International journal of environmental research and public health. 2021; 18(15). 21. Perloff D, Grim C, Flack J, Frohlich ED, Hill M, McDonald M, Morgenstern BZ. **Human blood pressure determination by sphygmomanometry**. *Circulation* (1993.0) **88** 2460-70. DOI: 10.1161/01.CIR.88.5.2460 22. 22.WHO STEPS surveillance manual: the WHO. STEPwise approach to chronic disease risk factor surveillance [https://apps.who.int/iris/handle/10665/43376 Accessed 5 Aug 2022 23. 23.Lu J, Lu Y, Wang X, Li X, Linderman GC, Wu C, Cheng X, Mu L, Zhang H, Liu J, Su M, Zhao H, Spatz ES, Spertus JA, Masoudi FA, Krumholz HM, Jiang L. Prevalence, awareness, treatment, and control of hypertension in China: data from 1·7 million adults in a population-based screening study (China PEACE Million Persons Project). Lancet (London, England). 2017; 390(10112):2549-58. 24. 24.The Asia-Pacific Perspective. : Redefining Obesity and Its Treatment [https://apps.who.int/iris/handle/10665/206936 Accessed 5 Aug 2022 25. 25.Global recommendations on physical activity for health. [https://apps.who.int/iris/handle/10665/44399 Accessed 5 Aug 2022 26. Wang B, Liu MC, Li XY, Liu XH, Gao ZN. **Prevalence and risk factors of type 2 diabetes mellitus and pre-diabetes in people over 40 years old from Dalian city**. *J Dalian Med Univ* (2016.0) **38** 334-9 27. Li H, Lao W, Yang Y. **Status and associated rish factors of T2DM and PM in the elderly ≥ 60 years old in Panyu District, Guangzhou**. *J Community Med* (2021.0) **19** 901-5 28. Orces CH, Lorenzo C. **Prevalence of prediabetes and diabetes among older adults in Ecuador: analysis of the SABE survey**. *Diabetes & metabolic syndrome* (2018.0) **12** 147-53. DOI: 10.1016/j.dsx.2017.12.002 29. Pham NM, Eggleston K. **Prevalence and determinants of diabetes and prediabetes among vietnamese adults**. *Diabetes Res Clin Pract* (2016.0) **113** 116-24. DOI: 10.1016/j.diabres.2015.12.009 30. 30.Zhao M, Lin H, Yuan Y, Wang F, Xi Y, Wen LM, Shen P, Bu S. Prevalence of Pre-Diabetes and Its Associated Risk Factors in Rural Areas of Ningbo, China. International journal of environmental research and public health. 2016; 13(8). 31. Lee JE, Jung SC, Jung GH, Ha SW, Kim BW, Chae SC, Park WH, Lim JS, Yang JH, Kam S, Chun BY, Kim JY, Lee JJ, Lee KS, Ahn MY, Kim YA, Kim JG. **Prevalence of diabetes Mellitus and Prediabetes in Dalseong-gun, Daegu City, Korea**. *Diabetes & metabolism journal* (2011.0) **35** 255-63. DOI: 10.4093/dmj.2011.35.3.255 32. Hu X, Meng L, Wei Z, Xu H, Li J, Li Y, Jia N, Li H, Qi X, Zeng X, Zhang Q, Li J, Liu D. **Prevalence and potential risk factors of self-reported diabetes among elderly people in China: a national cross-sectional study of 224,142 adults**. *Front public health* (2022.0) **10** 1051445. DOI: 10.3389/fpubh.2022.1051445 33. Yan Y, Wu T, Zhang M, Li C, Liu Q, Li F. **Prevalence, awareness and control of type 2 diabetes mellitus and risk factors in chinese elderly population**. *BMC Public Health* (2022.0) **22** 1382. DOI: 10.1186/s12889-022-13759-9 34. 34.Sinclair A, Saeedi P, Kaundal A, Karuranga S, Malanda B, Williams R. Diabetes and global ageing among 65-99-year-old adults: Findings from the International Diabetes Federation Diabetes Atlas, 9(th) edition. Diabetes research and clinical practice. 2020; 162:108078. 35. Bai A, Tao J, Tao L, Liu J. **Prevalence and risk factors of diabetes among adults aged 45 years or older in China: a national cross-sectional study**. *Endocrinol diabetes metabolism* (2021.0) **4** e00265. DOI: 10.1002/edm2.265 36. Hou X, Qiu J, Chen P, Lu J, Ma X, Lu J, Weng J, Ji L, Shan Z, Liu J, Tian H, Ji Q, Zhu D, Ge J, Lin L, Chen L, Guo X, Zhao Z, Li Q, Zhou Z, Yang W, Jia W. **Cigarette smoking is Associated with a lower prevalence of newly diagnosed diabetes screened by OGTT than non-smoking in chinese men with Normal Weight**. *PLoS ONE* (2016.0) **11** e0149234. DOI: 10.1371/journal.pone.0149234 37. Willi C, Bodenmann P, Ghali WA, Faris PD, Cornuz J. **Active smoking and the risk of type 2 diabetes: a systematic review and meta-analysis**. *JAMA* (2007.0) **298** 2654-64. DOI: 10.1001/jama.298.22.2654 38. Venables MC, Jeukendrup AE. **Physical inactivity and obesity: links with insulin resistance and type 2 diabetes mellitus**. *Diab/Metab Res Rev* (2009.0) **25** 18-23. DOI: 10.1002/dmrr.983 39. Aune D, Norat T, Leitzmann M, Tonstad S, Vatten LJ. **Physical activity and the risk of type 2 diabetes: a systematic review and dose-response meta-analysis**. *Eur J Epidemiol* (2015.0) **30** 529-42. DOI: 10.1007/s10654-015-0056-z 40. Zhang X, Dupre ME, Qiu L, Zhou W, Zhao Y, Gu D. **Urban-rural differences in the association between access to healthcare and health outcomes among older adults in China**. *BMC Geriatr* (2017.0) **17** 151. DOI: 10.1186/s12877-017-0538-9 41. Wang KW, Cai L, Lu YC, Shu ZS, Dong J, Zhang SL. **Dietary Habits and the Relationship with Hypertension in a rural area of Kunming**. *Mod Prev Med* (2011.0) **38** 801-3 42. Wei GS, Coady SA, Goff DC, Brancati FL, Levy D, Selvin E, Vasan RS, Fox CS. **Blood pressure and the risk of developing diabetes in african americans and whites: ARIC, CARDIA, and the framingham heart study**. *Diabetes Care* (2011.0) **34** 873-9. DOI: 10.2337/dc10-1786
--- title: 'Effects of major depression and bipolar disorder on erectile dysfunction: a two-sample mendelian randomization study' authors: - Wei-Kang Chen - Tao Zhou - Dong-Dong Yu - Jing-Ping Li - Jing-Gen Wu - Le-Jun Li - Zhong-Yan Liang - Feng-Bin Zhang journal: BMC Medical Genomics year: 2023 pmcid: PMC10061895 doi: 10.1186/s12920-023-01498-8 license: CC BY 4.0 --- # Effects of major depression and bipolar disorder on erectile dysfunction: a two-sample mendelian randomization study ## Abstract ### Background and Aims There are currently no clear conclusions about whether major depression (MD) and bipolar disorder (BD) increase the risk of erectile dysfunction (ED). In our study, we used a Mendelian randomization (MR) analysis to discover the causal associations between MD, BD and ED. ### Methods We got single-nucleotide polymorphisms (SNPs) related to MD, BD and ED from the MRC IEU Open genome-wide association study (GWAS) datasets. After a series of selection, SNPs left were selected as instrumental variables (IVs) of MD and BD for the following MR test to evaluate the relationship of genetically predicted MD or BD with the incidence of ED. Among them, we used the random-effects inverse-variance weighted (IVW) method as the main analysis. Finally, sensitivity analyses were further performed using Cochran’s Q test, funnel plots, MR-Egger regression, Leave-one-out method and MR- pleiotropy residual sum and outlier (PRESSO). ### Results Genetically-predicted MD was causally related to the incidence of ED in the IVW methods (odds ratio (OR), 1.53; $95\%$ confidence interval (CI), 1.19–1.96; $$p \leq 0.001$$), while no causal impact of BD on the risk of ED (OR = 0.95, $95\%$ CI 0.87–1.04; $$p \leq 0.306$$). The results of sensitivity analyses supported our conclusion, and no directional pleiotropy were found. ### Conclusion The findings of this research found evidence of a causal relationship between MD and ED. However, we did not find a causal relationship between BD and ED in European populations. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12920-023-01498-8. ## Introduction Erectile dysfunction (ED) is an important part of sexual dysfunction and can cause a decrease in the life quality of the patient and his partner. From the National Institutes of Health, the most commonly cited definition of ED is the inability to obtain and maintain an erection for satisfactory sexual intercourse firm enough [1]. The European Association of Urology (EAU) 2021 Andrology Disease Guide indicated that the incidence of ED increased with age, ranging from 12 to $82.9\%$ [2]. Although ED can be considered a vascular disease essentially, it is also closely related to neurological and mental health. For example, several studies report that ED is commonly found in some men with mental illness, including major depression (MD) [3], anxiety [4, 5] and schizophrenia [6]. In the 2017 Global Burden of Disease Study [7], depression is the third cause of non-fatal health loss, and affect over 300 million people worldwide [8]. According to previous literature, patients with ED often have MD [9, 10] with a frequency ranging from $8.7\%$14 to $43.1\%$ [11]. Recently, a Meta-analysis reported that depression may lead to ED (OR = 1.39, $95\%$ CI: 1.35-42) [12], has further substantiated the association between MD and ED. Bipolar disorder (BD), always characterized by irritability or euphoria or elation and increased energy activity or levels, is a serious mental illness. Globally, the lifetime incidences of BD type I and type II are approximately $0.6\%$ and $0.4\%$, however this value in developed countries is higher [13, 14]. BD is related to significant functional impairment, a higher rate of suicide, lower quality of life and a likelihood of high comorbidity. There is currently a lack of literature describing the relationship between BD and ED, but the recently cohort study of Hou et al. [ 15] found that BD patients had a higher prevalence of ED than controls, attracting public attention. To the best of our knowledge, extant studies are largely based on observational epidemiological designs and are therefore susceptible to reverse causality and unmeasured confounding factors [16]. To avoid this situation, Mendelian randomization (MR) has the advantage of using genetic variation as an instrumental variable, addressing observational research bias, thereby providing an alternative approach to explore causality [17, 18]. In the study, we used a MR approach to investigate the causal relationship between MD and BD on the risk of developing ED. ## Study design We used a two-sample MR design to detect the potential causal association of MD and BD on the risk of ED. The hypothesis of the MR study consist of three conditions: (i) the instrumental variants (IVs) should be associated with exposures of MD and BD; (ii) No clear correlation between IVs and the confounders; (iii) IVs have an effect on risk of ED only through the exposure of interest (MD or BD) and not through other means [19]. Only when all three of these conditions are met can the MR design reverse causality, control for potential confounders and provide robust estimates of causal effects [20]. Data on the associations of single-nucleotide polymorphisms (SNPs) with MD, BD and ED were obtained from publicly available large-scale genome-wide association studies (GWAS) [21–23], which could be downloaded from the MRC IEU Open GWAS datasets (Supplementary Table 1). The summary statistics of MD (GWAS ID: ieu-b-102) were obtained from Psychiatric Genomics Consortium (PGC) and the UK Biobank (UKB), extracted from 170,756 cases and 329,443 controls based on European samples; the summary statistics of BD (GWAS ID: ieu-b-41) were obtained from the PGC, including 13,413,244 SNPs of 170,756 European cases and 329,443 European controls; the summary statistics of ED (GWAS ID: ebi-a-GCST006956) were extracted from the UKB and the Estonian Genome Center of the University of Tartu (EGCUT), which were obtained from 223,805 European Samples. All the data from MR are publicly accessible (https://gwas.mrcieu.ac.uk/; last accessed on September 7, 2022). Ethical approval were waived for this research, and all subjects in the original GWAS have obtained informed consent. ## Selection of genetic variants In this study, we obtained SNPs that are significantly related to MD ($p \leq 5$ × 10− 8) from GWAS summary data [24, 25], while we relaxed the GWAS p-value threshold to 5 × 10− 7 in BD in order to obtain a suitable number of SNPs for subsequent analysis [26]. Then, we used the PLINK clumping method to calculate the linkage disequilibrium (LD) through the two-sample MR package and selected independent SNPs with the following conditions (R2 < 0.001, window size = 10,000 kb) [27], to ensure that all the left IVs for MD and BD are not in LD. We estimate the strength of the IVs on the basis of the F statistic. The formula is as follows: F = R2(N-2) (1-R2) (R2: variance of exposure explained by selected instrumental variables; N:sample size) [28]; R2 = 2×EAF×(1-EAF)×beta^2/((2×EAF×(1-EAF)×beta^2) + 2×EAF×(1-EAF)×se×N×beta^2) (beta: effect size for SNP; se: standard error for SNP; N:sample size) [29]. IVs were selected whose F > 10. After harmonizing the SNPs in the data source by effector alleles [30], we discovered each instrument SNP in the PhenoScanner GWAS database [31] to assess any prior association ($P \leq 5$ × 10− 8) with possible confounding factors (that is sleeplessness or insomnia, body mass index, smoking status, education, hematocrit, cardiovascular diseases, et al.) [ 32–34] to avoid potential confounding. Finally, the SNPs left were selected as IVs for the following MR test. ## Statistical analysis In the study, we applied the random-effects inverse-variance weighted (IVW) method as the main analysis to evaluate the casual relation of genetically predicted MD and BD with the risk of ED [35]. Other methods including MR Egger [36], weighted-median [37], weighted mode [38] and simple mode [39] were also applied. The main principles are as follows: [1] In the absence of heterogeneity and pleiotropy, the estimation results of IVW are preferred;[2] When there is only heterogeneity and no pleiotropy, the results of Weighted Medium method are preferred (the random effect model of IVW can also be used);[3] When there is multiple validity, the results calculated by MR Egger method are preferred [40]. Besides, several sensitivity analyses were carried out to evaluate the strength of the association. First, Cochran’s Q test and funnel plots were performed to assess the heterogeneity [41]. Second, we applied MR-Egger regression to recognize the existence of directional pleiotropy by calculating whether the intercept was statistically away from zero [36]. Third, we used the Leave-one-out method to verify the robustness of the findings [42]. Fourth, in order to detect possible outliers, we apply the MR pleiotropy residual sum and outlier (MR-PRESSO) test [43]. We used odds ratios (ORs) with their $95\%$ confidence intervals (CIs) to present the associations between MD and BD and risk of ED and applied RStudio (version 2022.02.3) with ‘TwoSampleMR’ and ‘MR-PRESSO’ to perform MR analyses. In this study, $p \leq 0.05$ was considered a statistically significant difference. ## Genetically predicted MD on ED After the above selection (the specific flow chart is shown in Fig. 1), 37 IVs were left, accounting for approximately $24.4\%$ of the observed variance of MD and all the F-statistics were above 10, ranging from 339.5 to 86003.0 (Supplementary Table 2). Fig. 1Workflow of MR study revealing causality from MD and BD on ED. Genetically predicted MD was related to higher odds of ED (OR = 1.53, $95\%$ CI 1.19–1.96; $$p \leq 0.001$$) in the IVW analyses (Figs. 2 and 3A). Meanwhile, similar results were discovered by weighted median approaches (OR = 1.622, $95\%$ CI = 1.13–2.32, $$p \leq 0.008$$), weighted mode approaches (OR = 1.58, $95\%$ CI = 0.74–3.39, $$p \leq 0.245$$), simple mode (OR = 1.60, $95\%$ CI = 0.72–3.59, $$p \leq 0.259$$) and MR-Egger regression (OR = 2.12, $95\%$ CI = 0.42–10.68, $$p \leq 0.367$$) (Fig. 2). No heterogeneity was found in the study with a Cochran Q-test ($$P \leq 0.436$$ of MR-Egger; $$p \leq 0.475$$ of IVW) (Table 1) and funnel plots (Fig. 4A). The MR-Egger intercept did not deviate significantly from zero with a p-value of 0.688 (Table 1). The leave-one-out test found that no significant differences was discovered while we removed a single SNP and applied the MR analysis again, demonstrating our results’ robustness (Fig. 5A). By using the MR-PRESSO test, Outliers are not found, verifying the absence of unknown pleiotropic effects of the genetic instruments. After calculation, we found the MR analyses of MD had $100\%$ statistical power. Fig. 2OR plot for MD and BD. Fig. 3The causality of MD(A) and BD(B) on ED risk. The slope represents the magnitude of the causal effect Table 1Pleiotropy tests and heterogeneity of MRPleiotropy testHeterogeneity testMR EggerIVWegger_interceptsepvalQQ_dfQ_pvalQQ_dfQ_pval MD -0.0100.0250.68835.694350.43635.861360.475 BD -0.0130.0190.50022.777320.88523.242330.896Abbreviations: MD: Major depression; BD: Bipolar Disorder; IVW, inverse variance weighted; MR, Mendelian randomization. Fig. 4Funnel plot to assess the heterogeneity of MD(A) and BD(B). Fig. 5Leave-one-out analysis of the effect of MD(A) and BD(B) on ED. ## Genetically predicted BD on ED After the above selection (the specific flow chart is shown in Fig. 1), 34 IVs were left, accounting for approximately $3.9\%$ of the observed variance of BD (the F-statistics range from 33.8 to 74.6) (Supplementary Table 3). Genetically predicted BD was not related to ED (OR = 0.95, $95\%$ CI 0.87–1.04; $$p \leq 0.306$$) in the IVW analyses (Figs. 2 and 3B). The consistent results were obtained in the weighted median approaches (OR = 0.97, $95\%$ CI = 0.86–1.09, $$p \leq 0.617$$), weighted mode approaches (OR = 0.99, $95\%$ CI = 0.77–1.27, $$p \leq 0.920$$), simple mode (OR = 1.00, $95\%$ CI = 0.78–1.29, $$p \leq 0.991$$) and MR-Egger regression (OR = 1.10, $95\%$ CI = 0.72–1.67, $$p \leq 0.659$$) (Fig. 2). There was no heterogeneity found by a Cochran Q-test ($$P \leq 0.885$$ of MR-Egger; $$p \leq 0.896$$ of IVW) (Table 1) and funnel plots (Fig. 4B). The MR-Egger intercept did not deviate significantly from zero with a p-value of 0.896 (Table 1). The leave-one-out test showed that there were no significant differences (Fig. 5B) and the MR-PRESSO test did not find any outliers. However, statistical power failed to reach $80\%$ to discovery the weak associations. ## Discussion In the study, we used a MR approach to investigate the causal relationship between MD or BD on the risk of ED. The findings of this research found evidence of a causal relationship between MD and ED. However, we did not find a causal relationship between BD and ED. As far as we know, the relationship between depression and ED is currently unclear. Some scholars point out that depression can increases the risk of ED [44, 45], while others do not agree [46]. A recent meta-analysis indicated an association between depression and ED, which the overall OR for studies evaluating depression exposure and risk of ED was 1.39 ($95\%$ CI: 1.35–1.42) [12]. Since OR acts as an association measure, it can only prove the existence of an association. Therefore, the above study cannot clarify the causal relationship between ED and depression and its direction. In our study, we took advantage of MR, a better study design method, which is free from bias and can accurately reveal causal relationships. Recently, a newly published article also used MR method, and further confirmed that MD plays a potentially causal role in the occurrence of ED [47]. In their study, they used the data of three institutions (PGC, the UKB and 23andMe) and did not remove possible confounding factors. However, in our study, we selected the data of two consortiums (PGC and UKB), considering the reliability of data sources and the potential overlap of data between consortiums. In addition, we discovered each instrument SNP in the PhenoScanner GWAS database to assess any prior association with possible confounding factors to avoid potential confounding. Finally, we also found that MD could increase the risk of ED with the OR was 1.53 ($95\%$ CI 1.19–1.96). Our findings further clarify the impact of MD on ED and provide more evidence for clinical practice. The mechanisms underlying how MD leads to ED remain to be elucidated and established [3]. However, some scholars have proposed relevant behavioral and biological models to explain the mechanism of the increased risk of ED in patients with depression [48]. Makhlouf et al. suggest that depressed patients often exhibit a lack of confidence and negative thinking, which in turn leads to decreased erectile function [48]. Biological models suggest the hypothalamic-pituitary-adrenal (HPA) axis is affected by depression, resulting in a high production of catecholamines, which in turn causes cavernosal muscle dysregulation and ED [49]. Depression may inhibit the activity of parasympathetic nerves, thereby decreasing the inflow of blood to the penis and inhibiting penile smooth muscle relaxation [50]. Moreover, most antidepressants have also been found to have some adverse effect on erectile function [50]. Depending on the various drugs, the incidence of ED may range from 25.8 to $80.3\%$ [51]. Unfortunately, ED may persist after selective serotonin reuptake inhibitors (SSRIs) are discontinued, with this treacherous condition being only recently defined as post-SSRI sexual dysfunction [52, 53]. Besides, studies have found that depressed patients have lower levels of testosterone than non-depressed patients, and low testosterone is thought to be associated with ED [54, 55]. As for BD, there was currently a lack of literature describing the relationship between BD and ED. Recently, Hou et al. found that the incidence of ED in BD patients was significantly higher than that in the control group (HR = 2.24, $95\%$ CI: 1.71–2.94) [15], through a research of 5,150 BD male patients in the Taiwan’s National Health Insurance. The specific relevant mechanism may be as follows. In clinical practice, BD is primarily treated with antipsychotics, mood stabilizers, and antidepressants, which have been found to cause ED. In addition, a large proportion of BD patients are accompanied by sleep disturbances, which in turn reduce testosterone levels in men and cause and lead to sexual dysfunction. Considering the above potential mechanism and the potential causal relationship between MD and ED we discovered, our team assume that BD can also increase the risk of ED and made effort to study the causal relationship between BD and ED by using MR method. However, based on our study, we did not find the clear evidence that BD has a direct contribution to the risk of ED. Therefore, it suggests that further research is needed on the relationship between BD and ED. ## Strengths and limitations The MR study design is one of the greatest strengths of this study. This approach can reverse causality inherent and minimize residual confounding in observational studies. Besides, it can allow us to discovery potential causal relationships between ED and MD or BD. The study can further support the results through other secondary analytical approaches and sensitivity analyses, increasing the reliability of our conclusions. In addition, we extracted the instrumental variables from the most recent GWAS available with confidence to minimize weak instrumental bias. However, there were some several limitations. First, the data from GWASs of this study came from European, so that the similar study should be investigated in other populations. Second, there are different subtypes of ED (non-vasculogenic or vasculogenic), MD and BD, which were not distinguished in this study. Subsequent studies could be devoted to ED analysis of different subgroups. Thirdly, only $3.9\%$ of the observed variance in BD was explained by IVs, so the statistical power may be insufficient. Therefore, for this negative result, we need to interpret it with caution to avoid drawing this conclusion due to insufficient power. ## Conclusion The findings of this research found evidence of a causal relationship between MD and ED. But the mechanism of the association between MD and ED remains to be discovered. On the other hand, we did not find a causal relationship between BD and ED in European populations, which need further in-depth research to verify. ## Electronic supplementary material Below is the link to the electronic supplementary material. Supplementary Material 1 ## References 1. 1.NIH Consensus Conference. Impotence. NIH Consensus Development Panel on Impotence. JAMA. 1993;270(1):83–90. 2. Salonia A, Bettocchi C, Boeri L. **European Association of Urology Guidelines on sexual and Reproductive Health-2021 update: male sexual dysfunction**. *Eur Urol* (2021.0) **80** 333-57. DOI: 10.1016/j.eururo.2021.06.007 3. Huang SS, Lin CH, Chan CH. **Newly diagnosed major depressive disorder and the risk of erectile dysfunction: a population-based cohort study in Taiwan**. *Psychiatry Res* (2013.0) **210** 601-6. DOI: 10.1016/j.psychres.2013.06.012 4. Mourikis I, Antoniou M, Matsouka E. **Anxiety and depression among greek men with primary erectile dysfunction and premature ejaculation**. *Ann Gen Psychiatry* (2015.0) **14** 34. DOI: 10.1186/s12991-015-0074-y 5. Wang YT, Chen HH, Lin CH. **Newly diagnosed panic disorder and the risk of erectile dysfunction: a population-based cohort study in Taiwan**. *Psychiatry Res* (2016.0) **244** 229-34. DOI: 10.1016/j.psychres.2016.07.037 6. Montejo AL, Majadas S, Rico-Villademoros F. **Frequency of sexual dysfunction in patients with a psychotic disorder receiving antipsychotics**. *J Sex Med* (2010.0) **7** 3404-13. DOI: 10.1111/j.1743-6109.2010.01709.x 7. **Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the global burden of Disease Study 2019**. *Lancet* (2020.0) **396** 1204-22. DOI: 10.1016/S0140-6736(20)30925-9 8. 8.World Health Organization. Depression and Other Mental Disorders: Gobal Health Estimates. Geneva; 2017. 9. Seftel AD, Sun P, Swindle R. **The prevalence of hypertension, hyperlipidemia, diabetes mellitus and depression in men with erectile dysfunction**. *J Urol* (2004.0) **171** 2341-5. DOI: 10.1097/01.ju.0000125198.32936.38 10. Martin-Morales A, Sanchez-Cruz JJ, Saenz de Tejada I. **Prevalence and independent risk factors for erectile dysfunction in Spain: results of the Epidemiologia de la Disfuncion Erectil Masculina Study**. *J Urol* (2001.0) **166** 569-75. DOI: 10.1016/s0022-5347(05)65986-1 11. Weber MF, Smith DP, O’Connell DL. **Risk factors for erectile dysfunction in a cohort of 108 477 australian men**. *Med J Aust* (2013.0) **199** 107-11. DOI: 10.5694/mja12.11548 12. Liu Q, Zhang Y, Wang J. **Erectile Dysfunction and Depression: a systematic review and Meta-analysis**. *J Sex Med* (2018.0) **15** 1073-82. DOI: 10.1016/j.jsxm.2018.05.016 13. Merikangas KR, Akiskal HS, Angst J. **Lifetime and 12-month prevalence of bipolar spectrum disorder in the National Comorbidity Survey replication**. *Arch Gen Psychiatry* (2007.0) **64** 543-52. DOI: 10.1001/archpsyc.64.5.543 14. Merikangas KR, Jin R, He JP. **Prevalence and correlates of bipolar spectrum disorder in the world mental health survey initiative**. *Arch Gen Psychiatry* (2011.0) **68** 241-51. DOI: 10.1001/archgenpsychiatry.2011.12 15. Hou PH, Mao FC, Chang GR. **Newly diagnosed bipolar disorder and the subsequent risk of Erectile Dysfunction: a Nationwide Cohort Study**. *J Sex Med* (2018.0) **15** 183-91. DOI: 10.1016/j.jsxm.2017.12.013 16. Davey Smith G, Phillips AN. **Correlation without a cause: an epidemiological odyssey**. *Int J Epidemiol* (2020.0) **49** 4-14. DOI: 10.1093/ije/dyaa016 17. Burgess S, Foley CN, Zuber V. **Inferring Causal Relationships between Risk factors and outcomes from genome-wide Association Study Data**. *Annu Rev Genomics Hum Genet* (2018.0) **19** 303-27. DOI: 10.1146/annurev-genom-083117-021731 18. Davey Smith G, Holmes MV, Davies NM. **Mendelian randomization and causal inference in observational data: substantive and nomenclatural issues**. *Eur J Epidemiol* (2020.0) **35** 99-111. DOI: 10.1007/s10654-020-00622-7 19. Lawlor DA. **Two-sample mendelian randomization: opportunities and challenges**. *Int J Epidemiol* (2016.0) **45** 908-15. DOI: 10.1093/ije/dyw127 20. Lawlor DA, Harbord RM, Sterne JA. **Mendelian randomization: using genes as instruments for making causal inferences in epidemiology**. *Stat Med* (2008.0) **27** 1133-63. DOI: 10.1002/sim.3034 21. Howard DM, Adams MJ, Clarke TK. **Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions**. *Nat Neurosci* (2019.0) **22** 343-52. DOI: 10.1038/s41593-018-0326-7 22. Stahl EA, Breen G, Forstner AJ. **Genome-wide association study identifies 30 loci associated with bipolar disorder**. *Nat Genet* (2019.0) **51** 793-803. DOI: 10.1038/s41588-019-0397-8 23. Bovijn J, Jackson L, Censin J. **GWAS identifies risk locus for Erectile Dysfunction and implicates hypothalamic neurobiology and diabetes in etiology**. *Am J Hum Genet* (2019.0) **104** 157-63. DOI: 10.1016/j.ajhg.2018.11.004 24. Jostins L, Ripke S, Weersma RK. **Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease**. *Nature* (2012.0) **491** 119-24. DOI: 10.1038/nature11582 25. Liu JZ, van Sommeren S, Huang H. **Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations**. *Nat Genet* (2015.0) **47** 979-86. DOI: 10.1038/ng.3359 26. Zhu Z, Zheng Z, Zhang F. **Causal associations between risk factors and common diseases inferred from GWAS summary data**. *Nat Commun* (2018.0) **9** 224. DOI: 10.1038/s41467-017-02317-2 27. Li H, Wen Z. **Effects of ulcerative colitis and Crohn’s disease on neurodegenerative diseases: a mendelian randomization study**. *Front Genet* (2022.0) **13** 846005. DOI: 10.3389/fgene.2022.846005 28. Burgess S, Thompson SG. **Avoiding bias from weak instruments in mendelian randomization studies**. *Int J Epidemiol* (2011.0) **40** 755-64. DOI: 10.1093/ije/dyr036 29. 29.Papadimitriou N, Dimou N, Tsilidis KK et al. Physical activity and risks of breast and colorectal cancer: a Mendelian randomisation analysis. Nat Commun. 2020;11(1):597. Published 2020 Jan 30. doi:10.1038/s41467-020-14389-8 30. Hartwig FP, Davies NM, Hemani G. **Two-sample mendelian randomization: avoiding the downsides of a powerful, widely applicable but potentially fallible technique**. *Int J Epidemiol* (2016.0) **45** 1717-26. DOI: 10.1093/ije/dyx028 31. Kamat MA, Blackshaw JA, Young R. **PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations**. *Bioinformatics* (2019.0) **35** 4851-3. DOI: 10.1093/bioinformatics/btz469 32. Minhas S, Bettocchi C, Boeri L. **European Association of Urology Guidelines on male sexual and Reproductive Health: 2021 update on male infertility**. *Eur Urol* (2021.0) **80** 603-20. DOI: 10.1016/j.eururo.2021.08.014 33. Wang M, Jian Z, Gao X. **Causal Associations between Educational Attainment and 14 urological and Reproductive Health Outcomes: a mendelian randomization study**. *Front Public Health* (2021.0) **9** 742952. DOI: 10.3389/fpubh.2021.742952 34. Xiong Y, Zhang FX, Zhang YC. **Genetically predicted insomnia causally increases the risk of erectile dysfunction**. *Asian J Androl* (2022.0). DOI: 10.4103/aja202261 35. Burgess S, Butterworth A, Thompson SG. **Mendelian randomization analysis with multiple genetic variants using summarized data**. *Genet Epidemiol* (2013.0) **37** 658-65. DOI: 10.1002/gepi.21758 36. Bowden J, Davey Smith G, Burgess S. **Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression**. *Int J Epidemiol* (2015.0) **44** 512-25. DOI: 10.1093/ije/dyv080 37. Bowden J, Davey Smith G, Haycock PC. **Consistent estimation in mendelian randomization with some Invalid Instruments using a weighted median estimator**. *Genet Epidemiol* (2016.0) **40** 304-14. DOI: 10.1002/gepi.21965 38. Hartwig FP, Davey Smith G, Bowden J. **Robust inference in summary data mendelian randomization via the zero modal pleiotropy assumption**. *Int J Epidemiol* (2017.0) **46** 1985-98. DOI: 10.1093/ije/dyx102 39. Zhu G, Zhou S, Xu Y. **Mendelian randomization study on the causal effects of omega-3 fatty acids on rheumatoid arthritis**. *Clin Rheumatol* (2022.0) **41** 1305-12. DOI: 10.1007/s10067-022-06052-y 40. Burgess S, Thompson SG. **Interpreting findings from mendelian randomization using the MR-Egger method**. *Eur J Epidemiol* (2017.0) **32** 377-89. DOI: 10.1007/s10654-017-0255-x 41. 41.Sterne JA, Sutton AJ, Ioannidis JP et al. Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials. BMJ. 2011;343:d4002. Published 2011 Jul 22. doi:10.1136/bmj.d4002 42. Burgess S, Bowden J, Fall T. **Sensitivity analyses for robust causal inference from mendelian randomization analyses with multiple genetic variants**. *Epidemiology* (2017.0) **28** 30-42. DOI: 10.1097/EDE.0000000000000559 43. Verbanck M, Chen CY, Neale B. **Detection of widespread horizontal pleiotropy in causal relationships inferred from mendelian randomization between complex traits and diseases**. *Nat Genet* (2018.0) **50** 693-8. DOI: 10.1038/s41588-018-0099-7 44. Nwakanma NC, Ofoedu JN. *J Psychiatr* (2016.0) **22** 979. DOI: 10.4102/sajpsychiatry.v22i1.979 45. Pietrzyk B, Olszanecka-Glinianowicz M, Owczarek A. **Depressive symptoms in patients diagnosed with benign prostatic hyperplasia**. *Int Urol Nephrol* (2015.0) **47** 431-40. DOI: 10.1007/s11255-015-0920-5 46. Kim M, Kim SY, Rou WS. **Erectile dysfunction in patients with liver disease related to chronic hepatitis B**. *Clin Mol Hepatol* (2015.0) **21** 352-7. DOI: 10.3350/cmh.2015.21.4.352 47. Ma K, Song P, Liu ZH. **Genetic evidence suggests that depression increases the risk of erectile dysfunction: a mendelian randomization study**. *Front Genet* (2022.0) **13** 1026227. DOI: 10.3389/fgene.2022.1026227 48. Makhlouf A, Kparker A, Niederberger CS. **Depression and erectile dysfunction**. *Urol Clin North Am* (2007.0) **34** 565-vii. DOI: 10.1016/j.ucl.2007.08.009 49. Goldstein I. **The mutually reinforcing triad of depressive symptoms, cardiovascular disease, and erectile dysfunction**. *Am J Cardiol* (2000.0) **86** 41F-5. DOI: 10.1016/s0002-9149(00)00892-4 50. Shiri R, Koskimäki J, Tammela TL, Häkkinen J. **Bidirectional relationship between depression and erectile dysfunction**. *J Urol* (2007.0) **177** 669-73. DOI: 10.1016/j.juro.2006.09.030 51. Serretti A, Chiesa A. **Treatment-emergent sexual dysfunction related to antidepressants: a meta-analysis**. *J Clin Psychopharmacol* (2009.0) **29** 259-66. DOI: 10.1097/JCP.0b013e3181a5233f 52. Csoka AB, Bahrick A, Mehtonen OP. **Persistent sexual dysfunction after discontinuation of selective serotonin reuptake inhibitors**. *J Sex Med* (2008.0) **5** 227-33. DOI: 10.1111/j.1743-6109.2007.00630.x 53. Healy D, Bahrick A, Bak M. **Diagnostic criteria for enduring sexual dysfunction after treatment with antidepressants, finasteride and isotretinoin**. *Int J Risk Saf Med* (2022.0) **33** 65-76. DOI: 10.3233/JRS-210023 54. Chou PS, Chou WP, Chen MC. **Newly diagnosed erectile dysfunction and risk of depression: a population-based 5-year follow-up study in Taiwan**. *J Sex Med* (2015.0) **12** 804-12. DOI: 10.1111/jsm.12792 55. Tsujimura A. **The relationship between Testosterone Deficiency and Men’s Health**. *World J Mens Health* (2013.0) **31** 126-35. DOI: 10.5534/wjmh.2013.31.2.126
--- title: circACTR2 attenuates gemcitabine chemoresiatance in pancreatic cancer through PTEN mediated PI3K/AKT signaling pathway authors: - Chao Xu - Qinwen Ye - Chao Ye - Shaojun Liu journal: Biology Direct year: 2023 pmcid: PMC10061898 doi: 10.1186/s13062-023-00368-8 license: CC BY 4.0 --- # circACTR2 attenuates gemcitabine chemoresiatance in pancreatic cancer through PTEN mediated PI3K/AKT signaling pathway ## Abstract ### Background Recently, accumulating studies have unveiled that circRNAs exert critical function in a variety of tumor biological processes including chemoresistance. Our previous study has found circACTR2 is significantly down-regulated in acquired gemcitabine (GEM)- resistant pancreatic cancer (PC) cells, which has not been well-explored. Our study aimed to research the function and molecular mechanism of circACTR2 in PC chemoresistance. ### Methods qRT-PCR and western blot analysis was performed to detect gene expression. The effect of circACTR2 on PC GEM resistance were examined by CCK-8 and flow cytometry assays. Whether circACTR2 could sponge miR-221-3p and regulate PTEN expression were determined by bioinformatics analysis, RNA pull-down, and Dual-luciferase reporter assay. ### Results circACTR2 was notably down-regulated in a panel of GEM-resistant PC cells lines, and negatively associated with aggressive phenotype and poor prognosis of PC. circACTR2 downregulation contributed to GEM chemoresistance of PC cells with decreased S phase ratio of cell cycle and cell apoptosis, as confirmed by gain- and loss-of-function assays in vitro. In addition, circACTR2 overexpression retarded GEM resistance in vivo. Further, circACTR2 acted as a ceRNA against miR-221-3p, which directly targeted PTEN. The mechanistic studies revealed that loss of circACTR2 promoted GEM resistance in PC through activating the PI3K/AKT signaling pathway by downregulating PTEN expression in a miR-221-3p dependent manner. ### Conclusions circACTR2 reversed the chemoresistance of PC cells to GEM through inhibiting PI3K/AKT signaling pathway by sponging miR-221-3p and upregulating PTEN expression. ### Supplementary Information The online version contains supplementary material available at 10.1186/s13062-023-00368-8. ## Introduction Pancreatic cancer (PC) is one of the most lethal human malignancies, with an overall five-year survival rate of less than $5\%$ [1]. The high mortality of PC could be largely attributive to its highly aggressive nature, wherein local invasion and remote metastasis may occur during the early stages of carcinogenesis, and its inherent chemoresistance [2]. Thus, most patients diagnosed with PC are not operable and chemotherapy is thus the main treatment option. At present, gemcitabine (GEM) is the first-line drug used in the treatment of PC. However, its therapeutic efficacy is far from satisfactory due to the inherent chemoresistance of PC [3]. A previous study revealed that only $23.8\%$ of GEM-treated patients received therapeutic benefits in their early stages of treatment [4]. What’s worse, acquired GEM resistance makes PC treatment more difficult. The molecular mechanisms of GEM resistance in PC include aberrant gene expression, mutations, and deregulation of key signaling pathways such as nuclear factor κB (NF-κB), Akt and Notch pathways [5]. However, a better understanding of the molecular mechanisms underlying the development of GEM chemoresistance is still necessary to develop novel-targeted therapies to ‘flip the switch’ from drug resistance to susceptibility in PC. Circular RNA (circRNA) is a special class of non-coding RNA molecules, characterized by covalently closed circular structures through special selective splicing. Due to the lack of 3 ‘end and 5’ end, circRNA is not easy to be degraded by exonuclease and is more stable than linear RNA. Accumulating studies have shown that circRNA can regulate gene expression at transcription and post-transcription level by cis-regulation of transcription, alternative splicing of RNAs, encoding peptides, and acting as competing endogenous RNAs [6]. In addition, circRNA has been found to play an important role in tumorigenesis and tumor progression by modulating tumor cell proliferation, differentiation, apoptosis, invasion, and drug resistance [7, 8]. Recently, several circRNAs have been discovered to participate in chemoresistance of PC. For example, hypoxic exosomal HIF-1α-stabilizing circZNF91 promotes chemoresistance of normoxic PC cells via enhancing glycolysis [9]. Another study reported that circLMTK2 knockdown attenuated GEM resistance of PC cells by regulating PAK1 via miR-485-5p [10]. Our previous study has revealed the functional profiles of differentially expressed circRNAs associated with GEM resistance in PC cells [11]. However, the biological functions and potential mechanisms of these dysregulated circRNAs associated with PC chemoresistance remain unclear, thus need to be clarified to to make a breakthrough in the clinical application for PC treatment. In the present study, we found that circRNA_102747 (also named circACTR2) was notably downregulated in GEM-resistant PC cells, and was necessary for the maintenance of GEM resistance. By acting as a ceRNA by sponging miR-221-3p, circACTR2 upregulated PTEN expression and consequently inhibited PI3K/AKT signal pathway to attenuate GEM resistance of PC. Our study provides a novel therapeutic target for overcoming PC GEM cheomresistance. ## Cell culture and transfection Human PC cell lines (PANC-1, SW1990, BxPC-3, CFPAC-1 and AsPC-1) were purchased from the Type Culture Collection of the Chinese Academy of Sciences (Shanghai, China). SW1990/GZ cells are acquired GEM-resistant SW1990 cells which were established in our lab [11]. PC cells were cultured in DMEM (Thermo Fisher Scientific, Waltham, MA, USA) supplemented with $10\%$ fetal bovine serum (Thermo Fisher Scientific), 100 U/mL penicillin, and 100 µg/mL streptomycin. Cultures were maintained at 37 °C in a humidified atmosphere of $5\%$ CO2. To knockdown and overexpress circACTR2, lentiviral vector (GenePharma Corp, shanghai, China) was used to express shRNA specifically targeting the junction region of the circACTR2 sequence (AAACTTTCTGATCTTATG) and full length of circACTR2, respectively. Only the empty lentiviral vector was used as a negative control (NC). The mimic and inhibitor of miRNA-221-3p, Phosphatase and tensin homolog (PTEN) overexpression vector and sh-PTEN vector were synthesized by GenePharma (Shanghai, China). For stable transfection, cells were seeded in a 24-well plate until $90\%$ confluent, then transfected with vector using lipofectamine™ 2000 reagent (Invitrogen, Carlsbad, USA) according to the manufacturer’s instructions. ## Clinical specimens In total, 25 PC samples were collected from the First Affiliated Hospital of USTC between 2018 and 2020. All samples were immediately frozen in liquid nitrogen after resection and stored at − 80 °C for RNA extraction. We have obtained informed consent from all patients and this study was approved by the Ethics Committee of the First Affiliated Hospital of USTC (Hefei, China). ## qRT-PCR Total RNA was extracted from cells and tissues using TRIzol Reagent (Invitrogen). cDNA was then synthesized using 1 µg samples of the total RNA with SuperScript III Reverse Transcriptase (invitrogen). Real time-PCR was performed using a SYBR primescript qRT-PCR kit (TaKaRa, Japan) on the Step One platform (Applied Biosystems, Shanghai, China). Primer sequences for candidate genes were listed in Additional file 2: Table S1. GAPDH was used as internal reference for quantification of circRNA and mRNA, while U6 for miRNA. Relative expression levels of genes was calculated by 2–ΔΔCt values. ## Western blot assay The total protein lysate extracted from the PC cells was separated by SDS-PAGE and transferred to PVDF membranes. After blocking membranes, PVDF were incubated with appropriate dilutions of specific primary antibodies against PTEN, GAPDH (Abcam, Cambridge, MA) and Akt, p-Akt (Ser 473) (Cell Signaling, Danvers, MA). For protein visualization, the blots were incubated with HRP-conjugated secondary antibodies after extensive washing with TBS and then visualized using the ECL system. ## Cell viability assay The viability of PC cells was determined by Cell Counting Kit 8 (Dojindo, Japan) and measured at OD 450 nm with the BioTek Gen5 system (BioTeck, USA). 96-well plates in triplicates at a density of 1.5 × 103 cells per well. The cells were then grow in complete media for another 72 h with indicated concentration of GEM treatment. ## Apoptosis assay FITC Annexin V cell apoptosis detection kit I (BD Biosciences, CA, USA) was used for cell apoptosis evaluation. Briefly, Cells were resuspended in binding buffer at a concentration of 1 × 106 cells/mL, then incubated with 0.5 mg/mL Annexin V-FITC and 2 mg/ml PI for 10 min and examined by FACSCalibur flow cytometry (BD Biosciences, CA, USA) to analyze the cell apoptosis. ## Cell cycle assay Cell cycle kit (BD Biosciences, CA, USA) was used for cell cycle distribution evaluation. Briefly, Cells were collected and pre-cooled $70\%$ ethanol, fixed at 4 °C overnight. Then cells were washed by PBS once and added with 0.5 mL propidium iodide (PI) solution (25 µL 20 × PI and 10 µL 50 × Rnase A), examined by FACSCalibur flow cytometery (BD Biosciences, CA, USA) to analyze the DNA content using the PI channel. ## In vivo assay for drug sensitivity SW1990/GZ cells transfected with OE-circ and OE-NC were injected subcutaneously into the right flank of 6-week-old female BALB/c mice (Chinese Academy of Sciences, Shanghai, China) (5 × 106 cells in 250 µl of PBS per mouse). Each experimental group included four mice. All mice received an i.p. injection of 50 mg/kg GEM twice a week after tumor formation (tumor size between 100 and 200 mm3) for 4 weeks. Animals were monitored daily, and tumor volume was measured every fourth day after treatment. All tumor-bearing mice were euthanized on the 28 th day after treatment [12]. ## Fluorescence in situ hybridization (FISH) The Cy3-labeled probes specific for circACTR and miR-221-3p was synthesized by Genepharma (Shanghai, China). SW1990 cells were hybridized with Cy3-labeled probe overnight and then dyed by DAPI. The signals of the probes were detected by a Fluorescent In Situ Hybridization Kit (Genepharma, Shanghai, China) and observed under a fluorescence microscope (Leica, Wetzlar, Germany). ## Immunohistochemistry (IHC) The xenograft tumor tissue was fixed with $10\%$ neutral formalin, embedded in paraffin and cut into into 4 μm thick sections. Immunohistochemistry was performed to stain PCNA and tunnel using the procedure as previously described [13]. ## Tissue microarray (TMA) and in situ hybridization (ISH) The tissue microarray containing 99 paraffin-embedded PC samples were purchased from Outdo Biotech (Shanghai, China). The probe for circACTR2 containing a biotin label synthesized by Genepharma (Shanghai, China) was used for ISH analysis. Concisely, TMA was dewaxed and rehydrated, digested using proteinase K and hybridized with the specific circACTR2 probe at 4 °C overnight, then incubated with anti-Digoxin-AP (Roche, Basel, Switzerland) at 4 °C overnight. The tissues were stained with NBT/BCIP (Roche, Basel, Switzerland) and quantified. ## RNA pull-down assay The biotinylated probe specifically bind to the junction area of circACTR2 was synthesized by Tsingke Biotech (Wuhan, China). The probe was incubated with the beads at room temperature for 10 min for immobilization. Then, the biotinylated beads were incubated with SW1990 cell lysate at 4 °C overnight. The biotinylated beads were magnetically separated and washed, then the bound miRNAs in the pull-down materials were extracted using Trizol reagent and analyzed by qRT-PCR assay. ## Dual-luciferase reporter assay The sequences of circACTR2 and PTEN-3’UTR and their corresponding mutant versions without miR-221-3p binding sites were synthesized and subcloned into luciferase reporter vector psiCHECK2 (Promega, Madison, WI, USA), respectively. All these plasmids were validated by sequencing. The relative luciferase activity was examined by Dual Luciferase Assay Kit (Promega, WI, USA) in accordance with the manufacturer’s protocols. ## Statistical analysis Differences between various groups were assessed using one-way ANOVA or Student’s test. The Chi-square was used for testing correlations between circACTR2 expression and clinicopathological variables. The survival rates were evaluated by Kaplane-Meier method and tested by log-rank test. The effects of the clinicopathological variables on overall survival of PC patients were determined by univariate and multivariate Cox proportional hazards regression model. Pearson correlation analysis was performed to determine correlations. All statistical analyses were performed using SPSS 19.0 software. P-values less than 0.05 were considered to be statistically significant. ## circACTR2 is down-regulated in GEM-resistant PC cells Our previous study revealed that 78 circRNAs (fold change ≥ 2) were observed in acquired GEM-resistant SW1990/GZ cells compared with parental SW1990 cells by high throughput circRNA microarray chip (Fig. 1A), and qRT-PCR verification further showed that the expression of circACTR2 has the largest difference with 4.12 times reduction in SW1990/GZ cells among these dysregulated circRNAs (Fig. 1B). We then determined the expression of circACTR2 in a panel of PC cells by qRT-PCR analysis. It showed that the expression level of circACTR2 in GEM-sensitive PC cell lines (IC50 < 10 µM/L GEM) such as SW1990, BxPC-3 and CFPAC-1 is generally higher than that in GEM-resistant PC cell lines (IC50 > 100 µM/L GEM) such as Panc-1 and SW1990/GZ cells (Fig. 1C, D). To determine the potential role of circACTR2 in PC, based on our previous circRNA microarray profile data, Gene Ontology (GO) functional analysis showed that circACTR2 was related to several biological processes related to chemo-resistance, such as cell stress response, cell apoptosis, and intracellular metabolism (Fig. 1E). In addition, pathway analysis showed that it was significantly associated with PI3K/AKT signal pathway (Fig. 1F), which is often abnormally activated to promote chemoresistance of PC. Thus, these results demonstrated that circACTR2 is downregulated in GEM-resistant PC cells, and suggested potential role for circACTR2 in modulating PC GEM resistance. Fig. 1circACTR2 is decreased in GEM-resistant PC cellsA Scatter plot shows the up-regulated and down-regulated circRNAs in acquired GEM-resistant SW1990/GZ PC cells compared with parental SW1990 cells. B The differentially expressed circRNAs with most change fold in SW1990/GZ PC cells compared with SW1990 cells were validated by qRT-PCR. C Relative cell viability of a panel of PC cell lines treated with increased concentration of gemcitabine for 72 h was determined by CCK-8 assay. D circACTR2 expression was determined in a panel of PC cell lines by qRT-PCR assay. E Gene *Ontology analysis* and F pathway analysis of circACTR2 based on microarray profile data. * $P \leq 0.05$ and **$P \leq 0.01$ vs. SW1990/GZ; #$P \leq 0.05$ and ##$P \leq 0.01$ vs. Panc-1 ## circACTR2 is negatively associated with aggressive phenotype and poor prognosis According to the UCSC Genome Browser, circACTR2 was spliced from ACTR2 located at chr2:65473657–65,492,309 and finally formed a circular transcript of 855 bp. circACTR2 is generated from back-splicing of the 5th and 6th exons of the ACTR2 gene, the back-spliced regions of circACTR2 was confirmed by Sanger sequencing, and all were in agreement with circBase (Fig. 2A). Resistance to digestion with actinomycin D (Fig. 2B) or RNase R (Fig. 2C) further confirmed this RNA was circular in form. To study the location of circACTR2, hybridization (FISH) assay demonstrated its cytoplasmic localization (Fig. 2D). Fig. 2circACTR2 is negatively associated with aggressive cell phenotype and poor prognosisA circACTR2 information from the UCSC Genome Browser. The amplified product was sequenced using Sanger sequencing to validate the circularized junction of circACTR2. B qRT-PCR analysis of circACTR2 and ACTR2 mRNA in SW1990 cells with actinomycin D treatment for 30 min at 37 °C. C qRT-PCR analysis of circACTR2 and ACTR2 mRNA in SW1990 cells treated with RNase R. D FISH images showed that circACTR2 labeled with Cy3 (red) was mainly distributed in the cytoplasm of SW1990 cells. The nuclei was stained with DAPI (blue). Scale bar, 25 μm. E Representative images of circACTR2 expression in PC tissues were detected by ISH. F Percentages of specimen with low or high expression of circACTR2 in PC compared with NP tissues and in PC tissues with different pathological grade. G Kaplan-Meier’s analyze of correlation between circACTR2 expression level and overall survival of patients with PC ($$n = 99$$, $p \leq 0.01$, log-rank test). * $P \leq 0.05$; **$P \leq 0.01$ We further examined the circACTR2 expression in 99 pairs of PC tissue and adjacent non-cancer (NC) TMA tissue using ISH (Fig. 2E), showing that the expression level of circACTR2 is downregulated in PC tissues compared with NC tissues (Fig. 2F). We then analyzed the correlation between circACTR2 expression and the clinicopathological characteristics as shown in Table 1, demonstrating that circACTR2 expression was significantly correlated with pathological grade with significant downregulation in Grade III compared with Grade I-II (Fig. 2F), while no other significant associations were obtained between circACTR2 expression with gender, age, location of tumor, T stage, N stage, and TNM stage. Additionally, Kaplan-Meier survival analysis showed that patients with low circACTR2 expression had significantly poor overall survival compared with patients with high circACTR2 expression (Fig. 2G). Further univariate and multivariate Cox regression analysis showed that TNM stage, pathological grade and circACTR2 expression levels were independent prognostic factors for PC patients (Table 2). Table 1The correlation between circACTR2 expression and clinicopathological characteristics of PC patientsClinicopathological variablesNumber of patients in each groupcircACTR2 expressionP valueLow (n)High (n)Age (years)> 604827210.893≤ 60512823GenderMale6336270.674Female361917Location of tumorHead5933260.927Body/tail402218Pathological gradeІ-ІІ6632340.045ІІІ332310T stageT1/T27946330.288T3/T420911N stageN05628280.204N1432716TNM stageІ4021190.614ІІ593425* $P \leq 0.05$ Table 2Univariate and multivariate Cox regression analysis of circACTR2 and survival in patients with PCClinicopathological variablesUnivariateanalysis P *Multivariate analysis* P HR ($95\%$ CI)HR ($95\%$ CI)Age (> 60 vs. ≤ 60)0.785(0.497–1.240)0.299Gender (Male vs. Female)0.887(0.550–1.432)0.624Tumor location (Head vs. Body/tail)0.916(0.577–1.454)0.710Pathological grade (І-ІІ vs. III)0.515(0.321–0.828)0.006*0.530(0.326–0.862)0.010*T stage (T1/T2 vs. T3/T4)1.080(0.603–1.935)0.795N stage (N0 vs. N1)0.576(0.362–0.915)0.019*TNM stage (І vs. II)0.583(0.360–0.944)0.028*0.474(0.287–0.782)0.003*circACTR2 (High vs. Low)0.178(0.105–0.304)< 0.001*0.170(0.098–0.296)< 0.001*Abbreviations: HR hazard ratio, CI confidence interval*$P \leq 0.05$ ## circACTR2 downregulation promotes GEM resistance of PC cells in vitro To determine whether circACTR2 involved in GEM resistance of PC cells, we stably transfected circACTR2 overexpression vector (OE-circ) in SW1990/GZ and PANC-1 cells respectively, we observed a remarkable increase of circACTR2 expression in OE-circ group cells compared with negative control (OE-NC) vector group cells (Fig. 3A). As expected, relative cell viability decreased in OE-circ group cells compared with OE-NC group cells treated with 10 µM/L GEM for 72 h (Fig. 3B). Consistently, the apoptosis rate of OE-circ group cells was increased compared with OE-NC group cells after incubating with gemcitabine (10 µM/L) for 72 h (Fig. 3C). In addition, OE-circ group cells showed reduced G1 phase ratio and increased S phase ratio compared with OE-NC group cells (Fig. 3D). Knockdown of circACTR2 with shRNA (sh-circ) vector transfection led to almost opposite results in SW1990 and BxPC-3 cells (Fig. 3E-H). Thus, these results suggest that circACTR2 downregulation is essential for sustaining GEM resistance and that circACTR2 overexpression substantially increased the efficacy of GEM by inducing tumor cell apoptosis. Fig. 3circACTR2 over-expression attenuates GEM-Resistance of PC cells in vitroA The relative expression levels of circACTR2 in PC cells stably transfected with circACTR2 over-expression vector (OE-circ) and corresponding negative control (NC) vector were detected by qRT-PCR. B Relative cell viability of PC cell transfected with OE-circ vector and OE-NC vector under GEM treatment for 72 h detected by CCK-8 assay. C Apoptosis rate and D cell cycle distribution in PC cells transfected with OE-circ vector and OE-NC vector detected by flow cytometry. E circACTR2 expression in PC cells stably transfected with sh-circACTR2 (sh-circ) vector and sh-NC vector were detected by qRT-PCR. F Relative cell viability of PC cell transfected with sh-circ vector and sh-NC vector under GEM treatment for 72 h. G Apoptosis rate and H cell cycle distribution in PC cells transfected with sh-circ vector and sh-NC vector detected by flow cytometry. * $p \leq 0.05$ and **$p \leq 0.01$ ## circACTR2 reverses GEM resistance by acting as miRNA sponge for miR-221-3p Given that circACTR2 localizes to the cytoplasm, we hypothesized that miRNA sponge activity could be a possible mechanism for its functional effects. 59 miRNAs were predicated and listed as possible targets of circACTR2 based on Arraystar’s miRNA target prediction software based on TargetScan & miRanda (Additional file 2: Table S2). Among above miRNAs, we focused on these miRNAs that have been previously found to be involved in tumor chemo-resistance and AKT signal pathway, such as miR-221-3p, miR-185-5p, miR-138-5p, miR-21-3p, miR-222-3p, miR-7-5p, miR-885-5p, miR-485-5p, miR-22-3p and miR-511-3p. RNA pull-down assay were further performed to analyse the 10 candidate miRNAs, which showed a specific enrichment of miR-221-3p as compared to the controls (Fig. 4A). Thus we speculated that circACTR2 may regulated GEM resistance by acting as miRNA sponge for miR-221-3p. According to the predicted miRNA binding sites on circACTR2, luciferase reporter assays showed that miR-221-3p could specifically target the wild-type linear form of circACTR2 resulting in decreased luciferase activity, but not the mutant form (Fig. 4B, C). Consistently, RNA FISH indicated that circACTR and miR-221-3p were co-localized in the cytoplasm (Fig. 4D). Additionally, circACTR2 overexpression led to markedly decrease of miR-221-3p and circACTR2 knockdown could significantly increase the expression of miR-221-3p in PC cells (Fig. 4E, F). Pearson correlation analysis further indicated that the expression levels of circACTR2 were negatively associated with those of the miR-221-3p in PC tissues (Fig. 4G). These results suggested circACTR2 could act as an miRNA sponge for miR-221-3p. Fig. 4circACTR2 reverse GEM resistance by acting as an miRNA sponge for miR-221-3pA qRT-PCR analysis for predicted miRNAs pulled-down by circACTR2. B Schematic illustration of wild-type and mutant circACTR2 luciferase reporter vectors. C Dual-luciferase reporter assays using the linear form of wild-type and mutant circACTR2 in SW1990 cells transfected with NC or miR-221-3p mimic. D FISH images showed the co-localization of circACTR2 labeled with Cy3 (red) and miR-221-3p labeled with FITC (green) in the cytoplasm of PC cells. nuclei were stained with DAPI. Scale bar, 25 μm. E and F The relative expression levels of miR-221-3p in PC cells stably transfected with OE-circ or sh-circ vector were detected by qRT-PCR. G Pearson correlation analysis of circACTR2 and miR-221-3p expression in 25 PC tissues. H and I Relative cell viability of PC cell transfected with miR-221-3p inhibitor or mimic compared with NC controls under GEM treatment was determined by CCK-8 assay. * $P \leq 0.05$; **$P \leq 0.01$ To determine whether circACTR2 overexpression reverse GEM resistance by sponging miR-221-3p, we first transfected miR-221-3p mimic in SW1990/GZ cells, which showed increased GEM resistance, and miR-221-3p mimic could largely attenuates the inhibitory effect of circACTR2 overexpression on GEM resistance in SW1990/GZ cells (Fig. 4H). Conversely, miR-221-3p inhibitor could increase GEM sensitivity in SW1990 cells and abolish the promoting effect of circACTR2 knockdown on GEM resistance in SW1990 cells (Fig. 4I). These data demonstrated miR-221-3p as target for circACTR2 to mediated its regulatory role in the GEM resistance. ## CircACTR2 reverses GEM resistance by inhibiting PI3K/AKT pathway via miR-221-3p/PTEN axis To elucidate the downstream mechanisms of circACTR2, based on above GO and pathway analysis suggesting circACTR2 might negatively regulate PI3K/AKT signal which is often abnormally activated to promote chemo-resistance of PC (Fig. 1F). What draws our attention is that target gene prediction shows that miR-221-3p contains complementary sequences of PTEN 3’UTR region, a key negative upstream regulator of PI3K/AKT signaling pathway [14]. To valid this, reporter assays demonstrated that miR-221-3p decreased the luciferase activity of the wild-type reporter, while no significant changes were found using mutant reporters (Fig. 5A, B). Consistently, the expression of PTEN mRNA and protein could be regulated by altering miR-221-3p expression with miR-221-3p mimic and inhibitor transfection (Fig. 5C-F). Pearson correlation analysis further indicated that the expression levels of circACTR2 were positively associated with those of the PTEN in PC tissues (Fig. 5G). Furthermore, circACTR2 overexpression inhibited the PI3K/AKT signal pathway shown as remarkable decrease of p-AKT expression, while circACTR2 knockdown led to the opposite results, which could be largely abolished by co-transfected with miRNA-221-3p mimic and inhibitor respectively (Fig. 5H, I). Fig. 5circACTR2 reverse GEM resistance by inhibiting PI3K/AKT pathway via miR-221-3p/PTEN axisA Schematic graph illustrated the mutation of potential binding site between miR-221-3p and the 3’-UTR regions of PTEN. B The relative luciferase activities of wild type PTEN 3’UTR and its mutant after transfected with miR-221-3p mimic in SW1990 cells. C and D PETN expression in PC cells after over-expressing and silencing miR-221-3p were detected by qRT-PCR, respectively. E and F PETN expression in PC cells after over-expressing and silencing miR-221-3p were detected by Western blotting, respectively. G Pearson correlation analysis of circACTR2 and PTEN expression in 25 PC tissues. H and I Western blotting for PTEN, AKT and p-AKT in PC cell lines transfected with indicated vectors, miR mimic or inhibitors. J and K Relative cell viability of PC cells were determined after treated with sh-PTEN transfection or PTEN OE-vector/LY294002 by CCK-8 assays, respectively. * $P \leq 0.05$; **$P \leq 0.01$ To further assess the role of PTEN/PI3K/AKT pathway in mediating circACTR2 functions, rescue assays indicated that PTEN knockdown reversed the inhibitory effect of circACTR2 overexpression on GEM resistance in SW1990/GZ cells, while PTEN overexpression or PI3K inhibitor LY294002 abolished the promoting effect of circACTR2 knockdown on GEM resistance in SW1990 cells (Fig. 5J, K). These results together showed that circACTR2 can upregulate PTEN expression by specifically sponging miR-221-3p, thereby inhibit the PI3K/AKT pathway to attenuate GEM resistance. ## circACTR2 overexpression retards PC GEM resistance in vivo To further determine the effects of circACTR2 on GEM resistance in vivo, we subcutaneously injected OE-circ and OE-NC transfected SW1990/GZ cells into nude mice exposed to GEM treatment. The results showed a significant tumor growth inhibition in OE-circ group as compared OE-NC group (Fig. 6A), indeed, the tumor weight was significantly less in the OE-circ group than that in OE-NC group (Fig. 6B, C). Similar to in vitro results, IHC assay showed that the percentage of PCNA-positive cells in OE-circ group was lower than OE-NC group (Fig. 6D). Conversely, the percentage of PTEN-positive and tunnel-positive cells in OE-circ group was significantly higher than OE-NC group (Fig. 6E). These in vivo findings supported that circACTR2 has potential as a novel therapeutic target for GEM resistance in PC. Fig. 6Targeting circACTR2 in vivo retards PC GEM resistanceA Tumor volume was measured every four days in OE-circ and OE-NC groups after the gemcitabine injections ($$n = 4$$). B Pictures of tumors and C tumors weight in OE-circ and OE-NC groups 4 weeks after the GEM treatment. Immunohistochemical staining of PCNA D and tunnel E in OE-circ and OE-NC group tumors (×200). Percentage of PCNA-positive and tunnel-positive cells was shown for each group. * $P \leq 0.05$; **$P \leq 0.01$ ## Discussion PC has a high degree of malignancy without obvious symptoms at the early stage of disease, thus most patients are diagnosed at the late stage when chemotherapy has become the main treatment. GEM is still the first-line drug for patients with advanced unresectable PC. Although most patients have initial response to treatment, chemotherapy resistance will gradually appear, which is the main reason for treatment failure [15]. The exploration of the mechanism underlying GEM resistance has been continuing, including the decrease of drug uptake efficiency, the expression of drug efflux transporters, the increase of enzymatic drug inactivation, the damage of cell apoptosis, the activation of epithelial mesenchymal transformation, and the stem pathway [16]. In recent years, non-coding RNA, especially circRNA, as a new epigenetic regulatory mechanism, has been found to play an important role in the pathogenesis of cancer, and can regulate the sensitivity of cancer cells to different chemotherapy drugs [17]. For example, circ_0013587 reverses erlotinib resistance in PC cells through regulating the miR-1227/E-Cadherin pathway [18]. Another study reported that circHIPK3 can promote GEM resistance in PC cells by sponging miR-330-5p and targets rassf1 [19]. However, the role of circRNA in acquired drug resistance of PC is still largely unclear. Thus, we have previously performed high-throughput microarray analysis in acquired GEM-resistant cells, showing several circRNAs are significantly dysregulated [11]. Among them, our study showed that circACTR2 is negatively associated with aggressive cell phenotype and poor prognosis in PC patients. A previous study showed that circACTR2 knockdown significantly decreased high glucose-induced pyroptosis, inflammation and fibrosis in proximal tubular cells [20]. A recent study demonstrated that circACTR2 activated macrophage inflammation, and stimulated macrophage-induced EMT and fibrosis of renal tubular epithelial cells [21]. In our study, functional analysis based on microarray profile of acquired GEM-resistant PC cells suggests that circACTR2 is related to multiple drug resistance related biological processes, which lead us to speculate that circACTR2 is involved in GEM resistance. To validate that, the gain/loss of function experiments showed that circACTR2 could negatively regulate the sensitivity of PC cells to GEM. Induction of cell apoptosis is one of the main mechanisms of GEM. As a nucleoside analogue, GEM affects cell cycle by inducing cell cycle arrest in S phase when used at low/moderate doses and exerts its cytotoxicity on cells in S phase [22, 23]. Increased S phase ratio of cell cycle by circACTR2 overexpression, indicating that PC cells were blocked in S phase, might be responsible for significant increase in PC cell apoptosis at relatively low doses of GEM, suggesting the potential of circACTR2 as a chemosensitizer of GEM. CircRNAs have been suggested to function as miRNA sponges to regulate the expression of downstream target genes with following characteristics: [1] derived from protein encoding exons; [2] predominantly located in cytoplasm [24]. Given that circACTR2 derives from the 5th and 6th exons of ACTR2 gene and mainly distributes in the cytoplasm, thus circACTR2 may act as miRNA sponge. circRNA requires specific binding sites for multiple miRNAs to conduct diverse biological roles. Numerous studies reported that circACTR2 could serve as sponge for several miRNAs, such as miR-561 and miR-205-5p [21, 25]. Our study firstly demonstrated that miR-221-3p was a direct target of circACTR2. Notably, miR-221-3p has been reported to act as a tumor promoter in multiple types of cancers, involved in tumor invasion, metastasis, proliferation and chemotherapy resistance by regulating the expression of target genes such as VASH1, PARP1, RB1 [26–28]. Recent studies have demonstrated that miR-221-3p is upregulated and predicts poor prognosis in PC [29, 30]. However, the role of miR-221-3p in GEM resistance in PC is unclear. Indeed, our results suggested that miR-221-3p could negatively regulate sensitivity of PC cells to GEM, and circACTR2 sensitized PC cells to GEM in a miR-221-3p dependent manner. To elucidate the downstream mechanisms of circACTR2, pathway analysis suggested circACTR2 negatively regulate PI3K/AKT signal pathway, which is often abnormally activated to promote chemoresistance of cancers [31]. Bioinformatics prediction further suggested that miR-221-3p may directly target PTEN, which is usually inactivated in a variety of human cancers including PC, results in the activation of the PI3K/Akt pathway [32, 33] Activated Akt can promote cell proliferation, invasion and angiogenesis, but inhibit cell apoptosis, through catalyzing phosphorylation of a series of effectors [34, 35]. Our study confirmed that miR-221-3p could sponge PTEN and downregulate its expression, thereby activate the PI3K/AKT signal pathway, consistent with previous studies [36, 37]. In summary, circACTR2 was significantly downregulated in GEM-resistant PC cells, contributing to PC GEM resistance. circACTR2 overexpression reversed the chemoresistance of PC cells to GEM through inhibiting PI3K/AKT signaling pathway by sponging miR-221-3p and upregulating PTEN expression (Fig. 7), which might provide an essential hint for circACTR2 as therapeutic target to overcome PC cheomresistance. Fig. 7Proposition of a model in which circACTR2 acts as a sponge for miR-221-3p to reduce PC GEM resistance by regulating the PTEN/PI3K/AKT signaling pathway ## Electronic supplementary material Below is the link to the electronic supplementary material. Supplementary Material 1: Table S1. List of primers used for qRT-PCR in this study Supplementary Material 2: Table S2. List of predicted miRNA targets of circACTR2 based on TargetScan & miRanda in this study ## References 1. Siegel RL, Miller KD, Fuchs HE. **Cancer statistics. 2021**. *CA Cancer J Clin* (2021.0) **71** 7-33. DOI: 10.3322/caac.21654 2. Yeo TP, Hruban RH, Leach SD. **Pancreatic cancer**. *Curr Probl Cancer* (2002.0) **26** 176-275. DOI: 10.1067/mcn.2002.129579 3. Rosenberg L. **Pancreatic cancer: a review of emerging therapies**. *Drugs* (2000.0) **59** 1071-89. DOI: 10.2165/00003495-200059050-00004 4. Mittal A, Chitkara D, Behrman SW. **Efficacy of gemcitabine conjugated and miRNA-205 complexed micelles for treatment of advanced pancreatic cancer**. *Biomaterials* (2014.0) **35** 7077-87. DOI: 10.1016/j.biomaterials.2014.04.053 5. Long J, Zhang Y, Yu X. **Overcoming drug resistance in pancreatic cancer**. *Expert Opin Ther Targets* (2011.0) **15** 817-28. DOI: 10.1517/14728222.2011.566216 6. Lasda E, Parker R. **Circular RNAs: diversity of form and function**. *RNA* (2014.0) **20** 1829-42. DOI: 10.1261/rna.047126.114 7. Lei M, Zheng G, Ning Q. **Translation and functional roles of circular RNAs in human cancer**. *Mol Cancer* (2020.0) **19** 30. DOI: 10.1186/s12943-020-1135-7 8. Ma S, Kong S, Wang F. **CircRNAs: biogenesis, functions, and role in drug-resistant tumours**. *Mol Cancer* (2020.0) **19** 119. DOI: 10.1186/s12943-020-01231-4 9. Zeng Z, Zhao Y, Chen Q. **Hypoxic exosomal HIF-1α-stabilizing circZNF91 promotes chemoresistance of normoxic pancreatic cancer cells via enhancing glycolysis**. *Oncogene* (2021.0) **40** 5505-17. DOI: 10.1038/s41388-021-01960-w 10. Lu Y, Zhou S, Cheng G. **CircLMTK2 silencing attenuates Gemcitabine Resistance in Pancreatic Cancer by sponging mir-485-5p and to target PAK1**. *J Oncol* (2022.0) **2022** 1911592. DOI: 10.1155/2022/1911592 11. Xu C, Yu Y, Ding F. **Microarray analysis of circular RNA expression profiles associated with gemcitabine resistance in pancreatic cancer cells**. *Oncol Rep* (2018.0) **40** 395-404. PMID: 29781033 12. Wang J, Zhu Y, Chen J. **Identification of a novel PAK1 inhibitor to treat pancreatic cancer**. *Acta Pharm Sin B* (2020.0) **10** 603-14. DOI: 10.1016/j.apsb.2019.11.015 13. Wang Y, Jiang F, Xiong Y. **LncRNA TTN-AS1 sponges miR-376a-3p to promote colorectal cancer progression via upregulating KLF15**. *Life Sci* (2020.0) **244** 116936. DOI: 10.1016/j.lfs.2019.116936 14. 14.Song MS, Salmena L, Pandolfi PP. The functions and regulation of the PTEN tumour suppressor.Nat Rev Mol Cell Biol. 2012 Apr4;13(5):283–96. 15. Binenbaum Y, Na’ara S, Gil Z. **Gemcitabine resistance in pancreatic ductal adenocarcinoma**. *Drug Resist Updat* (2015.0) **23** 55-68. DOI: 10.1016/j.drup.2015.10.002 16. Jia Y, Xie J. **Promising molecular mechanisms responsible for gemcitabine resistance in cancer**. *Genes Dis* (2015.0) **2** 299-306. DOI: 10.1016/j.gendis.2015.07.003 17. Wang S, Qian L, Cao T. **Advances in the study of CircRNAs in Tumor Drug Resistance**. *Front Oncol* (2022.0) **12** 868363. DOI: 10.3389/fonc.2022.868363 18. Xu H, Chen R, Shen Q. **Overexpression of circular RNA circ_0013587 reverses Erlotinib Resistance in Pancreatic Cancer cells through regulating the miR-1227/E-Cadherin pathway**. *Front Oncol* (2021.0) **11** 754146. DOI: 10.3389/fonc.2021.754146 19. Liu Y, Xia L, Dong L. **CircHIPK3 promotes gemcitabine (GEM) resistance in pancreatic Cancer cells by sponging mir-330-5p and targets RASSF1**. *Cancer Manag Res* (2020.0) **12** 921-9. DOI: 10.2147/CMAR.S239326 20. Wen S, Li S, Li L. **circACTR2: a novel mechanism regulating high Glucose-Induced Fibrosis in Renal tubular cells via pyroptosis**. *Biol Pharm Bull* (2020.0) **43** 558-64. DOI: 10.1248/bpb.b19-00901 21. Fu H, Gu YH, Tan J. **CircACTR2 in macrophages promotes renal fibrosis by activating macrophage inflammation and epithelial-mesenchymal transition of renal tubular epithelial cells**. *Cell Mol Life Sci* (2022.0) **79** 253. DOI: 10.1007/s00018-022-04247-9 22. Hamed SS, Straubinger RM, Jusko WJ. **Pharmacodynamic modeling of cell cycle and apoptotic effects of gemcitabine on pancreatic adenocarcinoma cells**. *Cancer Chemother Pharmacol* (2013.0) **72** 553-63. DOI: 10.1007/s00280-013-2226-6 23. Miao X, Koch G, Ait-Oudhia S. **Pharmacodynamic modeling of Cell Cycle Effects for Gemcitabine and Trabectedin Combinations in Pancreatic Cancer cells**. *Front Pharmacol* (2016.0) **7** 421. DOI: 10.3389/fphar.2016.00421 24. Arnaiz E, Sole C, Manterola L. **CircRNAs and cancer: biomarkers and master regulators**. *Semin Cancer Biol* (2019.0) **58** 90-9. DOI: 10.1016/j.semcancer.2018.12.002 25. Yun J, Ren J, Liu Y. **Circ-ACTR2 aggravates the high glucose-induced cell dysfunction of human renal mesangial cells through mediating the miR-205-5p/HMGA2 axis in diabetic nephropathy**. *Diabetol Metab Syndr* (2021.0) **13** 72. DOI: 10.1186/s13098-021-00692-x 26. Zhou CF, Ma J, Huang L. **Cervical squamous cell carcinoma-secreted exosomal mir-221-3p promotes lymphangiogenesis and lymphatic metastasis by targeting VASH1**. *Oncogene* (2019.0) **38** 1256-68. DOI: 10.1038/s41388-018-0511-x 27. Deng L, Lei Q, Wang Y. **Downregulation of mir-221-3p and upregulation of its target gene PARP1 are prognostic biomarkers for triple negative breast cancer patients and associated with poor prognosis**. *Oncotarget* (2017.0) **8** 108712-25. DOI: 10.18632/oncotarget.21561 28. Zhao L, Zou D, Wei X. **MiRNA-221-3p desensitizes pancreatic cancer cells to 5-fluorouracil by targeting RB1**. *Tumour Biol* (2016.0) **39** 16053-63. DOI: 10.1007/s13277-016-5445-8 29. Wu X, Huang J, Yang Z. **MicroRNA-221-3p is related to survival and promotes tumour progression in pancreatic cancer: a comprehensive study on functions and clinicopathological value**. *Cancer Cell Int* (2020.0) **20** 443. DOI: 10.1186/s12935-020-01529-9 30. Li F, Xu JW, Wang L. **MicroRNA-221-3p is up-regulated and serves as a potential biomarker in pancreatic cancer**. *Artif Cells Nanomed Biotechnol* (2018.0) **46** 482-87. DOI: 10.1080/21691401.2017.1315429 31. Liu R, Chen Y, Liu G. **PI3K/AKT pathway as a key link modulates the multidrug resistance of cancers**. *Cell Death Dis* (2020.0) **11** 797. DOI: 10.1038/s41419-020-02998-6 32. De Felici M, Klinger FG. **PI3K/PTEN/AKT signaling pathways in germ cell development and their involvement in germ cell tumors and ovarian dysfunctions**. *Int J Mol Sci* (2021.0) **22** 9838. DOI: 10.3390/ijms22189838 33. Asano T, Yao Y, Zhu J. **The PI3-kinase/Akt signaling pathway is activated due to aberrant pten expression and targets transcription factors NF-kappaB and c-Myc in pancreatic cancer cells**. *Oncogene* (2004.0) **23** 8571-80. DOI: 10.1038/sj.onc.1207902 34. Jiang T, Wang H, Liu L. **CircIL4R activates the PI3K/AKT signaling pathway via the miR-761/TRIM29/PHLPP1 axis and promotes proliferation and metastasis in colorectal cancer**. *Mol Cancer* (2021.0) **20** 167. DOI: 10.1186/s12943-021-01474-9 35. Wang C, Yang Z, Xu E. **Apolipoprotein C-II induces EMT to promote gastric cancer peritoneal metastasis via PI3K/AKT/mTOR pathway**. *Clin Transl Med* (2021.0) **11** e522. DOI: 10.1002/ctm2.522 36. Shi J, Zhang Y, Jin N. **MicroRNA-221-3p plays an oncogenic role in gastric carcinoma by inhibiting PTEN expression**. *Oncol Res* (2017.0) **25** 523-36. DOI: 10.3727/096504016X14756282819385 37. Sun L, Zhu W, Zhao P. **Down-regulated exosomal MicroRNA-221-3p derived from senescent mesenchymal stem cells impairs Heart Repair**. *Front Cell Dev Biol* (2020.0) **8** 263. DOI: 10.3389/fcell.2020.00263
--- title: 'Reported health, social support, stress and associations with choline intake in pregnant women in central South Africa: the NuEMI study 2018–2019' authors: - Liska Robb - Elizabeth Margaretha Jordaan - Gina Joubert - Jennifer Ngounda - Corinna May Walsh journal: Archives of Public Health year: 2023 pmcid: PMC10061903 doi: 10.1186/s13690-023-01061-y license: CC BY 4.0 --- # Reported health, social support, stress and associations with choline intake in pregnant women in central South Africa: the NuEMI study 2018–2019 ## Abstract ### Background The health and well-being of pregnant women can influence pregnancy outcomes and are closely associated with social support and experiences of stress. Poor nutrition predisposes to poor health with choline intake affecting pregnancy outcome. This study determined reported health, social support, and stress and how these factors are associated with choline intake in pregnancy. ### Methods A cross sectional study was performed. Pregnant women in their second and third trimesters attending a high-risk antenatal clinic at a regional hospital in Bloemfontein, South Africa, were included. Trained fieldworkers obtained information during structured interviews using standardised questionnaires. Logistic regression with backward selection ($p \leq 0.05$) was used to select significant independent factors associated with choline intake. Variables with a p-value < 0.15 in bivariate analysis were considered for inclusion in the model. ### Results Median age and gestation in the sample ($$n = 682$$) were 31.8 years and 32.0 weeks, respectively. Most participants ($84.7\%$) consumed less than the adequate intake (AI) of 450 mg of choline per day. Most participants ($69.0\%$) were either overweight or obese. One in eight participants ($12.6\%$) reported not having anyone that could help them in times of need, more than one third ($36.0\%$) reported having unpayable debt and one in twelve ($8.4\%$) reported experiencing physical abuse by their partners. Normotensive participants and those using anti-retroviral therapy (ART) (thus HIV-infected), were more likely to consume choline in amounts below the AI ($$p \leq 0.042$$ and $$p \leq 0.011$$, respectively). Logistic regression analysis showed that the odds of consuming choline in amounts below the AI were lower for participants that were not using ART versus those using ART, with an odds ratio of 0.53. ### Conclusion HIV-infected participants were more likely to consume choline in levels below the AI. This vulnerable group should be the focus of targeted efforts to improve choline intake. ## Background Maternal and foetal health outcomes are significantly affected by nutritional status during pregnancy and inadequate intake of essential nutrients during pregnancy impacts negatively on maternal and child health in the short- and long-term [1]. Rapid and significant developmental and physiological changes occur during this period and to support these changes, nutritional requirements increase [2]. Several nutrients, including the micronutrient choline, are especially important to support optimal short- and long-term pregnancy outcomes. Choline is a methyl donor involved in one-carbon metabolism affecting DNA and histone methylation – processes that ultimately regulate gene expression [3]. Choline is also involved in normal placental functioning, as a high choline intake is important for placental angiogenesis [4]. There is a higher need for choline during pregnancy as this nutrient is also vital for normal foetal brain development [5]. Zeisel [2017] suggests that our understanding of developmental abnormalities and the origins of chronic diseases can be better informed by appreciating the influence of methyl-donor nutrients, such as choline, on epigenetic programming [6]. Non-communicable diseases (NCDs) are responsible for the majority of deaths in the world. The primary NCDs, including cardiovascular and lung diseases, cancers and diabetes, account for 38 of the 41 million NCD-related deaths every year. The World Health Organization (WHO) focuses on preventing NCD-related deaths by attempting to reduce the major risk factors which include unhealthy diets, tobacco use, the harmful use of alcohol and physical inactivity [7]. In South Africa, NCDs are highly prevalent and a major cause for concern. The 2016 South African Demographic and Health Survey (SADHS) (participants > 15 years) revealed that $68.0\%$ of women were overweight or obese, while the prevalence of hypertension and diabetes in women was $46.0\%$ and $13.0\%$ respectively [8]. In addition to the challenge of NCDs in South Africa, prevalence rates of human immunodeficiency virus (HIV) remain high, especially in women of reproductive age. Prevalence of HIV in women between the ages of 25 and 44 years ranges from 36.0 to $40.3\%$ [9]. In order to contribute to the health of the mother and infant, antenatal care (ANC) is vital as it provides an opportunity to monitor and screen pregnant women and to identify health risks at an early stage when interventions can positively impact on pregnancy outcome. According to the most recent SADHS, most women in South Africa ($94.0\%$) received ANC from a skilled provider during their most recent pregnancy. Almost all of these women had their blood pressure measured, provided a blood and urine sample and were advised about alcohol and tobacco use [9]. In addition to the mentioned variables, maternal psychological and social stresses are important health-related aspects that must not be neglected during pregnancy. There is evidence to suggest that maternal stress during pregnancy can have major epigenetic effects, contributing to adverse physical and mental effects in the child [10]. As stated by Kuddus and Rahman [2016], health and nutrition cannot entirely be separated. Poor health can cause poor nutrition, as intake and use of nutritious foods and nutrients may be decreased, while poor nutrition can contribute to poor health [11]. Adequate intake of choline during pregnancy contributes to overall good nutrition and as such may contribute to good health of the mother and optimal development of the foetus. Thus, the main aim of the study was to determine reported health, social support and stress and associations with choline intake of pregnant women that participated in the *Nutritional status* of Expectant Mothers and their newborn Infants (NuEMI) study. ## Methodology A cross-sectional study was undertaken. All pregnant women attending the high-risk antenatal clinic at Pelonomi Hospital, Bloemfontein, South Africa from May 2018 to April 2019 were eligible to participate in this study. This is a high-risk clinic to which older women (> 35 years), and women with multiple pregnancies, previous poor pregnancy outcomes (neonatal death and preterm delivery), two or more previous caesarean sections, a gravida of six or more, obesity, hypertension and/or diabetes mellitus are referred from surrounding areas and towns. A consecutive convenience sample of 682 pregnant women in their second and third trimesters was included. ## Dietary intake Information about dietary intake ($$n = 681$$) was determined using a quantitative food frequency questionnaire (QFFQ). Choline intake was determined using data obtained from the QFFQ. The South African Food Composition Database (SA-FCDB) does not contain data on choline content of foods; thus, all food items were first matched to foods in the USDA Database for the Choline Content of Common Foods (Release 2) [12] using the FAO/INFOODS Guidelines for Food Matching [13]. Choline intake was compared to the 1998 dietary reference intake (DRI) values set by the Institute of Medicine (IOM) of the United States of America (USA), currently known as the National Academy of Medicine (NAM) [14]. Although the estimated average requirement (EAR) should be used to evaluate population intakes of nutrients, no EAR values are yet available for choline and therefore the AI value for pregnant women (19–70 years) of 450 mg/day and the tolerable upper intake level (UL) for all adults of 3500 mg/day [14] were used in this study. ## Reported health, social support and stress Information related to social support (group membership, network of friends, family structure), tobacco and alcohol use, medical and pregnancy history, medications, presence of stress and behaviours related to the control of stress were obtained using the Reported Health and Lifestyle Questionnaire. This questionnaire was adapted from the Antenatal Questionnaire of the Birth to Twenty (BTT) study [15], a longitudinal study focussed on child and adolescent health and development in Africa. ## Gestational body mass index (GBMI) Current weight and height of each participant were measured and entered into an algorithm of Davies et al. [ 2013] along with gestation in weeks to calculate GBMI. Gestational body mass index was categorised as follows: ≥ 10 to < 19.8 kg/m2 (underweight), ≥ 19.8 to < 26.1 kg/m2 (normal weight), ≥ 26.1 to < 29 kg/m2 (overweight), and ≥ 29 to < 50 kg/m2 (obese) [16]. Participants with a GBMI ≥ 50 kg/m2) were included in the “obese” category. ## Validity and reliability The QFFQ used to determine nutrient intake was adapted from a QFFQ previously validated for use in South Africa [17–19]. Researchers and fieldworkers received comprehensive training on dietary intake assessment methodology. The use of dietary intake assessment kits specifically developed for the current study increased reliability, as all fieldworkers used comparable measures for such items as different sized spoons, cups, plates, and bean bags with known volumes to obtain the dietary intake information. Household food measures that were available in local shops were included in the kits. All dietary intake data were checked and coded by only two researchers, both registered dietitians. Equipment used to obtain anthropometric data were calibrated and the measurements were taken using standardised, established techniques as described in the literature [20]. To further ensure reliability, each measurement was taken three times and the mean value used in analysis. The scale was calibrated with a known weight after every 20th participant was weighed. Interviews were conducted in the participant’s preferred language (English, Afrikaans, or Sesotho) to ensure that participants understood what was being asked and provided reliable answers. ## Data analysis Collected data were entered into Excel documents. Data cleaning and statistical analysis were performed by the Department of Biostatistics, Faculty of Health Sciences, University of the Free State. Data were analysed using SAS/STAT software, Version 9.4 of the SAS system for Windows, Copyright © 2013 SAS Institute Inc. SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc., Cary, NC, USA [21]. Descriptive statistics, including frequencies and percentages for categorical data, and means and standard deviations (SDs) for symmetrical numerical variables, or medians and interquartile range (IQR) for skew numerical variables were calculated. Due to occasional missing values (for example one participant did not have dietary information) and subgroup analyses, the number of cases with information are indicated throughout. Differences between groups were assessed by p-values (t-tests for symmetrical numerical variables, Mann-Whitney tests for skew numerical variables, chi-squared tests for categorical variables or Fisher’s exact tests for categorical variables with sparse data) or $95\%$ confidence intervals (CIs) for median, mean or percentage differences. Logistic regression with backward selection ($p \leq 0.05$) was used to select significant independent factors associated with choline intake. Variables with a p-value of < 0.15 in bivariate analysis were considered for inclusion in the model. ## Ethical considerations Ethics approval was obtained from the Health Sciences Research Ethics Committee of the University of the Free State (UFS-HSD$\frac{2018}{0625}$/2603) and the Free State Department of Health. Participants who provided written informed consent were included. To ensure confidentiality, each participant was allocated a unique number and no information that could identify individuals was used in data analysis. ## Results The median age of the sample ($$n = 682$$) was 31.8 years (IQR 26.8–36.5 years). Median gestation was 32.0 weeks (IQR 26–36 weeks). Most participants were pregnant with one baby ($93.4\%$), while $6.6\%$ expected twins. ## Choline intake Median daily choline intake ($$n = 681$$) was 275.0 mg (IQR: 84.7 mg – 386.7 mg). Most participants consumed less than the adequate intake (AI) of 450 mg/day for choline ($84.7\%$), while $15.3\%$ consumed a value between the AI and the UL (3500 mg/day) [14]. ## Alcohol and tobacco use Sample prevalence of alcohol use and smoking and associations thereof with choline intake are presented in Table 1. Considering all participants, $6.2\%$ smoked, and $9.0\%$ consumed alcohol during the current pregnancy. Most of the current smokers did so daily ($\frac{35}{42}$; $83.3\%$). Almost one in ten participants ($8.8\%$) snuffed or chewed tobacco during pregnancy. Smoking and alcohol consumption were not significantly associated with choline intake in this sample. Table 1Sample prevalence of alcohol use and smoking and associations thereof with choline intake in pregnant women in central South Africa: the NuEMI study 2018–2019Sample prevalence($$n = 680$$)Choline intake< 450 mgCholine intake≥ 450 mgp-valuen%n%n% Current cigarette smoker 0.799Yes426.23583.3716.7No63893.854184.89715.2 Current alcohol user 0.902Yes619.05285.3914.8No61991.052484.79515.4 Combined use of cigarettes and alcohol 0.728Both142.11178.57321.4Either7511.06586.71013.3Neither59186.950084.69115.4p-value for Chi-square or Fisher’s exact test for categorical data with significance set at $p \leq 0.05.$ ## Pregnancy health Most participants ($89.6\%$) had been pregnant before. Almost a quarter of participants were hospitalised at some stage during the current pregnancy ($23.5\%$), with the main reported reasons for hospitalisation being abdominal pain ($26.6\%$), hypertension ($18.5\%$) and vaginal bleeding ($5.2\%$). Symptoms experienced during the current pregnancy included loss of appetite ($60.8\%$), nausea ($56.6\%$), vomiting ($54.8\%$), swelling of the feet ($49.6\%$) and constipation ($39.2\%$). Several participants experienced weight loss of more than 3 kg ($17.2\%$), and diarrhoea for more than three days ($13.8\%$). Self-reported disease diagnoses of participants are summarised in Table 2. Hypertension ($23.0\%$) and sexually transmitted diseases (STDs) ($18.5\%$) were the main conditions that had been diagnosed. Table 2Self-reported diagnoses of pregnant women in central South Africa: the NuEMI study 2018–2019CurrentlyPreviouslyNevern%n%n%Hypertension ($$n = 682$$)15723.09513.943063.1Heart disease ($$n = 681$$)40.6172.566096.9Diabetes mellitus ($$n = 681$$)314.671.064394.4Tuberculosis (TB) ($$n = 681$$)71.0294.564594.7Asthma ($$n = 681$$)213.1263.863493.1Any sexually transmitted disease (STD) ($$n = 682$$)12618.5610.148771.4Vaginal infection/discharge ($$n = 682$$)10615.49113.348571.1Cancer ($$n = 681$$)0040.667799.4Lung diseases ($$n = 681$$)10.260.967499.0Elevated cholesterol ($$n = 680$$)30.471.067098.5Stroke ($$n = 680$$)10.2101.566998.4 A relatively large percentage of participants used medication for hypertension management ($23.7\%$) and antiretroviral therapy (ART) ($31.8\%$), while $3.7\%$ used oral glucose lowering medication. Table 3 summarises associations between reported health related to pregnancy and choline intake. Participants who did not have hypertension and those who were using ART (thus HIV-infected) had significantly lower choline intakes. Table 3Associations between reported health related to pregnancy and choline intake in pregnant women in central South Africa: the NuEMI study 2018–2019nCholine intake< 450 mgCholine intake≥ 450 mgp-valuen%n% Previous pregnancy 0.091Yes61051283.99816.1No716591.668.5 Hospitalisation during pregnancy 0.887Yes16013584.82515.6No12344284.87915.2 Diarrhoea lasting > three days 0.414Yes947781.91718.1No58750085.28714.8 Constipation 0.357Yes26722283.24516.9No41435585.85914.3 Nausea 0.278Yes38632283.46416.6No29525586.44013.6 Vomiting 0.378Yes37432185.85314.2No30725683.45116.6 Appetite loss 0.903Yes41335185.06215.0No26722684.64115.4 Weight loss > 3 kg 0.165Yes11710489.01311.1No56247183.89116.2 Heartburn 0.306Yes907381.11718.9No59150485.38714.7 Hypertension *0.042Yes15712579.63220.4No52445286.37213.7 Diabetes 0.302Yes312477.4722.6No64955285.19715.0 Any sexually transmitted disease 0.051Yes12511390.4129.6No55646483.59216.6 Tuberculosis 0.603Yes77100.000.0No67356984.610415.5 Use of antiretroviral therapy *0.011Yes21619489.82210.2No46338182.38217.7 Use of Tuberculosis medication 0.228Yes212095.214.8No65855584.410315.7 Use of glucose-lowering medication 1.000Yes2512184.0416.0No65455484.710015.3p-value for Chi-square or Fisher’s exact test for categorical data with significance set at $p \leq 0.05$; *indicates significance. ## Gestational body mass index Gestational body mass index classification and associations between GBMI of participants and choline intake are presented in Table 4. Gestational body mass index was only calculated for participants who were pregnant with one baby, as the algorithm was not developed for multiple gestations. Most participants ($69.0\%$) were either overweight or obese. Median GBMI ($$n = 637$$) was 30.8 kg/m2 (obese) with an interquartile range of 24.7 kg/m2 – 37.2 kg/m2. Although not significant, there was a trend suggesting that obese participants were less likely to consume choline in amounts below the AI than participants in the other GBMI categories. Table 4Gestational body mass index and choline intake in pregnant women in central South Africa: the NuEMI study 2018–2019Sample prevalence($$n = 636$$)Choline intake< 450 mgCholine intake≥ 450 mgp-valuen%n%n% Gestational body mass index 0.086Underweight (≥ 10 to < 19.8 kg/m2)426.63685.7614.3Normal weight (≥ 19.8 to < 26.1 kg/m2)15524.413989.71610.3Overweight (≥ 26.1 to < 29 kg/m2)7712.16888.3911.7Obese (≥ 29 kg/m2)36256.929581.56718.5p-value for Chi-square or Fisher’s exact test for categorical data with significance set at $p \leq 0.05.$ Although the majority of participants were overweight or obese, $6.6\%$ were underweight. Both overweight [32] and underweight [33] during pregnancy are associated with a higher risk for adverse effects and poor birth outcomes. Specifically, maternal obesity may increase levels of adiposity in the foetus which may contribute to poorer metabolic health in the offspring. Jack-Roberts et al. [ 2017] investigated the effect of choline supplementation on lipid metabolism in pregnant mouse dams. It was shown that supplementation of choline in obese dams improved indices of foetal adiposity, probably due to changes in the expression of specific genes. The authors recommend further studies to elucidate all related mechanisms by which choline might affect foetal adiposity during periods of maternal high-fat feeding and obesity [34]. Although a statistically significant difference was not observed regarding GBMI and choline intake in the current study, there was a trend indicating that obese participants had higher choline intakes than other GBMI categories. It is likely that obese participants consumed a larger volume of food, and subsequently more choline, than others. ## Social support and stress Data related to social support and stress are presented in Table 5. One in eight participants ($12.6\%$) did not have anybody that could help them in times of need. More than one third of participants ($36.0\%$) experienced recent unpayable debt. A concerning finding was that one in every twelve participants experienced physical abuse at the hands of their partners ($8.4\%$). No significant associations were found between social support and stress, and choline intake. Table 5Sample prevalence of social support and stress as well as associations thereof with choline intake in pregnant women in central South Africa: the NuEMI study 2018–2019Sample prevalenceCholine intake< 450 mgCholine intake≥ 450 mgp-valuen%n%n% Availability of people who could help the participant if a significant problem arose ($$n = 681$$) 0.584Nobody8612.67688.41011.6Unsure355.13085.7514.3Multiple people56082.247184.18915.9 *Has a* husband or partner with whom she can talk to about any problem she might have ($$n = 665$$) 0.293Never446.63579.6920.5Sometimes15022.613288.01812.0Always47170.839483.77716.4 Belongs to a church or religious organisation ($$n = 681$$) 0.918Yes50874.643084.77815.4No17325.414785.02615.0 Has been in danger of being killed by criminals during the past six months ($$n = 681$$) 0.850Yes436.33683.7716.3No63893.754184.89715.2 During the past six months, participant witnessed a violent crime (e.g., murder, robbery, assault, rape) ($$n = 679$$) 0.335Yes7911.66481.01519.0No60088.651185.28914.8 Unpayable debt during the previous six months ($$n = 679$$) 0.321Yes24536.020382.94217.1No43463.937285.76214.3 Participant or close family members had not been able to find a job for more than six months ($$n = 680$$) 0.524Yes48270.941185.37114.7No19829.116583.33316.7 During the last six months, participant or anyone in her close family was seriously ill ($$n = 681$$) 0.442Yes27239.923486.03814.0No40960.134383.96616.1 During the last six months, any member of participant’s close family died ($$n = 681$$) 0.407Yes20029.417386.52713.5No48170.640484.07716.0 Someone in participant’s close family has a problem with drugs or alcohol ($$n = 679$$) 0.315Yes21331.417682.63717.4No46668.639985.66714.8 During the last six months, participant had a break-up with her partner ($$n = 676$$) 0.893Yes10715.89185.11615.0No56984.248185.58815.5 During the last six months, participant’s partner hit or beat her ($$n = 669$$) 0.884Yes568.44783.9916.1No61391.651984.79415.3p-value for Chi-square or Fisher’s exact test for categorical data with significance set at $p \leq 0.05.$ ## Reported health, social support and stress factors associated with inadequate choline intake: logistic regression The following reported health, social support and stress variables were considered for inclusion in a logistic regression analysis: pregnant before (no versus yes), current hypertension (no versus yes), current sexually transmitted disease (no versus yes), current ART use (no versus yes) and GBMI (≥ 10 to < 19.8 kg/m2, ≥ 19.8 to < 26.1 kg/m2, ≥ 26.1 to < 29 kg/m2, ≥ 29 kg/m2). Current ART use was selected in the model with an odds ratio as indicated in Table 6. The odds of having a choline intake below the AI were lower for participants that were not using ART compared to those using ART (HIV-infected) with an odds ratio of 0.53. Table 6Reported health, social support and stress factors associated with inadequate choline intake in pregnant women in central South Africa: the NuEMI study 2018–2019: logistic regressionVariableDescriptionOdds ratio ($95\%$ CI)p-valueCurrent antiretroviral therapy useno vs. yes0.53 (0.32; 0.87)0.011 ## Discussion Median daily choline intake was 275.0 mg which is considerably lower than the AI of 450 mg. Adequate nutrient intake is important to support the health of the mother and baby and promotes optimal development in utero and during the early childhood years. For example, there is evidence that choline is required for placental health, and an optimally functioning placenta decreases the risk of preeclampsia and poor foetal growth [22]. A higher maternal choline intake can also affect gene expression, causing changes that regulate placental vascularisation, angiogenesis and stress reactivity, that may reduce stress-related disease risk in the offspring in adult life [22]. ## Reported health and lifestyle Alcohol and tobacco are well-known teratogens and can cause an array of poor health outcomes. South Africa has the highest rate of foetal alcohol spectrum disorder (FASD) in the world [23], and almost one in ten participants ($9.0\%$) in the current study consumed alcohol during the current pregnancy. Ideally, pregnant women who consume alcohol should consume higher amounts of choline, because it has been shown that higher choline intake may protect against some of the adverse consequences of FASD [24, 25]. However, no significant associations were found between alcohol use and/ smoking cigarettes and choline intake in the current study. Almost one quarter of participants were hospitalised during the current pregnancy, mainly due to abdominal pain, hypertension, vaginal bleeding, and vomiting. Furthermore, about one quarter of participants reported hypertension as a current diagnosis. Hypertensive participants were less likely to consume choline in amounts below the AI. Participants with a higher choline intake could have had an overall higher food (and choline) intake, which may have contributed to overweight or obesity – known risk factors for hypertension [26]. An animal study by Liu et al. [ 2017] found that choline ameliorated cardiovascular damage in hypertensive rats. The authors proposed that the mechanisms by which this occurred could be related to improved vagal activity and inhibition of the inflammatory response. One of the positive cardiovascular outcomes of choline supplementation was a decreased systolic blood pressure and the authors suggest choline as a possible adjunct therapy for hypertension [27]. Although it has been shown that the consumption of choline may increase cardiovascular (CVD) risk through the production of trimethylamine N-oxide (TMAO) [22], a recent meta-analysis by Meyer and Shea [2017] found no association between choline intake and CVD [28]. More research is therefore needed to determine the overall association between choline intake and CVD. Approximately a third of participants reported using ART at the time of the study. This finding is in line with national statistics, as $25.0\%$ of South African women of reproductive age were HIV-infected in 2019 [29]. A statistically significant association was found between the use of ART and choline intake. The odds of having a choline intake below the AI were lower for participants that were not using ART compared to those that were using ART (HIV-infected) with an odds ratio of 0.53. Various factors can contribute to a poor nutrient intake and a compromised nutritional status in HIV-infected individuals, who are more likely to eat less due to food insecurity, complications of HIV infection, or side effects of medication [30]. A study performed in rural communities in the Eastern Cape, South Africa, demonstrated that individuals from HIV-afflicted households ($$n = 68$$) consumed fewer kilojoules and had lower dietary diversity than non-HIV-afflicted households [31]. This highlights the possible effect that HIV might have on food intake causing a lower choline intake. Additionally, common symptoms occurring in pregnancy, such as nausea, constipation and appetite loss, can exacerbate a poor nutrient intake in HIV-infected women. ## Stress and social support Situations that can cause psychological stress were common in the participants. For example, approximately one in ten participants recently witnessed a violent crime or experienced physical abuse from their partner. Joblessness, death and unpayable debt in the household or family were also commonly reported. However, most participants did have a support system in place in the form of individuals they could ask for assistance or through their membership of a church or contacts with a social organisation. A large body of evidence suggests that maternal stress can have a profound influence on birth outcomes [35–37]. Women who experienced high levels of psychological and social stresses during pregnancy have been shown to be at higher risk of preterm delivery [38–40]. Prenatal stress exposure may also adversely affect short- and long-term foetal neurodevelopment. Hormonal and immune stress mediators play an important role in normal brain development and inappropriate levels of these mediators can negatively affect brain development [41]. Moreno Gudiño et al. [ 2017] demonstrated that choline supplementation in male rat pups attenuated the memory dysfunction provoked by stress caused by maternal separation during important developmental periods (post-natal days 1 to 14 and days 21 to 60). The authors propose the possibility of supplementing choline to stressed adolescents to mitigate the cognitive damage caused by stress in early life [42]. Conversely, van Lee et al. [ 2017] found that higher maternal plasma choline levels were associated with more symptoms related to anxiety and depression during pregnancy, but no association was found between maternal plasma choline and postnatal mental well-being [43]. More research is needed regarding the possible interactions between stress, choline intake, and foetal neurodevelopment. ## Limitations All data (except weight and height measurements) were self-reported. Convenient sampling was used instead of random sampling. The antenatal clinic where data were collected is considered a high-risk clinic, thus results cannot be generalised to low-risk pregnancies. However, participants in the current study reside in the same communities as other women with low-risk pregnancies who may be subjected to similar levels of stress and access to social support. ## Conclusions and recommendations Few participants consumed choline in amounts above the AI, which is a concerning finding, as the importance of choline during pregnancy is well established. The AI level can generally not be used to determine prevalence of deficiency in a population. However, if the intake is above the AI, prevalence of inadequate nutrient intakes is likely to be low. Alcohol use in this sample was relatively high. As choline supplementation has been shown to mitigate the effects of ethanol exposure on the foetus, it has been proposed that fortifying staple foods with choline might be a strategy to decrease the devastating effects of ethanol on the foetus [44]. This may be of benefit in a country such as South Africa that has very high levels of FASD. Hypertension was prevalent and the possible benefits of choline supplementation on hypertension management should be investigated. As it was found that participants who used ART were more likely to consume below adequate amounts of choline, use of ART, or HIV-infection, might be considered as a risk factor for a low choline intake. The median GBMI indicated that obesity is prevalent in the sample. Strategies to promote attaining a normal weight before conception require urgent attention, considering the various health risks associated with being overweight. Finally, pregnant women should be screened for psychological and social stress, and appropriate referrals and support provided in order to prevent short- and long-term adverse effects of maternal stress on their offspring. ## References 1. De Castro MBT, Freitas Vilela AA, Oliveira ASD, De, Cabral M, Souza RAG, De, Kac G. **Sociodemographic characteristics determine dietary pattern adherence during pregnancy**. *Public Health Nutr* (2016.0) **19** 1245-51. DOI: 10.1017/S1368980015002700 2. Mousa A, Naqash A, Lim S. **Macronutrient and micronutrient intake during pregnancy: an overview of recent evidence**. *Nutrients* (2019.0) **11** 1-20. DOI: 10.3390/nu11020443 3. Korsmo HW, Jiang X. **Choline: exploring the growing science on its benefits for moms and babies**. *Nutrients* (2019.0) **11** 1-15. DOI: 10.3390/nu11081823 4. Zeisel SH. **Nutrition in pregnancy: the argument for including a source of choline**. *Int J Womens Health* (2013.0) **5** 193-9. DOI: 10.2147/IJWH.S36610 5. 5.Zeisel S. Modern Nutrition in Health and Disease. 11th editi. Philadelphia: Lippincott Williams & Wilkins; 2014. 6. Zeisel SH. **Choline, other Methyl-Donors and Epigenetics**. *Nutrients* (2017.0) **9** 1-10. DOI: 10.3390/nu9050445 7. 7.World Health Organization (WHO). Preventing noncommuncable diseases. 2020. https://www.who.int/activities/preventing-noncommunicable-diseases. Accessed 15 Apr 2020. 8. 8.National Department of Health (NDoH)., Statistics South Africa (Stats SA), South African Medical Research Council (SAMRC). South Africa Demographic and Health Survey (SADHS). Pretoria, South Africa and Rockville, Maryland, USA; 2019. 9. 9.National Department of Health (NDoH)Statistics South Africa (Stats SA), south African Medical Research Council (SAMRC), ICF. South Africa demographic and Health Survey 20162019Maryland, USAPretoria, South Africa and Rockville. *Statistics South Africa (Stats SA), south African Medical Research Council (SAMRC), ICF. South Africa demographic and Health Survey 2016* (2019.0) 10. Entringer S, Buss C, Wadhwa P. **Prenatal stress, development, health and disease risk: a psychobiological perspective**. *Psyconeuroendocrinology* (2015.0) **62** 366-75. DOI: 10.1016/j.psyneuen.2015.08.019 11. Kuddus A, Rahman A. **Affect of urbanization on Health and Nutrition**. *Int J Stat Syst* (2016.0) **10** 165-75 12. 12.Patterson KY, Bhagwat S, Williams JR, Howe JC, Holden JM, Zeisel SH, et al. USDA Database for the Choline Content of Common Foods (Release two). Beltsville, Maryland; 2008. 13. 13.FAO/INFOODS. FAO/INFOODS Guidelines for Food Matching. Version 1. Rome; 2012. 14. 14.Institute of Medicine (IOM)Dietary reference intakes for thiamin, riboflavin, niacin, vitamin B6, folate, vitamin B12, pantothenic acid, biotin and choline1998Washington, DCNational Academy Press. *Dietary reference intakes for thiamin, riboflavin, niacin, vitamin B6, folate, vitamin B12, pantothenic acid, biotin and choline* (1998.0) 15. 15.University of Witwatersrand. Birth to Twenty. 2017. https://www.wits.ac.za/health/research-entities/birth-to-20/birth-to-twenty/. Accessed 24 Jan 2018. 16. Cruz MLS, Harris DR, Read JS, Mussi-Pinhata MM, Succi RCM. **Association of Body Mass Index of HIV-1-Infected pregnant women and infant weight, body Mass Index, length, and Head Circumference: the NISDI Perinatal Study**. *Nutr Res* (2007.0) **27** 685-91. DOI: 10.1016/j.nutres.2007.09.005 17. Wentzel-Viljoen E, Laubscher R, Kruger A. **Using different approaches to assess the reproducibility of a culturally sensitive quantified food frequency questionnaire**. *South Afr J Clin Nutr* (2011.0) **24** 143-8. DOI: 10.1080/16070658.2011.11734366 18. MacIntyre U, Kruger H, Venter C, Vorster H. **Dietary intakes of an african population in different stages of transition in the North West Province, South Africa: the THUSA study**. *Nutr Res* (2002.0) **22** 239-26. DOI: 10.1016/S0271-5317(01)00392-X 19. Hattingh Z, Walsh CM, Bester CJ, Oguntibeju OO. **Evaluation of energy and macronutrient intake of black women in Bloemfontein: a cross-sectional study**. *Afr J Biotechnol* (2008.0) **7** 4019-24 20. Stewart A, Marfell-Jones M, Olds T, de Ridder H. *International Standards for Anthropometric Assessment* (2011.0) 21. 21.SAS Institute Inc. SAS/ACCESS® 9.4 Interface to ADABAS: Reference. 2013. 22. Wallace TC, Blusztajn JK, Caudill MA, Klatt KC. **The underconsumed and underappreciated essential nutrient**. *Nutr Today* (2018.0) **53** 240-53. DOI: 10.1097/NT.0000000000000302 23. Adebiyi BO, Mukumbang FC, Beytell AM. **A guideline for the prevention and management of fetal alcohol spectrum disorder in South Africa**. *BMC Health Serv Res* (2019.0) **19** 1-13. DOI: 10.1186/s12913-019-4677-x 24. Jacobson SW, Carter RC, Molteno CD, Stanton ME, Herbert JS, Lindinger NM. **Efficacy of maternal choline supplementation during pregnancy in mitigating adverse Effects of prenatal Alcohol exposure on growth and cognitive function: a Randomized, Double-Blind, placebo-controlled clinical trial**. *Alcohol Clin Exp Res* (2018.0) **42** 1327-41. DOI: 10.1111/acer.13769 25. Idrus NM, Breit KR, Thomas JD. **Dietary choline levels modify the effects of prenatal alcohol exposure in rats**. *Neurotoxicol Teratol* (2016.0) **59** 43-52. DOI: 10.1016/j.ntt.2016.11.007 26. 26.World Health Organization (WHO). Hypertension. 2020. https://www.who.int/news-room/fact-sheets/detail/hypertension. Accessed 28 Jul 2020. 27. Liu L, Lu Y, Bi X, Xu M, Yu X, Xue R. **Choline ameliorates cardiovascular damage by improving vagal activity and inhibiting the inflammatory response in spontaneously hypertensive rats**. *Sci Rep* (2017.0) **7** 1-13. PMID: 28127051 28. Meyer KA, Shea JW. **Dietary choline and betaine and risk of CVD: prospective studies**. *Nutrients* (2017.0) **9** 1-13. DOI: 10.3390/nu9070711 29. 29.Joint United Nations Programme on HIV/AIDS (UNAIDS). South Africa: Country fact sheet. 2020. https://www.unaids.org/en/regionscountries/countries/southafrica. Accessed 28 Jul 2020. 30. Thuppal SV, Jun S, Cowan A, Bailey RL. **The Nutritional Status of HIV-Infected US adults**. *Curr Dev Nutr* (2017.0) **1** 1-6. DOI: 10.3945/cdn.117.001636 31. Ncube K, Shackleton CM, Swallow B, Dassanayake W. **Impacts of HIV / AIDS on food consumption and wild food use in rural South Africa**. *Food Secur* (2016.0) **8** 1135-51. DOI: 10.1007/s12571-016-0624-4 32. Macinnis N, Woolcott CG, McDonald S, Kuhle S. **Population attributable risk fractions of maternal overweight and obesity for adverse perinatal outcomes**. *Sci Rep* (2016.0) **6** 1-7. DOI: 10.1038/srep22895 33. Agrawal S, Singh A. **Obesity or underweight—what is worse in pregnancy?**. *J Obstet Gynecol India* (2016.0) **66** 448-52. DOI: 10.1007/s13224-015-0735-4 34. Jack-Roberts C, Joselit Y, Nanobashvili K, Bretter R, Malysheva OV, Caudill MA. **Choline supplementation normalizes fetal adiposity and reduces lipogenic gene expression in a mouse model of maternal obesity**. *Nutrients* (2017.0) **9** 1-15. DOI: 10.3390/nu9080899 35. Bock J, Wainstock T, Braun K, Segal M. **Stress in Utero: prenatal programming of Brain plasticity and cognition**. *Biol Psychiatry* (2015.0) **78** 315-26. DOI: 10.1016/j.biopsych.2015.02.036 36. Marques AH, Bjørke-Monsen AL, Teixeira AL, Silverman MN. **Maternal stress, nutrition and physical activity: impact on immune function, CNS development and psychopathology**. *Brain Res* (2015.0) **1617** 28-46. DOI: 10.1016/j.brainres.2014.10.051 37. Provençal N, Binder EB. **The effects of early life stress on the epigenome: from the womb to adulthood and even before**. *Exp Neurol* (2015.0) **268** 10-20. DOI: 10.1016/j.expneurol.2014.09.001 38. Cobo T, Kacerovsky M, Jacobsson B. **Risk factors for spontaneous preterm delivery**. *Int J Gynecol Obstet* (2020.0) **150** 17-23. DOI: 10.1002/ijgo.13184 39. Vollrath ME, Sengpiel V, Landolt MA, Jacobsson B, Latal B. **Is maternal trait anxiety a risk factor for late preterm and early term deliveries?**. *BMC Pregnancy Childbirth* (2016.0) **16** 1-6. DOI: 10.1186/s12884-016-1070-1 40. Wadhwa P, Entringer S, Buss C, Lu M. **The contribution of maternal stress to Preterm Birth: issues and considerations**. *Clin Perinatol* (2011.0) **38** 351-84. DOI: 10.1016/j.clp.2011.06.007 41. Buss C, Entringer S, Wadhwa P. **Fetal programming of Brain Development: intrauterine stress and susceptibility to psychopathology**. *Sci Signal* (2013.0) **5** 1-7 42. Moreno Gudiño H, Carías Picón D, de Brugada Sauras I. **Dietary choline during periadolescence attenuates cognitive damage caused by neonatal maternal separation in male rats**. *Nutr Neurosci* (2017.0) **20** 327-35. DOI: 10.1080/1028415X.2015.1126444 43. van Lee L, Quah P, Saw S, Yap F, Godfrey K, Chong Y. **Maternal choline status during pregnancy, but not that of betaine, is related to antenatal mental well-being: the growing up in Singapore toward healthy outcomes cohort**. *Depress Anxiety* (2017.0) **34** 877-87. DOI: 10.1002/da.22637 44. Bearer C, Wellmann K, Tang N, He M, Mooney S. **Choline ameliorates deficits in Balance caused by Acute neonatal ethanol exposure**. *The Cerebellum* (2015.0) **14** 413-20. DOI: 10.1007/s12311-015-0691-7
--- title: Discrete patterns of microbiome variability across timescales in a wild rodent population authors: - Jonathan Fenn - Christopher Taylor - Sarah Goertz - Klara M. Wanelik - Steve Paterson - Mike Begon - Joe Jackson - Jan Bradley journal: BMC Microbiology year: 2023 pmcid: PMC10061908 doi: 10.1186/s12866-023-02824-x license: CC BY 4.0 --- # Discrete patterns of microbiome variability across timescales in a wild rodent population ## Abstract Mammalian gastrointestinal microbiomes are highly variable, both within individuals and across populations, with changes linked to time and ageing being widely reported. Discerning patterns of change in wild mammal populations can therefore prove challenging. We used high-throughput community sequencing methods to characterise the microbiome of wild field voles (Microtus agrestis) from faecal samples collected across 12 live-trapping field sessions, and then at cull. Changes in α- and β-diversity were modelled over three timescales. Short-term differences (following 1–2 days captivity) were analysed between capture and cull, to ascertain the degree to which the microbiome can change following a rapid change in environment. Medium-term changes were measured between successive trapping sessions (12–16 days apart), and long-term changes between the first and final capture of an individual (from 24 to 129 days). The short period between capture and cull was characterised by a marked loss of species richness, while over medium and long-term in the field, richness slightly increased. Changes across both short and long timescales indicated shifts from a Firmicutes-dominant to a Bacteroidetes-dominant microbiome. Dramatic changes following captivity indicate that changes in microbiome diversity can be rapid, following a change of environment (food sources, temperature, lighting etc.). Medium- and long-term patterns of change indicate an accrual of gut bacteria associated with ageing, with these new bacteria being predominately represented by Bacteroidetes. While the patterns of change observed are unlikely to be universal to wild mammal populations, the potential for analogous shifts across timescales should be considered whenever studying wild animal microbiomes. This is especially true if studies involve animal captivity, as there are potential ramifications both for animal health, and the validity of the data itself as a reflection of a ‘natural’ state of an animal. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12866-023-02824-x. ## Introduction Gastrointestinal microbiome composition is complex and understanding the causes and consequences of changes in microbiome structure can be challenging. The microbiome can be influenced by a range of environmental factors, including infection status, nutrition, and life history, all of which can be drivers of microbiome structure and diversity [1]. Changes in the microbiome can in turn have impacts on a variety of host phenotypes [2] including infection responses, food metabolism, pathogenicity of the bacterial taxa themselves, and ultimately host fitness. While some microbial populations may be intrinsically more dynamic in abundance than others, some may also show specific shifts clearly associated with factors such as ageing [3–5], helminth infection [6, 7], and diet [8–10]. In contrast, a significant proportion of the bacterial community is comparatively stable, comprising a ‘core-microbiome’ of established taxa. The term ‘core microbiome’ is used to refer to communities that are stable either within, or between individuals (or both), and so high-resolution sampling both within and between individuals is required to expose any shifts in this core [11]. Although the important role of archaea and fungal species in microbiome communities is becoming increasingly well understood, this study focusses solely on bacterial communities, and as such ‘microbiome’ will hereafter refer to bacterial taxa. While temporal variation around this core has been extensively examined in human populations [12–14] and laboratory model species [15, 16], the prominence and nature of consistent patterns of change across varying timescales in wild mammal populations is currently underexplored and invites further investigation. These shifts can affect both α-diversity (the local diversity of species in a community, and β-diversity (the difference in taxa between communities.) Human populations are analogous to wild animal populations, being genetically diverse, and experiencing a diversity of lifestyles, diets and immune challenges. The fact that modern humans are typically far more long-lived means ageing effects may not be as prevalent in typical wild animal populations. Despite this, age-related shifts to Bacteroidetes-dominant microbiomes have been recorded in laboratory studies of mice ZHANG, and non-model species including marmosets [17], and pigs [18]. Ageing effects may, therefore, scale somewhat to the ages of the host species, as is observed in metabolism [19], physical function [20], and mutation rates [21, 22]. Age-associated changes reported in human populations vary in nature, for instance depending on existing infections [23], but are typically characterised by increased proportions of Bacteroidetes species [24–26]. Such shifts may not be a linear, as the Bacteroidetes/Firmicutes ratio has been shown to initially decrease from infancy to adulthood, before increasing again in old age [27]. These changes are associated with dysbiosis, characterised by increased abundance of pathogenic taxa, and have been implicated or associated with poorer health [28], typically coincident with increased levels of frailty and inflammation [29], and more specific pathologies, such as kidney disease, which is associated with reduced Firmicutes and increased Fusobacteria and Proteobacteria [30]. α-diversity of gut microbiomes increases with age in humans [31], lab mice [32], and non-model species [18, 33]. Despite this, studies on some species have shown the opposite effect [17, 34]. These discrepancies may be species-specific, but may also be due to conflation of ‘chronological age’ (a simple measure of time), which is associated with increased richness and ‘biological age’ (maturation of host physiology and increasing physical frailty) which is associated with reductions in richness and associated pathology—while biological and chronological age are strongly associated, they will not always necessarily increase together in a linear fashion [3, 35]. Short-term changes, over the scale of days and hours, have been demonstrated in lab mice in response to stress [36], changes in diet [37–39], as a result of host diurnal rhythmicity [40], and in human patients following severe injury [41]. Rapid changes in faecal community structure can also occur external to the host, depending on exposure to varying environmental conditions [42]. Microbiome studies of wild populations commonly involve some element of captivity [43] (Morgan and Tromborg 2007)to allow for ease of monitoring or sample/tissue collection, and so it is important to know whether such changes are occurring following capture. Marked differences between the microbiome of laboratory and wild populations have been recorded in a range of species, with domestication or captive breeding commonly associated with reduction in microbial diversity or loss of taxa, relative to wild populations, and changes in translocated individuals occurring relatively rapidly [44–48]. Again, these shifts are not universal, with some species showing increased microbiome richness under captivity [49], and more robust microbiomes, showing reduced captivity-associated differences over time [50, 51]. Studies exploring these patterns in detail across timescales in wild populations are lacking, or are often limited. For example, a longitudinal analysis of mouse lemur microbiomes (Microcebus rufus) showed reduced α-diversity in older individuals, but was restricted to 15 individuals [52]. Studies of populations thatare more reliably trappable at high numbers allow us to build upon work on human, lab and wild populations, and more reliably understand the prevalence and significance of characteristic patterns of change in microbiome communities across timescales. Microbiome studies are typically cross-sectional, allowing for invasive sampling of the gut post-cull, and thus providing what is hopefully the most accurate snapshot of the live animal’s GI microbiome community. While longitudinal microbiome studies must typically rely on faecal samples rather than direct sampling of the gut, diversity metrics have been shown to be highly correlated between faeces and caecum samples, making faeces a suitable representation of microbiome communities in the live animal [53]. Dynamic changes in the microbiome, and their environmental and host-intrinsic causes, are becoming increasingly well characterised, and so the importance of emphasising longitudinal experimental design in wild animal microbiome studies is becoming more apparent [54]. We investigated the gastrointestinal microbiome of a wild population of the field vole, Microtus agrestis. Using both longitudinal faecal samples from mark-recapture trapping, and faecal samples taken after capture and dissection, we examined the level of between- and within-individual variation associated with different bacterial phyla. We explored how levels of species richness, and the balance between representation of Bacteroidetes and Firmicutes as dominant phyl constitute patterns of change in microbiome structure over different timescales, as well as what ecological factors such as host condition and infection status might be associated with this variation. Patterns of changes over different timescales in wild animals are likely to be complex and somewhat population-specific, but by characterising those patterns in a well-studied population, we aim to provide an example of how such changes might manifest, and how they might be studied. ## Trapping & dissection Field methods are based on previous studies of this population [55, 56], and are also described elsewhere [57]. Voles were live-trapped using a grid composed of 197 Ugglan traps, over approximately one hectare of a clear-felled area in Kielder Forest, Northumbria, UK. Trapping was conducted over twelve 3-day sessions between March and August 2017, with traps checked twice each day, in the morning and evening. Traps were baited with mixed seed and chopped carrot. Newly-trapped animals were tagged with Passive Integrated Transponders (PIT Tags) on first capture to allow for re-identification. Upon first capture, animals were visually inspected, and sex and reproductive status were recorded. At each capture, snout-vent body length was measured, and total body mass was recorded. These values were used to calculate the scaled mass index (SMI), a measure of body condition [58]. For our longitudinal sampling, 428 faecal samples were collected from 206 voles. The mean number of samples per individual was 2.28, and 3.26 when excluding individuals with only one capture. In most cases these animals were then released, but at the end of each trapping session a small number of the animals were sent to the University of Nottingham for dissection, to perform gastrointestinal helminth surveys, collect caecum samples, and extract eye lenses as a proxy measure of animal age [59]. A total of 60 voles were sampled in this way, forming the cross-sectional dataset. They were housed for either one or two nights prior to dissection, fed bird seed mix and chopped carrot, and provided with water ad libitum. Animals were killed by increasing CO2 concentration in a sealed chamber, with death confirmed by exsanguination. Procedures were performed with approval from the University of Liverpool Animal Welfare Committee, under a UK Home Office license (PPL $\frac{40}{3235}$ to MB. Field-to-lab workflow is shown in Fig S1.) Morphometric measurements taken at cull include mass, snout-vent length and tail length. Eyes were removed and stored in formalin. Later, eye lenses were removed, desiccated at 60 °C for 48 h, and weighed with an electronic balance for use as an age proxy [59, 60]. Gastrointestinal tracts were removed and stored in $80\%$ ethanol. These animals had further faecal samples taken at cull, and for 44 of those animals, caecum tissue was also taken. All faeces and caecum samples were stored at -80 °C. ## Gastrointesintal helminth surveys Gastrointestinal tracts were dissected under dissection microscopes, and any helminth macroparasites identified morphologically and counted. Descriptions, keys or other literature on which the identifications were based are given alongside the respective helminth taxa below. Of the animals included in this study, two types of macroparasite were commonly observed – the pinworm *Syphacia nigeriana* ($64.3\%$ prevalence) (Chromadorea: Oxyuridae) [61, 62] & tapeworms ($50\%$ prevalence) (Cestoda: Hymenolepidae and Anoplocephalidae) [63]. Other species recorded in this population include *Heligmosomoides laevis* (Chromadorea: Heligmosomidae) [64]and *Trichuris arvicolae* (Enoplea: Trichuridae) [65], but were not present in the animals recorded in this study. ## DNA extraction & microbiome sequencing DNA was extracted from faeces and caecum tissue using the DNeasy Powersoil extraction kit (Qiagen Cat. 47,016) and sent for 16S community sequencing at University of Liverpool Centre for Genomic Research. Alongside these samples, positive and negative controls were included, provided in-house at the Centre for Genomic Research, Liverpool. Primers described by Caporaso et al., 2011 [66] were used to amplify and barcode the V4 region of 16 s (detail of primers for every stage are provided in Table S1.) A total of 658 samples were submitted and 5 μl of each DNA sample at 1 ng/μl was entered into the first-round PCR with total reaction volume of 20ul, and the following conditions: 98 °C for 2 min, 20 s at 95 °C, 15 s at 65 °C, 30 s at 70 °C for 10 cycles then a 5 min extension at 72 °C. Samples were then purified with AMPure SPRI beads in a 1:1 volume ratio (Beckman Coulter, Indiana, USA), and a secondary, nested PCR was then performed to incorporate i5 & i7 Illumina adapter sequences, using the same conditions for a further 15 cycles. Samples were again purified with SPRI beads in a 1:1 volume ratio and quantified by Qubit dsDNA HS Assay (Thermo Fisher Scientific, Massachusetts, US) using the Agilent Fragment Analyzer (Agilent Technologies, Santa Clara, US). Samples which failed to amplify were not sequenced. Final amplified libraries and controls were pooled in equimolar amounts into 8 pools according to the Qubit data, followed by size selection on a $1.5\%$ Pippin prep gel (Sage Science Inc., Massachusetts) using a size range of 300-600 bp. Quantity of the size selected pools of amplicon libraries was completed using a Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific, Massachusetts, US), while the quality and average size was assessed using the High Sensitivity DNA Kit on the Agilent Bioanalyzer (Agilent Technologies, California, US). Subsequently, a quantitative real-time PCR (qPCR) assay, designed to specifically detect adapter sequences flanking the Illumina libraries, was performed using an Illumina KAPA Library Quantification Kit (Kapa Biosystems, Wilmington, USA). A 20 μl PCR reaction (performed in triplicate for each pooled library) was prepared on ice with 12 μl SYBR Green Master Mix and 4 μl diluted pooled DNA (1:1000 to 1:100,000 depending on the initial concentration determined by the Qubit dsDNA HS Assay Kit). PCR thermal cycling conditions consisted of initial denaturation at 95 °C for 5 min, 35 cycles of 95 °C for 30 s (denaturation) and 60 °C for 45 s (annealing and extension), melt curve analysis to 95 °C (continuous) and cooling at 37 °C (LightCycler LC48011, Roche Diagnostics Ltd, Burgess Hill, UK). Template DNA was denatured for 5 min at room temperature using freshly diluted 0.1 N-sodium hydroxide (NaOH) and the reaction was subsequently terminated by the addition of hybridization buffer. Following calculation of the molarity using qPCR data, template DNA was diluted to a loading concentration of 8 pM using the hybridization buffer. The amplicon libraries were sequenced on an Illumina HiSeq 2500 platform (Illumina Inc., California, US) with version 2 chemistry using sequencing by synthesis (SBS) technology to generate 2 × 300 bp paired-end reads. Fragmented PhiX phage genome was added to the sequence library to increase the sequence complexity. ## Bioinformatics Base-calling and de-multiplexing of indexed reads was performed by CASAVA version 1.8.2 (Illumina) to produce 658 samples across the two runs, in FASTQ format. The raw FASTQ files were trimmed to remove Illumina adapter sequences using Cutadapt version 1.2.1 [67]. Any reads which matched the adapter sequence over at least 3 bp were trimmed off. The reads were further trimmed to remove low quality bases, using Sickle version 1.200 [68] with a minimum window quality score of 20. After trimming, reads shorter than 20 bp were removed. If both reads from a pair passed this filter, each was included in the R1 (forward reads) or R2 (reverse reads) file. If only one of a read pair passed this filter, it was included in the R0 (unpaired reads) file. To improve base quality in both read pairs, sequencing errors were corrected in both forward and reverse reads using the error-correct module within SPAdes assembler, version 3.1.0 [69] using options '–careful' and '–only-error-correction'. The average number of paired-end reads per sample was 512,850 (SD = 161,805, IQR = 145,881). Read pairs were merged to produce a single sequence for each pair that would entirely span the amplicon using PEAR, version 0.9.10 [70]. Additionally, sequences with uncalled bases (Ns) were removed. To remove sequences originating from potential PCR primer dimers or from any spurious amplification events, a size selection was applied to select sequences between 200 and 600 bp. To remove PhiX sequences associated with indices, each sample was compared with the complete PhiX sequence (GenBank gi9626372) using BLASTN [71]. Sequences matching PhiX (E-value < 10–5) were filtered out of the dataset. An average of $99.67\%$ of reads per-sample, were successfully aligned, which was reduced to $98.57\%$ post PhiX-filtering. Sequences passing the filters for each sample were concatenated into a single file, which was used subject to a custom analysis pipeline based on QIIME 1.9.1 [66]. The RDP classifier was used against the GreenGenes database (version 13.8).throughout the analysis. To identify sequence variability in each sample, amplicon sequences and assigned to clusters according to sequence similarity, using SWARM version 2.2.1, [72]. To calculate the abundance of each cluster, sequences were aligned on the identified centroid clusters sequences, using a minimum similarity threshold of $97\%$ for the entire length of the sequence. Taxonomic assignment of each cluster (now referred to as operational taxonomic unit, OTU) was carried out using the QIIME script ‘assign_taxonomy.py’, using the RDP classifier [73] to match the centroid sequence of each cluster obtained by swarm, to a sequence from the database. The abundance table was post-processed to remove any OTU below $0.005\%$ of the total sequence count of sequences [74]. ## Statistical analysis All statistical analyses were carried out in R 3.6.2 [75], and read counts were centered log-ratio (CLR) transformed using the ‘SleuthALR’ package [76]. The package ‘phyloseq’ was used to calculate measures of α-diversity. Three metrics were chosen to assess different aspects of α-diversity, with Shannon index values emphasising taxa evenness, phylogenetic diversity (hereafter, ‘PD’) being correlated with evenness [77] and Chao1 being suitable for datasets skewed to low-abundance taxa [78] Bray–Curtis and weighted UniFrac (wUniFrac) distances were calculated with phyloseqand used in Non-Metric Multidimensional Scaling (NMDS) to provide individual (site) scores (wUniFrac $K = 5$, stress = 0.0012, Bray–Curtis $K = 3$, stress = 0.164. In addition, robust principal component analysis (RPCA) was performed using the ‘rospca’ package [79]. UniFrac distance incorporates OTU relatedness data from a provided phylogenetic tree, and wUniFrac adjusts this distance to reduce the influence of rare OTUs and alleviate any oversized influence of rare taxa by taking abundances into account. Bray–*Curtis is* an abundance-based metric, whereby distance values give a measure of between-sample dissimilarity, but which are sensitive to presence of rare taxa. RPCA is also abundance-based, but can better deal with sparse, highly-dimensional datasets. Three OTUs of a total 1321 were excluded due to severe overrepresentation in specific individuals, and causing issues with MDS and RPCA ordination. For both α- and β-diversity measures, multiple indices were calculated to provide a more comprehensive picture of microbial diversity, and capture any variation more weighted to specific diversity metrics. Using CLR abundance data, the coefficient of variance (CoV) was calculated for each OTU, based on within-individual variation from longitudinal faecal samples, and cross-sectionally between individuals at cull. These values were normalised using Box-Cox transformation, a power-transformation for positive non-normal data (Longitudinal λ = 0.51, Cross-Sectional Lambda λ = -0.42) in R [75] using the ‘car’ and ‘MASS’ packages [80, 81]. Transformed data was then used to measure correlation between variation in OTUs from cross-sectional faecal samples and longitudinal faecal samples, to ascertain bywhether OTUs which were variable between individuals were the same as those which were variable within the same individuals over time. Average OTU variance per phylum was compared by linear model, with post-hoc *Tukey analysis* to determine differences in variability between major phyla (phyla with fewer than 10 OTUs present, and unclassified OTUs, were excluded). Both cross-sectional faecal and longitudinal faecal community datasets were subject to variance partitioning using the ‘VariancePartition’ R package to ascertain the ecological factors associated with variation in CLR abundance [82]. These factors include age category (designated as ‘mature’ or ‘juvenile’ from physical inspection of body size), the within-year Julian date (i.e. day number 1–365), sex and scaled mass index (SMI) [58] as a measure of condition. Julian date was chosen to account for seasonal affects as it offers a more precise continuous measure of time than simply including the trapping session, and as all samples in this analysis were collected in 2017, there was no need to account for inter-annual patterns of variation. For cross-sectional cull samples, the days kept in captivity and prevalence of gastrointestinal helminth infections were included, and for longitudinal samples, the individual ID. Subsequent analyses were carried out to determine changes in α- and β-diversity over three time-scales; short-term changes from field to lab following capture, medium-term changes between sequential trapping sessions, and long-term changes between the first and last longitudinal samples of individual voles. All changes were assessed in R through Gaussian linear mixed models (LMER) using the lme4 package [83] and tested for significance with ‘lmerTest’ package [84], with individual ID as a random effect, and incorporating the Julian date from the start of the year as a fixed effect to account for any seasonal changes. These models were performed on α-diversity metrics, including total unique OTU count as a measure of richness, and Bray–Curtis, wUniFracand RPCA scores to measure changes in β-diversity. Short-term models compared samples taken at cull with the most recent longitudinal sample from the same animal (1–2 days prior). Medium-term models incorporated all consecutive longitudinal faecal samples with no missed trapping sessions between sessions (12–16 days between each sample), and assessed the size of changes observed from the first capture in the sequence as explained by the number of sessions passed. Long-term models compared paired longitudinal samples from the first and last captures, excluding all cases where that time difference was only one trapping session, with time between first and last samples also included as a factor. Trapping date was included as a factor in all models, in an effort to show changes in diversity, regardless of the time of year in which the trapping sequence occurred. However, seasonal effects can still not be completely discounted, as all traps were recorded within 2017, and as such, a general seasonal change in microbiome diversity affecting all animals from early Spring to Autumn would be difficult to distinguish from any effects due purely to ageing. In order to assess associations between diversity metrics and other ecological and environmental factors, linear models were performed on measures of cull faecal α-diversity and Bray–Curtis and wUniFrac site scores to measure changes in β-diversity, which incorporated Julian date, scaled mass index (SMI) as a measure of condition, sex and prevalence of two gastrointestinal helminths which are common in the population – the pinworm *Syphacia obvelata* and tapeworms (Class: Cestoda). In order to incorporate infection data from gut dissections, these analyses were performed on a reduced cross-sectional dataset, using only cull faecal data. ## Taxonomic structure & variance Across all sample samples, Firmicutes and Bacteroidetes constituted the majority of OTUs sequenced, (summarised in Figs. 4 and 6). Bray–Curtis MDS1, wUniFrac MDS2 and RPC1 allprovided an axis which clearly distinguished relative prevalence of these two phyla, with higher scores of both Bray–Curtis MDS1 and wUniFrac MDS2 and RPC1 representing a significant shift towards a more Bacteroidetes-dominant microbiome (Fig. 1). ( OTUs most strongly represented in loadings are in Tables S3, S4 & S5).Fig. 1Bar chart showing inter-phylum differences represented in loadings of Bray–Curtis MDS1, Weighted UniFrac MDS2 and RPC1, distinguishing centered log-ratio abundances of *Bacteroidetes taxa* relative to other phyla. Error bars show standard error values Within-individual OTU variance, measured from longitudinal faecal samples, was consistent with between-individual variance of OTUs from cull faecal samples, indicating that bacterial taxa which are more variable between individuals are also more variable within an individual over time (Linear model, p = < 2.6 × 10–16, Adjusted R2 = 0.78; see Fig S2). OTUs of phylum Firmicutes were significantly more variable than those of Bacteroidetes (GLM with Tukey post-hoc: within-individual $p \leq 0.0001$, faeces $p \leq 0.0001$) (Cross-sectional differences shown in Fig. 2).Fig. 2Differences across phyla in coefficient of variance in OTU CLR abundance, as sampled from cross-sectional faecal samples. Boxes represent interquartile range (IQR) with median line. Upper and lower whiskers correspond to highest and lowest values at no more than 1.5 × IQR. Individual points represent outlier OTUs. Firmicutes show a higher level of variance than the other dominant phylum Bacteroidetes (*$p \leq 0.05$, ** $p \leq 0.01$, *** $p \leq 0.001$) ## Variance partitioning Variance partitioning was performed to show the percentage of variation in theCLR abundance of each OTU that is explained by each variable used in statistical models (Fig. 3). For the longitudinal data, these variables include individual ID, the Julian date, body condition (SMI), while for the cross-sectional data they include Julian date, the days spent in captivity prior to cull, SMI, eye lens mass as a proxy for age, sex, and prevalence of pinworm (Syphacia obvelata) and tapeworm infections. In the longitudinal data individual ID typically explained 8–$25\%$ of variation in most OTUs, indicating a substantial level of within-individual consistency in microbiome structure. A small number of OTUs were strongly associated with sex – of 46 OTUs which had over $50\%$ of their variation explained by sex in the longitudinal dataset, over half [29] were of order Clostridiales. Variance partitioning of cross-sectional data found that ecological factors explained a small amount of variation for most OTUs, with 3 OTUs having over $50\%$ of their variation explained (2 of order Bacteroidales, 1 of phylum Tenericutes). There is, however, still a great deal of unexplained residual variation across both datasets for the majority of OTUs. Fig. 3Variance partitioning of OTU CLR abundance from faecal samples. Violin plots are composed according to the percentage of variation in the CLR abundance of each OTU explained by the corresponding factor listed on the x-axis. Variation which is unexplained by the provided parameters is shown in the ‘Residuals’ violin on the right-hand side of the plot. Variance partitioning is shown for longitudinal and cull samples in the top and bottom plots, respectively ## Short-term changes (Field to Lab) (Summary of model outputs is provided in Table S2). Shifts in microbial α-diversity were recorded between faecal samples from the field and matched samples from the lab 1–2 days later. Faecal samples taken at cull showed significant decreases in α-diversity compared to paired longitudinal samples (LMER Chao1 $$p \leq 1.60$$ × 10–8, PD $$p \leq 2.35$$ × 10–12, Shannon $$p \leq 6.29$$ × 10–15). Significant differences inBray-Curtis (GLMER $p \leq 2$ × 10–16),wUniFrac (GLMER $$p \leq 2.33$$ × 10–7) and RPCA (GLMER $$p \leq 6.12$$ × 10–8) indicate shifts in β-diversity corresponding to a more uniform, Bacteroidetes-dominant microbiome upon arrival in captivity. Observed short-term changes are summarised in Fig. 4.Fig. 4A Within-individual changes in α-diversity observed between paired faecal samples taken at cull, and the most recent longitudinal sample (1–2 days prior), showing a decrease in α-diversity (Chao1 index). Boxes represent interquartile range (IQR) with median line. Upper and lower whiskers correspond to highest and lowest values at no more than 1.5 × IQR. B By-phylum differences in mean CLR abundances of OTUs between cull faecal samples and faecal samples taken from live-captures preceding cull. Biplots showing within-individual changes in β-diversity observed between paired faecal samples taken at cull, and the most recent longitudinal sample (1–2 days prior) in C RPCA, D wUniFrac and E Bray–Curtis distances. Significance values are reported on RPC1, wUniFrac MDS2 and Bray–Curtis MDS1 (*$p \leq 0.05$, ** $p \leq 0.01$, *** $p \leq 0.001$) ## Medium-term changes (between successive trapping sessions) Microbiomes showed increases in measures of α-diversity between successive trapping sessions, though only in Chao1(LMER, Chao1 $$p \leq 0.0177$$, PD = 0.279, Shannon $$p \leq 0.97$$). The inclusion of individual ID as a random effect means this is independent of any population level effects such as survival bias, and less likely to be due to seasonal effects, as any observed shifts are relative to each animal’s starting state, regardless of how it is being influenced by environmental variables at that time. While all three measures of β-diversity did show slight shifts towards Bacteroidetes dominance between sessions, these changes were not statistically significant. Observed medium-term changes are summarised in Fig. 5.Fig. 5A Within-individual changes in α-diversity observed across sequential trapping sessions, showing increases in Chao1 α-diversity over subsequent captures from the first in the continuous sequence. B Within-individual changes in β-diversity observed across sequential trapping sessions, showing increases in site scores of B RPCA, C wUniFrac and D Bray-Curtis distances over time, are non-significant (lines represent fitted linear model, with shaded area representing $95\%$ confidence intervals, *$p \leq 0.05$, ** $p \leq 0.01$, *** $p \leq 0.001$) ## Long-term changes α-diversity was significantly higher at the final longitudinal capture relative to the first, indicating an accrual of bacterial species over long periods (LMER, $$p \leq 0.0035$$, PD $$p \leq 7.97$$ × 10–4, Shannon $$p \leq 0.0248$$). A significant difference is observed in Bray–Curtis distance between first and last captures (GLMER, $$p \leq 0.043$$), indicating a shift towards a more Bacteroidetes-dominant microbiome. No significant difference was observed in wUniFrac RPCA distances (GLMER: wUniFrac $$p \leq 0.058$$, RPCA $$p \leq 0.149$$). Observed long-term changes are summarised in Fig. 6.Fig. 6A Within-individual changes in α-diversity observed between paired first and last longitudinal faecal samples, showing an increase in α-diversity (Chao1 index). Boxes represent interquartile range (IQR) with median line. Upper and lower whiskers correspond to highest and lowest values at no more than 1.5 × IQR. Individual points represent outliers. B By-phylum differences in mean CLR abundances of OTUs between first and last live-capture faecal samples. C Biplot showing within-individual changes in β-diversity observed between paired first and last longitudinal faecal samples by site scores on the first two axes of variation observed in C RPCA, D wUniFrac, and E Bray–Curtis distances. Significant associations are found on axis 1 of Bray–Curtis ordination.. (*$p \leq 0.05$, ** $p \leq 0.01$, *** $p \leq 0.001$) ## Ecological associations Cull faeces α-diversity was positively associated with body condition, measured as SMI, though not in Shannon diversity (GLM, Chao1 $$p \leq 0.00323$$, PD $$p \leq 0.00861$$, Shannon $$p \leq 0.51884$$), indicating that richer microbiome communities are associated with better host health. SMI was also significantly associated with the Bray–Curtis distance, with Bacteroidetes dominance associated with lower condition (GLM, $$p \leq 0.0126$$), but not wUniFrac (GLM $$p \leq 0.162$$) or RPCA (GLM $$p \leq 0.798$$) distance. Tapeworm infection was positively associated with Bacteroidetes dominance as explained by Bray–Curtis (GLM, $$p \leq 0.0313$$), and RPCA (GLM, $$p \leq 0.00931$$) distances, but not wUniFrac (GLM, $$p \leq 0.162$$) (These associations are summarised in Fig S4). ## Discussion Differences between bacterial taxa in how they are affected by temporal patterns are a crucial element of wild animal ecology, as taxon-specific shifts in microbiome structure have been implicated in many aspects of host health [85]. Here we have shown clear and defined patterns of change in gastrointestinal microbial diversity across different time scales, including long-term ageing-associated increases in α-diversity and shifts in β-diversity towards Bacteroidetes-dominance, and rapid reductions in α-diversity associated with capture and captivity. Firmicutes taxa showed significantly higher levels of variation than those of Bacteroidetes, and so while increases in α-diversity suggest an accrual of bacteria over time, it is likely that shifts in β-diversity are at least in part explained by loss of Firmicutes species. These phylum-level differences in stability have been reported in human populations, presumably due to differences in susceptibility to changes in environmental conditions [86]. These observed changes increase our understanding of natural variation in microbiome structures in wild populations, show clear parallels to analogous health-related shifts observed in ageing humans, and provide important context to consider when performing any analysis of wild animals involving captivity or rehousing. Increases in α-diversity with age, over both medium-term and long-term timescales show a pattern of constant species accrual throughout an animal’s lifetime, while positive associations with species richness and body condition suggest an important role in this process for maintaining host health. The underlying taxonomic composition was broadly what would be expected of mammalian gastrointestinal microbiomes [87, 88], and has been recorded in other wild rodent populations [89] with Firmicutes and Bacteroidetes being the dominant phyla. While Bacteroidetes was generally more prevalent than Firmicutes in this population, in domesticated populations of Microtus ochrogaster Firmicutes was the more dominant phylum [90]. Most variation in OTU abundance was either associated with the specific animal, or was unexplained by any of the accompanying data. This unexplained variation may arise from purely stochastic processes, and/or there may be factors not captured in this study which play a role in shaping microbiome communities, such as dietary variation [89]. While field voles are predominantly herbivorous, variation within the plant species consumed, or occasional consumption of other foods like insect larvae may influence microbiome composition [91–93], and so alteration in diet after capture may have an impact on microbiome communities. Recent research in wild rodent population has also highlighted a potentially significant role for social network structure in determining population-level microbiome diversity [94]. While host-genetic influences not accounted for in this study, the impact of the genome on microbiome structure has shown to be secondary to environmental and temporal factors in both humans [1] and rodents [95]. While we have characterised distinct patterns of variability across different timescales, it should be noted that the observed short-term changes are likely to have limited relevance to ecological processes that would occur without human intervention, as the conditions and circumstances of animal capture and rehousing are not directly analogous to any naturally occurring process. Despite this, they do still illustrate that dramatic shifts in microbiome structure can occur in short timescales dependent on surrounding conditions, and that any microbiome study involving captivity and capture may be significantly confounded, even when efforts are made to minimise interference. With these caveats, we have been able to identify specific and directed changes in the microbiome across multiple time scales. ## Short-term changes and effects of capture α-diversity was reduced between paired final live-capture and cull samples, across multiple richness indices, indicating rapid diversity loss associated with capture and 1–2 days of captivity. Reduced α-diversity associated with captivity has also been reported in a number of mammals [96], though this effect is not universal, and in some instance the opposite may be observed [97, 98]. The changes observed in this study both confirm that captivity-associated changes in microbiome are common across species and populations, and show how rapidly these changes can arise. There are many potential direct causes of these changes; changes in diet have been suggested as a key contributor [38, 96], but other factors such as stress [99, 100], disruption to diurnal sleep cycles [101], or combinations of these factors [102] may also play a role. These changes confirm that the microbiome can be highly volatile, and captivity-associated changes are essential to consider, both in terms of health of captive animals, and for studies of wild populations. This is particularly relevant for studies involving live trapping and rehousing of animals prior to sample collection, as changes from ‘natural’ conditions could significantly impact the microbiome of the animal in question within a short window of time. It is unlikely that these changes will be universal in nature or magnitude across study species, as captivity has been shown to have differing impacts on the gut microbiome of different host species, even within the same genus, potentially having a reduced impact on generalists compared to specialists [103, 104]. A significant taxonomic shift in microbiome communities was also observed following capture. While such shifts are in the same direction as what would be expected from ageing (increased Bacteroidetes-dominance), the magnitude and speed of the change suggests that capture and captivity itself is impacting the microbiome. Alongside the decreases in species, this indicates a loss of Firmicutes, resulting in increased prominence of Bacteroidetes. Shifts towards Bacteroidetes-dominant microbiome following captivity have been observed in deer mice (Peromyscus maniculatus) examined pre- and post-captivity [48], as well as in comparisons between captive mammals and counterpart wild populations [96, 97, 105, 106]. ## Medium- & long-term changes in microbiome structure Both the medium-term (between successive trapping sessions) and long-term (between first and last live-traps) models showed that microbiome α-diversity increases with age, regardless of the time of year, with no obvious drop-off observed in the oldest voles. They also showed that β-diversity shifted toward a more Bacteroidetes-dominant microbiome throughout an animal’s life. Due to the diversity metrics used in this study, some of these patterns may be driven in part by rare taxa having an outsized impact. One Bacteroidetes OTU in particular has a very strong representation in Bray–Curtis MDS1, but was not classified below phylum level (Table S 3). OTUs which strongly contributed to Firmicutes dominance in Bray–Curtis and RPCA distances were of the genus Ruminococcus, a taxon which is important in cellulolytic metabolism, and so loss of these bacteria over time may have an impact on host digestion and health [107] Age-associated increases in α-diversity are recorded in humans, from infancy to adulthood [26]. Age-related shifts in taxonomic structure of the microbiome have been recorded in laboratory mice [108] and captive mammals [17, 109], with multiple human studies showing specific age-related shifts to Bacteroidetes [26, 27]. The combination of changes in both α- and β-diversity observed in this study suggest that as the voles age, they accrue new bacterial OTUs, primarily of Phylum Bacteroidetes. ## Other correlates of microbiome diversity and fitness implications While most ecological factors were not associated with microbiome diversity, body condition, measured as SMI, was found to be positively associated with both α-diversity, and a more Firmicutes-dominant microbiome. The association with α-diversity may suggest that while animals in different life stages and under different constraints may harbour qualitatively different microbiome communities, it is the richness of those communities which is most significant for host condition. The positive impacts of age and infection on α-diversity highlight how the state of the gut can be associated with multiple ecological factors which may be having indirect effects on host condition via the microbiome. On the other hand, rapid reductions in α-diversity indices associated with capture and captivity are essential to consider as context for microbiome analyses of wild and captive populations, and when considering questions of captive animal health and welfare. The negative association between Bacteroidetes and scaled mass index mirrors what has been observed in human populations, although in humans, high-BMI, particularly in obese individual, is associated with Firmicutes-dominance, and thus higher mass indices broadly corresponded to pathology, rather than healthy condition [110, 111]. The Bacteroidetes/Firmicutes ratio is commonly implicated as a key factor affecting gastrointestinal, and general, health, and so this change may be relevant to health and survival over time in wild populations. Many studies have looked at the Bacteroidetes/Firmicutes ratio as an important correlate of gut health, with alterations in this ratio linked to obesity and other pathologies [112, 113]. How these associations relate to ageing-related shifts in microbiome, in which both richness and Bacteroidetes-dominance increase is unclear, and this element of the analysis is somewhat limited by the reduced cross-sectional dataset. Further investigation could elucidate whether microbiome-associated variation in condition is primarily the result of inter-individual differences, or could be relevant to within-individual changes throughout an animal’s life. ## Conclusion The gastrointestinal microbiome is complex and dynamic, particularly in wild, heterogeneous populations, and the factors underlying temporal changes in its composition are often difficult to determine. Using both longitudinal and cross-sectional data from a well-characterised wild rodent population, we have established robust temporal patterns of changes in microbiome structure, both in short periods following change in environmental conditions, and over the course of an animal’s life in the wild. While microbiome structure was found to be highly individual, broad patterns of change over different timescales can be observed in both α- and β-diversity. Ageing is associated with slight shifts towards Bacteroidetes-dominance and increase in species richness, while captivity was associated with marked and larger short-term increases in Bacteroidetes-dominance and drops in species richness. Firmicutes OTUs, being significantly more variable than those of Bacteroidetes, are likely responsible for many of the temporal patterns of change in microbiome structure which were observed. These results provide a useful framework with which to understand time-sensitive, consistent changes in microbiome structure within wild animal populations. The specific characteristics of changes observed in this population invite direct comparisons with analogous findings from laboratory rodent models and human populations, and suggest a potentially significant role for temporal microbiome dynamics on host health and fitness. ## Supplementary Information Additional file 1. ## References 1. 1.Rothschild D, Weissbrod O, Barkan E, Kurilshikov A, Korem T, Zeevi D, et al. Environment dominates over host genetics in shaping human gut microbiota. Nature 2018 555:7695. 2018 28;555(7695):210–5. 2. Suzuki TA. **Links between natural variation in the microbiome and host fitness in wild mammals**. *Integr Comp Biol* (2017.0) **57** 756-769. DOI: 10.1093/icb/icx104 3. Kim S, Jazwinski SM. **The Gut Microbiota and Healthy Aging: A Mini-Review**. *Gerontology* (2018.0) **64** 513-520. DOI: 10.1159/000490615 4. 4.García-Peña C, Álvarez-Cisneros T, Quiroz-Baez R, Friedland RP. Microbiota and Aging. A Review and Commentary. Arch Med Res. 2017 48(8):681–9. 5. 5.Bana B, Cabreiro F. The Microbiome and Aging. 101146/annurev-genet-112618-043650. 2019 53:239–61. 6. Zaiss MM, Harris NL. **Interactions between the intestinal microbiome and helminth parasites**. *Parasite Immunol* (2016.0) **38** 5-11. DOI: 10.1111/pim.12274 7. Rapin A, Harris NL. **Helminth-Bacterial Interactions: Cause and Consequence**. *Trends Immunol* (2018.0) **39** 724-733. DOI: 10.1016/j.it.2018.06.002 8. Voreades N, Kozil A, Weir TL. **Diet and the development of the human intestinal microbiome**. *Front Microbiol* (2014.0) **5** 494. PMID: 25295033 9. Singh RK, Chang HW, Yan D, Lee KM, Ucmak D, Wong K. **Influence of diet on the gut microbiome and implications for human health**. *J Transl Med* (2017.0) **15** 73. DOI: 10.1186/s12967-017-1175-y 10. David LA, Maurice CF, Carmody RN, Gootenberg DB, Button JE, Wolfe BE. **Diet rapidly and reproducibly alters the human gut microbiome**. *Nature* (2014.0) **505** 559. DOI: 10.1038/nature12820 11. Risely A. **Applying the core microbiome to understand host-microbe systems**. *J Anim Ecol* (2020.0) **89** 1549-1558. DOI: 10.1111/1365-2656.13229 12. David LA, Materna AC, Friedman J, Campos-Baptista MI, Blackburn MC, Perrotta A. **Host lifestyle affects human microbiota on daily timescales**. *Genome Biol* (2014.0) **15** 1-15 13. Caporaso JG, Lauber CL, Costello EK, Berg-Lyons D, Gonzalez A, Stombaugh J. **Moving pictures of the human microbiome**. *Genome Biol* (2011.0) **12** 1-8. DOI: 10.1186/gb-2011-12-5-r50 14. Costello EK, Lauber CL, Hamady M, Fierer N, Gordon JI, Knight R. **Bacterial community variation in human body habitats across space and time**. *Science* (2009.0) **326** 1694-7. DOI: 10.1126/science.1177486 15. Wang J, Lang T, Shen J, Dai J, Tian L, Wang X. **Core gut bacteria analysis of healthy mice**. *Front Microbiol* (2019.0) **10** 887. DOI: 10.3389/fmicb.2019.00887 16. Schloss PD, Schubert AM, Zackular JP, Iverson KD, Young VB, Petrosino JF. **Stabilization of the murine gut microbiome following weaning**. *Gut Microbes* (2012.0) **3** 383-393. DOI: 10.4161/gmic.21008 17. Reveles KR, Patel S, Forney L, Ross CN. **Age-related changes in the marmoset gut microbiome**. *Am J Primatol* (2019.0) **81** e22960. DOI: 10.1002/ajp.22960 18. Lim MY, Song EJ, Kang KS, Do Nam Y. **Age-related compositional and functional changes in micro-pig gut microbiome**. *Geroscience* (2019.0) **41** 935-44. DOI: 10.1007/s11357-019-00121-y 19. 19.Ma S, Yim SH, Lee SG, Kim EB, Lee SR, Chang KT, et al. Organization of the Mammalian Metabolome according to Organ Function, Lineage Specialization, and Longevity. Cell Metab. 2015;22(2). 20. 20.Justice JN, Cesari M, Seals DR, Shively CA, Carter CS. Comparative Approaches to Understanding the Relation Between Aging and Physical Function. J Gerontol A Biol Sci Med Sci. 2016 Oct 1 [cited 2022 Nov 28];71(10):1243. Available from: /pmc/articles/PMC5018556/ 21. Cagan A, Baez-Ortega A, Brzozowska N, Abascal F, CoorensSanders THHMA. **Somatic mutation rates scale with lifespan across mammals**. *Nature* (2022.0) **604** 517-524. DOI: 10.1038/s41586-022-04618-z 22. 22.MacRae SL, Croken MMK, Calder RB, Aliper A, Milholland B, White RR, et al. DNA repair in species with extreme lifespan differences. Aging. 2015;7(12). 23. Liu J, Johnson R, Dillon S, Kroehl M, Frank DN, Tuncil YE. **Among older adults, age-related changes in the stool microbiome differ by HIV-1 serostatus**. *EBioMedicine* (2019.0) **40** 583-594. DOI: 10.1016/j.ebiom.2019.01.033 24. Claesson MJ, Cusack S, O’Sullivan O, Greene-Diniz R, De Weerd H, Flannery E. **Composition, variability, and temporal stability of the intestinal microbiota of the elderly**. *Proc Natl Acad Sci U S A* (2011.0) **108** 4586-4591. DOI: 10.1073/pnas.1000097107 25. La-ongkham O, Nakphaichit M, Nakayama J, Keawsompong S, Nitisinprasert S. **Age-related changes in the gut microbiota and the core gut microbiome of healthy Thai humans**. *3 Biotech* (2020.0) **10** 276. DOI: 10.1007/s13205-020-02265-7 26. Odamaki T, Kato K, Sugahara H, Hashikura N, Takahashi S, Xiao JZ. **Age-related changes in gut microbiota composition from newborn to centenarian: A cross-sectional study**. *BMC Microbiol* (2016.0) **16** 1-12. PMID: 26728027 27. Mariat D, Firmesse O, Levenez F, Guimarǎes VD, Sokol H, Doré J. **The firmicutes/bacteroidetes ratio of the human microbiota changes with age**. *BMC Microbiol* (2009.0) **9** 1-6. PMID: 19121223 28. O’Toole PW, Jeffery IB. **Gut microbiota and aging**. *Science* (2015.0) **350** 1214-5. DOI: 10.1126/science.aac8469 29. 29.Claesson MJ, Jeffery IB, Conde S, Power SE, O’connor EM, Cusack S, et al. Gut microbiota composition correlates with diet and health in the elderly. 2012; 30. Zhang J, Luo D, Lin Z, Zhou W, Rao J, Li Y. **Dysbiosis of gut microbiota in adult idiopathic membranous nephropathy with nephrotic syndrome**. *Microb Pathog* (2020.0) **147** 104359. DOI: 10.1016/j.micpath.2020.104359 31. De La Cuesta-Zuluaga J, Kelley ST, Chen Y, Escobar JS, Mueller NT, Ley RE. **Age- and Sex-Dependent Patterns of Gut Microbial Diversity in Human Adults**. *mSystems* (2019.0) **4** e00261-e319. PMID: 31098397 32. Scott KA, Ida M, Peterson VL, Prenderville JA, Moloney GM, Izumo T. **Revisiting Metchnikoff: Age-related alterations in microbiota-gut-brain axis in the mouse**. *Brain Behav Immun* (2017.0) **65** 20-32. DOI: 10.1016/j.bbi.2017.02.004 33. Ke S, Fang S, He M, Huang X, Yang H, Yang B. **Age-based dynamic changes of phylogenetic composition and interaction networks of health pig gut microbiome feeding in a uniformed condition**. *BMC Vet Res* (2019.0) **15** 1-13. DOI: 10.1186/s12917-019-1918-5 34. Smith P, Willemsen D, Popkes M, Metge F, Gandiwa E, Reichard M. **Regulation of life span by the gut microbiota in the short-lived african turquoise killifish**. *Elife* (2017.0) **22** 6 35. Jackson MA, Jeffery IB, Beaumont M, Bell JT, Clark AG, Ley RE. **Signatures of early frailty in the gut microbiota**. *Genome Med* (2016.0) **8** 1-11. PMID: 26750923 36. Bastiaanssen TFS, Gururajan A, van de Wouw M, Moloney GM, Ritz NL, Long-Smith CM. **Volatility as a Concept to Understand the Impact of Stress on the Microbiome**. *Psychoneuroendocrinology* (2021.0) **124** 105047. DOI: 10.1016/j.psyneuen.2020.105047 37. Carmody RN, Gerber GK, Luevano JM, Gatti DM, Somes L, Svenson KL. **Diet dominates host genotype in shaping the murine gut microbiota**. *Cell Host Microbe* (2015.0) **17** 72-84. DOI: 10.1016/j.chom.2014.11.010 38. Zarrinpar A, Chaix A, Yooseph S, Panda S. **Diet and feeding pattern affect the diurnal dynamics of the gut microbiome**. *Cell Metab* (2014.0) **20** 1006-1017. DOI: 10.1016/j.cmet.2014.11.008 39. Shang Y, Khafipour E, Derakhshani H, Sarna LK, Woo CW, Siow YL. **Short Term High Fat Diet Induces Obesity-Enhancing Changes in Mouse Gut Microbiota That are Partially Reversed by Cessation of the High Fat Diet**. *Lipids* (2017.0) **52** 499-511. DOI: 10.1007/s11745-017-4253-2 40. Liang X, Bushman FD, FitzGerald GA. **Rhythmicity of the intestinal microbiota is regulated by gender and the host circadian clock**. *Proc Natl Acad Sci U S A* (2015.0) **112** 10479-10484. DOI: 10.1073/pnas.1501305112 41. Howard BM, Kornblith LZ, Christie SA, Conroy AS, Nelson MF, Campion EM. **Characterizing the gut microbiome in trauma: significant changes in microbial diversity occur early after severe injury**. *Trauma Surg Acute Care Open* (2017.0) **2** 1-6 42. Wong K, Shaw TI, Oladeinde A, Glenn TC, Oakley B, Molina M. **Rapid Microbiome Changes in Freshly Deposited Cow Feces under Field Conditions**. *Front Microbiol* (2016.0) **7** 1-12. PMID: 26834723 43. Morgan KN, Tromborg CT. **Sources of stress in captivity**. *Appl Anim Behav Sci* (2007.0) **102** 262-302. DOI: 10.1016/j.applanim.2006.05.032 44. Uenishi G, Fujita S, Ohashi G, Kato A, Yamauchi S, Matsuzawa T. **Molecular analyses of the intestinal microbiota of chimpanzees in the wild and in captivity**. *Am J Primatol* (2007.0) **69** 367-376. DOI: 10.1002/ajp.20351 45. Wienemann T, Schmitt-Wagner D, Meuser K, Segelbacher G, Schink B, Brune A. **The bacterial microbiota in the ceca of Capercaillie (Tetrao urogallus) differs between wild and captive birds**. *Syst Appl Microbiol* (2011.0) **34** 542-551. DOI: 10.1016/j.syapm.2011.06.003 46. Xenoulis PG, Gray PL, Brightsmith D, Palculict B, Hoppes S, Steiner JM. **Molecular characterization of the cloacal microbiota of wild and captive parrots**. *Vet Microbiol* (2010.0) **146** 320-325. DOI: 10.1016/j.vetmic.2010.05.024 47. Villers LM, Jang SS, Lent CL, Lewin-Koh SC, Norosoarinaivo JA. **Survey and comparison of major intestinal flora in captive and wild ring-tailed lemur (Lemur catta) populations**. *Am J Primatol* (2008.0) **70** 175-184. DOI: 10.1002/ajp.20482 48. Schmidt E, Mykytczuk N, Schulte-Hostedde AI. **Effects of the captive and wild environment on diversity of the gut microbiome of deer mice (Peromyscus maniculatus)**. *ISME J* (2019.0) **13** 1293-1305. DOI: 10.1038/s41396-019-0345-8 49. Edenborough KM, Mu A, Mühldorfer K, Lechner J, Lander A, Bokelmann M. **Microbiomes in the insectivorous bat species Mops condylurus rapidly converge in captivity**. *PLoS ONE* (2020.0) **15** e0223629. DOI: 10.1371/journal.pone.0223629 50. Kohl KD, Dearing MD. **Wild-caught rodents retain a majority of their natural gut microbiota upon entrance into captivity**. *Environ Microbiol Rep* (2014.0) **6** 191-195. DOI: 10.1111/1758-2229.12118 51. Dhanasiri AKS, Brunvold L, Brinchmann MF, Korsnes K, Bergh Ø, Kiron V. **Changes in the Intestinal Microbiota of Wild Atlantic cod Gadus morhua L**. *Upon Captive Rearing Microb Ecol* (2011.0) **61** 20-30. PMID: 20424834 52. Aivelo T, Laakkonen J, Jernvall J. **Population-and individual-level dynamics of the intestinal microbiota of a small primate**. *Appl Environ Microbiol* (2016.0) **82** 3537-3545. DOI: 10.1128/AEM.00559-16 53. Čížková D, Ďureje Ľ, Piálek J, Kreisinger J. **Experimental validation of small mammal gut microbiota sampling from faeces and from the caecum after death**. *Heredity (Edinb)* (2021.0) **127** 141-150. DOI: 10.1038/s41437-021-00445-6 54. Björk JR, Dasari M, Grieneisen L, Archie EA. **Primate microbiomes over time: Longitudinal answers to standing questions in microbiome research**. *Am J Primatol* (2019.0) **81** e22970. PMID: 30941803 55. Jackson JA, Hall AJ, Friberg IM, Ralli C, Lowe A, Zawadzka M. **An Immunological Marker of Tolerance to Infection in Wild Rodents**. *PLoS Biol* (2014.0) **12** 1-13. DOI: 10.1371/journal.pbio.1001901 56. Jackson JA, Begon M, Birtles R, Paterson S, Friberg IM, Hall A. **The analysis of immunological profiles in wild animals: A case study on immunodynamics in the field vole**. *Microtus agrestis Mol Ecol* (2011.0) **20** 893-909. DOI: 10.1111/j.1365-294X.2010.04907.x 57. Wanelik KM, Begon M, Bradley JE, Friberg IM, Jackson JA, Taylor CH. **Effects of an IgE receptor polymorphism acting on immunity, susceptibility to infection, and reproduction in a wild rodent**. *Elife* (2023.0) **16** 12 58. Peig J, Green AJ. **New perspectives for estimating body condition from mass/length data: The scaled mass index as an alternative method**. *Oikos* (2009.0) **118** 1883-1891. DOI: 10.1111/j.1600-0706.2009.17643.x 59. Rowe FP, Bradfield A, Quy RJ, Swinney T. **Relationship Between Eye Lens Weight and Age in the Wild House Mouse (Mus musculus)**. *J Appl Ecol* (1985.0) **22** 55-61. DOI: 10.2307/2403326 60. 60.Augusteyn RC. Growth of the eye lens: I. Weight accumulation in multiple species. Mol Vis. 2014;20(October 2013):410–26. 61. 61.Behnke JM, Stewart A, Smales L, Cooper G, Lowe A, Kinsella JM, et al. Parasitic nematodes of the genus Syphacia Seurat, 1916 infecting Cricetidae in the British Isles: the enigmatic status of Syphacia nigeriana. Parasitology. 2022 Jan 5 [cited 2023 Jan 26];149(1):76–94. Available from: https://pubmed.ncbi.nlm.nih.gov/34608855/ 62. 62.Baylis HA. On a Collection of Nematodes from Nigerian Mammals (chiefly Rodents). Parasitology [Internet]. 1928 [cited 2023 Jan 26];20(3):280–304. Available from: https://www.cambridge.org/core/journals/parasitology/article/abs/on-a-collection-of-nematodes-from-nigerian-mammals-chiefly-rodents/1F1E5D14DFF6766E9171D14B7C06B238 63. 63.Schmidt GD. CRC Handbook of Tapeworm Identification. [Internet]. Vol. 61, 10.1086/415201. Stony Brook Foundation, Inc.; 1986 [cited 2023 Jan 26]. 556–556 p. Available from: https://www.journals.uchicago.edu/doi/10.1086/415201 64. Mészáros F. **Parasitic Nematodes of Microtus arvalis (Rodentia) in Hungary**. *Parasitologica hungarica* (1977.0) **10** 67-83 65. 65.Feliu C, et al. Genetic and morphological heterogeneity in small rodent whipworms in southwestern Europe: characterization of Trichuris muris and description of Trichuris arvicolae n. sp. (Nematoda: Trichuridae), Journal of Parasitology. American Society of Parasitologists. 2000;86(3):442–9. 66. Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ. **Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample**. *Proc Natl Acad Sci* (2011.0) **108** 4516-4522. DOI: 10.1073/pnas.1000080107 67. Martin M. **Cutadapt removes adapter sequences from high-throughput sequencing reads**. *EMBnet J* (2011.0) **17** 10-12. DOI: 10.14806/ej.17.1.200 68. 68.Joshi NA, Fass JN. Sickle: A sliding-window, adaptive, quality-based trimming tool for FastQ files. 2011. 69. Prjibelski A, Antipov D, Meleshko D, Lapidus A, Korobeynikov A. **Using SPAdes De Novo Assembler**. *Curr Protoc Bioinformatics* (2020.0) **70** e102. DOI: 10.1002/cpbi.102 70. Zhang J, Kobert K, Flouri T, Stamatakis A. **PEAR: a fast and accurate Illumina Paired-End reAd mergeR**. *Bioinformatics* (2014.0) **5** 614-620. DOI: 10.1093/bioinformatics/btt593 71. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. **Basic local alignment search tool**. *J Mol Biol* (1990.0) **215** 403-410. DOI: 10.1016/S0022-2836(05)80360-2 72. Mahé F, Rognes T, Quince C, de Vargas C, Dunthorn M. **Swarm: robust and fast clustering method for amplicon-based studies**. *PeerJ* (2014.0) **2** e593. DOI: 10.7717/peerj.593 73. Wang Q, Garrity G, Tiedje J, Cole J. **Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy**. *Appl Environ Microbiol* (2007.0) **73** 5261-5267. DOI: 10.1128/AEM.00062-07 74. Bokulich N, Subramanian S, Faith J, Gevers D, Gordon J, Knight R. **Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing**. *Nat Methods* (2013.0) **10** 57-59. DOI: 10.1038/nmeth.2276 75. 75.Team RC. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.; 2018. 76. 76.Mcgee WA, Pimentel H, Pachter L, Wu JY. Compositional Data Analysis is necessary for simulating and analyzing RNA-Seq data. [cited 2023 Jan 12]; Available from: 10.1101/564955 77. 77.Faith DP. Phylogenetic diversity and conservation evaluation: Perspectives on multiple values, indices, and scales of application. In: Phylogenetic Diversity: Applications and Challenges in Biodiversity Science. Springer International Publishing; 2018. p. 1–26. 78. Kim BR, Shin J, Guevarra RB, Lee JH, Kim DW, Seol KH. **Deciphering Diversity Indices for a Better Understanding of Microbial Communities**. *J Microbiol Biotechnol* (2017.0) **27** 2089-2093. DOI: 10.4014/jmb.1709.09027 79. Hubert M, Reynkens T, Schmitt E, Verdonck T. **Sparse PCA for High-Dimensional Data With Outliers**. *Technometrics* (2016.0) **58** 424-434. DOI: 10.1080/00401706.2015.1093962 80. Fox J, Weisberg S. *An R Companion to Applied Regression* (2019.0) 81. Venables WN, Ripley BD. *Modern Applied Statistics with S* (2002.0) 82. Hoffman GE, Schadt EE. **variancePartition: Interpreting drivers of variation in complex gene expression studies**. *BMC Bioinformatics* (2016.0) **17** 1-13. DOI: 10.1186/s12859-016-1323-z 83. 83.Bates D, Mächler M, Bolker BM, Walker SC. Fitting linear mixed-effects models using lme4. J Stat Softw. 2015 Oct 1;67(1). 84. Kuznetsova A, Brockhoff PB, Christensen RHB. **lmerTest Package: Tests in Linear Mixed Effects Models**. *J Stat Softw* (2017.0) **82** 1-26. DOI: 10.18637/jss.v082.i13 85. Hanning I, Diaz-Sanchez S. **The functionality of the gastrointestinal microbiome in non-human animals**. *Microbiome* (2015.0) **3** 51. DOI: 10.1186/s40168-015-0113-6 86. Faith JJ, Guruge JL, Charbonneau M, Subramanian S, Seedorf H, Goodman AL. **The long-term stability of the human gut microbiota**. *Science* (2013.0) **341** 1237439. DOI: 10.1126/science.1237439 87. Ley RE, Hamady M, Lozupone C, Turnbaugh P, Ramey RR, Bircher JS. **Evolution of mammals and their gut microbes**. *Science* (2008.0) **320** 1647-51. DOI: 10.1126/science.1155725 88. Ley RE, Bäckhed F, Turnbaugh P, Lozupone CA, Knight RD, Gordon JI. **Obesity alters gut microbial ecology**. *Proc Natl Acad Sci U S A* (2005.0) **102** 11070-11075. DOI: 10.1073/pnas.0504978102 89. Maurice CF, Cl Knowles S, Ladau J, Pollard KS, Fenton A, Pedersen AB. **Marked seasonal variation in the wild mouse gut microbiota**. *ISME J* (2015.0) **9** 2423-2434. DOI: 10.1038/ismej.2015.53 90. 90.Curtis JT, Assefa S, Francis A, Kö Hler GA. Fecal microbiota in the female prairie vole (Microtus ochrogaster). 2018 [cited 2022 Nov 29]; Available from: 10.1371/journal.pone.0190648 91. 91.IUCN Red List - Field Vole [Internet]. [cited 2022 Nov 29]. Available from: https://www.iucnredlist.org/species/13426/115112050 92. 92.The Wildlife Trusts - Mammals: Field Vole [Internet]. [cited 2022 Nov 29]. Available from: https://www.wildlifetrusts.org/wildlife-explorer/mammals/field-vole 93. 93.Mammal Society - Discover Mammals: Field Vole [Internet]. [cited 2022 Nov 29]. Available from: https://www.mammal.org.uk/species-hub/full-species-hub/discover-mammals/species-field-vole/ 94. Raulo A, Allen BE, Troitsky T, Husby A, Firth JA, Coulson T. **Social networks strongly predict the gut microbiota of wild mice**. *ISME J* (2021.0) **15** 2601-2613. DOI: 10.1038/s41396-021-00949-3 95. Lees H, Swann J, Poucher SM, Nicholson JK, Holmes E, Wilson ID. **Age and Microenvironment Outweigh Genetic Influence on the Zucker Rat Microbiome**. *PLoS ONE* (2014.0) **9** 1-11 96. McKenzie VJ, Song SJ, Delsuc F, Prest TL, Oliverio AM, Korpita TM. **The effects of captivity on the mammalian gut microbiome**. *Integr Comp Biol* (2017.0) **57** 690-704. DOI: 10.1093/icb/icx090 97. Guan Y, Yang H, Han S, Feng L, Wang T, Ge J. **Comparison of the gut microbiota composition between wild and captive sika deer (Cervus nippon hortulorum) from feces by high-throughput sequencing**. *AMB Express* (2017.0) **7** 212. DOI: 10.1186/s13568-017-0517-8 98. Li Y, Zhang K, Liu Y, Li K, Hu D, Wronski T. **Community Composition and Diversity of Intestinal Microbiota in Captive and Reintroduced Przewalski’s Horse (Equus ferus przewalskii)**. *Front Microbiol* (1821.0) **2019** 10 99. Bharwani A, Firoz Mian M, Foster JA, Surette MG, Bienenstock J, Forsythe P. **Structural & functional consequences of chronic psychosocial stress on the microbiome & host**. *Psychoneuroendocrinology* (2016.0) **63** 217-227. DOI: 10.1016/j.psyneuen.2015.10.001 100. Bailey MT, Dowd SE, Parry NMA, Galley JD, Schauer DB, Lyte M. **Stressor Exposure Disrupts Commensal Microbial Populations in the Intestines and Leads to Increased Colonization by Citrobacter rodentium**. *Infect Immun* (2010.0) **78** 1509-1519. DOI: 10.1128/IAI.00862-09 101. Maki KA, Burke LA, Calik MW, Watanabe-Chailland M, Sweeney D, Romick-Rosendale LE. **Sleep fragmentation increases blood pressure and is associated with alterations in the gut microbiome and fecal metabolome in rats**. *Physiol Genomics* (2020.0) **52** 280-292. DOI: 10.1152/physiolgenomics.00039.2020 102. 102.Voigt RM, Forsyth CB, Green SJ, Mutlu E, Engen P. Circadian Disorganization Alters Intestinal Microbiota. Vol. 9, PLoS ONE. 2014. 103. 103.Weinstein SB, Mart Inez-Mota R, Stapleton TE, Klure DM, Greenhalgh R, Orr TJ, et al. Microbiome stability and structure is governed by host phylogeny over diet and geography in woodrats (Neotoma spp.). The Proceedings of the National Academy of Sciences. 2021;118(47):e2108787118:1–9. 104. 104.Kohl KD, Skopec MM, Dearing MD. Captivity results in disparate loss of gut microbial diversity in closely related hosts. Conserv Physiol [Internet]. 2014 [cited 2022 Jun 21];2(1):1–11. Available from: /pmc/articles/PMC4806740/ 105. Gibson KM, Nguyen BN, Neumann LM, Miller M, Buss P, Daniels S. **Gut microbiome differences between wild and captive black rhinoceros – implications for rhino health**. *Sci Rep* (2019.0) **9** 7570. DOI: 10.1038/s41598-019-43875-3 106. Clayton JB, Vangay P, Huang H, Ward T, Hillmann BM, Al-Ghalith GA. **Captivity humanizes the primate microbiome**. *PNAS* (2016.0) **113** 10376-10381. DOI: 10.1073/pnas.1521835113 107. 107.Helaszek CT, White BA. Cellobiose uptake and metabolism by Ruminococcus flavefaciens. Appl Environ Microbiol [Internet]. 1991 [cited 2022 Dec 15];57(1):64–8. Available from: https://journals.asm.org/doi/10.1128/aem.57.1.64-68.1991 108. Conley MN, Wong CP, Duyck KM, Hord N, Ho E, Sharpton TJ. **Aging and serum MCP-1 are associated with gut microbiome composition in a murine model**. *PeerJ* (2016.0) **4** e1854. DOI: 10.7717/peerj.1854 109. Adriansjach J, Baum ST, Lefkowitz EJ, van Der Pol WJ, Buford TW, Colman RJ. **Age-related differences in the gut microbiome of rhesus macaques**. *J Gerontology A Biol Sci Med Sci* (2020.0) **75** 1293-1298. DOI: 10.1093/gerona/glaa048 110. Koliada A, Syzenko G, Moseiko V, Budovska L, Puchkov K, Perederiy V. **Association between body mass index and Firmicutes/Bacteroidetes ratio in an adult Ukrainian population**. *BMC Microbiol* (2017.0) **17** 1-6. PMID: 28049431 111. Indiani CMDSP, Rizzardi KF, Castelo PM, Ferraz LFC, DarrieuxParisotto MTM. **Childhood Obesity and Firmicutes/Bacteroidetes Ratio in the Gut Microbiota: A Systematic Review**. *Child Obes* (2018.0) **14** 501-9. DOI: 10.1089/chi.2018.0040 112. Stojanov S, Berlec A, Štrukelj B. **The influence of probiotics on the firmicutes/bacteroidetes ratio in the treatment of obesity and inflammatory bowel disease**. *Microorganisms* (2020.0) **8** 1715. DOI: 10.3390/microorganisms8111715 113. Indiani CMDSP, Rizzardi KF, Castelo PM, Ferraz LFC, DarrieuxParisotto MTM. **Childhood Obesity and Firmicutes/Bacteroidetes Ratio in the Gut Microbiota: A Systematic Review**. *Childhood Obesity* (2018.0) **14** 501-9. DOI: 10.1089/chi.2018.0040
--- title: Investigation of the effects of physical activity level on functionality level and quality of life in the postpartum period authors: - Halil I. Bulguroglu - Merve Bulguroglu - P. T. Cansu Gevrek journal: Journal of Health, Population, and Nutrition year: 2023 pmcid: PMC10061954 doi: 10.1186/s41043-023-00368-4 license: CC BY 4.0 --- # Investigation of the effects of physical activity level on functionality level and quality of life in the postpartum period ## Abstract ### Background Physical activity, known to have positive effects in every period of life, may decrease due to anatomical and physiological changes and increased responsibilities in the postpartum period. This study aimed to understand how women's physical activity levels, functional levels, and quality of life are affected in the postpartum period and to emphasize the importance of physical activity levels in the postpartum period. ### Methods The population of our study was planned as a cross-sectional study of postpartum women who applied to a private center. The sample consists of 101 volunteer postpartum women participating in the study. Physical activity levels; with the International Physical Activity Questionnaire (IPAQ), postpartum functional levels; with the Inventory of Functional Status After Childbirth (IFSAC), postpartum quality of life level; with Maternal Postpartum Quality of Life (MAPP-QOL) were evaluated. ### Results It was determined that the amount of physical activity of postpartum women was 928.347 ± 281.27 MET-min/week, which means low physical activity level, and $35.64\%$ were not physically active. The mean total score of IFSAC was 2.13 ± 0.79, and the mean total score of MAPP-QOL was 16.93 ± 6.87. It was determined that there was a positive and significant correlation ($p \leq 0.05$) between IPAQ and IFSAC ($r = 0.034$) and MAPP-QOL ($r = 0.214$). A significant difference was found when the IFSAC and MAPP-QOL scores were compared between the three groups with different physical activity levels ($p \leq 0.05$). ### Conclusions As a result, it was observed that the physical activity levels of women in the postpartum period were low, negatively affecting their functionality and quality of life. ## Introduction The postpartum period is a process that starts after birth and covers the first six months. It is a period in which women experience physiological changes and undertake roles and responsibilities that they have not experienced before. The part of motherhood gained during this period is one of the most challenging roles women experience throughout their lives [1]. At the end of the nine months, the physical changes and discomforts of the mother in transition are added to adapting to the newly acquired roles and responsibilities. Generally, health professionals focus more on the physical health of mothers during this period and give less place to social and emotional needs [2]. The level of functionality is a concept based on realizing the daily vital functions, especially the woman’s basic needs, and the mother must continue her everyday life. The decrease in this level negatively affects the mother's ability to cope with physical changes and the mother-infant harmony, causing a delay in social and emotional recovery [3, 4]. All these affect the mother's quality of life, a multidimensional concept that can affect many aspects of life and cause the postpartum process to be adversely affected [5]. Physical activity, defined as all kinds of body movements performed by contraction of skeletal muscles, requiring energy expenditure above the basal level, has positive effects on human life physiologically and psychologically in every period [6]. Changes in the postpartum period and newborn responsibilities cause a decrease in the physical activity level of the mother. It is known that regular physical activity during this period accelerates the mother's physical recovery and positively affects her mood and quality of life [7]. Physical activity in the postpartum period; improves blood circulation, strengthens abdominal and spinal muscles, stimulates lactation, accelerates uterine recovery, prevents urogynecological dysfunction, and improves mothers' mental and physical condition. All these effects make it easier for mothers to perform their daily activities [8]. In the guidelines published by the American Society of Gynecology and Obstetrics, it is stated that women should do moderate-intensity exercise for at least 150 min, spread over every day of the week, if possible, during the postpartum period [9]. Several studies examine the effects of physical activity on women's quality of life and depression levels in the postpartum period [10, 11]. However, there is no study in the literature evaluating the relationship between the level of functionality of the mother, which is important for mother and child health, and the level of physical activity. This study aims to understand how the physical activity levels, functional levels, and quality of life of postpartum women affect and to emphasize the importance of interventions that increase physical activity levels by drawing attention to the adverse effects of decreased physical activity, especially in new mothers. ## Study design and population Before the study, the power analysis performed to determine the sample size determined that 100 people were needed for the correlation analysis to be performed by taking the Pearson correlation coefficient $r = 0.30$ with $80\%$ power (alpha = 0.05, bidirectional) in the G*power program. Considering the $20\%$ dropout assumption, 126 postpartum volunteer women who applied to a private center between $\frac{22}{06}$/2022 and $\frac{22}{07}$/2022 were invited to our cross-sectional study. Twenty of them wanted to be excluded from the study. Five women left the study unwillingly to make the evaluations, and the study was completed with 101 postpartum women. Inclusion criteria were defined as being between 6 weeks and six months postpartum, aged between 20 and 38 years, having given birth for the first time, having a single baby, not having any birth anomaly in oneself or the baby, and accepting to be included in the study. Women with multiple pregnancies and chronic diseases such as hemodynamically significant heart disease, restrictive lung diseases, diabetes, and hypertension were excluded from the study. Before starting the study, ethical approval was obtained from Ankara Medipol University Non-Interventional Clinical Research Ethics Committee (Date: $\frac{20}{06}$/2022 Decision No: 0120). The study was conducted by the Helsinki Principles. ## Measuring methods The individuals included in the study were evaluated with data collection forms filled out through questionnaires. Information was obtained from the individuals' demographic (age, height, weight, body mass index, education level, postpartum week). In addition, physical activity levels, postpartum functional levels, and quality of life were evaluated. The International Physical Activity Questionnaire (IPAQ) was used to assess physical activity level [12]. The questionnaire, consisting of seven questions covering activities in the last seven days, can be administered by individuals and provides information about the time people spend in moderate to vigorous activities. The Turkish version of IPAQ was used in our study [13]. In the classification of physical activity levels; physically inactive (< 600 MET-min/week), low physical activity level (600–3000 MET-min/week), and sufficient physical activity level (beneficial for health) (> 3000 MET-min/week) are used [14]. Postnatal functional levels of individuals were evaluated with the Inventory of Functional Status After Childbirth (IFSAC) [3]. The IFSAC consists of five subscales, including five dimensions of functional status and 36 four-point Likert-type questions to determine postpartum recovery. These include domestic, social, and community activities, baby care responsibilities, self-care, and professional activities. The total score is calculated by dividing the scores of all answered items by the number of answered items. Each question of the IFSAC has been evaluated over four points (one to four). A high score (close to four) indicates high functional status. The Turkish version of the IFSAC was used in our study [15]. Individuals' postpartum quality of life was evaluated with the Maternal Postpartum Quality of Life (MAPP-QOL) [16]. Postpartum quality of life is a scale that is evaluated according to the perception of the mother and consists of five sub-dimensions and a total of 40 items. Sub-dimensions of the scale; kinship consists of family-friend (nine items), socioeconomic (nine items), spouse (five items), health (eight items), and psychological (nine items) dimensions. The scale assesses how satisfied and important mothers feel at four to six weeks post-discharge postpartum. The scale consists of two parts. In the first part, satisfaction with each item is questioned, and in the second part, the importance is questioned. To calculate the quality of life scale scores; 3.5 is subtracted from each of the satisfaction items from one to six (thus, the figures are -2.5, -1.5, -0.5, 0.5, 1.5, 2.5), and the scores obtained from the satisfaction dimension are multiplied with the same items in the significance dimension of the scale. The scores obtained after the procedure are summed up and divided by the number of scale questions (40 items), and a fixed value [15] is added to the number obtained from the section to avoid negative results, and the result is found. Thus, the Quality of Life *Scores is* in the range of 0–30. The higher the score obtained from the scale, the higher the quality of life after birth; lower scores indicate low quality of life after birth [15, 16]. Our study used the Turkish version of the MAPP-QOL [17]. ## Statistical analyses Statistical analyses of the study were performed using the "Statistical Package for Social Sciences" (SPSS) version 26.0 (SPSS inc., Chicago, IL, USA). Visual (histogram, probability graphs) and analytical methods (Kolomogrov-Smirnov/Shapiro–Wilk's test) were used to define whether the variables were normally distributed or not. Numerical variables with normal distribution are shown as mean ± standard deviation. Pearson correlation analysis determined the relationship between physical activity levels, quality of life, and functionality levels. One-way ANOVA analysis of variance was used to determine the relationship between the quality of life and functionality levels of the groups with three different physical activity levels. ## Results 101 volunteer postpartum women were included in the study. Age, height, weight, BMI, postpartum weeks, and education levels of the individuals included in the study are given in Table 1.Table 1Demographics of postpartum womenPostpartum women ($$n = 101$$)X ± SDAge (years)26.32 ± 2.98Height (cm)165.72 ± 4.12Weight (kg)64.228 ± 7.84BMI (kg/ cm2)24.25 ± 2.17n%Education levelPrimary School43.96High School1615.84University7372.28Master's Degree87.92Postpartum week4th week87.925th week2827.726th week3433.677th week3130.69X ± SD: mean ± SD, cm: centimeters, kg: kilograms, BMI: body mass index, n:sample size The average amount of physical activity of postpartum women was found to be 928.347 ± 281.27 MET-min/week (Table 2). The mean total score of IFSAC, in which postnatal functional levels were evaluated, was 2.13 ± 0.79. The mean total score of MAPP-QOL was 16.93 ± 6.87 (Table 2).Table 2Physical activity, postpartum functional status and postpartum quality of life measurement results of postpartum womenPostpartum women($$n = 101$$)X ± SDIPAQ Total physical activity(MET-min/week)928.347 ± 281.27IFSAC Total (1–4)2.13 ± 0.79MAPP-QOL Total (0–30)16.93 ± 6.87X ± SD: mean ± SD, IPAQ: International Physical Activity Questionnaire, MET: metabolic equivalent, min: minute, IFSAC: Inventory of Functional Status After Childbirth, MAPP-QOL: Maternal Postpartum Quality of Life, n:sample size When Table 3 is examined, it is seen that $35.64\%$ of postpartum women are not physically active, $50.5\%$ have low physical activity levels, and $13.86\%$ are sufficient to maintain their health. Table 3Physical activity levels of postpartum womenPostpartum women($$n = 101$$)n%Physical activity level Physically Inactive (< 600 MET- min/week)3635.64 Low Physical Activity Level (600 – 3000 MET-min/week)5150.5 Physical Activity Level Sufficient (> 3000 MET-min/week)1413.86MET: metabolic equivalent, min: minute, n: sample size Table 4 shows the correlation coefficients between women's IPAQ mean scores and the total mean scores of IFSAC and MAPP-QOL. It was determined that there was a moderate ($r = 0.034$) positive correlation between IPAQ and IFSAC ($p \leq 0.05$), and a weak ($r = 0.214$) positive correlation between IPAQ and MAPP-QOL ($p \leq 0.05$).Table 4The relationship between average amount of physical activity of postpartum women and postpartum functional status and postpartum quality of life total scoresPostpartum women($$n = 101$$)IPAQ (MET-min/week)IFSAC Totalr: 0.034p: 0.031*MAPP-QOL Totalr: 0.214p: 0.027*IPAQ: International Physical Activity Questionnaire, MET: metabolic equivalent, min: minute, IFSAC: Inventory of Functional Status After Childbirth, MAPP-QOL: Maternal Postpartum Quality of Life*$p \leq 0.05$ Table 5 compares the mean scores of IFSAC and MAPP-QOL according to the level of physical activity. When IFSAC and MAPP-QOL scores were compared according to physical activity levels, a significant difference was found between the three groups according to physical activity levels ($p \leq 0.05$, Table 5).Table 5Investigation of the relationship between postpartum functional status and postpartum quality of life total scores according to physical activity levels of postpartum womenPhysical activity levelPhysically Inactive (< 600 MET- min/week)Low Physical Activity Level (600–3000 MET-min/week)Physical Activity Level Sufficient (> 3000 MET-min/week)Test and p-valueIFSAC Total2.01 ± 0.932.38 ± 1.013.20 ± 0.37F = 9.467p = 0.035MAPP-QOL Total12.43 ± 6.4415.93 ± 5.9720.15 ± 4.31F = 8.328p = 0.011Bold values indicate $p \leq 0.05$MET: metabolic equivalent, min: minute, IFSAC: Inventory of Functional Status After Childbirth, MAPP-QOL: Maternal Postpartum Quality of Life ## Discussion This study showed that women's physical activity levels during the postpartum period were low, and their functional and quality of life levels were adversely affected. Our study found that the physical activity levels of postpartum women were low, especially $35.64\%$ of physically inactive women, and the mean IPAQ scores were 928.347 ± 281.27. Similar to our study, studies show that the physical activity levels of postpartum women are insufficient [10, 18]. However, studies are showing that lack of physical activity in the postpartum period leads to decreased weight loss [19], increased pain [20], and deterioration in sleep quality [21], especially depression [10, 11]. The results of these studies show us the negativities of inactive life and emphasize the importance of physical activity during motherhood, which is one of the critical processes of life. In our study, similar to other studies, the functional and quality of life levels of women with low physical activity levels and who are not physically active were found to be low. In our study, the mean total score of MAPP-QOL was 16.93 ± 6.87. In addition, a positive weak correlation was found between IPAQ and MAPP-QOL, and it was observed that the mean quality of life score of postpartum women with high physical activity levels was higher than other levels. We think that the reason for the low correlation is due to the low number of participants. There are studies in the literature that have similar results to our study. Bahadoran et al., in their study with 91 pregnant women, stated that an increase in the level of physical activity increases the well-being of women [22]. In another study, it was stated that the quality of life of women in the postpartum period is low, and this low level may cause various problems in women [23]. Another study determined that the quality of life levels of women with insufficient physical activity levels were lower than those of women with higher physical activity levels [10]. We think the increase in physical activities during the postpartum period will make women feel more energetic, strong, and fit and will positively affect many parameters, such as motherhood roles and quality of life. Although a few studies in the literature emphasize the importance of the functional level of the mother in the postpartum period [1, 2], no study evaluating its relationship with physical activity has been found. As a result of our study, the mean IFSAC total score was 2.13 ± 0.79. In addition, a positive moderate correlation was found between IPAQ and IFSAC, and it was observed that the functional levels of postpartum women decreased with the decrease in physical activity. The level of functionality, which means the mother's readiness to undertake self-care, social, social, and professional activities, and baby care, gradually decreases after birth. In their study, Sanli and Oncel pointed out that it takes longer than six months for the mother to reach her pre-pregnancy functional level and stated the importance of providing the necessary support to mothers quickly adapt to the postpartum period [2]. Fathi et al. stated that the increase in depression levels negatively affected the functional levels of women and reported that treatments for depression would increase the functional levels of women [1]. It is known that physical activity has positive effects on depression in the whole population. In addition, the decrease in the physical activity levels of postpartum women and the process they are in may decrease the satisfaction levels they receive from life, and their functional levels may be negatively affected. These results may affect the motivational aspects of women in the postpartum period and their motherhood roles. Increasing the physical activity levels of postpartum women provides both the physiological benefits of physical activity in women and the motivational contribution provided by activity and mental relaxation. This will positively affect the mother's care and social and professional functions, especially mother-infant development. The fact that the questionnaires used in our study to evaluate both qualities of life and functional level are specific to the postpartum period is one of the strengths of our study. In addition, our study is the first study investigating the effect of physical activity level on functional status in postpartum women. The most important limitation of our study was not questioning the types of physical activity performed by postpartum women. ## Conclusions Our study will guide the literature that with the decrease in women's physical activity levels during the postpartum period, their quality of life and functional levels decrease and that directing postpartum women to physical activities is at least as necessary as other roles. In future studies, examining the effects of different types of physical activity on various factors in postpartum women is necessary. ## References 1. Fathi F, Mohammad-Alizadeh-Charandabi S, Mirghafourvand M. **Maternal self-efficacy, postpartum depression, and their relationship with functional status in Iranian mothers**. *Women Health* (2018) **58** 188-203. DOI: 10.1080/03630242.2017.1292340 2. Sanli Y, Oncel S. **Evaluation of the functional status of woman after childbirth and effective factors**. *J Turk Soc Obstet Gynecol* (2014) **2** 105-114. DOI: 10.4274/tjod.82574 3. Fawcett J, Tulman L, Myers ST. **Development of the inventory of functional status after childbirth**. *J Nurse Midwifery* (1988) **33** 252-260. DOI: 10.1016/0091-2182(88)90080-8 4. Doering Runquist JJ, Morin K, Stetzer FC. **Severe fatigue and depressive symptoms in lower-income urban postpartum women**. *West J Nurs Res* (2009) **31** 599-612. DOI: 10.1177/0193945909333890 5. Edhborg M, Seimyr L, Lundh W, Widstrom AM. **Fussy child-difficult parenthood? Comparisons between families with a “depressed” mother and non-depressed mother 2 month postpartum**. *J Reprod Infant Psychol* (2000) **18** 225-238. DOI: 10.1080/713683036 6. Mikkelsen K, Stojanovska L, Polenakovic M, Bosevski M, Apostolopoulos V. **Exercise and mental health**. *Maturitas* (2017) **106** 48-56. DOI: 10.1016/j.maturitas.2017.09.003 7. Blum JW, Beaudoin CM, Caton-Lemos L. **Physical activity patterns and maternal well-being in postpartum women**. *Matern Child Health* (2004) **8** 163-169. DOI: 10.1023/B:MACI.0000037649.24025.2c 8. Evenson KR, Mottola MF, Owe KM, Rousham EK, Brown WJ. **Summary of international guidelines for physical activity following pregnancy**. *Obstet Gynecol Surv* (2014) **69** 407-414. DOI: 10.1097/OGX.0000000000000077 9. **Physical activity and exercise during pregnancy and the postpartum period**. *Obstet Gynecol* (2020) **135** e178-e188. DOI: 10.1097/AOG.0000000000003772 10. Okyay EK, Ucar T. **The effect of physical activity level at postpartum period on quality of life and depression level**. *Med Sci* (2018) **7** 587-593. DOI: 10.5455/medscience.2018.07.8822 11. Kołomańska-Bogucka D, Mazur-Bialy AI. **Physical activity and the occurrence of postnatal depression—a systematic review**. *Medicina* (2019) **55** 560. DOI: 10.3390/medicina55090560 12. Craig CL, Marshall AL, Sjöström M, Bauman AE, Booth ML, Ainsworth BE, Pratt M, Ekelund U, Yngve A, Sallis JF, Oja P. **International physical activity questionnaire: 12-country reliability and validity**. *Med Sci Sports Exerc* (2003) **35** 1381-1395. DOI: 10.1249/01.MSS.0000078924.61453.FB 13. Saglam M, Arikan H, Savci S, Inal-Ince D, Bosnak-Guclu M, Karabulut E, Tokgozoglu L. **International physical activity questionnaire: reliability and validity of the turkish version**. *Percept Mot Skills* (2010) **111** 278-284. DOI: 10.2466/06.08.PMS.111.4.278-284 14. Hagströmer M, Oja P, Sjöström M. **The International Physical Activity Questionnaire (IPAQ): a study of concurrent and construct validity**. *Public Health Nutr* (2006) **9** 755-762. DOI: 10.1079/PHN2005898 15. Ozkan S, Sevil U. **The study of validity and reliability of inventory of functional status after childbirth**. *TAF Prev Med Bull* (2007) **6** 199-208 16. Hill PD, Aldag JC, Hekel B, Riner G, Bloomfield P. **Maternal postpartum quality of life questionnaire**. *J Nurs Meas* (2006) **14** 205-220. DOI: 10.1891/jnm-v14i3a005 17. Altuntug K, Ege E. **The validity and reliability of the Turkish version of the postpartum quality of life scale**. *J Anatolia Nurs Health Sci* (2012) **15** 214-222 18. Adeniyi AF, Ogwumike OO, Bamikefa TR. **Postpartum exercise among nigerian women: ıssues relating to exercise performance and self-efficacy**. *ISRN Obstet Gynecol* (2013) **1** 1-7. DOI: 10.1155/2013/294518 19. Ha AVV, Zhao Y, Binns CW, Pham NM, Nguyen P, Nguyen CL, Chu TK, Lee AH. **Postpartum physical activity and weight retention within one year: a prospective cohort study in Vietnam**. *Int J Environ Res Public Health* (2020) **17** 1105. DOI: 10.3390/ijerph17031105 20. Girard MP, O'Shaughnessy J, Doucet C, Ruchat SM, Descarreaux M. **Association between physical activity, weight loss, anxiety, and lumbopelvic pain in postpartum women**. *J Manipulative Physiol Ther* (2020) **43** 655-666. DOI: 10.1016/j.jmpt.2019.11.008 21. Bay H, Eksioglu A, Sogukpinar N, Turfan CE. **The effect of postpartum sleep quality on mothers’ breastfeeding self-efficacy level**. *Early Child Dev Care* (2022) **2022** 1-12. DOI: 10.1080/03004430.2022.2078319 22. Bahadoran P, Tirkesh F, Oreizi HR. **Association between physical activity 3–12 months after delivery and postpartum well-being**. *Iran J Nurs Midwifery Res* (2014) **19** 82-87. PMID: 24554965 23. Ercel O, Sut HK. **Sleep quality and quality of life in postpartum woman**. *J Turk Sleep Med* (2020) **1** 23-30. DOI: 10.4274/jtsm.galenos.2019.92400
--- title: 'Effectiveness of eHealth weight management interventions in overweight and obese adults from low socioeconomic groups: a systematic review' authors: - Richard Myers-Ingram - Jade Sampford - Rhian Milton-Cole - Gareth David Jones journal: Systematic Reviews year: 2023 pmcid: PMC10061957 doi: 10.1186/s13643-023-02207-3 license: CC BY 4.0 --- # Effectiveness of eHealth weight management interventions in overweight and obese adults from low socioeconomic groups: a systematic review ## Abstract ### Background Low socioeconomic status (SES) is associated with increased rates of overweight and obesity. Proponents of electronic health (eHealth) hypothesise that its inclusion in weight management interventions can improve efficacy by mitigating typical barriers associated with low SES. ### Objectives To establish the scope of eHealth weight management interventions for people with overweight and obesity from a low SES. Secondary objectives were to determine the efficacy of eHealth interventions in facilitating weight loss, physical activity and fitness improvements. ### Methods Four databases and grey literature were systematically searched to identify eligible studies published in English from inception to May 2021. Studies examining an eHealth intervention with low SES participants were included. Outcomes included temporal change in weight and BMI, anthropometry, physiological measures and physical activity levels. The number and heterogeneity of studies precluded any meta-analyses; thus, a narrative review was undertaken. ### Results Four experimental studies with low risk of bias were reviewed. There was variance in how SES was defined. Study aims and eHealth media also varied and included reducing/maintaining weight or increasing physical activity using interactive websites or voice responses, periodic communication and discourse via telephone, social media, text messaging or eNewsletters. Irrespectively, all studies reported short-term weight loss. eHealth interventions also increased short-term physical activity levels where it was assessed, but did not change anthropometry or physiological measures. None reported any effect on physical fitness. ### Conclusions This review revealed short-term effects of eHealth interventions on weight loss and increased physical activity levels for low SES participants. Evidence was limited to a small number of studies, with small to moderate sample sizes. Inter-study comparison is challenging because of considerable variability. Future work should prioritise how to utilise eHealth in the longer term either as a supportive public health measure or by determining its long-term efficacy in engendering volitional health behaviour changes. ### Systematic review registration PROSPERO CRD42021243973 ### Supplementary Information The online version contains supplementary material available at 10.1186/s13643-023-02207-3. ## Introduction Overweight and obesity, defined as abnormal or excessive fat accumulation that may impair health, are typically measured using body mass index (BMI) (the ratio of mass (kg) to squared height (m2)) [1]. Overweight is classified as a BMI ≥ 25 kg/m2 and obese ≥ 30 kg/m2 [2]. Overweight and obesity global prevalence are high [3–5], with an estimated $60\%$ of females and $67\%$ of males overweight or obese in England [6]. Overweight and obesity are a significant risk factor for noncommunicable diseases including type 2 diabetes, cardiovascular disease, specific cancers, liver disease and some respiratory disease [7] as well as depression [8]. Addressing overweight and obesity is therefore essential for the individual themselves, clinicians and policy makers [9, 10]. The World Health Organization (WHO) has prioritised the prevention and reduction of obesity as a key public health agenda, recommending nations make substantial improvements to tackle the current obesity trends [11]. Socioeconomic status (SES) is a complex concept involving several domains, including an individual’s or family’s income, occupational status, locality, and educational level [12]. Low SES is disproportionately associated with increased rates of overweight and obesity in high-income countries [5, 13], and individuals experience higher levels of obesity-related diseases, especially cardiovascular disease [14]. A meta-analysis demonstrated that those living in a low SES neighbourhood had a $30\%$ increased risk of being overweight (pooled OR 1.30, $95\%$ CI; 1.16–1.47, $p \leq 0.001$) and a $45\%$ increased risk of being obese (pooled OR 1.45, $95\%$ CI; 1.21–1.74, $p \leq 0.001$) compared with individuals living in high SES neighbourhoods [15]. People living in deprived areas are more likely to have unhealthy lifestyle behaviours (e.g. smoking, increased alcohol consumption) and lower healthy behaviours (e.g. physical activity, healthy diet) compared to less deprived areas [16]. It has been suggested that the built environment that someone lives in directly influences their lifestyle behaviours. Indeed, areas of higher deprivation have a higher concentration of features that are harmful to health, such as more fast food outlets and limited physical activity opportunities, termed the obesogenic environment [17, 18]. Good quality evidence-based interventions are lacking for people living with overweight and obesity from lower SES. Low SES individuals have worse outcomes and higher dropout rates in health promotion programmes compared to individuals from higher SES [19, 20] due to financial costs of travelling to face-to-face sessions [21], childcare issues and taking time out of work [22] as well as programmes not addressing the structural barriers faced by those with a low SES [23]. These barriers need to be considered in the development of health promotion interventions. Electronic health (eHealth) is one approach that aims to overcome these barriers, allowing participants to access weight management programmes at times and locations that suit the individual [24]. While results of eHealth interventions have been inconsistent, a recent meta-analysis of 9 pooled studies demonstrated that eHealth weight loss interventions resulted in modest weight loss compared with no treatment (mean difference: −2.70 kg ($95\%$ CI: −3.33 to −2.08kg); $p \leq 0.001$); however, their analysis did not account for SES [25]. eHealth interventions vary but utilise technology to provide remote health care to individuals. This may be through the mode of delivery such as computer or mobile phone, utilising websites/web applications, mobile and/or social media applications, email or SMS text messaging [26, 27]. It offers the potential for a wide-reaching, low-cost and efficacious intervention, while also addressing specific barriers associated with people with low SES [22, 28]. But it is unknown whether any eHealth approaches exist for people living with low SES especially as a digital divide still exists where people with low SES are less likely to be able to access eHealth [24]. Furthermore, it is also unknown whether any eHealth interventions have any effect on overweight or obesity in people with low SES. ## Objectives A systematic review was therefore undertaken to identify eHealth weight management interventions for people living with overweight and obesity from a low SES. The primary aim was to establish what eHealth weight management interventions exist for people with overweight and obesity from a low SES. The secondary aim was to determine the efficacy of interventions in facilitating weight loss and physical activity and fitness improvements in people living with overweight and obesity from a low SES background. ## Protocol and registration The protocol for this systematic review was registered with the International Prospective Register of Systematic Reviews (PROSPERO, registration number: CRD42021243973). This systematic review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) statement guidelines [29] (Supplementary material) and follows a predetermined published protocol [30]. ## Eligibility criteria This review included studies of eHealth weight management interventions in adults over the age of 18 living with overweight or obesity from a low SES background. The PICOS (Population, Intervention, Comparison, Outcomes, and Study design) framework was used to structure the eligibility criteria [31]. Retrieved work was reviewed if it met the inclusion criteria, or was otherwise excluded as per our published protocol [30]. Studies were excluded if they involved bariatric surgery or pharmacology-only interventions, and did not include or report on participants based on SES. Physiological measures were added as an inclusion criterion, and non-eHealth interventions (i.e. face-to-face components) were also added as an exclusion criterion for completeness (Table 1).Table 1Study eligibility criteria using the PICOS criteriaPICOSInclusionExclusionPopulation• Adults ≥ 18 years old with BMI > 25 kg/m2• Low SES• Pregnancy or postpartum (within 3 months)• Any SES other than low SESIntervention• Weight management intervention delivered using eHealth technology• Bariatric surgery• Medication-only interventions• Face-to-face componentsComparator• N/Aa• N/AOutcome• Weight (kg), BMI (kg/m2) and/or percentage weight change• A range of anthropometric, physiological and physical activity/fitness measures• N/AStudy design• Experimental studies• Observational studies• Case studies/series• Reviews• Secondary analysisaN/A Not applicable ## Population Studies were included if participants were adults over the age of 18, had a BMI greater than 25 kg/m−2 and were from a low SES background. Studies were required to explicitly state their criteria of low SES to be included, or outcomes had been reported by SES. Low SES was defined through multiple constructs, including, but not limited to, low income, low educational level, low occupational status or a combination of these [12] (Table 2).Table 2Outline of domains that relate to socioeconomic statusDomainExplanationIncomeThe earnings received through employment by an individual or family, typically compared against the nation’s average earnings [32]EducationAn indicator for knowledge and educational attainment, generally measured using the individuals highest level of schooling achieved, such as primary, secondary and tertiary education [19]Occupational statusInvolves specific aspects related to the job role itself such as power, income and educational requirements as well as the physical or hazardous demands related to that job [33] ## Intervention types We included studies that deployed weight management protocols designed to have an effect on weight loss or maintenance, increase in physical fitness and/or physical activity. Interventions involved one or more of the weight management domains as outlined by NICE [34] including diet and nutrition advice/education, physical activity and behaviour change techniques. Eligible studies delivered their interventions via eHealth inclusive of web-based, mobile applications, text, social media or other related modalities. Bariatric surgery and medicine-only trials were excluded, as well as those that had any face-to-face contact. ## Comparator Studies with or without a control group were considered for eligibility, and no limitation was placed on the control group. ## Outcomes The primary outcome domains were weight, weight change and BMI. Secondary outcome domains included anthropometric, physiological, fitness or physical activity measures. Outcome domains within included studies were assessed at baseline and at any reported follow-up time point(s) upon completion of the intervention. Studies with multiple time points were reported and the maximum follow-up time selected. ## Study design Experimental and observational cohort studies that aimed to investigate the efficacy of eHealth weight management interventions that were written in the English language were included. Experimental studies included randomized controlled trials (RCTs), quasi-experimental studies, controlled clinical trials or cluster trials. Quasi-experimental study designs differ from RCTs in that they do not directly manipulate the independent variable, therefore may not include a control group or randomisation [35]. Observational studies comprised of prospective and retrospective comparative cohort studies as well as cross-sectional, case-control or nested case-control studies. A range of study designs were included to identify the breadth of research available. Review articles, secondary analyses and case studies were excluded. ## Search strategy The systematic literature search was completed in May 2021. The electronic literature search strategy was based on the eligibility criteria using Medical Subject Headings (MeSH) and text words. Electronic databases included MEDLINE, Embase, EmCare and CINAHL. Subject header and free text searches were completed, using Boolean search techniques such as “AND” and “OR”, based on the PICOS framework (Table 1) and previous literature [36]. The detailed search strategy is presented in Supplementary material. Reference lists, grey literature and completed theses were also searched. Databases were searched from their respective inception dates. ## Study selection After the initial search, results were transferred to reference manager software (EndNote X8.0.1, Bld 10444, Clarivate™, London, UK) and duplicates removed. Two authors (R. M. I. and J. S.) independently screened titles and abstracts before full-text articles according to the eligibility criteria, using proprietary systematic review software (Rayyan Systems Inc., Cambridge, MA, USA). Reasons for exclusions were collated, and discrepancies were resolved following discussion and consensus by two authors (R. M. I. and J. S.). If consensus could not be reached, then a third author (GDJ) was available to assess and resolve the discrepancy. In total, 2256 studies were identified. After 711 duplicates were removed, 1545 articles remained for title and abstract review, and 1464 were excluded for not meeting the inclusion criteria. Therefore, 81 articles were subjected to full-text assessment of their eligibility. In 62 articles, the population did not include overweight or obese participants from a low SES, 7 did not include eHealth and/or had elements of face-to-face interaction as part of the intervention, 3 did not include the eligible primary or secondary outcomes, 1 did not meet the study design criteria, 3 were not full-text articles and there was 1 duplicate. Four studies were therefore eligible for full-narrative review (Fig. 1).Fig. 1PRISMA flowchart ## Data extraction An adapted data extraction form was created based on the Cochrane Data Extraction Form for RCTs and non-RCTs [37]. Data included study details (author, year of publication and country), design, participant characteristics (sample size, baseline characteristics including age, ethnicity and SES), interventions and all outcomes post-intervention and any follow-up time points. The same two authors independently extracted data using the form, with any discrepancies settled following an assessment by a third author (G. D. J.). ## Quality The same two authors independently assessed the risk of bias of included publications using the Joanna Briggs Institute (JBI) Critical Appraisal Tool, Checklist for Randomised Controlled Trials and Checklist for Quasi-Experimental Studies [38]. Each domain within the JBI *Checklist is* assigned 0 for low risk of bias, 1 for unclear and 2 for high risk of bias. The total score was calculated into a percentage dependent on the individual checklist used. A final rating of > $50\%$ was deemed as high risk. ## Data analysis While meta-analyses of standardised post-intervention outcomes and any similarly-timed follow-ups were intended, the heterogeneity of studies was evaluated and was found to be high for aims, outcome time points and intervention components. Therefore, meta-analyses were not performed, and a narrative synthesis was performed on work included for review [39]. ## Study characteristics There were 373 participants in total (Table 4). Participants were predominately female ($99\%$); in 3 studies, all participants were females [40, 41, 42] and represented $95\%$ of participants in the remaining article [43]. Ethnicity varied. All participants identified as African American in 1 article [41], as Latinas in another [40] and as multiple ethnicities in the remaining articles [42, 43]. All studies were conducted in the USA. Designs included 3 quasi-experimental [40, 42, 43] and 1 randomised controlled trial [41]. Intervention aims varied between studies; 2 focused on weight loss [42, 43], 1 on weight maintenance [41] and 1 on increasing physical activity [40]. The definition of SES varied. Two articles adopted a percentage of income compared to the national poverty line approach [41, 43], 1 used eligibility for a national nutritional benefits scheme [42] and SES was operationalised as a combination of income, education and employment in the remaining article [40]. The duration of intervention ranged from 1 month [40] to 12 months [41]. Reported attrition rates varied across the included studies from as low as $5\%$ [41], to $12.5\%$ [40], $15\%$ [43] and up to as much as $48.5\%$ [42]. ## Risk of bias All studies had a low risk of bias (Table 3). In the three quasi-experimental studies [40, 42, 43], there were no differences in terms of care received, all included multiple measurements of outcomes (pre and post intervention), outcomes were measured in a standardised way and assessed using appropriate statistical analyses, but none included a control group. In the only RCT [41], participants were randomized, between-group characteristics were insignificantly different at baseline, they were treated identically except for the intervention, follow-up was complete and appropriate statistical analyses were deployed; however, neither assessors nor participants were blinded to the treatment assignment. Table 3Quality assessment scores of included studiesStudyStudy designCritical appraisal toolScoreBenitez et al. [ 40]Quasi experimentalJBI Checklist for Quasi-Experimental Studies$\frac{2}{16}$Bennett et al. [ 41]RCTJBI Checklist for Randomized Controlled Trials$\frac{8}{26}$Cavallo et al. [ 43]Quasi experimentalJBI Checklist for Quasi-Experimental Studies$\frac{2}{16}$Griffin et al. [ 42]Quasi experimentalJBI Checklist for Quasi-Experimental Studies$\frac{2}{16}$ ## Intervention components The components of interventions included in this review varied considerably and included an interactive website [40], interactive voice response and monthly telephone calls [41], social media [43], and text messages and eNewsletters [42] (Table 4). Benitez et al. [ 40] conducted a 1-month intervention providing access to a culturally and linguistically adapted, theory-driven website promoting physical activity. In contrast, Bennett et al. [ 41] conducted an intervention known as the SHAPE programme where participants were assigned behaviour change goals by a computer algorithm from a library of goals (such as no sugar-sweetened beverages, no fast food and increase fruit and vegetable intake) at baseline and 6 months, as well as monthly telephone calls with a dietitian. Cavallo et al. [ 43] used a different approach called the INSHAPE CLE intervention. Here, access to a private social media group was provided, with daily online posts focusing on healthy eating advice using different themes such as Education Only, Recipes, Testimonials/Goal Setting, “*Ask a* Dietitian” and Competitions. Finally, Griffin et al. [ 42] developed a simple approach called the MyQuest intervention utilising 2 to 3 daily text messages and eNewsletters. Table 4Characteristic of eHealth weight management studies in low SES adultsStudyPopulationInterventionAuthor (year), countryStudy designSettingnMean ageSex% FEthnicitySES domain (s)Duration/follow-upType of technologyTheoryBehaviour aims/ targetComponent(s)Benitez et al. [ 2015] [40], USAQuasi exp. Community2435.2100LatinasCombination1 month/noneWebsiteSCTTTMPromote PA1). Personalised physical activity counselling messages and strategies2). Access to culturally adapted exercise videos3). Provided with pedometers4). Guest access to websiteBennett et al. [ 2013] [41], USARCTCommunity18535.4100African-AmericanLow income12 months/18 monthsIVRSelf-efficacy theoryImprove well-beingmaintain weightIntervention1). Obesogenic behaviour change goals2). Self-monitoring via IVR phone calls3). Tailored skills training materials4). 12 interpersonal counselling calls5). 12-month gym membershipControl1). General wellness newsletters every 6 monthsCavallo et al. [ 2021] [43], USAQuasi exp. Community5546.495White = $23.6\%$Black or African American = $69.1\%$More than one race = $5.9\%$Low income3 months/noneSocial mediaNRWeight loss1). Access to a private social media group — ~3 daily posts by moderator2). Provided with a Fitbit3). Monetary incentiveGriffin et al. [ 2020] [42], USAQuasi exp. Community10936.1100White = $43.1\%$, African American = $54.1\%$Low income3 months/noneText messagingeNewsletterSCTIncrease PAWeight loss1). Prescribed 1600 kcal/day meal plan2). Daily text messages including goal setting, healthy eating and PA reminders3). Weekly eNewsletters4). Provided with a pedometerTotal:37338.398.8IVR, Interactive voice response, n sample size; NR not reported, PA, Physical activity, SCT Social cognitive theory, TTM Transtheoretical model, RCT Randomised controlled trial ## Weight loss and maintenance effects Two studies aimed to achieve weight loss [42, 43], and 1 aimed to maintain weight [41]. All reported a significant weight loss at the end of the intervention [41–43] with one observing significant weight loss at 18-month follow-up [41] (Table 5). Mean (±SD) body weight loss ranged from 1.07 (3.96) kg to 1.81 (5.76) kg. Cavallo et al. [ 43] observed that participants lost ≥ $5\%$ of baseline body weight in $16\%$ of participants, while Griffin et al. [ 42] observed it in $32\%$ of participants and ≥ $10\%$ in $5\%$ of participants. Table 5Outcomes of eHealth weight management interventions in low SES adultsStudyResultsAuthor (year)AttritionTime pointsWeight (kg)(Mean (±SD/$95\%$ CI)BMI (kg/m2) ($95\%$ CI)Physical activity measurePhysical activityBenitez et al. [ 2015] [40]$12.5\%$Baseline1 monthNot reportedNRSeven- Day Physical Activity Recall (moderate to vigorous physical activity)Pre: 12.5 (0–120) min/weekPost: 67.5 (0–510) min/weekChange: +55 min/weekBennett et al. [ 2013] [41]$5\%$Baseline12 months18 months12 monthsIG: −1. 0 (0.5)CG: 0.5 (0.5)Mean difference: −1.4 (−2.8 to −0.1)18 monthsIG: −0.9 (0.6)CG: 0.8 (0.6)Mean difference: −1.7 (−3.3 to −0.2)12 monthsIG: −0.3 (0.2)CG: 0.3 (0.2)Mean difference: −0.6 (−1.1 to −0.1)18 monthsIG: −0.2 (0.2)CG: 0.4 (0.2)Mean difference: −0.6 (−1.2 to −0.1)N/AN/ACavallo et al. [ 2021] [43]$15\%$Baseline3 monthsPre: 95.38 (12.33)Post: 94.31 (13.21)Change: −1.07 (−2.14 to 0.0)NRN/AN/AGriffin et al. [ 2020] [42]$48.5\%$Baseline3 monthsPre: 92.35Post: 89.9Change: −1.81 (5.76)NRPedometerPre: 6819 steps/dayPost: 8980 steps/dayChange: +1689 [689] steps/dayCG, control group, IG, Intervention group, NR, Not reported, N/A, Not applicable ## Physical activity and fitness effects Two studies aimed to increase physical activity [40, 42]. Both observed a statistically significant increase in physical activity, although methods of measurements differed. Benitez et al. [ 40] reported a median (range) increase in moderate to vigorous physical activity using the 7-Day Physical Activity Recall from 12.5 (0–120) to 67.5 min (0–510) (p = < 0.05). In contrast, Griffin et al. [ 42] reported physical activity using pedometers to measure daily steps. There was a significant mean (±SD) difference in daily steps between baseline [6819] and post intervention [8980] of 1689 (±689) steps ($$p \leq 0.19$$). No studies reported any effects on physical fitness. ## Anthropometry and physiological effects Only 1 study [41] reported outcomes for anthropometric and physiological measures. No significant differences between intervention and control groups were found in waist circumference, blood pressure, blood pressure control, glucose or lipid levels at any time point. ## Main findings To the authors’ knowledge, this is the first attempt to systematically review the literature of weight management interventions using eHealth specifically in people from a low SES background and living with overweight and obesity. It is important because low SES individuals are disproportionately affected by overweight and obesity [13]. The main findings are that eHealth interventions specifically designed for low SES groups are scarce with only 4 low risk-of-bias studies meeting our inclusion criteria, comprising a total of 373 participants. eHealth interventions aiming to reduce/maintain weight or increase physical activity varied. They included interactive websites or voice responses, periodic communication and discourse via telephone, social media, text messaging or eNewsletters. All studies reported a significant effect of their respective eHealth interventions on weight loss. Generalisations should be made with caution however as the review revealed only USA-centric studies with predominantly female participants and sample sizes were small to modest (ranging between $$n = 24$$ and $$n = 185$$). Given that SES spectra are not invariant across nation states nor equally distributed between biological sex [44], and overweight and obesity affect males more than females in the UK [6], future eHealth studies specific to the UK and that include both sexes are required. ## Effect on weight loss Intervention duration was relatively short (1–3 months, with one exception of 12 months and follow-up at 18 months), yet all interventions demonstrated statistically significant weight loss during the intervention. In the longer intervention, the effect was sustained at 18 months [41]. There was a significant effect of interventions on physical activity which improved at 3 months in two articles [40, 42]. Despite the sample sizes being modest, these findings are welcome and collectively supports the premise that eHealth interventions are a successful approach for people with low SES. Our findings are in keeping with an earlier narrative systematic review (6 studies, $$n = 4899$$ [36]). It observed that eHealth weight management interventions had a positive effect on weight loss in participants who identified as being part of an ethnic minority group. Given that ethnic minorities are also associated with higher risk of deprivation and obesity [45], there is further evidence eHealth is an efficacious approach for vulnerable groups within the general population. Although in our review we found interventions led to statistically significant weight loss, these findings need to be interpreted with respect to a clinically significant weight loss. According to UK clinical guidance, 3–$5\%$ body weight loss is associated with clinically meaningful health benefits [34], and aiming for $30\%$ of participants achieving $5\%$ weight loss is a desirable service outcome [46]. Two studies reviewed [42, 43] reported $16\%$ and $32\%$ of participants achieved ≥ $5\%$ of body weight loss respectively, meaning a minority of low SES participants achieved a clinically significant weight, and one did not meet the UK national guidance. There is a need therefore to develop successful interventions to achieve clinically meaningful weight loss in a greater proportion of participants. ## Effect on physical activity Economic, social and political factors influence and, to some degree, drive the amount of physical activity and exercise completed at the population level, seeing as uptake of global recommendations (e.g. [47]) remains low [48]. No study reviewed assessed the effect on physical fitness which is presumably because physical fitness is defined as a subset of physical activity [49]. It might also be due to the recognition of attitudinal differences towards exercise compared with physical activity in people with long-term conditions [50, 51]. Irrespectively, physical activity increased significantly as an effect of eHealth programmes in two studies included in the current review [40, 42]. Since optimising physical activity and exercise as a behaviour change is desirable to support and maintain weight loss and reduces the risk of noncommunicable diseases [52], evidencing eHealth’s effectiveness in increasing physical activity for low SES participants supports targeting physical activity in the design of interventions for this group. People with low SES face specific barriers to sustained physical activity changes such as the cost of gym membership, perceived neighbourhood safety and availability of green spaces to be physically active in [22, 53]. Efforts to modulate these barriers should be included in the design of interventions. Improving self-efficacy is a positive predictor of increasing physical activity in low SES groups [54]. So, it was welcome that self-efficacy was included within the eHealth interventions in the reviewed studies by provision of tailored physical activity feedback, pedometer self-monitoring and setting physical activity goals [40, 42]. But it was disappointing that neither were able to report whether physical activity changes were sustained after 1-month [40] and 3-month [42] intervention periods. A previous systematic review and meta-analysis with low-income participants identified that while interventions resulted in a small but significant increase in physical activity levels, the effect was modest compared to interventions involving the general population, and it was not maintained at 6 months [55]. Furthermore, interventions were not limited to solely eHealth, and some included studies containing face-to-face components. Evidence supporting the relative effect of eHealth on physical activity levels in low compared to higher SES participants, and whether any increases are sustained, therefore remains elusive. ## eHealth interventions and media The reviewed studies supported behaviour change through increasing self-monitoring behaviours (e.g. interactive voice response (IVR) and text messages) and information provision (e.g. social media posts and eNewsletters). Three studies provided equipment to support self-monitoring of physical activity [40, 42, 43]. One provided access to a gym with reimbursement of travel costs for follow-up visits [41]. Weight loss outcomes in this study were compelling and sustained at 18 months which suggests that providing financial support could be a significant behavioural modifier given that absorbing travel costs is a specific barrier identified in low SES groups. Access to gyms, walking groups and community involvement are effective strategies to prevent weight gain in low SES groups [23]. Thus, it is no surprise that interventions that consider environmental, social, economic and/or structural issues are more likely to improve outcomes across SES. In the development of future interventions, clinicians, researchers and funders have an obligation to consider factors associated with low SES, such as insufficient financial agency to purchase interventions and self-monitoring equipment. At a national level, financial support for sustained public health could be provided as part of welfare systems. There is debate whether the advanced welfare tax burden that egalitarian societies sustain offsets health inequalities due to socioeconomic status compared to more neoliberal welfare states [56]. Our belief is that the investigations into the causes for health inequalities should continue and are welcome because they will provide testable theories that can explain, for example, how physical activity improvements due to eHealth interventions wane differently depending on SES and why. These may well indicate that provision of sustained financial support programmes for eHealth as a public health intervention is indicated for subgroups of society, and if so, programmes should be duly scrutinised for their cost-effectiveness. eHealth has the potential to improve health at local, national and international levels by using the developing technology effectively. Counterintuitively though, an expanding eHealth landscape could widen social health inequalities because not all individuals are able to use eHealth well due to inequity and inequality in environmental factors, access, cost and utilisation [24]. Inequality exists in the dissemination of intervention results to the public too. Due respect to the spectrum of health literacy in the public to whose behaviours the results are aimed at modifying is not always made. What’s more, our results identify the scarcity of studies that included low SES participants. This potential bias is vexing because individuals with low SES are at a greater risk of social health inequalities. There is therefore a clear need to focus eHealth interventions tailored to this group. The delivery method of eHealth should be an important factor when developing interventions due to differing utilisation of technology across SES. eHealth that is not accessible, easy to use and/or targeted to the population may further the digital divide [57]. Using smartphones as the only access to the internet is high among low-income groups [58]. This means the use of mobile technology and applications may be more appropriate and acceptable in this population. While the interventions revealed in this review could all have been practically accessed using a smartphone, only one study was specifically designed for smartphone use via direct text messaging — a modality which incidentally caused the largest mean change in body weight loss [42]. Three studies did not specifically describe the use of smartphone use or accessibility despite the potential this has in this population. Utilising or adapting eHealth for smartphone compatibility should be supported because it is a strong candidate to improve the efficacy of interventions while minimising health inequalities among low SES groups [59]. ## Uptake and attrition Uptake and attrition are key challenges in investigating weight management interventions in individuals with low SES due to the complex behaviour change required [60]. Attrition rates were generally low in included studies compared to traditional weight management interventions where attrition rates can be up to $80\%$ [60]. Bennett et al. [ 41] reported the lowest attrition rate ($5\%$), presumably due to the strict exclusion criteria removing any participants that were suspected of being “uninterested”. Griffin et al. [ 42] observed the highest attrition rate ($48.5\%$) among participants who identified as African American and participants with the lowest education and incomes. This suggests there may be sub-groups within low SES along ethnicity, education and income demographics and presumably their intersections. Understanding the reasons for the demographic differences in completing programmes is an important area for further research. Engaging sub-groups in the development of interventions, and understanding their specific needs, is likely to improve retention of participants and outcomes. Barriers to participation in interventional studies are well documented [61]. In addition to experiencing significant time demands to attend and travel to study appointments, people with low SES report mistrust of, and poor communication with, physicians and nurses [62], and it would be interesting to see if similar barriers exist for people with low SES in their interactions with other health professionals for instance exercise physiologists and prescribers or nutritionists. Irrespectively, eHealth has the potential to overcome some of these barriers because it can offset time and costs and provides autonomy in selecting to participate at convenient times. ## Strengths and limitations This systematic review was registered with an international systematic review register which is one of its strengths. It has been written following the PRISMA guidelines [29], and the protocol has been previously published [30]. We do however acknowledge some limitations. The review only included adults. Given that the burden of overweight and obesity is growing, there is a need to identify how eHealth can be utilised across the lifespan including younger populations who have different digital habits. This review identified only a small number of eligible studies. This was mainly due to many studies not specifying the SES criteria used or involving participants across the SES spectrum. We specifically wanted interventions that solely targeted people from low SES as we defined it. The complex nature of SES and its varying constructs and domains mean a standardised definition of low SES remains elusive, and we acknowledge that our definition may not have yielded all relevant studies. It is therefore possible that studies were not identified within our search strategy that analysed participants in subgroupings that might have satisfied our inclusion criteria. ## Conclusions In summary, there is a small amount of evidence with low risk of bias within the literature supporting eHealth interventions for weight management in people with low SES — a group of society who are often under represented within research. This systematic review has demonstrated that eHealth weight management interventions can lead to short-term weight loss and increases in physical activity in people with low SES. It must be recognised, however, that this interpretation is based on a small number of studies with small to modest sample sizes, as well as generally low-quality study designs. Hence, more thoroughly designed experimental studies are indicated. eHealth has the potential to deliver evidence-based interventions with high reach and low cost, but intervention designers and funders should be mindful of widening social health inequalities if there are members of society who are inadvertently subjected to discrimination based on their ability to access eHealth. Our findings, in contrast, have shown that it is feasible for people with low SES to utilise eHealth. This review therefore supports the idea of promoting of eHealth interventions to support people living with overweight and obesity in low SES groups with specific consideration of the delivery components (e.g. smart phones, mobile applications and social media), the structural factors associated with SES, the specification and tailoring of interventions and the assessment of sustained behaviour change. ## Supplementary Information Additional file 1. PRISMA_2020_checklist. Completed PRISMA checklist for systematic review’s and meta-analyses. Additional file 2. Search Strategy- eHealth interventions for weight management in adults with low socio-economic status. Example search strategies completed on MEDLINE and CINAHL databases. Additional file 3. Excluded Studies. A list of articles that may have appeared to meet the inclusion criteria, but which were excluded following full text review. ## References 1. 1.World Health Organization (WHO): Obesity. https://www.who.int/health-topics/obesity. 2. 2.National Institute for Health and Care Excellence (NICE): Obesity: identification, assessment and management. www.nice.org.uk/guidance/cg189. 3. Chooi YC, Ding C, Magkos F. **The epidemiology of obesity**. *Metabolism* (2019.0) **92** 6-10. DOI: 10.1016/j.metabol.2018.09.005 4. 4.World Health Organization (WHO): The Global Health Observatory: mean BMI (kg/m2) (crude estimate). https://www.who.int/data/gho/data/indicators/indicator-details/GHO/mean-bmi-(kg-m-)-(crude-estimate). 5. Swinburn BA, Sacks G, Hall KD, McPherson K, Finegood DT, Moodie ML, Gortmaker SL. **The global obesity pandemic: shaped by global drivers and local environments**. *Lancet* (2011.0) **378** 804-814. DOI: 10.1016/S0140-6736(11)60813-1 6. 6.NHS Digital: Statistics on obesity, physical activity and diet. https://digital.nhs.uk/data-and-information/publications/statistical/statistics-on-obesity-physical-activity-and-diet/england-2020#. 7. Guh DP, Zhang W, Bansback N, Amarsi Z, Birmingham CL, Anis AH. **The incidence of co-morbidities related to obesity and overweight: a systematic review and meta-analysis**. *BMC Public Health* (2009.0) **9** 88. DOI: 10.1186/1471-2458-9-88 8. Luppino FS, de Wit LM, Bouvy PF, Stijnen T, Cuijpers P, Penninx BW, Zitman FG. **Overweight, obesity, and depression: a systematic review and meta-analysis of longitudinal studies**. *Arch Gen Psychiatry* (2010.0) **67** 220-229. DOI: 10.1001/archgenpsychiatry.2010.2 9. Loring B, Robertson A. *Obesity and inequities: guidance for addressing inequities in overweight and obesity* (2014.0) 10. Theis DRZ, White M. **Is obesity policy in England fit for purpose? Analysis of government strategies and policies, 1992-2020**. *Milbank Q* (2021.0) **99** 126-170. DOI: 10.1111/1468-0009.12498 11. Alleyne G, Binagwaho A, Haines A, Jahan S, Nugent R, Rojhani A, Stuckler D. **Embedding non-communicable diseases in the post-2015 development agenda**. *Lancet (London, England)* (2013.0) **381** 566-574. DOI: 10.1016/S0140-6736(12)61806-6 12. Baker EH. **Socioeconomic status, definition**. *The Wiley Blackwell Encyclopedia of Health, Illness, Behavior, and Society* (2014.0) 2210-2214 13. Bann D, Johnson W, Li L, Kuh D, Hardy R. **Socioeconomic inequalities in body mass index across adulthood: coordinated analyses of individual participant data from three British birth cohort studies initiated in 1946, 1958 and 1970**. *PLoS Med* (2017.0) **14** e1002214. DOI: 10.1371/journal.pmed.1002214 14. de Mestral C, Stringhini S. **Socioeconomic status and cardiovascular disease: an update**. *Curr Cardiol Rep* (2017.0) **19** 115. DOI: 10.1007/s11886-017-0917-z 15. Mohammed SH, Habtewold TD, Birhanu MM, Sissay TA, Tegegne BS, Abuzerr S, Esmaillzadeh A. **Neighbourhood socioeconomic status and overweight/obesity: a systematic review and meta-analysis of epidemiological studies**. *BMJ Open* (2019.0) **9** e028238. DOI: 10.1136/bmjopen-2018-028238 16. Giskes K, Avendano M, Brug J, Kunst AE. **A systematic review of studies on socioeconomic inequalities in dietary intakes associated with weight gain and overweight/obesity conducted among European adults**. *Obes Rev* (2010.0) **11** 413-429. DOI: 10.1111/j.1467-789X.2009.00658.x 17. Morland K, Wing S, Diez Roux A. **The contextual effect of the local food environment on residents’ diets: the atherosclerosis risk in communities study**. *Am J Public Health* (2002.0) **92** 1761-1767. DOI: 10.2105/AJPH.92.11.1761 18. Townshend T, Lake A. **Obesogenic environments: current evidence of the built and food environments**. *Perspect Public Health* (2017.0) **137** 38-44. DOI: 10.1177/1757913916679860 19. 19.Robertson A, Lobstein T, Knai C. Obesity and socio-economic groups in Europe: evidence review and implications for action. Report to the European Commission (SANCO/2005/C4-NUTRITION-03). https://ec.europa.eu/health/ph_determinants/life_style/nutrition/documents/ev20081028_rep_en.pdf. 20. Blane DN, McLoone P, Morrison D, Macdonald S, O’Donnell CA. **Patient and practice characteristics predicting attendance and completion at a specialist weight management service in the UK: a cross-sectional study**. *BMJ Open* (2017.0) **7** e018286. DOI: 10.1136/bmjopen-2017-018286 21. Brightman L. **Huang H-CC, Dugdale P: Determining patient attendance, access to interventions and clinical outcomes in a publicly funded obesity programme: results from the Canberra Obesity Management Service**. *Clin Obes* (2019.0) **9** e12325. DOI: 10.1111/cob.12325 22. Coupe N, Cotterill S, Peters S. **Tailoring lifestyle interventions to low socio-economic populations: a qualitative study**. *BMC Public Health* (2018.0) **18** 967. DOI: 10.1186/s12889-018-5877-8 23. Beauchamp A, Backholer K, Magliano D, Peeters A. **The effect of obesity prevention interventions according to socioeconomic position: a systematic review**. *Obes Rev* (2014.0) **15** 541-554. DOI: 10.1111/obr.12161 24. Latulippe K, Hamel C, Giroux D. **Social health inequalities and eHealth: a literature review with qualitative synthesis of theoretical and empirical studies**. *J Med Internet Res* (2017.0) **19** e136. DOI: 10.2196/jmir.6731 25. Hutchesson MJ, Rollo ME, Krukowski R, Ells L, Harvey J, Morgan PJ, Callister R, Plotnikoff R, Collins CE. **eHealth interventions for the prevention and treatment of overweight and obesity in adults: a systematic review with meta-analysis**. *Obes Rev* (2015.0) **16** 376-392. DOI: 10.1111/obr.12268 26. Arem H, Irwin M. **A review of web-based weight loss interventions in adults**. *Obes Rev* (2011.0) **12** e236-e243. DOI: 10.1111/j.1467-789X.2010.00787.x 27. Neve M, Morgan PJ, Jones PR, Collins CE. **Effectiveness of web-based interventions in achieving weight loss and weight loss maintenance in overweight and obese adults: a systematic review with meta-analysis**. *Obes Rev* (2010.0) **11** 306-321. DOI: 10.1111/j.1467-789X.2009.00646.x 28. Bukman AJ, Teuscher D, Feskens EJ, van Baak MA, Meershoek A, Renes RJ. **Perceptions on healthy eating, physical activity and lifestyle advice: opportunities for adapting lifestyle interventions to individuals with low socioeconomic status**. *BMC Public Health* (2014.0) **14** 1036. DOI: 10.1186/1471-2458-14-1036 29. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE. **The PRISMA 2020 statement: an updated guideline for reporting systematic reviews**. *Syst Rev* (2021.0) **10** 89. DOI: 10.1186/s13643-021-01626-4 30. Myers-Ingram R, Sampford J, Milton-Cole R, Jones GD. **Outcomes following eHealth weight management interventions in adults with overweight and obesity from low socioeconomic groups: protocol for a systematic review**. *JMIR Res Protoc* (2022.0) **11** e34546. DOI: 10.2196/34546 31. 31.Centre for Reviews and DisseminationSystematic reviews: CRD’s guidance for undertaking reviews in health care20093Centre for Reviews and DisseminationUniversity of York. *Systematic reviews: CRD’s guidance for undertaking reviews in health care* (2009.0) 32. 32.American Psychological Association - Task Force on Socioeconomic Status: Report of the APA Task Force on Socioeconomic Status. http://www.apa.org/pi/ses/resources/publications/task-force-2006.pdf. 33. Geyer S, Peter R. **Income, occupational position, qualification and health inequalities---competing risks? (Comparing indicators of social status)**. *J Epidemiol Community Health* (2000.0) **54** 299-305. DOI: 10.1136/jech.54.4.299 34. 34.National Institute for Health and Care Excellence (NICE): Weight management: lifestyle services for overweight or obese adults [Online: Public health guideline PH53] https://www.nice.org.uk/guidance/ph53. 35. 35.Reichardt CS. Quasi-Experimental Design, in: Millsap, R.E., Maydeu-Olivares, A. (Eds.), The Sage handbook of quantitative methods in psychology. Thousand Oaks: SAGE; 2009. pp. 46–71. 36. Bennett GG, Steinberg DM, Stoute C, Lanpher M, Lane I, Askew S, Foley PB, Baskin ML. **Electronic health (eHealth) interventions for weight management among racial/ethnic minority adults: a systematic review**. *Obes Rev* (2014.0) **15** 146-158. DOI: 10.1111/obr.12218 37. Li T, Higgins JPT, Deeks JJ, Higgins JP, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA. **Collecting data**. *Cochrane handbook for systematic reviews of interventions version 62* (2019.0) 38. Moola S, Munn Z, Tufanaru C, Aromataris E, Sears K, Sfetcu R, Currie M, Lisy K, Qureshi R, Mattis P, Aromataris E, Munn Z. **Chapter 7: Systematic reviews of etiology and risk**. *JBI Manual for Evidence Synthesis* (2020.0) 39. Siddaway AP, Wood AM, Hedges LV. **How to do a systematic review: a best practice guide for conducting and reporting narrative reviews, meta-analyses, and meta-syntheses**. *Annu Rev Psychol* (2019.0) **70** 747-770. DOI: 10.1146/annurev-psych-010418-102803 40. Benitez TJ, Cherrington AL, Joseph RP, Keller C, Marcus B, Meneses K, Marquez B, Pekmezi D. **Using web-based technology to promote physical activity in Latinas: results of the Muévete Alabama pilot study**. *Comput Inform Nurs* (2015.0) **33** 315-324. DOI: 10.1097/CIN.0000000000000162 41. Bennett GG, Foley P, Levine E, Whiteley J, Askew S, Steinberg DM, Batch B, Greaney ML, Miranda H, Wroth TH. **Behavioral treatment for weight gain prevention among black women in primary care practice: a randomized clinical trial**. *JAMA Intern Med* (2013.0) **173** 1770-1777. DOI: 10.1001/jamainternmed.2013.9263 42. Griffin JB, Struempler B, Funderburk K, Parmer SM, Tran C, Wadsworth DD. **My Quest, a community-based mHealth intervention to increase physical activity and promote weight loss in predominantly rural-dwelling, low-income, Alabama women**. *Fam Community Health* (2020.0) **43** 131-140. DOI: 10.1097/FCH.0000000000000251 43. Cavallo DN, Martinez R, Webb Hooper M, Flocke S. **Feasibility of a social media-based weight loss intervention designed for low-SES adults**. *Transl Behav Med* (2021.0) **11** 981-992. DOI: 10.1093/tbm/ibaa070 44. Bambra C, Eikemo TA. **Welfare state regimes, unemployment and health: a comparative study of the relationship between unemployment and self-reported health in 23 European countries**. *J Epidemiol Community Health* (2009.0) **63** 92-98. DOI: 10.1136/jech.2008.077354 45. Wang Y, Beydoun MA, Min J, Xue H, Kaminsky LA, Cheskin LJ. **Has the prevalence of overweight, obesity and central obesity levelled off in the United States? Trends, patterns, disparities, and future projections for the obesity epidemic**. *Int J Epidemiol* (2020.0) **49** 810-823. DOI: 10.1093/ije/dyz273 46. OaFP B. *Obesity and Food Policy Branch: Developing a specification for lifestyle weight management services: best practice guidance for tier 2 services* (2013.0) 42 47. 47.World Health Organization (WHO)Global recommendations on physical activity for health2010GenevaWHO Press. *Global recommendations on physical activity for health* (2010.0) 48. Lowe A, Gee M, McLean S, Littlewood C, Lindsay C, Everett S. **Physical activity promotion in physiotherapy practice: a systematic scoping review of a decade of literature**. *Br J Sports Med* (2018.0) **52** 122-127. DOI: 10.1136/bjsports-2016-096735 49. Caspersen CJ, Powell KE, Christenson GM. **Physical activity, exercise, and physical fitness: definitions and distinctions for health-related research**. *Public Health Rep* (1985.0) **100** 126-131. PMID: 3920711 50. 50.Berry R. Attitudes to exercise and activity: shift learning highlight report https://www.csp.org.uk/system/files/documents/2018-07/csp_concept_testing_highlight_report_both_phases_v1.pdf. 51. 51.Chartered Socity of Physiotherapy (CSP): Love activity, hate exercise? https://www.csp.org.uk/public-patient/keeping-active-and-healthy/love-activity-hate-exercise-campaign. 52. Swift DL, McGee JE, Earnest CP, Carlisle E, Nygard M, Johannsen NM. **The effects of exercise and physical activity on weight loss and maintenance**. *Prog Cardiovasc Dis* (2018.0) **61** 206-213. DOI: 10.1016/j.pcad.2018.07.014 53. Miles R, Panton L. **The influence of the perceived quality of community environments on low-income women's efforts to walk more**. *J Community Health* (2006.0) **31** 379-392. DOI: 10.1007/s10900-006-9021-9 54. Craike M, Bourke M, Hilland TA, Wiesner G, Pascoe MC, Bengoechea EG, Parker AG. **Correlates of physical activity among disadvantaged groups: a systematic review**. *Am J Prev Med* (2019.0) **57** 700-715. DOI: 10.1016/j.amepre.2019.06.021 55. Bull ER, Dombrowski SU, McCleary N, Johnston M. **Are interventions for low-income groups effective in changing healthy eating, physical activity and smoking behaviours? A systematic review and meta-analysis**. *BMJ Open* (2014.0) **4** e006046. DOI: 10.1136/bmjopen-2014-006046 56. 56.Kelly-Irving M, Ball WP, Bambra C, Delpierre C, Dundas R, Lynch J, et al. Falling down the rabbit hole? Methodological, conceptual and policy issues in current health inequalities research. Crit Public Health. 2022;33(1):1–11. 57. 57.Reiners F, Sturm J, Bouw LJW, Wouters EJM. Sociodemographic factors influencing the use of eHealth in people with chronic diseases. Int J Environ Res Public Health. 2019;16(4). 58. 58.Centre PR: Mobile fact sheet .https://www.pewresearch.org/internet/fact-sheet/mobile/. 59. 59.Vasselli JR, Juray S, Trasino SE. Success and failures of telehealth during COVID-19 should inform digital applications to combat obesity. Obes SciPract. 2021;8(2):254-8. 60. Moroshko I, Brennan L, O'Brien P. **Predictors of dropout in weight loss interventions: a systematic review of the literature**. *Obes Rev* (2011.0) **12** 912-934. DOI: 10.1111/j.1467-789X.2011.00915.x 61. Sullivan-Bolyai S, Bova C, Deatrick JA, Knafl K, Grey M, Leung K, Trudeau A. **Barriers and strategies for recruiting study participants in clinical settings**. *West J Nurs Res* (2007.0) **29** 486-500. DOI: 10.1177/0193945907299658 62. Ejiogu N, Norbeck JH, Mason MA, Cromwell BC, Zonderman AB, Evans MK. **Recruitment and retention strategies for minority or poor clinical research participants: lessons from the healthy aging in neighborhoods of diversity across the life span study**. *Gerontologist* (2011.0) **51 Suppl 1** S33-S45. DOI: 10.1093/geront/gnr027
--- title: New stress-induced hyperglycaemia markers predict prognosis in patients after mechanical thrombectomy authors: - Yi Sun - Yapeng Guo - Yachen Ji - Kangfei Wu - Hao Wang - Lili Yuan - Ke Yang - Qian Yang - Xianjun Huang - Zhiming Zhou journal: BMC Neurology year: 2023 pmcid: PMC10061963 doi: 10.1186/s12883-023-03175-w license: CC BY 4.0 --- # New stress-induced hyperglycaemia markers predict prognosis in patients after mechanical thrombectomy ## Abstract ### Objective Stress-induced hyperglycaemia (SIH) is a frequent phenomenon that occurs in patients with acute ischaemic stroke. The aim of this study was to investigate the relationship between SIH and the prognosis of mechanical thrombectomy (MT) patients according to the stress hyperglycemia ratio (SHR) and glycaemic gap (GG) indicators, as well as explore its relationship with haemorrhagic transformation (HT). ### Methods Patients were enrolled from January 2019 to September 2021 in our centre. SHR was calculated as fasting blood glucose divided by the A1c-derived average glucose (ADAG). GG was calculated as fasting blood glucose minus ADAG. Logistic regression was used to analyse SHR, GG with outcome and HT. ### Results A total of 423 patients were enrolled in the study. The incidence of SIH was as follows: $\frac{191}{423}$ of patients with SHR > 0.89, $\frac{169}{423}$ of patients with GG > -0.53. SHR > 0.89 (OR: 2.247, $95\%$ CI: 1.344–3.756, $$P \leq 0.002$$) and GG>-0.53 (OR: 2.305, $95\%$ CI: 1.370–3.879, $$P \leq 0.002$$) were both associated with poor outcomes (modified Rankin Scale > 2) at Day 90 and an increase risk of HT. Additionlly, receiver operating characteristic curves were used to assess the predictive performance of the SHR and GG on outcomes. The area under the curve for SHR to predict poor outcomes was 0.691, with an optimal cut-off value of 0.89. The area under the curve for GG was 0.682, with an optimal cut-off value of -0.53. ### Conclusion High SHR and high GG are strongly associated with poor 90-day prognosis in MT patients and an increased risk of HT. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12883-023-03175-w. ## Introduction With the successive publication of several randomized controlled trials, the safety and efficacy of mechanical thrombectomy (MT) have been demonstrated, which has revolutionized the treatment of acute anterior circulation large vessel occlusive stroke [1, 2]. Despite achieving successful recanalization, approximately $50\%$ of patients who are treated with MT fail to achieve a favourable outcome[1]. Moreover admission hyperglycaemia is associated with worse prognosis in patients receiving intravenous thrombolysis [3]. Additionally, it is well acknowledged that hyperglycaemia is strongly associated with poor prognosis and an increased risk of hemorrhagic transformation after MT [3, 4]. Hyperglycaemia is common in ischaemic stroke, not only in diabetic patients with high blood glucose or chronically high blood glucose levels that are undiagnosed, but also in nondiabetic patients with short-term elevated blood glucose levels due to stressful conditions, which we refer to as stress-induced hyperglycaemia (SIH) [5]. SIH is caused by a cascade of reactions resulting from stressors such as trauma and acute illness (stroke), which subsequently cause sympathetic excitation and activation of the pituitary-adrenal axis[5]. Thus, the concentrations of catecholamines, cortisol and inflammatory factors are higher, which leads to a transient increase in glucose, mainly via the process of gluconeogenesis[5, 6].Additionally, the role of insulin resistance (IR) in inflammatory and stressful states should not be ignored [7]. However, some studies have not been able to distinguish well between chronic hyperglycaemic status and stress-induced hyperglycaemia. There is no uniform definition of SIH. Some studies have defined admission glucose > 200 mg/dl (definitions vary between studies) without diabetes as SIH [8, 9], diabetes diagnosed via previous medical history and/or glycated haemoglobin (HbA1c) > $6.5\%$ [10], which seems to be reasonable, thus ignoring the stressful conditions occurring in diabetic patients. Recently, two new markers have been proposed to assess SIH, which are known as the stress hyperglycaemia ratio (SHR) and glycaemic gap (GG) [11]; these markers, are not based simply on absolute blood glucose levels, but rather on relative values derived from background blood glucose as a reference. SHR and GG markers have been validated in the prognosis of patients with intravenous thrombolysis [12]. Therefore, the purpose of this study was to examine the incidence of SHR and GG in patients undergoing MT and to explore the impact of SIH assessed by SHR and GG on their outcomes. ## Study participants We continuously enrolled patients with anterior circulation large vessel occlusive stroke who underwent MT at the First Affiliated Hospital of Wannan Medical College from January 2019 to September 2021. The ethics of the study were reviewed and approved by the Ethics Committee of Wannan Medical College. Due to its retrospective nature, informed consent of the patients was waived. The exclusion criteria were as follows: [1] age < 18 years; [2] prestroke modified Rankin Scale (mRS) score ≥ 2; [3] CTA or DSA confirmed occlusion of the anterior cerebral artery, occlusion farther than the M2 segment of the middle cerebral artery, or multivessel occlusion; [4] absence of postoperative imaging data; [5] no postoperative laboratory indices of fasting blood glucose and glycated haemoglobin levels; and [6] no postoperative 90-day mRS score assessed by clinical follow-ups or hospital visit records(Fig. 1). Fig. 1Flow chart of the inclusion of the study population For all of the included patients, we collected demographic data (age and sex), personal disease history (hypertension, atrial fibrillation, diabetes and antiplatelet/anticoagulant history), clinical data (IT, baseline blood pressure, Trial of Org 10,172 in Acute Stroke Treatment [TOAST] classification, admission National Institutes of Health Stroke Scale [NIHSS] score and admission Alberta Stroke Program Early CT [ASPECT] score), and laboratory data within 24 hours (fasting blood glucose, FBG and glycated hemoglobin, among other parameters.). Assessments and recordings of relevant surgical data by surgical participants included symptom onset-to-puncture time (OTP), onset-to-reperfusion time (OTR), site of occluded vessel, degree of recanalization and collateral status. ## Variable definitions The condition of the collateral status was assessed according to the extent of contrast reversal within the occluded arterial basin at delayed DSA angiography, with the following parameters being defined: grade 0 was defined as little or no significant contrast reversal within the confines of the occluded vessel; grade 1 was assigned if the collateralization reached the middle cerebral artery M3 segment, and grade 2 was defined if the collaterals reached the middle cerebral artery M2 segment or the distal main stem[13]. We defined a thrombolysis in cerebral infarction (mTICI) score of 2b or 3 as successful recanalization [14]. Functional prognosis was assessed via the 90-day mRS, which we defined as good prognosis for 0–2 and poor prognosis for 3–6. A1c-derived average glucose(ADAG) is an indicator derived from glycated haemoglobin to assess blood glucose levels over an 8–12 week period [15], and it is calculated as 1.59*HbA1c-2.59.Moreover, SHR is calculated by dividing FBG by ADAG, and GG is calculated by FBG minus ADAG [11]. ## Statistical analysis All of the patients were grouped into favourable and poor outcome groups according to the 90-day mRS scores. Normally distributed continuous variables are expressed as the mean ± standard deviation, nonnormally distributed continuous variables are expressed as quartiles and medians and categorical variables are expressed as percentages/frequencies. For comparisons between the groups (if not specified), chi-square and Fisher tests were used for the categorical variables, t tests were used for normal variables, and nonparametric tests for nonnormal variables. Variables were assessed for normality by using the Kolmogorov-Smirnov test. Univariate regression analysis was used to assess the relationship between prognosis and clinical characteristics, and variables with $P \leq 0.05$ in the univariate analysis were included in the regression models. Receiver operating characteristic (ROC) curves were used to evaluate SHR/GG to predict outcomes. Furthermore, the optimal test cut-off point was determined by calculating Youden’s index. Statistical significance was set at $P \leq 0.05$ (two-tailed). Statistical analyses were performed by using SPSS 26.0 (IBM, Armonk, NY, USA). ## Baseline characteristics A total of 490 patients with LVOS who received MT were enrolled in this study, and 67 patients were excluded based on the exclusion criteria, with 423 patients ultimately meeting the study criteria. The median age of all of the examined patients was 70 years, and 250 ($59.1\%$) were male. Of all of the patients, 269 ($63.6\%$) had a history of hypertension, 59 ($13.9\%$) had a history of diabetes mellitus, and 221 ($52.2\%$) had a history of atrial fibrillation. At admission, the median NIHSS and ASPECT scores were 14 [11,17] and 9 [7,10], respectively. The median SHR was 0.87 (0.76, 1.05), and the median GG was − 0.84 (-1.67,0.33). Moreover, 191 ($45.2\%$) of all of the patients had an SHR > 0.89, and 169 ($40.0\%$) patients had a GG > -0.53. In the study population, 379 ($89.6\%$) patients achieved recanalization with grade 2b/3 mTICI (Table 1). Table 1Univariate analysis of 90⁃day functional independence (mRS ≤ 2) in mechanical thrombectomy patientsAll patients($$n = 423$$)Good outcomes($$n = 231$$)Poor outcomes($$n = 192$$)P-value [year, M (Q1, Q3)]70[62,77]68[58,75]74[66,78]< 0.001Male [n, (%)]250(59.1)154(66.7)96(50.0)0.001Medical history [n, (%)]Hypertension269(63.6)134(58.0)135(70.3)0.009Diabetes mellitus59(13.9)26(11.3)33(17.2)0.080Atrial fibrillation221(52.2)97(42.0)124(64.6)< 0.001Antiplatelets/anticoagulants history [n, (%)]0.523No340(80.4)188(81.4)152(79.2)Antiplatelets58(13.7)28(12.1)30(15.6)Anticoagulants25(5.9)15(6.5)10(5.2)IT [n, (%)]53(12.1)28(12.1)23(12.0)0.964Baseline SBPa[mmHg, M (Q1, Q3)]152[139,168]150[138,165]156[140,172]0.033Baseline DBPb[mmHg, M (Q1, Q3)]83[74,92]83[74,92]82[74,93]0.708Admission NIHSS [M (Q1, Q3)]14[11,17]12[10,15]15[12,19]< 0.001Admission ASPECT [M (Q1, Q3)]9[7,10]9[8,10]8[6,9]< 0.001TOAST classification [n, (%)]< 0.001LAA119(28.1)87(37.7)32(16.7)Cardioembolic257(60.8)116(50.2)141(73.4)Others47(11.1)28(12.1)19(9.9)Occlusion location [n, (%)]0.036ICA164(38.8)78(33.8)86(44.8)MCA(M1)216(51.1)131(56.7)85(44.3)MCA(M2)43(10.2)22(9.5)21(10.9)OTP [min, M (Q1, Q3)]300[230,390]300[221,360]300[240,392]0.424OTRc [min, M (Q1, Q3)]356[280,456]350[270,454]360[300,493]0.105Collateral scored [n, (%)]< 0.001Grade 049(11.6)5(2.2)44(22.9)Grade 1101(24.0)43(18.8)58(30.2)Grade 2271(64.4)181(79.0)90(46.9)mTICI,2b/3 [n, (%)]379(89.6)216(93.5)163(84.9)0.004SHR [M (Q1, Q3)]0.87(0.76,1.05)0.83(0.72,0.94)0.97(0.83,1.13)< 0.001GG [M (Q1, Q3)]− 0.84 (-1.67,0.33)-1.81(-1.07,-0.50)-1.26 (-0.17,0.86)< 0.001SHR (> 0.89) [n, (%)]191(45.2)69(29.9)122(63.5)< 0.001GG (>-0.53) [n, (%)]169(40.0)59(25.5)110(57.3)< 0.001Abbreviations: mRS, modified Rankin Scale score; SBP, systolic blood pressure; DBP, diastolic blood pressure; 1mmHg = 0.133 kPa; IT, Intravenous Thrombolysis; NIHSS, National Institutes of Health Stroke Scale; ASPECT, Alberta Stroke Program Early CT; TOAST, Trial of Org 10172 in Acute Stroke Treatment; LAA, large-artery atherosclerosis; ICA, internal carotid artery; MCA(M1/M2) M1/M2 middle cerebral artery segment, OTP, onset-to-puncture time; OTR, onset-to-reperfusion time; mTICI, modified Thrombolysis in Cerebral Infarction; SHR, stress hyperglycaemia ratio; GG, glycaemic gapa:13 patients lost data on SBPb:13 patients lost data on DBPc:1 patient lost data on OTRd:2 patients lost data on Collateral score ## Relationship between SHR, GG and outcome All of the patients in this study were divided into two groups based on 90-d mRS scores: 231($54.6\%$) were classified in the favourable outcome group, and the other 192 ($45.4\%$) were classified in the poor outcome group. In the univariate analysis, the percentage of the population with SHR > 0.89 significantly differed between the favourable and poor outcome groups ($29.9\%$ vs. $63.5\%$, $P \leq 0.001$), and so did GG>-0.53($25.5\%$ vs$.57.3\%$, $P \leq 0.001$). In addition, compared with the good prognosis group, patients in the poor prognosis group were older ($P \leq 0.001$), and had a higher proportion of male patients ($$P \leq 0.001$$), previous hypertensive disease ($$P \leq 0.009$$), embolism ($P \leq 0.001$) and ICA occlusion($P \leq 0.036$);however, they had a lower proportion of good collateral circulation ($P \leq 0.001$) and successful reperfusion ($$P \leq 0.004$$), as well as, a higher median admission NIHSS ($P \leq 0.001$), a higher median baseline systolic blood pressure ($$P \leq 0.033$$), and a lower median admission ASPECT ($P \leq 0.001$). In both regression models, after adjusting for variables with $P \leq 0.05$ in the univariate analysis, Model 1 for, SHR > 0.89(OR: 2.247, $95\%$CI: 1.344–3.756, $$P \leq 0.002$$) and Model 2 for GG>-0.53 (OR: 2.305, $95\%$CI: 1.370–3.879, $$P \leq 0.002$$) demonstrated reduced 90-day good functional outcomes (Table 2). The distribution of modified Rankin Scale (mRS) scores at Day 90 according to the SHR and GG groups is shown in (Figs. 2 and 3), respectively. In addition, in Model 1, male (OR: 1.731, $95\%$CI: 1.029–2.912, $$P \leq 0.039$$), high baseline NIHSS (OR: 1.118, $95\%$CI: 1.050–1.190, $P \leq 0.001$), low baseline ASPECT (OR: 0.706, $95\%$CI: 0.604–0.825, $P \leq 0.001$), and poor collateral score (Grade 1 vs. Grade 0, OR: 0.187, $95\%$CI: 0.054–0.650, $$P \leq 0.008$$; Grade 2 vs. Grade 0, OR: 0.119, $95\%$CI: 0.036–0.396, $$P \leq 0.001$$) were independently associated with poor functional outcomes at 90 days, and in Model 2, male (OR: 1.767, $95\%$CI: 1.050–2.972, $$P \leq 0.032$$), high baseline NIHSS (OR: 1.122, $95\%$CI: 1.054–1.194, $P \leq 0.001$), low baseline ASPECT (OR: 0.707,$95\%$CI: 0.604–0.826, $P \leq 0.001$), and poor collateral circulation (Grade 1 vs. Grade 0, OR: 0.190, $95\%$CI: 0.055–0.655, $$P \leq 0.009$$; Grade 2 vs. Grade 0, OR: 0.120,$95\%$CI: 0.037–0.399, $$P \leq 0.001$$)were similar. Table 2Multivariate analysis of 90⁃day functional independence (mRS ≤ 2) in mechanical thrombectomy patients in Model 1 and Model 2Model 1Model 2Odds Ratio$95\%$CIP-valueOdds Ratio$95\%$CIP-valueAge1.0230.995 ~ 1.0510.1101.0240.996 ~ 1.0520.093Male1.7311.029 ~ 2.9120.0391.7671.050 ~ 2.9720.032Hypertension1.3240.771 ~ 2.2740.3091.3160.766 ~ 2.2610.319TOAST classification0.3510.388Cardioembolic vs. LAA1.4740.764 ~ 2.8420.2471.3900.717 ~ 2.6950.329Others vs. LAA1.7940.715 ~ 4.5000.2131.8140.728 ~ 4.5180.201Admission NIHSS1.1181.050 ~ 1.190< 0.0011.1221.054 ~ 1.194< 0.001Admission ASPECT0.7060.604 ~ 0.825< 0.0010.7070.604 ~ 0.826< 0.001Baseline SBPa1.0111.000 ~ 1.0230.0601.0110.999 ~ 1.0220.073Occlusion location0.3660.380M1 vs. ICA0.7180.420 ~ 1.2260.2250.7140.418 ~ 1.2200.218M2 vs. ICA0.6100.254 ~ 1.4630.2680.6310.263 ~ 1.5110.301mTICI,2b/30.7160.303 ~ 1.6940.4460.7420.311 ~ 1.7680.501Collateral scoreb< 0.0010.001Grade 1vs Grade 00.1870.054 ~ 0.6500.0080.1900.055 ~ 0.6550.009Grade 2vs Grade 00.1190.036 ~ 0.3960.0010.1210.037 ~ 0.3990.001SHR (> 0.89)2.2471.344 ~ 3.7560.002GG (>-0.53)2.3051.370 ~ 3.8790.002mRS, modified Rankin Scale score; SBP, systolic blood pressure; DBP, diastolic blood pressure; 1mmHg = 0.133 kPa; NIHSS, National Institutes of Health Stroke Scale; ASPECT, Alberta Stroke Program Early CT; TOAST, Trial of Org 10172 in Acute Stroke Treatment; LAA, large-artery atherosclerosis; ICA, internal carotid artery; MCA(M1/M2) M1/M2 middle cerebral artery segment, mTICI, modified Thrombolysis in Cerebral Infarction; ADAG, A1c-Derived Average Glucose; SHR, stress hyperglycemia ratio; GG, glycemic gapa:13 patients lost data on SBP.b:2 patients lost data on Collateral score. Fig. 2Distribution of modified Rankin Scale (mRS) score at Day 90, according to SHR Fig. 3Distribution of modified Rankin Scale (mRS) score at Day 90, according to GG ## SHR and GG for Predicting the functional outcomes Receiver operating characteristic (ROC) curve analysis was used to determine the functional outcomes of SHR and GG for LVOS patients who received MT. The optimal cut-off points for SHR and GG to predict prognostic outcome after MT in LVOS patients were 0.89(sensitivity of 0.635 and specificity of 0.701) and − 0.53(sensitivity of 0.573 and specificity of 0.749) respectively. Moreover, the area under the curve (AUC) for the ability of SHR and GG levels to predict poor outcomes was 0.691 and 0.682, respectively (Fig. 4). Fig. 4Receiver operating characteristic (ROC) curves showed predictive SHR and GG for functional outcomes. MT, mechanical thrombectomy; mRS, modified Rankin Scale score; SHR, stress hyperglycemia ratio; GG, glycemic gap ## Relationship between SHR, GG and HT All of the patients were divided into two groups according to HT, of which 111($26.2\%$) patients experienced HT. In the univariate analysis, we found that antiplatelet/anticoagulant history ($$P \leq 0.040$$), IT ($$P \leq 0.003$$), OTP ($$P \leq 0.001$$), OTR ($P \leq 0.001$), SHR > 0.89 ($P \leq 0.001$) and GG>-0.53 ($$P \leq 0.001$$) differed between the two groups (Online Supplemental Table S1). We included variables with $P \leq 0.05$ in a multifactorial analysis, wherein we initially included clinical data (admission NIHSS, admission ASPECT and SHR > 0.89/GG>-0.53) and subsequently included surgical data (OTR; due to the fact the time period of OTR contains OTP, we only included OTR); in addition, pharmacological treatment was included (IT, and antiplatelet/anticoagulant history). After adjusting for the inclusion of different variables, all of the results showed that SHR > 0.89 and GG>-0.53 increased the risk of HT. In addition, IT, antiplatelet history and low ASPECT increased the risk of HT (Online Supplemental Tables S2 and S3). ## Discussion A number of studies have mentioned that SIH impairs the prognosis of MT patients; however, only a few studies have reasonably quantified this effect. We evaluated SHR and GG to assess the occurrence of SIH, and we found that the incidence of SIH was high base on either the occurrence of $45.2\%$ (SHR) or $40.2\%$ (GG), which was much higher than the proportion of patients with diabetes and also higher than the proportion of patients with SIH as defined by previous studies. In our study, we found that high SHR and high GG independently predicted the outcome after MT. Second, SHR and GG increased the risk of HT in patients who underwent MT. There have been a number of studies on SIH, but the definition of SIH has been shown to vary. In recent years, some studies have used an increase in absolute blood glucose (fasting blood glucose or random blood glucose levels) to indicate the occurrence of SIH[9, 16]. The increase in absolute blood glucose does suggest that stress is occurring in some patients, but it also has the obvious drawback of failing to take into consideration the effects of background blood glucose, which corresponding affects the final outcome. Similarly, the SHINE study demonstrated that positive interventions based on the blood glucose range did not improve the prognosis, but rather increased the incidence of hypoglycaaemic events [17]. Thus, absolute glucose elevation alone is not a good response to stressful events, and these glucose interventions may need to be considered with respect to the background glucose conditions. In our study, both SHR and GG referenced both background glucose (ADAG) and absolute glucose (FBG) levels, which may be a relatively reasonable choice, however, this may require a multicentre prospective study to confirm. Although some studies have also used SHR (FBG/HbA1c, RBG/HbA1c), these studies were mostly conducted in nondiabetic patients [18, 19], and it is clearly unadvisable to ignore the occurrence of SIH in diabetic patients. A single-centre, small sample study by Yang et al. [ 11] showed that the SHR and GG can be used to assess the prognosis of acute stroke patients with diabetes. Additionally, a study based on the Chinese Stroke Center Alliance (CSCA) database showed similar findings, with the SHR being a prognostic indicator for diabetic patients with AIS and associated with the risk of in-hospital death[20]. Our study was conducted in a total population and the prevalence of SIH in diabetic patients was over $50\%$, regardless of whether it was assessed by SHR > 0.89 or GG > -0.53 (specific data not provided in the text). Again, our study showed that both the SHR and GG were reliable indicators for assessing the outcomes of all MT patients. Several studies have supported our viewpoint; for example, Yuan et al. [ 21] showed that a higher SHR increased the risk of haemorrhagic transformation in patients with AIS regardless of whether or not they had diabetes. A recent study has also shown that the SHR is an important predictor of outcome for patients receivingIVT[22].Also Chen et al. showed that the SHR was associated with the prognosis of MT[23]. The detailed mechanism of the SHR and prognosis of MT patients is not clear, however, some viewpoints have been accepted by most scholars. SIH involves an elevation of blood glucose secondary to major illnesses such as trauma, stroke or even surgery[5]. SIH is partially a manifestation of the stress response. Therefore, it leads to activation of the sympathetic and hypothalamic pituitary-adrenal axis[5]. The release of factors such as cortisol and catecholamines during this process leads to an increase in blood glucose, which is mainly accomplished via gluconeogenesis[5, 6]. Incidentally, glucose is a proinflammatory mediator in the body[6]. SIH causes an increase in reactive oxygen species in the body,thus leading to oxidative stress and an inflammatory cascade that disrupts the immune system[21]; thus pathway, ultimately lead to the development of various infection-related complications[24, 25]. It has also been suggested that a higher SHR leads to an increased incidence of stroke-associated pneumonia and a poor outcome[25]. SIH can leads to the destruction of the vascular endothelium and a decrease in nitric oxide release, thus limiting arterial dilatory reserve and affecting reperfusion[25, 26]. Moreover, SIH affects the action of clotting factors, whereas hyperglycaemic states induce platelet aggregation, which together lead to a prothrombotic state[5, 27]. Furthermore, it has been widely accepted that SIH increases the incidence of cerebral edema and haemorrhagic transformation[4, 28]. IR is a common phenomenon of elevated blood glucose in stroke patients[7]. Insulin regulates blood glucose by promoting glucose uptake by skeletal muscle, cardiac muscle and other adipose tissues, as well as by inhibiting hepatic glycogenolysis and gluconeogenesis. When these effects are reduced, insulin resistance occurs, which results in an increase in blood glucose[7, 29]. The inflammatory state in patients with stroke and cardiovascular disease reduces insulin sensitivity[29]. Further pro-inflammatory factors exacerbate ischaemic injury in insulin-resistant patients[30]. It is noteworthy that complete recanalization was a factor that improved prognosis in previous studies; however, mTICI had no statistical significance in our multifactorial analysis, but was significant in the univariate analysis. We may be able to explain this conclusion from a statistical point of view. The reperfusion success rate of the 5 major randomized trials was approximately $70\%$[1], whereas the recently published RESCUE BT study was as high as $90\%$ [31], which may be attributed to the maturation and refinement of MT-related techniques. In contrast, in our study, the reperfusion success rate was $89.6\%$ (favourable outcome,$93.5\%$ vs. poor outcome, $84.9\%$; $$P \leq 0.004$$). Moreover, the small sample size of reperfusion failure resulted in its nonsignificance in the multiple regression analysis. It is worth noting that the present study had some limitations. First, our study was a single-centre retrospective study that requires a larger sample size and prospective studies for support. Second, due to the small sample size of diabetic patients in this study, we failed to analyse in different background glucose groups. In addition, although we did not perform glycaemic interventions prior to FBG collection whenever possible, we intervened in a very small number of patients by using subcutaneous insulin injection in consideration of their condition, however we did not exclude these patients from the study. Finally, we were unable to collect blood glucose related metrics over multiple time periods, and a subcutaneous implantable blood glucose monitoring device may be a good option. ## Conclusion This study showed that both SIH markers(SHR and GG) were significantly associated with poor outcomes in MT patients and increased risk of HT. ## Electronic supplementary material Below is the link to the electronic supplementary material. Supplementary Material 1 Supplementary Material 2 Supplementary Material 3 ## References 1. Goyal M, Menon BK, van Zwam WH, Dippel DWJ, Mitchell PJ, Demchuk AM. **Endovascular thrombectomy after large-vessel ischaemic stroke: a meta-analysis of individual patient data from five randomised trials**. *The Lancet* (2016.0) **387** 1723-31. DOI: 10.1016/S0140-6736(16)00163-X 2. Powers WJ, Rabinstein AA, Ackerson T, Adeoye OM, Bambakidis NC, Becker K. **Guidelines for the early management of patients with Acute ischemic stroke: 2019 update to the 2018 guidelines for the early management of Acute ischemic stroke: a Guideline for Healthcare Professionals from the American Heart Association/American Stroke Association**. *Stroke* (2019.0) **50** e344-e418. DOI: 10.1161/STR.0000000000000211 3. Goyal N, Tsivgoulis G, Pandhi A, Dillard K, Katsanos AH, Magoufis G. **Admission hyperglycemia and outcomes in large vessel occlusion strokes treated with mechanical thrombectomy**. *J Neurointerv Surg* (2018.0) **10** 112-7. DOI: 10.1136/neurintsurg-2017-012993 4. Montalvo M, Mistry E, Chang AD, Yakhkind A, Dakay K, Azher I. **Predicting symptomatic intracranial haemorrhage after mechanical thrombectomy: the TAG score**. *J Neurol Neurosurg Psychiatry* (2019.0) **90** 1370-4. PMID: 31427365 5. Dungan KM, Braithwaite SS, Preiser JC. **Stress hyperglycaemia**. *Lancet* (2009.0) **373** 1798-807. DOI: 10.1016/S0140-6736(09)60553-5 6. 6.Farshad K, Mojtaba M,Mohammad A.Mechanisms underlying stress-induced hyperglycemia in critically ill patients.Therapy. 2007;4(1):97–106. 7. Jing J, Pan Y, Zhao X, Zheng H, Jia Q, Mi D. **Insulin resistance and prognosis of nondiabetic patients with ischemic stroke: the ACROSS-China Study (abnormal glucose regulation in patients with Acute Stroke across China)**. *Stroke* (2017.0) **48** 887-93. DOI: 10.1161/STROKEAHA.116.015613 8. 8.Kerby JD, Griffin RL, MacLennan P, Rue LW 3. Stress-induced hyperglycemia, not diabetic hyperglycemia, is associated with higher mortality in trauma. Ann Surg. 2012;256(3):446–52. 9. Tziomalos K, Dimitriou P, Bouziana SD, Spanou M, Kostaki S, Angelopoulou SM. **Stress hyperglycemia and acute ischemic stroke in-hospital outcome**. *Metabolism* (2017.0) **67** 99-105. DOI: 10.1016/j.metabol.2016.11.011 10. American Diabetes A. **Diagnosis and classification of diabetes mellitus**. *Diabetes Care* (2012.0) **35** 64-71. DOI: 10.2337/dc12-s064 11. Yang CJ, Liao WI, Wang JC, Tsai CL, Lee JT, Peng GS. **Usefulness of glycated hemoglobin A1c-based adjusted glycemic variables in diabetic patients presenting with acute ischemic stroke**. *Am J Emerg Med* (2017.0) **35** 1240-6. DOI: 10.1016/j.ajem.2017.03.049 12. Chen G, Ren J, Huang H, Shen J, Yang C, Hu J. **Admission Random Blood glucose, fasting blood glucose, stress hyperglycemia ratio, and functional outcomes in patients with Acute ischemic stroke treated with intravenous thrombolysis**. *Front Aging Neurosci* (2022.0) **14** 782282. DOI: 10.3389/fnagi.2022.782282 13. Christoforidis GA, Mohammad Y, Kehagias D, Avutu B, Slivka AP. **Angiographic assessment of pial collaterals as a prognostic indicator following intra-arterial thrombolysis for acute ischemic stroke**. *AJNR Am J Neuroradiol* (2005.0) **26** 1789-97. PMID: 16091531 14. Yoo AJ, Simonsen CZ, Prabhakaran S, Chaudhry ZA, Issa MA, Fugate JE. **Refining angiographic biomarkers of revascularization: improving outcome prediction after intra-arterial therapy**. *Stroke* (2013.0) **44** 2509-12. DOI: 10.1161/STROKEAHA.113.001990 15. Nathan DM, Kuenen J, Borg R, Zheng H, Schoenfeld D, Heine RJ. **Translating the A1C assay into estimated average glucose values**. *Diabetes Care* (2008.0) **31** 1473-8. DOI: 10.2337/dc08-0545 16. Capes SE, Hunt D, Malmberg K, Pathak P, Gerstein HC. **Stress hyperglycemia and prognosis of stroke in nondiabetic and diabetic patients: a systematic overview**. *Stroke* (2001.0) **32** 2426-32. DOI: 10.1161/hs1001.096194 17. Johnston KC, Bruno A, Pauls Q, Hall CE, Barrett KM, Barsan W. **Intensive vs Standard Treatment of Hyperglycemia and functional outcome in patients with Acute ischemic stroke: the SHINE Randomized Clinical Trial**. *JAMA* (2019.0) **322** 326-35. DOI: 10.1001/jama.2019.9346 18. Li J, Quan K, Wang Y, Zhao X, Li Z, Pan Y. **Effect of stress hyperglycemia on neurological deficit and mortality in the Acute ischemic stroke people with and without diabetes**. *Front Neurol* (2020.0) **11** 576895. DOI: 10.3389/fneur.2020.576895 19. Zhu B, Pan Y, Jing J, Meng X, Zhao X, Liu L. **Stress hyperglycemia and outcome of non-diabetic patients after Acute ischemic stroke**. *Front Neurol* (2019.0) **10** 1003. DOI: 10.3389/fneur.2019.01003 20. Mi D, Li Z, Gu H, Jiang Y, Zhao X, Wang Y. **Stress hyperglycemia is associated with in-hospital mortality in patients with diabetes and acute ischemic stroke**. *CNS Neurosci Ther* (2022.0) **28** 372-81. DOI: 10.1111/cns.13764 21. Yuan C, Chen S, Ruan Y, Liu Y, Cheng H, Zeng Y. **The stress hyperglycemia ratio is Associated with Hemorrhagic Transformation in patients with Acute ischemic stroke**. *Clin Interv Aging* (2021.0) **16** 431-42. DOI: 10.2147/CIA.S280808 22. Shen CL, Xia NG, Wang H, Zhang WL. **Association of stress hyperglycemia ratio with Acute ischemic stroke outcomes post-thrombolysis**. *Front Neurol* (2021.0) **12** 785428. DOI: 10.3389/fneur.2021.785428 23. Chen X, Liu Z, Miao J, Zheng W, Yang Q, Ye X. **High stress hyperglycemia ratio predicts poor outcome after mechanical thrombectomy for ischemic stroke**. *J Stroke Cerebrovasc Dis* (2019.0) **28** 1668-73. DOI: 10.1016/j.jstrokecerebrovasdis.2019.02.022 24. Liu DD, Chu SF, Chen C, Yang PF, Chen NH, He X. **Research progress in stroke-induced immunodepression syndrome (SIDS) and stroke-associated pneumonia (SAP)**. *Neurochem Int* (2018.0) **114** 42-54. DOI: 10.1016/j.neuint.2018.01.002 25. Tao J, Hu Z, Lou F, Wu J, Wu Z, Yang S. **Higher stress hyperglycemia ratio is Associated with a higher risk of Stroke-Associated Pneumonia**. *Front Nutr* (2022.0) **9** 784114. DOI: 10.3389/fnut.2022.784114 26. Chen R, Ovbiagele B, Feng W. **Diabetes and stroke: Epidemiology, Pathophysiology, Pharmaceuticals and Outcomes**. *Am J Med Sci* (2016.0) **351** 380-6. DOI: 10.1016/j.amjms.2016.01.011 27. Lemkes BA, Hermanides J, Devries JH, Holleman F, Meijers JC, Hoekstra JB. **Hyperglycemia: a prothrombotic factor?**. *J Thromb Haemost* (2010.0) **8** 1663-9. DOI: 10.1111/j.1538-7836.2010.03910.x 28. Cannarsa GJ, Wessell AP, Chryssikos T, Stokum JA, Kim K, De Paula Carvalho H. **Initial stress hyperglycemia is Associated with Malignant Cerebral Edema, Hemorrhage, and poor functional outcome after mechanical thrombectomy**. *Neurosurgery* (2022.0) **90** 66-71. DOI: 10.1227/NEU.0000000000001735 29. Ding PF, Zhang HS, Wang J, Gao YY, Mao JN, Hang CH. **Insulin resistance in ischemic stroke: mechanisms and therapeutic approaches**. *Front Endocrinol (Lausanne)* (2022.0) **13** 1092431. DOI: 10.3389/fendo.2022.1092431 30. Ago T, Matsuo R, Hata J, Wakisaka Y, Kuroda J, Kitazono T. **Insulin resistance and clinical outcomes after acute ischemic stroke**. *Neurology* (2018.0) **90** e1470-e7. DOI: 10.1212/WNL.0000000000005358 31. Investigators RBT, Qiu Z, Li F, Sang H, Luo W, Liu S. **Effect of Intravenous Tirofiban vs Placebo before Endovascular Thrombectomy on Functional Outcomes in large vessel occlusion stroke: the RESCUE BT Randomized Clinical Trial**. *JAMA* (2022.0) **328** 543-53. DOI: 10.1001/jama.2022.12584
--- title: 'Associations between body circumference and testosterone levels and risk of metabolic dysfunction-associated fatty liver disease: a mendelian randomization study' authors: - Lin Ning - Jianguang Sun journal: BMC Public Health year: 2023 pmcid: PMC10061974 doi: 10.1186/s12889-023-15467-4 license: CC BY 4.0 --- # Associations between body circumference and testosterone levels and risk of metabolic dysfunction-associated fatty liver disease: a mendelian randomization study ## Abstract ### Backgroud Body circumference and testosterone levels have been reported as associated with metabolic dysfunction-associated fatty liver disease (MAFLD) risk. However, whether body circumference and testosterone levels play a role in the development of MAFLD remains inconclusive. ### Methods Using a large database of genome-wide association studies, genetic loci that are independent of each other and strongly associated with body circumference and testosterone levels were selected as instrumental variables, the causal relationship between body circumference and testosterone and risk of MAFLD was investigated by two-sample Mendelian randomization methods such as inverse variance weighted (IVW), MR-Egger regression, and weighted median estimator (WME), using the odds ratios (ORs) as evaluation indicators. ### Results A total of 344 SNPs were included as instrumental variables in this study, including 180 for waist circumference, 29 for waist-to-hip ratio, and 135 for testosterone levels. Using the above two-sample Mendelian Randomization method to derive the causal association between exposure and outcome. The results of this study showed that three exposure factors were causally associated with the risk of MAFLD. Waist circumference obtained three statistically significant results for IVW, WME and Weighted mode (IVW: OR = 3.53, $95\%$CI: 2.23–5.57, $P \leq 0.001$; WME: OR = 3.88, $95\%$CI: 1.81–8.29, $P \leq 0.001$; Weighted mode: OR = 3.58, $95\%$CI: 1.05–12.16, $$P \leq 0.043$$). Waist-to-hip ratio obtained one statistically significant result for IVW (OR = 2.29, $95\%$CI: 1.12–4.66, $$P \leq 0.022$$). Testosterone levels obtained one statistically significant result for IVW (OR = 1.93, $95\%$CI: 1.30–2.87, $$P \leq 0.001$$). Waist circumference, waist-to-hip ratio and testosterone level were considered as risk factors for MAFLD. The Cochran Q test for IVW and MR-Egger method indicated that there was no intergenic heterogeneity in SNPs. The test for pleiotropy indicated that the possibility of pleiotropy in the causal analysis was weak. ### Conclusion The results of the two-sample Mendelian randomization analysis showed that waist circumference was the exact risk factor for MAFLD, waist-to-hip ratio and testosterone levels were potential risk factors for MAFLD, the risk of developing MAFLD increases with these three exposure factors. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12889-023-15467-4. ## Introduction Metabolic dysfunction-associated fatty liver disease (MAFLD) was renamed from Non-alcoholic fatty liver disease (NAFLD) in March 2020 [1]. The disease is characterized by intrahepatocellular lipid deposition combined with systemic multisystemic metabolic disorders [2], the prevalence is increasing year by year and has become the number one chronic liver disease in China and the global prevalence is about $25\%$ [3]. MAFLD can not only cause steatohepatitis and liver fibrosis, liver cancer, but also cause extra-hepatic complications such as cardiovascular disease and chronic kidney disease due to MAFLD-related metabolic disorders, which are more serious to people’s health [4]. However, so far, there are no effective preventive and therapeutic drugs for MAFLD, therefore, it is significant to identify the factors affecting the development of MAFLD and to intervene early for the development of MAFLD. Obesity, especially abdominal obesity, is the primary risk factor for MAFLD. Body circumference, especially waist circumference and waist-to-hip ratio, are practical indicators of body BMI and obesity[5]. Previous studies[6–8] have found that the incidence of fatty liver disease increases with increasing waist circumference and that waist-to-hip ratio is also a risk factor for the development of MAFLD [9]. It has also been shown that sex hormone levels are significantly associated with various specific disorders in the metabolic syndrome, testosterone is an important regulator of glucose and lipid metabolism in the body and is an intrinsic indicator of hepatic lipid metabolism [10–12], and the severity of disease in MAFLD patients is significantly correlated with blood testosterone levels [13]. However, the perspective of the above studies is limited to traditional observational epidemiology, which is vulnerable to unknown confounding factors and reverse causation. In recent years, mendelian randomization (MR) has been developed as an important method for causal inference, and it uses exposure-related genotypes as instrumental variables (IVs) and can overcome the drawbacks of traditional epidemiological studies such as difficult data acquisition and poor extrapolation of results [14]. However, traditional MR requires genotypes from the same individual, as well as information on exposure and outcome, and data detection costs are high. Recently, the two-sample Mendelian randomization (2-sample MR) method has been gradually developed, which allows gene and exposure association data and gene and outcome association data from two independent sample populations of the same overall population, respectively, compared with traditional MR, greatly improving the efficiency and feasibility of etiological studies, and has been widely used in causal association studies of risk factors and disease outcomes[15]. This study used a two-sample MR method to explore the causal associations between three exposure factors of body circumference (including waist circumference and waist-to-hip ratio) and testosterone level and risk of MAFLD. ## Study design In this study, body circumference (waist circumference, waist-to-hip ratio) and testosterone levels were used as exposure factors, and single nucleotide polymorphisms (SNPs) loci significantly associated with the above exposure factors were selected as instrumental variables(IVs), and the outcome variable was MAFLD. The causal association analysis between exposure and outcome was performed using a two-sample MR analysis approach based on a publicly available genome wide association study (GWAS) database of large samples, and Cochran Q test to assess heterogeneity, and finally sensitivity analysis to verify the reliability of the causal association results. MR analysis needs to satisfy the following three core hypotheses: ①there is a strong association between instrumental variable Z and exposure factor X; ②instrumental variable Z is not associated with any confounding factor U of the exposure-outcome association; and ③the instrumental variable Z does not affect the outcome Y, except possibly by association with the exposure X. The two-sample MR study model is shown in Fig. 1. Fig. 1Model of the two-samples MR analysis ## Data sources In this study, significant body circumference (waist circumference, waist-hip ratio) and serum testosterone levels were used as exposure factors, SNPs significantly associated with the above exposure factors were used as IVs, and the outcome factor was MAFLD. The pooled data used to conduct the two-sample MR study were obtained from the IEU Open GWAS database summary website (https://gwas.mrcieu.ac.uk/), waist circumference (GWAS ID: ukb-a-382), waist-to-hip ratio (GWAS ID: ieu-a-79), testosterone (GWAS ID: ebi-a-GCST90012102), and MAFLD (GWAS ID: finn-b-NAFLD), all of the above databases were derived from European populations. All datasets used in this study were from the public domain, and summary information is presented in Table 1. Table 1Summary of the GWAS included in this two-sample MR studyVariableIDSample sizeNumber of SNPsConsortiumPopulationSexYearWaist circumferenceukb-a-382336,63910,894,596Neale LabEuropeanMales and Females2017Waist-to-hip ratioieu-a-79210,0822,542,432GIANTEuropeanMales and Females2015Testosterone levelsebi-a-GCST90012102188,50716,139,906-EuropeanMales and Females2020MAFLDfinn-b-NAFLD218,79216,380,466-EuropeanMales and Females2021 ## Selection of instrumental variables SNPs with significant correlation with body circumference and testosterone level ($P \leq 5.$ 0 × 10− 8) were screened, and the interference of linkage disequilibrium (LD) was excluded [16], setting parameter r2 = 0. 001, kb = 10,000, the echo SNPs and outlier SNPs were excluded, and the SNPs with significant heterogeneity were excluded by heterogeneity test. If the number of SNPs filtered according to the above criteria is large, each SNP should be queried on the PhenoScanner website (http://www.phenoscanner.medschl.cam.ac.uk/), SNPs affected by confounding factors that violated MR core hypothesis②and③were excluded. Finally valid SNPs significantly associated with exposure factors that met MR core hypothesis were obtained as IVs. F > 10 indicates the absence of weak instrumental variable bias, which is calculated as follows: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F = \frac{{N - k - 1}}{k} \times \frac{{{R^2}}}{{1 - {R^2}}}$$\end{document}, where N is the sample size of the exposed database, k is the number of SNPs, and R2 is the proportion of variance explained by SNPs in the exposed database. R2 is calculated as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${R^2} = \frac{{2 \times EAF \times \left({1 - EAF} \right) \times {\beta ^2}}}{{S{D^2}}}$$\end{document}, where EAF is the effect allele frequency, β is the allele effect value, and SD is the standard deviation. ## Statistical analysis for mendelian randomization We used the TwoSampleMR package (version 0.5.6) in R program (version 4.2.1) to integrate and analyze the data. In this study, inverse variance weighted (IVW) [17] was used as the main analysis method, while MR-Egger regression [18], weighted median estimator (WME) [19], simple mode and weighted mode [20] were used together for MR analysis. The principle of IVW is to weight the inverse of the variance of each IV as the weight while ensuring that all IVs are valid, the regression does not consider the intercept term, and the final result is the weighted average of the effect values of all IVs. The major difference between MR-Egger regression and IVW is that the regression takes into account the presence of the intercept term, and in addition, it also uses the inverse of the ending variance as a weight for the fit. The WME is defined as the median of the weighted empirical density function of the ratio estimates, which allows consistent estimation of causality if at least half of the valid instruments are present in the analysis. ## Heterogeneity and sensitivity test There may be heterogeneity in the 2-sample MR analysis due to differences in analysis platforms, experimental conditions, including populations and SNPs, which may bias the estimation of causal effects. Therefore, the main IVW and MR-Egger methods were tested for heterogeneity in this study. The heterogeneity test was used to test the differences between individual IVs, and Cochran’s Q statistic and P-value were used to determine whether there was heterogeneity, and $P \leq 0.1$ represented the presence of heterogeneity; Pleiotropy test mainly tests the presence of horizontal pleiotropy for multiple IVs [21], and the P-value of the pleiotropy test was used in this study to measure whether there was pleiotropy in the analysis, if $P \leq 0.05$, it is considered that the possibility of pleiotropy in the causal analysis is weak. Leave-one-out sensitivity test is mainly to calculate the MR results of the remaining IVs after eliminating them one by one [22], if the estimated MR results of other IVs after eliminating one IV are very different from the total results, it means that the MR results are sensitive to that IV. The presence of pleiotropy in the analysis was also determined in this study using the MR-pleiotropy residual sum outlier (MR-PRESSO) [23]. ## Instrumental variables After screening SNPs with strong correlation with exposure in the corresponding GWAS database and removing the interference of linkage disequilibrium, we initially screened 418 SNPs, including 232 for waist circumference, 38 for waist-hip ratio, and 148 for testosterone level. Extracting the information of the above SNPs from the GWAS database corresponding to MAFLD, we obtained 412 valid SNPs, including 230 for waist circumference, 38 for waist-hip ratio, and 144 for testosterone level. Next, we eliminated echo SNPs and outlier SNPs, and finally we queried each SNP on the PhenoScanner website (http://www.phenoscanner.medschl.cam.ac.uk/) to exclude SNPs affected by confounding factors such as “alcohol consumption, body mass index, type 2 diabetes mellitus, hyperlipidemia, hypothyroidism”, etc. We eventually obtained 344 IVs, including 180 for waist circumference, 29 for waist-hip ratio, and 135 for testosterone level. The F-statistics corresponding to the single SNPs were all more than 10, indicating that the causal associations were less likely to be affected by weak instrumental variable bias. Basic information on the instrumental variables is in the Supplementary Materials (Basic information on instrumental variables). ## Results of two-sample MR analysis In this study, the IVW method was used as the main analytical method, and the other four MR analytical methods were complementary to the IVW method. In the MR analysis results, with an OR value bigger than 1, exposure was considered a risk factor for the outcome, and vice versa as a protective factor for the outcome, and when the P value was less than 0.05, it was considered statistically significant and the causal association was established. The results of the analysis in this study showed that all three exposure factors were causally associated with MAFLD. Waist circumference obtained three statistically significant results for IVW, WME and Weighted mode (IVW: OR = 3.53, $95\%$CI: 2.23–5.57, $P \leq 0.001$; WME: OR = 3.88, $95\%$CI: 1.81–8.29, $P \leq 0.001$; Weighted mode: OR = 3.58, $95\%$CI: 1.05–12.16, $$P \leq 0.043$$). Waist-to-hip ratio obtained one statistically significant result for IVW (OR = 2.29, $95\%$CI: 1.12–4.66, $$P \leq 0.022$$). Testosterone level obtained one statistically significant result for IVW (OR = 1.93, $95\%$CI: 1.30–2.87, $$P \leq 0.001$$). The above results suggest that waist circumference is a definite risk factor for MAFLD, and waist-to-hip ratio and testosterone levels are also potential risk factors for MAFLD, and as they increase, the risk of developing MAFLD also increases. The results of the specific analysis of the five methods are shown in Table 2. Visualization of MR results is shown in the Supplementary Materials (MR results visualization charts). Table 2MR estimates of associations between 3 types of exposure and MAFLD riskExposureNumber of SNPsMR methodsoutcomeOR($95\%$CI)p-valueWaist circumference180MR EggerMAFLD1.74 (0.42,7.22)0.447WME3.88 (1.81,8.29)< 0.001IVW3.53 (2.23,5.57)< 0.001Simple mode3.42 (0.57,20.42)0.179Weighted mode3.58 (1.05,12.16)0.043Waist-to-hip ratio29MR Egger1.38 (0.04,44.83)0.858WME1.87 (0.69,5.12)0.22IVW2.29 (1.12,4.66)0.022Simple mode2.56 (0.39,16.62)0.333Weighted mode2.39 (0.495,11.49)0.288Testosterone level135MR Egger1.67 (0.77,3.62)0.199WME1.71 (0.86,3.41)0.125IVW1.93 (1.30,2.87)0.001Simple mode2.32 (0.59,9.09)0.227Weighted mode1.58 (0.80,3.14)0.192Abbreviations: CI, confidence interval. ## Result of heterogeneity and sensitivity test The Cochran Q test for IVW and MR-Egger method indicated that there was no intergenic heterogeneity in SNPs ($P \leq 0.1$); the test for pleiotropy indicated that the possibility of pleiotropy in the causal analysis was weak ($P \leq 0.05$), and the above results are detailed in Table 3. Table 3Results of Heterogeneity and sensitivity testExposureoutcomeMR methodsp of pleiotropyp of Cochran QWaist circumferenceMAFLD0.305MR Egger0.204IVW0.203Waist-to-hip ratio0.772MR Egger0.834IVW0.864Testosterone level0.662MR Egger0.161IVW0.173 In the visualization of MR results, the funnel plot showed that the points representing the causal association effect were roughly symmetrically distributed when a single SNP was used as the IV, indicating that the causal association was less likely to be affected by potential bias. The results of the “Leave-one-out” sensitivity analysis showed that after eliminating each SNP in turn, no SNP with a large effect on the causal association estimates was found. ## Discussion In this study, the causal relationship between body circumference and testosterone level and risk of MAFLD was investigated using a two-sample MR analysis method using publicly available databases and a large-scale GWAS study. The results showed that all the exposure factors we studied were causally associated with the outcome, with waist circumference being the exact risk factor for MAFLD, waist-to-hip ratio and testosterone levels being potential risk factors for MAFLD, the risk of developing MAFLD increases with these three exposure factors. Our finding is consistent with the results of several previous studies. A five-year study by S Wang et al. including 12,477 observers showed that the cumulative incidence of MAFLD increased with increasing waist circumference and concluded that waist circumference was an independent risk factor for MAFLD [24]. A prospective cohort study in Korea involving 5400 people aged 40 ~ 69 years showed that waist circumference was the most important risk factor for MAFLD among the physical indicators of middle-aged and elderly people in Korea, and the threshold of waist circumference values were 81 cm for men and 78.5 cm for women [25]. Results of a clinical study on risk factors for NAFLD in Urumqi, China, showed that waist-to-hip ratio was a clinically meaningful risk factor for the development of NAFLD[26]. A clinical study of MAFLD in Taiwan, China, which included 1,969 participants, showed that waist-to-hip ratio was of concern in the incidence and severity of MAFLD, and that the correlation between waist-to-hip ratio and MAFLD was more pronounced in women than in men [27]. A Korean cohort study involving 613 women of various ages showed that serum testosterone levels in premenopausal women were positively associated with the risk of developing MAFLD [28]. The results of the above studies suggest that waist circumference, waist-to-hip ratio and testosterone levels are risk factors for MAFLD, which is consistent with the results of our study. However, it should be noted that there are clinical studies showing gender differences in testosterone levels and the risk of MAFLD. A meta-analysis of 16 indicators involving 13,721 men and 5,840 women by Veeravich Jaruvongvanich et al. showed that the lower the testosterone level, the higher the risk of MAFLD in men, and in women, conversely, the lower the testosterone level, the lower the risk of developing MAFLD [29]. The multicenter clinical study by Monika Sarkar et al. showed that men with lower testosterone levels were more likely to develop MAFLD [30]. The above results also suggest that there may be gender differences in the causal association between testosterone levels and MAFLD, and the database of testosterone levels selected for this study includes both men and women, so the results of the analysis may be biased. To make the study results more reliable, the analysis should be performed separately according to gender. This study explores the causal relationship between body circumference, testosterone, and risk of MAFLD using the two-sample MR method, which avoids the disturbance of confounding factors such as social environment and lifestyle because genetic variation is a long-term and stable exposure and can be measured directly. Compared to randomized controlled trials, MR allows for truly random assignment and does not violate ethics. Two-sample MR has a relatively larger sample size, allows for greater confidence, and increases the specificity of genetic variants using multiple IVs compared to a single SNP. The datasets used in this study were of large sample size and publicly available, which ensured the quality of the IVs used in the analysis. The IVs in this study fully considered and excluded SNPs that could affect the results, and outlier SNPs were excluded using the MR-PRESSO method. Therefore, the selection of IVs in this study is more detailed, comprehensive and reliable than previous studies. Of course, there are limitations in this study, as we did not perform a subgroup analysis by sex, and a more specific effect relationship could have been obtained with a two-sample MR analysis by sex, and this study has limitations in explaining the biological mechanisms underlying the causal effects of exposure and outcome. In conclusion, this study validated the feasibility of applying MR methods to the study of the risk of developing MAFLD, and searching for key risk factors for preventable MAFLD at the genetic level, which has guiding implications for the early prevention of MAFLD in China. However, there are few GWAS data in Asian and Chinese populations, and data from other databases are difficult to obtain and organize, so the results of this study need to be further validated in Chinese populations in combination with clinical and randomized controlled trials. ## Electronic supplementary material Below is the link to the electronic supplementary material. MR results visualization charts Basic information of instrumental variables ## References 1. Mohammed Eslam PN, Newsome SK, Sarin QM, Anstee G. **A New Definition for Metabolic Dysfunction-Associated fatty liver disease: an International Expert Consensus Statement**. *J Hepatol* (2020.0) **73** 202-9. DOI: 10.1016/j.jhep.2020.03.039 2. Gregory A, Michelotti, Mariana V. **Anna Mae Diehl. NAFLD, NASH and Liver Cancer**. *Nat Rev Gastroenterol Hepatol* (2013.0) **10** 656-65. DOI: 10.1038/nrgastro.2013.183 3. 3.Zobair M, Younossi AB, Koenig D, Abdelatif Y, Fazel L, Henry et al. Global Epidemiology of Nonalcoholic Fatty Liver Disease-Meta-Analytic Assessment of Prevalence, Incidence, and Outcomes. Hepatology (Baltimore, Md.) (2016) 64(1):73–84. doi: 10.1002/hep.28431 4. Sun D-Q, Jin Y, Wang T-Y, Kenneth I, Zheng RS. **MAFLD and Risk of CKD**. *Metab Clin Exp* (2021.0) **115** 154433. DOI: 10.1016/j.metabol.2020.154433 5. Robert Ross IJ, Neeland S, Yamashita I, Shai J. **Waist circumference as a vital sign in clinical practice: a Consensus Statement from the IAS and ICCR Working Group on visceral obesity**. *Nat Rev Endocrinol* (2020.0) **16** 177-89. DOI: 10.1038/s41574-019-0310-7 6. SoJung Lee JL, Kuk C, Boesch SA. **Waist circumference is Associated with Liver Fat in Black and White Adolescents. Applied Physiology, Nutrition, and metabolism = physiologie Appliquee**. *Nutr Et Metab* (2017.0) **42** 829-33. DOI: 10.1139/apnm-2016-0410 7. 7.Nima Motamed M, Sohrabi H, Ajdarkosh G, Hemmasi M, Maadi et al. Fatty Liver Index vs Waist Circumference for Predicting Non-Alcoholic Fatty Liver Disease. World Journal of Gastroenterology (2106) 22(10):3023–3030. doi: 10.3748/wjg.v22.i10.3023 8. Jian C, Xu Y, Ma X, Shen Y, Wang Y. **Neck circumference is an effective supplement for nonalcoholic fatty liver Disease Screening in a community-based Population**. *Int J Endocrinol* (2020.0) **2020** 7982107. DOI: 10.1155/2020/7982107 9. Neves MM, Silva F, Mendonça T. **Waist-to-hip ratio and inflammatory parameters are Associated with risk of non-alcoholic fatty liver disease in patients with morbid obesity**. *Biomedicines* (2022.0) **10** 2416. DOI: 10.3390/biomedicines10102416 10. Daniel M, Kelly S, Akhtar DJ, Sellers V, Muraleedharan KS. **Testosterone differentially regulates targets of lipid and glucose metabolism in liver, muscle and adipose tissues of the testicular Feminised mouse**. *Endocrine* (2016.0) **54** 504-15. DOI: 10.1007/s12020-016-1019-1 11. Caldwell ASL, Middleton LJ, Jimenez M, Desai R, McMahon AC. **Characterization of Reproductive, metabolic, and endocrine features of polycystic ovary syndrome in female hyperandrogenic mouse models**. *Endocrinology* (2014.0) **155** 3146-59. DOI: 10.1210/en.2014-1196 12. Cao W, Xu Y, Shen Y, Wang Y, Ma X. **Associations between Sex Hormones and Metabolic-Associated fatty liver disease in a middle-aged and Elderly Community**. *Endocr J* (2022.0) **69** 1007-14. DOI: 10.1507/endocrj.EJ21-0559 13. Yim JY, Kim J, Kim D, Ahmed A. **Serum testosterone and non-alcoholic fatty liver disease in men and women in the US**. *Liver International: Official Journal of the International Association for the Study of the Liver* (2018.0) **38** 2051-9. DOI: 10.1111/liv.13735 14. Connor A, Emdin AV. **Sekar Kathiresan**. *Mendelian Randomization JAMA* (2017.0) **318** 1925-6. DOI: 10.1001/jama.2017.17219 15. Brandon L. **Efficient design for mendelian randomization studies: Subsample and 2-Sample instrumental variable estimators**. *Am J Epidemiol* (2013.0) **178** 1177-84. DOI: 10.1093/aje/kwt084 16. 16.Hemani G, Zheng J, Elsworth B, Wade Kh, Haberland V et al. The MR-Base Platform Supports Systematic Causal Inference across the Human Phenome. ELife (2018) 7: 34408. doi: 10.7554/eLife.34408 17. Burgess S, Bowden J, Fall T, Ingelsson E. **Thompson. Sensitivity analyses for robust causal inference from mendelian randomization analyses with multiple genetic variants**. *Epidemiol (Cambridge Mass)* (2017.0) **28** 30-42. DOI: 10.1097/EDE.0000000000000559 18. Slob Eaw G, Pjf THurik, Ar R. **A note on the Use of Egger regression in mendelian randomization studies**. *Int J Epidemiol* (2017.0) **46** 2094-7. DOI: 10.1093/ije/dyx191 19. Jack Bowden GD, Smith PC. **Consistent estimation in mendelian randomization with some Invalid Instruments using a weighted median estimator**. *Genet Epidemiol* (2016.0) **40** 301-14. DOI: 10.1002/gepi.21965 20. Hartwig FP, Smith GD, Bowden J. (2017.0). DOI: 10.1093/ije/dyx102 21. Stephen Burgess SG. **Interpreting findings from mendelian randomization using the MR-Egger Method**. *Eur J Epidemiol* (2017.0) **32** 377-89. DOI: 10.1007/s10654-017-0255-x 22. Quentin F, Gronau E-J. (2019.0). DOI: 10.1007/s42113-018-0011-7 23. Marie Verbanck C-Y, Chen B, Neale R. **Detection of widespread horizontal pleiotropy in Causal Relationships inferred from mendelian randomization between Complex Traits and Diseases**. *Nat Genet* (2018.0) **50** 693-98. DOI: 10.1038/s41588-018-0099-7 24. Wang S, Zhang J, Jiang XZ, Tong B, Wang Q. **Relationship between waist circumference trajectory and new-onset non alcoholic fatty liver disease in the non-obese population**. *Zhonghua Liu Xing Bing Xue Za Zhi = Zhonghua Liuxingbingxue Zazhi* (2020.0) **41** 824-8. DOI: 10.3760/cma.j.cn112338-20190630-00479 25. Lee J-H, Jeon S, Lee HS, Kwon Y-J. **Cutoff points of Waist circumference for Predicting Incident non-alcoholic fatty liver disease in Middle-Aged and older korean adults**. *Nutrients* (2022.0) **14** 2994. DOI: 10.3390/nu14142994 26. Sulan Lin Y, Xian Y, Liu W, Cai J. **Risk factors and community intervention for nonalcoholic fatty liver disease in Community residents of Urumqi, China**. *Medicine* (2018.0) **97** e0021. DOI: 10.1097/MD.0000000000010021 27. Lin I-Ting, Lee M-Y, Wang C-W, Wu D-W, Chen S-C. **Gender differences in the Relationships among metabolic syndrome and various obesity-related indices with nonalcoholic fatty liver disease in a Taiwanese Population**. *Int J Environ Res Public Health* (2021.0) **18** 857. DOI: 10.3390/ijerph18030857 28. 28.Park J-M, Lee HS, Oh J, Lee Y-J. Serum Testosterone Level Within Normal Range Is Positively Associated with Nonalcoholic Fatty Liver Disease in Premenopausal but Not Postmenopausal Women. Journal of Women’s Health (2002) (2019) 28(8): 1077–1082. doi: 10.1089/jwh.2018.7263 29. Veeravich Jaruvongvanich A, Sanguankeo T, Riangwiwat SU. **Testosterone, sex hormone-binding globulin and nonalcoholic fatty liver disease: a systematic review and Meta-analysis**. *Ann Hepatol* (2017.0) **16** 382-94. DOI: 10.5604/16652681.1235481 30. Monika Sarkar K, Yates A, Suzuki J, Lavine R. **Low testosterone is Associated with Nonalcoholic Steatohepatitis and Fibrosis Severity in Men. Clinical gastroenterology and hepatology: the Official Clinical Practice**. *J Am Gastroenterological Association* (2021.0) **19** 400-2. DOI: 10.1016/j.cgh.2019.11.053
--- title: Disrupted structural connectivity and less efficient network system in patients with the treatment-naive adult attention-deficit/hyperactivity disorder authors: - Takashi Ohnishi - Wataru Toda - Shuntaro Itagaki - Aya Sato - Junya Matsumoto - Hiroshi Ito - Shiro Ishii - Itaru Miura - Hirooki Yabe journal: Frontiers in Psychiatry year: 2023 pmcid: PMC10061975 doi: 10.3389/fpsyt.2023.1093522 license: CC BY 4.0 --- # Disrupted structural connectivity and less efficient network system in patients with the treatment-naive adult attention-deficit/hyperactivity disorder ## Abstract ### Introduction Attention-deficit/hyperactivity disorder (ADHD) is a neuropsychiatric disorder whose primary symptoms are hyperactivity, impulsivity, and inattention. Historically, ADHD was recognized as a disease of childhood and adolescence. However, many patients are known to have persistent symptoms into adulthood. Many researchers consider the neuropathology of ADHD to be based on abnormalities in multiple parallel and intersecting pathways rather than a single anatomical area, but such alterations remain to be clarified. ### Methods Using diffusion tensor imaging, we investigated differences in the global network metrics estimated by graph theory and the degree of connectivity between adjacent voxels within a white matter (WM) fascicle defined by the density of the diffusing spins (connectometry) between 19 drug-naive Japanese patients with adult ADHD and 19 matched healthy controls (HCs). In adult patients with ADHD, we examined the relationships between the symptomatology of ADHD and global network metrics and WM abnormalities. ### Results Compared with HCs, adult patients with ADHD showed a reduced rich-club coefficient and decreased connectivity in widely distributed WMs such as the corpus callosum, the forceps, and the cingulum bundle. Correlational analyses demonstrated that the general severity of ADHD symptoms was associated with several global network metrics, such as lower global efficiency, clustering coefficient, small worldness, and longer characteristic path length. The connectometry revealed that the severity of hyperactive/impulsive symptoms was associated with overconnectivity in the corticostriatal, corticospinal, and corticopontine tracts, the inferior fronto-occipital fasciculus, and the extreme capsule but dysconnectivity in the cerebellum. The severity of inattentive symptoms was associated with dysconnectivity in the intracerebellar circuit and some other fibers. ### Conclusion The results of the present study indicated that patients with treatment-naive adult ADHD showed disrupted structural connectivity, which contributes to less efficient information transfer in the ADHD brain and pathophysiology of ADHD. ### Trial registration UMIN Clinical Trials Registry (UMIN-CTR) UMIN000025183, Registered: 5 January 2017. ## 1. Introduction Attention-deficit/hyperactivity disorder (ADHD) is a neuropsychiatric disease that usually appears in childhood and is characterized by hyperactivity, increased impulsivity, and developmentally inappropriate inattention. ADHD is known to affect children; however, a recent World Health Organization (WHO) survey on global mental health found that the prevalence of patients with adult ADHD was $2.8\%$ on average [1]. Growing evidence suggests that structural abnormalities in the brain may contribute to the pathophysiology of ADHD [2]. However, a recent meta-analysis of functional and structural MRI studies on children and adolescents with ADHD found no significant convergence across structural and functional regional alterations in ADHD, suggesting that the pathophysiology of ADHD might be based on network interactions rather than a regional abnormality [3]. Indeed, a recent study demonstrated that the neuropathology of ADHD is based on multiple parallel and intersecting pathways rather than a single anatomical area, which demonstrated altered functional connectivity in ADHD brains [2]. Diffusion tensor imaging, which is believed to be an indicator of fiber tract integrity, reflecting coherence, organization, and/or density of fiber bundles in the white matter of the brain, is a promising in vivo method that has made it possible to investigate white matter abnormalities in neuropsychiatric disorders [4]. According to a recent meta-analysis, the most common WM abnormality in ADHD was a decrease in fractional anisotropy (FA) in the corpus callosum splenium [5]. However, DTI studies of patients with adult ADHD have been limited, and the results are relatively inconsistent [6, 7]. A possible factor contributing to inconsistent findings in adult patients with ADHD is differences between the ADHD participants with a history of stimulant treatment, such as the difference observed on the starting period of the administration of methylphenidate (MPH) [6]. A previous randomized study investigating the effects of MPH treatment in male patients with ADHD reported that MPH affects FA in association tracts of the left hemisphere and the corpus callosum in children but not in adolescent and adult patients [8]. Similar to the human study, an experimental study in adolescent rats reported increases in FA values in the corpus callosum after MPH administration [9]. Furthermore, a study showed that the striatal genes associated with axonal myelination are upregulated by the administration of MPH in juveniles [10]. Since patients with adult ADHD often consume stimulants for prolonged periods [6], such data indicated that WM integrity is affected by the treatment of MPH, and studies involving medication-naive patients with adult ADHD should be undertaken to understand the pathophysiology of adult ADHD. However, DTI studies conducted on medication-naive patients with adult ADHD are still limited [6]. Moreover, a previous DTI study estimated FA with the region of interest method and tract-based spatial statistics [6]. Therefore, to conduct more comprehensive analyses to investigate WM abnormalities and its associated abnormal brain network topology in treatment-naive patients with adult ADHD, we applied conventional graph theory analysis for connectomics and the “local connectometry” [11] to investigate differences in global network metrics estimated by graph theory and the degree of connectivity between adjacent voxels within a WM fascicle defined by the density of the diffusing spins [11] between healthy controls (HCs) and patients with adult ADHD. Furthermore, we examined relationships between the symptomatology of ADHD and global network metrics and WM abnormalities. Diffusion MRI connectometry was used to derive the correlational tractography that has qualitative anisotropy (QA) correlated with the diagnosis effect and symptom severity of ADHD [11]. Diffusion MRI connectometry is a method to estimate the diffusion quantities along the local tract orientation [11]. We hypothesize that, similar to untreated children with ADHD, untreated patients with adult ADHD show disintegrity of the white matter which is involved in the impairment of complex networks in the brain and contributes to the pathogenesis of adult patients with ADHD. ## 2.1. Participants The call for participants was opened on 1 February 2017 and closed with the entry of the final participant on 1 June 2022. Patients with adult ADHD were recruited not only from the outpatient unit of psychiatry at the Fukushima Medical University but also from neighboring psychiatric hospitals; after explaining the purpose of the study, potential study participants were referred to the outpatient unit of psychiatry at Fukushima Medical University. In the current study, 20 patients with treatment-naive adult ADHD (M:$F = 11$:9) and 20 healthy controls (HCs) (M:$F = 11$:9) participated. From the Fukushima Medical University Hospital's outpatient service, we recruited participants with adult ADHD. At least two qualified psychiatrists diagnosed patients using the criteria outlined in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). Participants with comorbid psychiatric disorders other than ADHD were excluded using the structured clinical interview for the DSM-5 clinical version. The healthy controls were recruited through local advertisements at Fukushima Medical University. Participants having neurological or medical conditions that could potentially affect the central nervous system, such as atypical headache, history of head trauma with loss of consciousness, thyroid disease, epilepsy, seizures, substance-related disorders, or mental retardation, were excluded. Participants with a history of illicit drug use, antidepressants (at least not over the last 3 months), or other psychoactive medication were excluded. The level of intellectual performance of the participants was evaluated with the Japanese version of the Wechsler Adult Intelligence Scale III (WAIS-III) test. The WAIS-III test provides a standardized full-scale intelligence quotient (IQ) based on subtests that measure the level of verbal [verbal IQ (VIQ)] and non-verbal knowledge and reasoning [performance IQ (PIQ)]. We obtained written informed consent from all participants before participation. The presence and severity of ADHD symptoms in both ADHD and HCs were assessed using the Conners' Adult Attention-Deficit/Hyperactivity Disorder Rating Scale (CAARS). The Research Ethics Committee of Fukushima Medical University approved this study (Approval code: no. 2693, Approval data: 13 April 2016). The study was organized and carried out in line with the Declaration of Helsinki. Due to the poor image quality of the diffusion tensor imaging, two participants (one ADHD and one HC) were excluded from the analyses. ## 2.2. MRI acquisition and preprocessing for connectometry analysis and graph theoretical analysis The MRI data were acquired on a Siemens 3T Biograph mMR scanner with a 12-channel phased array coil. DTI acquisition involved a single-shot, spin-echo planar imaging sequence in contiguous axial planes that covered the whole brain. Diffusion-sensitizing gradients were applied in 30 non-collinear directions, together with acquisition without diffusion weighting ($b = 0$). The imaging parameters were set to the following values: TR = 10,100 ms, TE = 78 ms, average = 1, b-value = 1,000 s/mm2, slice thickness = 2.5 mm, and 60 slices. The matrix resolution was acquired at 96 × 96 and reconstructed to 96 × 96. The resolution was 2.5 × 2.5 × 2.5 mm3. DICOM files were converted to SRC files by using DSI Studio. The SRC files were examined during the quality control procedure [12], and the data from two participants were excluded because they did not pass the quality control check. The SRC files were reconstructed in the MNI space using q-space diffeomorphic reconstruction [13] to obtain the spin distribution function [14]. Since the b-value of acquired DTI was lower than 4,000 s/mm2, we applied the advanced option of “no high b for DTI.” A diffusion sampling length ratio of 1.25 was used. The output resolution in diffeomorphic reconstruction was 2.5-mm isotropic. The restricted diffusion was quantified using restricted diffusion imaging [15]. The tensor metrics were calculated using DWI [11] with a b-value of lower than 1,000 s/mm2. The quantitative anisotropy (QA) was extracted as the local connectome fingerprint [16] and used in the connectometry analysis. ## 2.3. Data analyses As the sample size of this study was relatively small, the normal distribution and equal variances were evaluated using the Shapiro–Wilk test and Levene's test, and if these were not guaranteed, then non-parametric tests such as the Mann–Whitney U-test and Spearman's rank-order correlation were used. ## 2.3.1. Statistical analysis for demographic data The chi-squared tests were used for gender and handedness, and independent two-sample t-tests were used for age, IQ, and CARRS between patients with adult ADHD and HCs. Statistical analyses were performed using R Statistical Software Version 3.1.0 (Foundation for Statistical Computing, Vienna, Austria). ## 2.3.2. Network construction and analysis The connectivity matrix (adjacency matrix) and graph theoretical analyses were conducted using DSI Studio and the brain connectivity toolbox [12, 17]. The following procedures were used in the graph theoretical analysis. The first step was to create a tractography map from the DTI data, which included reading and parsing digital imaging and communications in medicine files, reconstructing it to characterize the main diffusion directions of the fibers and fiber tracking [11]. The next step was to generate a connectivity matrix, which was calculated using the counts of the connecting tracts [11]. The Desikan–Killiany–Tourville atlas was used for brain parcellation. This step included obtaining the whole-brain fiber tracks, placing seeding regions in the whole brain and spatial normalization, defining the region of interest, and creating a connectivity matrix [11]. The graph theoretical network measures were then calculated from the connectivity matrix by using the brain connectivity toolbox (brain-connectivity-toolbox.net) [17]. A graph is defined as a set of nodes and the edges or lines between them. The threshold was 0.001 to filter out matrix entries with a small number of connecting tracks. It is a ratio to the maximum connecting tracks in the connectivity matrix. The topology of graphs can be quantitatively described by a variety of measures. In this study, we used weighted graphs and evaluated the following global network metrics [17]: [1] graph density, [2] global efficiency, [3] clustering coefficient, [4] characteristic path length, [5] small worldness, and [6] rich-club coefficient [18]. An independent two-sample t-test or a Mann–Whitney U-test was used to evaluate the diagnostic effect on each global network metric. Pearson's correlation coefficient or Spearman's rank-order correlation was computed to assess the relationship between scores of ADHD symptoms (T-score of CAARS, DSM-IV inattentive symptoms, DSM-IV hyperactive/impulsive, DSM-IV total symptoms, and ADHD index), and global network metrics. These statistical analyses were performed using R Statistical Software Version 3.1.0 (Foundation for Statistical Computing, Vienna, Austria). ## 2.3.3. Local connectometry Local connectometry was conducted using DSI Studio [11]. To test the diagnostic effect, a non-parametric Spearman partial correlation was used to derive the correlation, and the effect of FIQ was removed using a multiple regression model. To explore the relationship between symptomatic severity in adult ADHD and QA, a non-parametric Spearman partial correlation was used to derive the correlation between T-scores of DSM-IV hyperactive–impulsive and DSM-IV inattentive score and QA, and the effects of FIQ, age, and sex were eliminated by using a multiple regression model. A T-score threshold of 3 was assigned and tracked using a deterministic fiber tracking algorithm [19] to obtain correlational tractography. The QA values were normalized. The seeding was placed on the whole brain. The tracks were filtered by topology-informed pruning [20] with four iteration(s). A false discovery rate (FDR) threshold of 0.05 was used to select tracks. A total of 4,000 randomized permutations were applied to the group label to obtain the null distribution of the track length to estimate the FDR. ## 3.1. Dimorphic data The Shapiro–Wilk test indicated that the data were consistent with a normal distribution at a significant level of 0.05. Levene's test indicated that the equality of the error variances was assumed at a significant level of 0.05. Table 1 shows the demographic and clinical characteristics of patients with adult ADHD and HCs. All participants were unrelated Japanese. Data obtained from 19 patients with adult ADHD (mean age: 23.89, men:women = 11:8) and 19 HCs (mean age: 25.00, men:women = 11:8) were analyzed (Table 1). Regarding the type of ADHD, 11 patients belonged to the inattentive type, and eight patients belonged to the combined type. Patients with adult ADHD and HCs did not differ significantly in age, gender, and handedness (Table 1). Patients with adult ADHD exhibited significantly lower IQ and higher CAARS than those with HCs (Table 1). **Table 1** | Unnamed: 0 | Health control | Adult ADHD | P-value (Two sample t-test, Chi-square test) | | --- | --- | --- | --- | | Demographic variables | Demographic variables | Demographic variables | Demographic variables | | Sex (Male: Female) | 11: 8 | 11: 8 | 1 | | Agen (mean s.d) | 23.89 (2.33) | 25.00 (3.52) | 0.262 | | Handedness (Right: Left) | 18: 1 | 17: 2 | 1 | | DSM-IV inattentive symptoms (mean s.d) | 48.47 (6.65) | 87.05 (4.56) | P < 0.001 | | DSM-IV hyperactive/impulsive (mean s.d) | 49.63 (6.66) | 70.57 (16.32) | P < 0.001 | | DSM-IV total symptoms (mean s.d) | 48.89 (5.87) | 83.21 (7.81) | P < 0.001 | | ADHD index (mean s.d) | 48.05 (5.73) | 77.05 (8.23) | P < 0.001 | | Verbal IQ | 122.68 (8.02) | 108.63 (15.93) | 0.0015 | | Performance IQ | 114.57 (11.10) | 101.2 (13.10) | 0.0017 | | Full IQ | 121.15 (7.88) | 106.10 (14.84) | 0.00039 | ## 3.2.1. Diagnostic effects on global network metrics Except for the clustering coefficient in the ADHD and HCs, the Shapiro–Wilk test indicated that the data were consistent with a normal distribution at a significant level of 0.05. Levene's test indicated that the equality of the error variances was assumed at a significant level of 0.05. Therefore, a comparison between adult ADHD and HCs in the clustering coefficient was done with a Mann–Whitney U-test, and a correlational analysis between the ADHD score and clustering coefficient was done with a Spearman's rank-order correlation. Other parameters were tested by parametric analysis (two-sample t-test and Pearson's correlation analysis). Table 2 shows the results of the graph theoretical analysis. We found a statistically significant difference between groups in the rich-club coefficient ($k = 15$) ($$P \leq 0.48$$, $95\%$ CI = −0.143–−0.0001). Patients with adult ADHD had significantly lower rich-club coefficients compared with HCs. Statistically significant differences between groups in the network density, global efficiency, clustering coefficient, characteristic path length, and small worldness were not found. **Table 2** | Network parameters of graph analysis | Health control | Adult ADHD | P-value (95% Confidence interval) | | --- | --- | --- | --- | | Network parameters of graph analysis | mean (standard deviation) | mean (standard deviation) | P-value (95% Confidence interval) | | Graph density | 0.388 (0.039) | 0.377 (0.057) | 0.473 (−0.043–0.0201) | | Global efficiency | 0.0938 (0.011) | 0.0871 (0.015) | 0.125 (0.087–0.0938) | | Clustering coefficient | 0.029 (0.003) | 0.029 (0.007) | 0.506* | | Characteristic path length | 14.637 (1.896) | 16.186 (3.257) | 0.081 (−0.205–3.3021) | | Small worldness | 0.0019 (0.0004) | 0.0018 (0.0006) | 0.529 (−0.00045–0.00023) | | Rich club | Rich club | Rich club | Rich club | | K = 5 | 0.999 (0.0028) | 0.995 (0.0119) | 0.166 (−0.0096–0.0017) | | K = 10 | 0.960 (0.0333) | 0.9408 (0.0725) | 0.283 (−0.057–0.017) | | K = 15 | 0.8202 (0.0690) | 0.7484 (0.1376) | 0.049 (−0.143–−0.0001) | | K = 20 | 0.5849 (0.1015) | 0.5385 (0.1476) | 0.267 (−0.051–0.1204) | ## 3.2.2. Relationship between ADHD symptoms and global network metrics in patients with adult ADHD There were negative correlations between the T-score of the ADHD index and global efficiency (r = −0.481, $95\%$ CI −0.768–−0.0346, $$P \leq 0.037$$) (Figure 1, upper left), clustering coefficient (r = −0.56, $$P \leq 0.0127$$, Spearman's rank-order correlation) (Figure 1, upper right), and small worldness (r = −0.603, $95\%$ CI −0.83–−0.205, $$P \leq 0.0062$$) (Figure 1, lower left). There was a positive correlation between the T-score of the ADHD index and characteristic path length ($r = 0.454$, $95\%$ CI 0.000629–0.753, $$P \leq 0.0495$$) (Figure 1, lower left). There were no statistically significant correlations between the T-score of the ADHD index and the rich-club coefficient. There were no significant correlations between inattentive symptoms, hyperactive/impulsive, DSM-IV total scores, and global network metrics. **Figure 1:** *Correlations between ADHD symptoms (X: T-score of ADHD index) and global network metrics (Y) in patients with adult ADHD. (A) A significantly negative correlation between the T-score of the ADHD index and the global efficiency was noted (r = −0.481, 95% CI −0.768–−0.0346, P = 0.037). (B) A significantly negative correlation between the T-score of the ADHD index and the clustering coefficient was noted (r = −0.56, P = 0.0127). (C) A significantly negative correlation between the T-score of the ADHD index and the small worldness was noted (r = −0.603, 95% CI −0.83–−0.205, P = 0.0062). (D) A significantly positive correlation between the T-score of the ADHD index and the characteristic path length was noted (r = 0.454, 95% CI 0.000629–0.753, P = 0.0495).* ## 3.3.1. Diagnostic effects on connectometry Compared with HCs, the connectometry analysis identified significantly decreased connectivity in patients with adult ADHD in the body of corpus callosum, the cingulum bundle, the forceps minor, the forceps major, the left fornix, the right corticospinal tract, the right superior longitudinal fasciculus III, the right medial lemniscus, and the right corticopontine tract (Figure 2, upper). The connectometry analysis identified no track with increased connectivity in patients with adult ADHD. **Figure 2:** *Results of local connectometry. Upper: Compared with HCs, the connectometry analysis identified significantly decreased connectivity in patients with adult ADHD in the body of the corpus callosum, the cingulum bundle, the forceps minor, the forceps major, the left fornix, the right corticospinal tract, the right superior longitudinal fasciculus III, the right medial lemniscus, and the right corticopontine tract. Middle: Significantly positive correlations between QA and severity of hyperactive–impulsive symptoms in the right corticostriatal tract, the right corticopontine tract, the right corticospinal tract, the right inferior fronto-occipital fasciculus, and the right extreme capsule (red fibers). The QA in the middle cerebellar peduncle and the left cerebellum was found to be negatively related to the severity of hyperactive–impulsive symptoms (blue fibers). Bottom: The connectometry analysis found negative correlations between QA and severity of inattentive symptoms in the bilateral corticospinal tract, the left inferior longitudinal fasciculus, the right corticopontine tract, the middle cerebellar peduncle, the bilateral cerebellum, the right medial lemniscus, and the right dentatorubrothalamic tract.* ## 3.3.2. Connectivity associated with symptoms of ADHD In patients with adult ADHD, positive correlations between QA and severity of hyperactive/impulsive symptoms (T-score of DSM-IV hyperactive–impulsive symptoms) were observed in the right corticostriatal tract, the right corticopontine tract, the right corticospinal tract, the right inferior fronto-occipital fasciculus, and the right extreme capsule (Figure 2, middle; red fibers). The QA in the middle cerebellar peduncle and left cerebellum was found to be negatively related to the severity of hyperactive–impulsive symptoms (Figure 2, middle; blue fibers). The connectometry analysis found negative correlations between QA and severity of inattentive symptoms in the bilateral corticospinal tract, the left inferior longitudinal fasciculus, the right corticopontine tract, the middle cerebellar peduncle, the bilateral cerebellum, the right medial lemniscus, and the right dentatorubrothalamic tract (Figure 2, bottom; blue fibers). There was no significant result in tracks with QA positively correlated with the severity of inattentive symptoms. On the contrary, there was no significant result in tracks with the QA correlated with the ADHD index. ## 4. Discussion In this study, we performed comprehensive analyses of DTI data, local connectometry analysis by using QA and graph theoretical analysis, and conducted comparisons of several measurements between patients with HCs and patients with adult ADHD. We first discuss the results of global network metrics estimated by graph theory and then discuss the results of local connectometry. ## 4.1. Global network metrics Patients with adult ADHD had a significantly lower rich-club coefficient than HCs. Other indicators, such as small worldness and global efficiency, which were found to be abnormal in previous studies of children and adolescents with ADHD [21, 22], were not significantly different in the present study. Network hubs that are members of the rich club are connected to each other, establishing a central rich club that serves as a hub for interregional and global neural signaling as well as whole-brain integration and communication [18]. The results of the graph theoretical analysis suggest that adults with ADHD have altered functional integration and global information communication in the brain. While children with ADHD have a deteriorated small-world network structure, with lower global efficiency and higher local efficiency (21–23), there were discrepancies between studies in the results of global network measures in patients with adult ADHD (23–27). Sidlauskaite et al. reported no differences between patients with adult ADHD and HCs in terms of global network metrics, such as small worldness, global efficiency, and clustering coefficient [24]. Meanwhile, other studies reported lower global efficiency, abnormal rich-club organization and reduced hemispheric asymmetry in adult ADHD [25, 27]. However, these three studies of positive results of global network metrics were reported from the same country, and two out of three studies were from the same laboratory. The results of this study, abnormal rich-club organization, and possible lower global communication in patients with adult ADHD, are similar to the results of those studies and may support the positive results of previous studies [25, 27]. Correlational analyses revealed correlations between global network metrics and the overall severity of symptoms. The more severe the overall symptoms of ADHD, global efficiency, clustering coefficient, and small worldness were lower, and characteristic path length was longer, suggesting that altered functional integration and global information communication in patients with adult ADHD contribute to the general symptomatic severity of ADHD and its close relationship with the pathophysiology of ADHD. Although patients with adult ADHD showed a lower rich-club coefficient than HCs, the correlational analysis showed no correlation between the rich-club coefficient and symptomatic severity. This appears to be some sort of contradictory result. We cannot provide a clear explanation at this time, but we speculated that the rich-club coefficient abnormality may be a trait marker rather than a state marker of ADHD. Further studies will be needed to clarify such speculation. ## 4.2. Local connectometry The local connectometry revelated decreased connectivity in the corpus callosum (CC) (body) and fibers near the corpus callosum, such as the forceps minor, the forceps major, the cingulum bundle, and the left fornix in patients with adult ADHD. In addition, the right corticospinal tract, the right corticopontine tract, the right medial lemniscus, and the right superior longitudinal fasciculus III (SLF III) also showed decreased connectivity in patients with adult ADHD. We speculated that widely distributed disrupted structural connectivity including the CC revealed by local connectometry could be related to the less efficient information transfer system revealed by graph theory in patients with adult ADHD and related to overall symptoms of ADHD. Except for median structure such as the CC and the cingulum, the white matter fiber abnormalities in adult ADHD in this study were found in the right hemisphere as in previous studies [23], suggesting a contribution to the problem of hemispheric lateralization problems in ADHD [25, 27]. The CC is responsible for hemispheric lateralization and communication between cerebral hemispheres [28]. Studies of connectome with agenesis of the corpus callosum demonstrated that patients with agenesis of the corpus callosum showed longer path length and lower global efficiency [29, 30]. This suggests that CC abnormalities may contribute to global network metrics that reflect functional integration. The abnormalities of CC in patients with ADHD have been consistently reported by previous studies, including meta-analyses [23]. The forceps major connects the bilateral occipital lobes via the splenium of the corpus callosum, and the forceps minor connects the lateral and medial surfaces of the frontal lobes via the genu of the corpus callosum [28]. Several studies, including in patients with adult ADHD, showed abnormality in the forceps major and minor [23, 31]. A previous study demonstrated the relationship between executive function and FA in the the right forceps minor in bilingual young adults and showed higher FA in the forceps minor predicted the high performance of a task requiring executive function [32]. Moreover, a previous study reported that the disintegrity of the white matter in both forceps major and minor contributed to more declined cognitive function in patients with type-2 diabetes [33]. These data suggest that impairment in the forceps contributes to cognitive disability, including executive function, which is often observed in patients with ADHD. The cingulum bundle and fornix are important parts of the limbic system [28, 30]. According to DTI studies, the cingulum takes longer to mature even after adolescence and frequently does not take on adult traits until the mid-20s or later [34, 35]. Neuroimaging studies have demonstrated that functions of the cingulum bundle are related to executive/attention functions, emotion, and memory, while clinical studies have demonstrated cingulum abnormalities in neuropsychiatric diseases, including ADHD [36]. The cingulum bundle and fornix are important components of the Papez circuit, which is believed to be essential for cognition, emotion, and episodic memory [36, 37]. The cingulum bundle connects the anterior thalamic nucleus to the cingulate gyrus, and it also connects the cingulate gyrus to the parahippocampal region, while the fornix connects the subiculum to the mammillary bodies [36, 37]. Therefore, the Papez circuit may also play a role in the pathophysiology of major depressive disorders [36, 38]. Although there were no obvious psychiatric comorbidities in our sample, adult ADHD is often associated with psychiatric comorbidities and a high prevalence of depression has also been reported [39]. *Common* genetic factors for ADHD and depression have been reported as contributing to this phenomenon [40]. Therefore, we speculate that abnormalities of these regions seem to be associated with not only cognitive disability but also susceptibility to depressive disorder in adult ADHD. Indeed, in depression, abnormalities in the cingulum bundle and fornix have been observed [36, 38]. The corticopontine tract and the corticospinal tract are associated with the coordination of planned motor functions [23, 28], and abnormalities of these tracts have been considered to be associated with deficits in fine motor control in patients with ADHD [23]. However, a recent study found that the corticopontine tract, a component of cortical-ponto-cerebellar pathways, also known as the cerebello-thalamo-cortical or cortico-ponto-cerebellar loop, serves as a medium for cerebro-cerebellar communication during cognitive processing [41]. We speculated that abnormality in the corticopontine tract might be associated with not only motor control disorder but also cognitive disability in patients with ADHD. The SLF III had possible connections between the supramarginal gyrus and the pars opercularis and bidirectional connections between the ventral prefrontal cortex and the inferior parietal lobule [28]. The SLF III may function to transfer somatosensory information, including language articulation via monitoring orofacial and hand motions [28]. The abnormalities in the SLF were also reported in patients with ADHD [23]. This study found disintegrity in the right medial lemniscus in adult ADHD. To the best of our knowledge, there has been no report of abnormality in this region in patients with ADHD; however, a previous study reported decreased FA in this region in patients with autistic spectrum disorder (ASD) [42]. The medial lemniscus serves as a major route for ascending sensory fibers to the ventroposterolateral thalamus [28]. Since ADHD and ASD are often concurrent and shared sensory symptoms such as hyper- and hyposensitivity to various types of sensory input [43], abnormality in the medial lemniscus might be associated with sensory symptoms in ADHD. Although global network metrics did not significantly correlate with hyperactive/impulsive or inattentive symptoms in the current study, local connectometry detected abnormal fibers associated with these symptoms. Regarding the hyperactive–impulsive symptoms, severe symptoms are associated with higher QA (suggesting overconnectivity) in the right corticostriatal tract, the corticospinal tract, vextreme capsule, and the right inferior fronto-occipital fasciculus. The corticostriatal projections are critical components of forebrain circuits that are extensively involved in motivated behavior [28]. Not only decreased connectivity but also overconnectivity may also disrupt structural connectivity and information transfer, and some studies demonstrated overconnectivity in patients with ASD [40]. The corticostriatal circuits have been linked to dopaminergic pathways that connect the striatum to the prefrontal cortex and other areas, and dysfunction of these circuits has been linked to impairments in cognitive functions and the ability to adapt behavior to changing circumstances [44]. Considering the function of the corticostriatal tract and its contribution to the pathophysiology of ADHD, the association between abnormality in this region and hyperactive–impulsive symptoms seems to be plausible. The inferior fronto-occipital fascia (IFOF) is a WM tract that originates in the occipital and parietal lobes, ends in the inferior frontal lobe, and is connected to the inferolateral insula via the extreme and external capsules with the uncinate fasciculus. While the IFOF and extreme capsule are primarily associated with semantic language processing and transmission [28], research has shown that the IFOF connects the salience network to the executive control network, a possible serving role in goal-oriented behavior [45]. We speculated that, together with the corticostriatal tract abnormalities, IFOF abnormalities may contribute to behavioral abnormalities, such as impulsiveness in ADHD-based disability of goal-oriented behaviors. Decreased connectivity in the cerebellum was associated with both hyperactive–impulsive and inattentive symptoms. As mentioned previously, cerebro-cerebellar communication should be an important component in cognitive processing [38]. Although dopamine receptors are not abundant in the cerebellum, it is involved in the indirect regulation of dopaminergic neurotransmission [44]. An animal study demonstrated that the vermis and paravermal areas in the cerebellum modulate the rate of dopamine and noradrenaline turnover in the caudate and nucleus accumbens [44]. Considering the intimate relationship between the dopaminergic system and the pathophysiology of ADHD, the association between abnormality in the cerebellar circuit and symptoms of ADHD seems to be plausible. Indeed, many studies have demonstrated abnormalities of the cerebellum in ADHD [23, 46]. The results for corticospinal and corticopontine tracts were ambiguous. Compared with HCs, these tracts showed significantly decreased connectivity in the adult ADHD group, but correlational analysis showed a positive correlation with the severity of hyperactive/impulsive symptoms. Furthermore, the corticospinal and corticopontine tracts showed opposite QA patterns in association with symptoms. As described earlier, higher QA in these areas was related to the severity of hyperactive–impulsive symptoms, whereas lower QA was related to the severity of inattentive symptoms. We have no clear explanation of these phenomena but speculate that different patterns of white matter abnormalities, decreased connectivity, and overconnectivity in the same region might be associated in different ways and might contribute to different symptoms in ADHD and patterns of the combination of overconnectivity/decreased connectivity of white matter fibers in each region, which might be associated with severity of clinical symptoms. However, there are no previous studies to support these speculations at this time, and further research is needed. Regarding other tracts, the inferior longitudinal fasciculus (associated with object recognition, face recognition, lexical and semantic processes, emotion, and visual memory) [47], the medial lemniscus (associated with sensory processing) [28], and the dentatorubrothalamic tract (associated with motor control and also is a known site of deep stimulation for the treatment of tremor) [28, 48], we have no clear explanation for associations between disintegrity in these fibers and severity of inattentive symptoms at present because there has been no study that reported a direct relationship between attention and these regions. ## 4.3. Strengths and limitations of this study The superior point of this study is the strict selection of treatment-naive patients with adult ADHD. Since WM integrity was affected by the treatment of MPH, it was important to evaluate DTI data in treatment-naive patients with adult ADHD. Meanwhile, there are some limitations in this study. First, as already mentioned in the Discussion section, the results for the corticospinal and corticopontine tracts were ambiguous, and we have no clear explanation for the phenomenon. Further research is needed to clarify such phenomena. The second limitation is a relatively small sample size of the study. Although Friston's loss-function analysis suggests that the optimal sample size for a neuroimaging study is 16 to 32 participants [49, 50], the small sample size in this study may underestimate the detection of changes in connectivity due to a lack of statistical power and may contribute to seemingly contradictory results, such as the difference between correlation analysis and group comparison results, and due to the fact that graph theory correlation analysis showed a correlation only with the ADHD index but no correlation with core symptoms. However, our findings are similar to previous studies with a larger sample size. Third, this study is a biological study with a small sample, and therefore, we excluded adult ADHD patients with comorbid psychiatric disorders. On the contrary, patients with adult ADHD have been reported to have a high frequency of various comorbid disorders, including affective disorders, anxiety disorders, and substance abuse [39, 51]. Therefore, there are limitations in generalizing the results of this study to patients with adult ADHD, and this point should be interpreted with caution. Fourth, the maximum b-value and the number of directions in this study were lower than those recommended by DSI Studio [13]. We cannot deny the possibility of reduced sensitivity in detecting WM due to our DTI acquisition parameters. However, previous studies that performed DTI like the DTI acquisition parameters in our study and performed similar analyses using DSI Studio demonstrated pathologically relevant results [52, 53]. Finally, healthy control participants in the present study had high IQs. Due to the existence of the relationship between white matter development and connectome and intelligence [54, 55], control participants with high IQ affected the results of this study. However, correlational analyses were conducted only on participants with adult ADHD, and we found relevant results in terms of the pathophysiology of ADHD. ## 5. Conclusion Treatment-naive patients with adult ADHD showed lower rich-club coefficients and decreased connectivity of the corpus callosum and fibers near the corpus callosum and several fibers in the right cerebral hemisphere. The widely distributed abnormal WM would contribute to the abnormal rich club and consequently less efficient information transfer in the ADHD brain. The severity of overall ADHD symptoms was associated with several global network metrics and several fibers associated with the severity of the hyperactive–impulsive symptom and the inattentive symptom, suggesting the contribution of WM abnormalities in the adult ADHD brain to the pathophysiology of adult ADHD. ## Data availability statement The datasets presented in this article are not readily available because Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data is not available. Requests to access the datasets should be directed to tohnish8@its.jnj.com. ## Ethics statement The studies involving human participants were reviewed and approved by Research Ethical Committee of Fukushima Medical University. The patients/participants provided their written informed consent to participate in this study. ## Author contributions TO: conceptualization, methodology, data analyses, writing—original draft preparation, and project administration. HY: writing—review and editing and supervision. HI, SIs, AS, and IM: data collection and writing—review and editing. JM: writing—review and editing. WT and SIt: conceptualization, data collection, and writing—review and editing. ## Conflict of interest TO has disclosed that they are a full-time employee of Janssen Pharmaceutical K.K. of Johnson and Johnson in Japan. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The authors declare that this study received funding from Janssen Pharmaceutical K.K. of Johnson and Johnson in Japan. The funder had the following involvement in the study via TO: study concept, design of study, data analyses, interpretation of data and drafting manuscript. ## Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## References 1. Fayyad J, Sampson NA, Hwang I, Adamowski T, Aguilar-Gaxiola S, Al-Hamzawi A. **WHO world mental health survey collaborators. The descriptive epidemiology of DSM-IV adult ADHD in the world health organization world mental health surveys**. *Atten Defic Hyperact Disord* (2017) **9** 47-65. DOI: 10.1007/s12402-016-0208-3 2. Alexander L, Farrelly N. **Attending to adult ADHD: a review of the neurobiology behind adult ADHD**. *Ir J Psychol Med.* (2018) **35** 237-44. DOI: 10.1017/ipm.2017.78 3. Samea F, Soluki S, Nejati V, Zarei M, Cortese S, Eickhoff SB. **Brain alterations in children/adolescents with ADHD revisited: a neuroimaging meta-analysis of 96 structural and functional studies**. *Neurosci Biobehav Rev.* (2019) **100** 1-8. DOI: 10.1016/j.neubiorev.2019.02.011 4. White T, Nelson M, Lim KO. **Diffusion tensor imaging in psychiatric disorders**. *Top Magn Reson Imaging.* (2008) **19** 97-109. DOI: 10.1097/RMR.0b013e3181809f1e 5. Zhao Y, Yang L, Gong G, Cao Q, Liu J. **Identify aberrant white matter microstructure in ASD, ADHD and other neurodevelopmental disorders: A meta-analysis of diffusion tensor imaging studies**. *Prog Neuropsychopharmacol Biol Psychiatry* (2022) **8** 113-110477. DOI: 10.1016/j.pnpbp.2021.110477 6. Bouziane C, Caan MWA, Tamminga HGH, Schrantee A, Bottelier MA, de Ruiter MB. **ADHD and maturation of brain white matter: a DTI study in medication naive children and adults**. *Neuroimage Clin.* (2017) **29** 53-9. DOI: 10.1016/j.nicl.2017.09.026 7. Schweren LJ, Hartman CA, Zwiers MP, Heslenfeld DJ, Franke B, Oosterlaan J. **Stimulant treatment history predicts frontal-striatal structural connectivity in adolescents with attention-deficit/hyperactivity disorder**. *Eur Neuropsychopharmacol.* (2016) **26** 674-83. DOI: 10.1016/j.euroneuro.2016.02.007 8. Bouziane C, Filatova OG, Schrantee A, Caan MW, Vos FM, Reneman L. **White matter by diffusion mri following methylphenidate treatment: a randomized control trial in males with attention-deficit/hyperactivity disorder**. *Radiology* (2019) **293** 186-192. DOI: 10.1148/radiol.2019182528 9. Van Der Marel K, Klomp A, Meerhoff GF, Schipper P, Lucassen PJ, Homberg JR. **Long-term oral methylphenidate treatment in adolescent and adult rats: differential effects on brain morphology and function**. *Neuropsychopharmacol.* (2014) **39** 263-73. DOI: 10.1038/npp.2013.169 10. Adriani W, Leo D, Guarino M, Natoli A, Di Consiglio E, De Angelis G. **Short-term effects of adolescent methylphenidate exposure on brain striatal gene expression and sexual/endocrine parameters in male rats**. *Ann N Y Acad Sci.* (2006) **1074** 52-73. DOI: 10.1196/annals.1369.005 11. Yeh FC, Badre D, Verstynen T. **Connectometry: a statistical approach harnessing the analytical potential of the local connectome**. *Neuroimage* (2016) **125** 162-171. DOI: 10.1016/j.neuroimage.2015.10.053 12. Yeh FC, Zaydan IM, Suski VR, Lacomis D, Richardson RM, Maroon JC. **Differential tractography as a track-based biomarker for neuronal injury**. *Neuroimage.* (2019) **202** 116131. DOI: 10.1016/j.neuroimage.2019.116131 13. Yeh FC, Tseng WY. **NTU-90: a high angular resolution brain atlas constructed by q-space diffeomorphic reconstruction**. *Neuroimage.* (2011) **58** 91-9. DOI: 10.1016/j.neuroimage.2011.06.021 14. Yeh FC, Wedeen VJ, Tseng WY. **Generalized q-sampling imaging**. *IEEE Trans Med Imaging.* (2010) **29** 1626-35. DOI: 10.1109/TMI.2010.2045126 15. Yeh FC, Liu L, Hitchens TK, Wu YL. **Mapping immune cell infiltration using restricted diffusion MRI**. *Magn Reson Med.* (2017) **77** 603-12. DOI: 10.1002/mrm.26143 16. Yeh FC, Vettel JM, Singh A, Poczos B, Grafton ST, Erickson KI. **Quantifying differences and similarities in whole-brain white matter architecture using local connectome fingerprints**. *PLoS Comput Biol 15;12.* (2016) e1005203. DOI: 10.1371/journal.pcbi.1005203 17. Rubinov M, Sporns O. **Complex network measures of brain connectivity uses and interpretations**. *Neuroimage.* (2010) **52** 1059-69. DOI: 10.1016/j.neuroimage.2009.10.003 18. Dennis EL, Jahanshad N, Toga AW, McMahon KL, de Zubicaray GI. **Development of the “rich club” in brain connectivity networks from 438 adolescents and adults aged 12 to 30**. *Proceedings of IEEE International Symposium Biomed Imaging* (2013) 624-7. DOI: 10.1109/ISBI.2013.6556552 19. Yeh FC, Verstynen TD, Wang Y, Fernández-Miranda JC, Tseng WY. **Deterministic diffusion fiber tracking improved by quantitative anisotropy**. *PLoS One 15;8.* (2013) e80713. DOI: 10.1371/journal.pone.0080713 20. Yeh FC, Panesar S, Barrios J, Fernandes D, Abhinav K, Meola A. **Automatic removal of false connections in diffusion MRI tractography using topology-informed pruning (TIP)**. *Neurotherapeutics.* (2019) **16** 52-8. DOI: 10.1007/s13311-018-0663-y 21. Cao Q, Shu N, An L, Wang P, Sun L, Xia MR. **Probabilistic diffusion tractography and graph theory analysis reveal abnormal white matter structural connectivity networks in drug-naive boys with attention deficit/hyperactivity disorder**. *J Neurosci 26;33.* (2013) 10676-87. DOI: 10.1523/JNEUROSCI.4793-12.2013 22. Beare R, Adamson C, Bellgrove MA, Vilgis V, Vance A, Seal ML. **Altered structural connectivity in ADHD: a network based analysis**. *Brain Imaging Behav.* (2017) **11** 846-58. DOI: 10.1007/s11682-016-9559-9 23. Bu X, Cao M, Huang X, He Y. **The structural connectome in ADHD**. *Psychoradiology* (2021) **1** 257-271. DOI: 10.1093/psyrad/kkab021 24. Sidlauskaite J, Caeyenberghs K, Sonuga-Barke E, Roeyers H, Wiersema JR. **Whole-brain structural topology in adult attention-deficit/hyperactivity disorder: preserved global - disturbed local network organization**. *Neuroimage Clin.* (2015) **13** 506-12. DOI: 10.1016/j.nicl.2015.10.001 25. Li D, Li T, Niu Y, Xiang J, Cao R, Liu B. **Reduced hemispheric asymmetry of brain anatomical networks in attention deficit hyperactivity disorder**. *Brain Imaging Behav.* (2019) **13** 669-84. DOI: 10.1007/s11682-018-9881-5 26. Li D, Cui X, Yan T, Liu B, Zhang H, Xiang J. **Abnormal rich club organization in hemispheric white matter networks of ADHD**. *J Atten Disord.* (2021) **25** 1215-29. DOI: 10.1177/1087054719892887 27. Wang B, Wang G, Wang X, Cao R, Xiang J, Yan T. **Rich-Club analysis in adults with ADHD connectomes reveals an abnormal structural core network**. *J Atten Disord.* (2021) **25** 1068-79. DOI: 10.1177/1087054719883031 28. Wycoco V, Shroff M, Sudhakar S, Lee W. **White matter anatomy: what the radiologist needs to know**. *Neuroimaging Clin N Am.* (2013) **23** 197-216. DOI: 10.1016/j.nic.2012.12.002 29. Owen JP, Li YO, Ziv E, Strominger Z, Gold J, Bukhpun P. **The structural connectome of the human brain in agenesis of the corpus callosum**. *Neuroimage.* (2013) **70** 340-55. DOI: 10.1016/j.neuroimage.2012.12.031 30. Shi M, Freitas L, Spencer-Smith MM, Kebets V, Anderson V, McIlroy A. **Intra- and inter-hemispheric structural connectome in agenesis of the corpus callosum**. *NeuroImage Clinical.* (2021) **31** 102709. DOI: 10.1016/j.nicl.2021.102709 31. Bode MK, Lindholm P, Kiviniemi V, Moilanen I, Ebeling H, Veijola J. **DTI abnormalities in adults with past history of attention deficit hyperactivity disorder: a tract-based spatial statistics study**. *Acta Radiol.* (2015) **56** 990-6. DOI: 10.1177/0284185114545147 32. Mamiya PC, Richards TL, Kuhl PK. **Right forceps minor and anterior thalamic radiation predict executive function skills in young bilingual adults**. *Front Psychol* (2018) **9** 118. DOI: 10.3389/fpsyg.2018.00118 33. Gao S, Chen Y, Sang F, Yang Y, Xia J, Li X. **White matter microstructural change contributes to worse cognitive function in patients with type 2 diabetes**. *Diabetes.* (2019) **68** 2085-94. DOI: 10.2337/db19-0233 34. Lebel C, Beaulieu C. **Longitudinal development of human brain wiring continues from childhood into adulthood**. *J Neurosci.* (2011) **31** 10937-47. DOI: 10.1523/JNEUROSCI.5302-10.2011 35. Lebel C, Gee M, Camicioli R, Wieler M, Martin W, Beaulieu C. **Diffusion tensor imaging of white matter tract evolution over the lifespan**. *Neuroimage.* (2012) **60** 340-52. DOI: 10.1016/j.neuroimage.2011.11.094 36. Bubb EJ, Metzler-Baddeley C, Aggleton JP. **The cingulum bundle: anatomy, function, and dysfunction**. *Neurosci Biobehav Rev.* (2018) **92** 104-27. DOI: 10.1016/j.neubiorev.2018.05.008 37. Senova S, Fomenko A, Gondard E, Lozano AM. **Anatomy and function of the fornix in the context of its potential as a therapeutic target**. *J Neurol Neurosurg Psychiatry.* (2020) **91** 547-59. DOI: 10.1136/jnnp-2019-322375 38. van Velzen LS, Kelly S, Isaev D, Aleman A, Aftanas LI, Bauer J. **White matter disturbances in major depressive disorder: a coordinated analysis across 20 international cohorts in the ENIGMA MDD working group**. *Mol Psychiatry.* (2020) **25** 1511-25. DOI: 10.1038/s41380-019-0477-2 39. Ohnishi T, Kobayashi H, Yajima T, Koyama T, Noguchi K. **Psychiatric comorbidities in adult attention-deficit/hyperactivity disorder: prevalence and patterns in the routine clinical setting**. *Innov Clin Neurosci.* (2019) **16** 11-6. PMID: 32082943 40. Balogh L, Pulay AJ, Réthelyi JM. **Genetics in the ADHD clinic: how can genetic testing support the current clinical practice?**. *Front Psychol* (2022) **8** 751041. DOI: 10.3389/fpsyg.2022.751041 41. Palesi F, De Rinaldis A, Castellazzi G, Calamante F, Muhlert N, Chard D. **Contralateral cortico-ponto-cerebellar pathways reconstruction in humans**. *Sci Rep.* (2017) **7** 12841. DOI: 10.1038/s41598-017-13079-8 42. Kleinhans NM, Pauley G, Richards T, Neuhaus E, Martin N, Corrigan NM. **Age-related abnormalities in white matter microstructure in autism spectrum disorders**. *Brain Res* (2012) **15** 1479.1-16. DOI: 10.1016/j.brainres.2012.07.056 43. Ohta H, Aoki YY, Itahashi T, Kanai C, Fujino J, Nakamura M. **White matter alterations in autism spectrum disorder and attention-deficit/hyperactivity disorder in relation to sensory profile**. *Mol Autism* (2020) **19** 11.77. DOI: 10.1186/s13229-020-00379-6 44. Solso S, Xu R, Proudfoot J, Hagler DJ, Campbell K, Venkatraman V. **Diffusion tensor imaging provides evidence of possible axonal overconnectivity in frontal lobes in autism spectrum disorder toddlers**. *Biol Psychiatry.* (2016) **79** 676-84. DOI: 10.1016/j.biopsych.2015.06.029 45. Conner AK, Briggs RG, Sali G, Rahimi M, Baker CM, Burks JD. **A Connectomic atlas of the human cerebrum-chapter 13: tractographic description of the inferior fronto-occipital fasciculus**. *Operative Neurosurgery.* (2018) **15** S436-43. DOI: 10.1093/ons/opy267 46. Ding L, Pang G. **Identification of brain regions with enhanced functional connectivity with the cerebellum region in children with attention deficit hyperactivity disorder: a resting-state fMRI study**. *Int J Gen Med.* (2021) **27** 14. DOI: 10.2147/IJGM.S303339 47. Herbet G, Zemmoura I, Duffau H. **Functional anatomy of the inferior longitudinal fasciculus: from historical reports to current hypotheses**. *Front Neuroanat.* (2018) **12** 77. DOI: 10.3389/fnana.2018.00077 48. Fenoy AJ, Schiess MC. **Deep brain stimulation of the dentato-rubro-thalamic tract: outcomes of direct targeting for tremor**. *Neuromodulation.* (2017) **20** 429-36. DOI: 10.1111/ner.12585 49. Friston KJ, Holmes AP, Worsley KJ. **How many subjects constitute a study?**. *Neuroimage10.* (1999) 1-5. DOI: 10.1006/nimg.1999.0439 50. Friston K. **Ten ironic rules for non-statistical reviewers**. *Neuroimage 16;61.* (2012) 1300-10. DOI: 10.1016/j.neuroimage.2012.04.018 51. Kooij JJS, Bijlenga D, Salerno L, Jaeschke R, Bitter I, Balázs J. **Updated European consensus statement on diagnosis and treatment of adult ADHD**. *Eur Psychiatry.* (2019) **56** 14-34. DOI: 10.1016/j.eurpsy.2018.11.001 52. Bigham B, Zamanpour SA, Zare H. **Alzheimer's disease neuroimaging initiative. Features of the superficial white matter as biomarkers for the detection of Alzheimer's disease and mild cognitive impairment: A diffusion tensor imaging study**. *Heliyon.* (2022) **8** 8.e08725. DOI: 10.1016/j.heliyon.2022.e08725 53. Zdanovskis N, Platkājis A, Kostiks A, Karelis G, Grigorjeva O. **Brain structural connectivity differences in patients with normal cognition and cognitive impairment**. *Brain Sci.* (2021) **11** 943. DOI: 10.3390/brainsci11070943 54. Northam GB, Liégeois F, Chong WK, Wyatt JS, Baldeweg T. **Total brain white matter is a major determinant of IQ in adolescents born preterm**. *Ann Neurol.* (2011) **69** 702-11. DOI: 10.1002/ana.22263 55. Jiang R, Calhoun VD, Fan L, Zuo N, Jung R, Qi S. **Gender differences in connectome-based predictions of individualized intelligence quotient and sub-domain scores**. *Cereb Cortex.* (2020) **30** 888-900. DOI: 10.1093/cercor/bhz134
--- title: Septin-3 autoimmunity in patients with paraneoplastic cerebellar ataxia authors: - Ramona Miske - Madeleine Scharf - Kathrin Borowski - Nicole Rieckhoff - Bianca Teegen - Yvonne Denno - Christian Probst - Kersten Guthke - Ieva Didrihsone - Brigitte Wildemann - Klemens Ruprecht - Lars Komorowski - Sven Jarius journal: Journal of Neuroinflammation year: 2023 pmcid: PMC10061979 doi: 10.1186/s12974-023-02718-9 license: CC BY 4.0 --- # Septin-3 autoimmunity in patients with paraneoplastic cerebellar ataxia ## Abstract ### Background Septins are cytoskeletal proteins with filament forming capabilities, which have multiple roles during cell division, cellular polarization, morphogenesis, and membrane trafficking. Autoantibodies against septin-5 are associated with non-paraneoplastic cerebellar ataxia, and autoantibodies against septin-7 with encephalopathy with prominent neuropsychiatric features. Here, we report on newly identified autoantibodies against septin-3 in patients with paraneoplastic cerebellar ataxia. We also propose a strategy for anti-septin autoantibody determination. ### Methods Sera from three patients producing similar immunofluorescence staining patterns on cerebellar and hippocampal sections were subjected to immunoprecipitation followed by mass spectrometry. The identified candidate antigens, all of which were septins, were expressed recombinantly in HEK293 cells either individually, as complexes, or combinations missing individual septins, for use in recombinant cell-based indirect immunofluorescence assays (RC-IIFA). Specificity for septin-3 was further confirmed by tissue IIFA neutralization experiments. Finally, tumor tissue sections were analyzed immunohistochemically for septin-3 expression. ### Results Immunoprecipitation with rat cerebellum lysate revealed septin-3, -5, -6, -7, and -11 as candidate target antigens. Sera of all three patients reacted with recombinant cells co-expressing septin-$\frac{3}{5}$/$\frac{6}{7}$/11, while none of 149 healthy control sera was similarly reactive. In RC-IIFAs the patient sera recognized only cells expressing septin-3, individually and in complexes. Incubation of patient sera with five different septin combinations, each missing one of the five septins, confirmed the autoantibodies’ specificity for septin-3. The tissue IIFA reactivity of patient serum was abolished by pre-incubation with HEK293 cell lysates overexpressing the septin-$\frac{3}{5}$/$\frac{6}{7}$/11 complex or septin-3 alone, but not with HEK293 cell lysates overexpressing septin-5 as control. All three patients had cancers (2 × melanoma, 1 × small cell lung cancer), presented with progressive cerebellar syndromes, and responded poorly to immunotherapy. Expression of septin-3 was demonstrated in resected tumor tissue available from one patient. ### Conclusions Septin-3 is a novel autoantibody target in patients with paraneoplastic cerebellar syndromes. Based on our findings, RC-IIFA with HEK293 cells expressing the septin-$\frac{3}{5}$/$\frac{6}{7}$/11 complex may serve as a screening tool to investigate anti-septin autoantibodies in serological samples with a characteristic staining pattern on neuronal tissue sections. Autoantibodies against individual septins can then be confirmed by RC-IIFA expressing single septins. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12974-023-02718-9. ## Introduction Paraneoplastic neurological syndromes (PNS) are rare autoimmune diseases of the nervous system associated with cancer outside the brain. Sera of patients with PNS often contain autoantibodies targeting neuronal proteins. Diagnostically, these autoantibodies can function as markers for distinct neurological autoimmune diseases as well as for the underlying tumors [1, 2]. Septin proteins as target antigens in neurological autoimmune diseases were first described in patients with cerebellar ataxia and anti-septin-5 autoantibodies [3, 4]. Recently, anti-septin-7 autoantibodies were identified in patients with encephalopathy and myelopathy [5], suggesting that autoantibodies targeting different septins may be associated with distinct neurological phenotypes. Septins belong to a large conserved family of guanosine triphosphate (GTP)-binding proteins widely expressed in all metazoan tissues. In humans, at least 13 different septins exist. They are divided into four groups according to sequence similarities: septin-2 group (septins-1,-2,-4, and -5), septin-3 group (septins-3,-9, and -12), septin-6 group (septins-6,-8,-10,-11, and -14) and septin-7 [6]. In vivo, septins self-assemble into hetero-oligomers that contain septins from three or four different groups [6]. These core particles are the building blocks for higher order structures such as filaments, rings and coils, which function in a variety of cellular processes including cell division, cellular polarization, morphogenesis, and membrane trafficking. In the nervous system, septins play a role in neurite formation, as well as pre- and post-synaptic signaling processes, including neurotransmitter exocytosis. Here, we report on septin-3 as a novel autoimmune target antigen in patients with paraneoplastic cerebellar ataxia. ## Methods Reagents were obtained from Merck, Darmstadt, Germany, or Sigma-Aldrich, Heidelberg, Germany, if not specified otherwise. ## Patients Serum samples from the three septin-3 IgG-positive patients were sent in for routine autoantibody testing for diagnostic purposes, including identification of antibody targets. One of the samples (PS1) was previously found to be positive for low-titer GABAB receptor and GAD65 antibodies [7]. All patients gave written informed consent. Anonymized sera of 149 healthy blood donors, 59 patients with multiple sclerosis (ethics committee of Charité—Universitätsmedizin Berlin, EA$\frac{4}{231}$/20 and EA$\frac{4}{018}$/17) and 52 patients with anti-neuronal antibodies (leftover material, laboratory Prof. Stöcker, Lübeck, Germany) were used as controls. ## Indirect immunofluorescence assays Indirect immunofluorescence assays (IIFAs) were performed using microscopy slides mounted with a biochip array consisting of brain tissue cryosections (rat hippocampus, rat or primate cerebellum, murine encephalon), recombinant HEK293 cells separately expressing brain antigens (Hu, Yo, Ri, CV2, PNMA2, ITPR1, Homer 3, CARP VIII, ARHGAP26, ZIC4, DNER/Tr, GAD65, GAD67, amphiphysin, recoverin, GABAB receptor, glycine receptor, DPPX, IgLON5, NMDA receptor, AMPA receptor, mGluR1 receptor, mGluR5 receptor, GLURD2 receptor, LGI1, CASPR2, M1-AQP4, M23-AQP4, MOG, ATP1A3, NCDN), recombinant acetone-fixed HEK293 cells expressing either different histidine-tagged septin combinations (septin-$\frac{3}{5}$/$\frac{6}{7}$/11; septin-$\frac{5}{6}$/$\frac{7}{11}$; septin-$\frac{3}{6}$/$\frac{7}{11}$; septin-$\frac{3}{5}$/$\frac{7}{11}$; septin-$\frac{3}{5}$/$\frac{6}{11}$; septin-$\frac{3}{5}$/$\frac{6}{7}$) or septin-3, -5, -6, -7, or -11 separately, and non-transfected HEK293 cells as control substrate. Each biochip mosaic was incubated with 70 µL of PBS-diluted serum or CSF at room temperature for 30 min, washed with PBS-Tween, and immersed in PBS-Tween for 5 min. In a second step, either Alexa Fluor 488 labelled goat anti-human IgG (Jackson Research, Suffolk, United Kingdom), fluorescein isothiocyanate (FITC)-labelled goat anti-human IgG (EUROIMMUN Medizinische Labordiagnostika AG, Lübeck, Germany) or IgG subclass specific FITC-labelled mouse anti-human IgG (1–4, Sigma-Aldrich) were applied and incubated at room temperature for 30 min. If required, nucleic acid stain (TO-PRO™-3 Iodide, Fisher Scientific, Waltham, USA) was added during the second incubation step. Slides were washed again with a flush of PBS-Tween and then immersed in PBS-Tween for 5 min. Slides were embedded in PBS-buffered, DABCO-containing glycerol (approximately 20 µL per field) and examined by fluorescence microscopy. Samples were classified as positive or negative based on fluorescence intensity of the transfected cells in direct comparison with non-transfected cells and control samples. Endpoint titers refer to the highest dilution showing visible fluorescence. For neutralization assays, diluted serum samples were pre-incubated with HEK293 cell extracts containing the overexpressed recombinant antigen or with empty vector-transfected HEK293 control extracts 1 h prior to tissue incubation. Results were evaluated by two independent observers using a EUROStar II microscope (EUROIMMUN Medizinische Labordiagnostika AG, Lübeck, Germany) or an LSM700 (Zeiss, Jena, Germany). ## Immunoprecipitation Cerebellum from rat was dissected and shock-frozen in liquid nitrogen. The tissues were homogenized in solubilization buffer (100 mmol/L tris–HCl pH 7.4, 150 mmol/L sodium chloride, 2.5 mmol/L ethylenediaminetetraacetic acid, $0.5\%$ (w/v) sodium deoxycholate, $1\%$ (w/v) Triton X-100) containing protease inhibitors (Complete mini, Roche Diagnostics, Penzberg, Germany) at 4 °C. Insoluble material was sedimented by centrifugation at 21,000×g at 4 °C for 15 min. For immunoprecipitation, 500 µL of the supernatant were incubated at 4 °C overnight with 15 µL of patient sera and then with Protein G Dynabeads (ThermoFisher Scientific, Dreieich, Germany) for another 3 h to capture immunocomplexes. Beads were washed 3 times with PBS, and eluted with NuPage LDS sample buffer (ThermoFisher Scientific, Schwerte, Germany) containing 25 mmol/L dithiothreitol at 70 °C for 10 min. Carbamidomethylation with 59 mM iodoacetamide (Bio-Rad, Hamburg, Germany) was performed prior to SDS-PAGE (NuPAGE, ThermoFisher Scientific, Schwerte, Germany). Separated proteins were visualized with Coomassie Brilliant Blue (G-250) (Merck), and identified by mass spectrometric analysis as described elsewhere [8]. ## Recombinant expression of septin proteins in HEK293 cells The cDNA encoding human septin-3, septin-5, septin-6, septin-7 and septin-11 was obtained from Source BioScience UK Limited (clones IRCMp5012E0331D, IRAUp969E0781D, IRAUp969G0159D, IRCMp5012B107D and IRATp970F0181D). The coding sequences were amplified by PCR using the template cDNA and DNA oligonucleotide primers (Additional file 6: Table S1). The amplification products were digested with Esp3I or with NcoI/XhoI as indicated and ligated with NcoI/XhoI linearized pTriEx-1 (Merck, Darmstadt, Germany). The septin proteins were transiently expressed in the human cell line HEK293 following PEI-mediated transfection (Exgene 500), according to the manufacturer’s instructions (Biomol GmbH, Hamburg, Germany). For IIFA, cells were grown on cover slides and acetone-fixed 2 days after transfection. For the production of recombinant cell lysates, cells were harvested 5 days after transfection and lysed by shear-stress. The lysates were stored in aliquots at − 80 °C until further use. His-tagged septin-3 was enriched from HEK293 cells expressing septin-3-His in addition to non-tagged septin-5, -6, -7, and -11 by immobilized metal ion affinity chromatography combined with anion exchange chromatography. ## Tumor tissue staining for septin-3 Formalin-fixed paraffin-embedded melanoma and lymph node metastasis tissue from patient 1 were sectioned (4 μm). As positive control, mouse cerebellum tissue was sectioned as well. Slices were placed onto slides, deparaffinized, rehydrated, and subjected to heat-induced epitope-retrieval using Target Retrieval Solution (pH 9, 3-in-1, Dako, Hamburg, Germany) according to the supplier’s instructions. Subsequently, the slides were washed with Tris-buffered saline (TBS) containing $0.05\%$ Tween-20 at room temperature. Blocking was performed with serum-free protein block (Thermo Fisher Scientific, Schwerte, Germany) for 10 min. Polyclonal rabbit anti-septin-3 (HPA003548, Sigma-Aldrich, Taufkirchen, Germany) was diluted 1:500 or 1:1,000 in Dako antibody diluent and then applied for 30 min. Polyclonal rabbit anti-septin-11 (HPA003459, Sigma-Aldrich) and monoclonal rabbit anti-GABARB2 receptor (ab75838, Abcam) were used as controls in a 1:75 and 1:250 dilution, respectively. As a negative control, rabbit immunoglobulin fraction (X0936, Dako, Hamburg, Germany) was used. Envision + HRP Rabbit detection system (Dako, Inc., Santa Clara, US) was used according to manufacturer’s instructions to detect bound rabbit IgG. This system is based on a horseradish peroxidase-labelled, avidin/biotin-free polymer conjugated with the secondary antibody to reduce background staining from endogenous peroxidase and pseudoperoxidase; 3,3′-diaminobenzidine (DAB +) chromogen is used for visualization. Hematoxylin (Leica Biosystems, Wetzlar, Germany) was used for counterstaining. Slides were mounted with the water-free mounting medium Neo-Mount (VWR, Darmstadt, Germany). ## Clinical and paraclinical features Clinical and paraclinical findings, tumor associations, therapy and outcome of the three patients are summarized in Table 1. All patients were male and ≥ 60 years of age at onset of neurological symptoms (64, 69, and 60 years, respectively). All presented with a subacute progressive cerebellar syndrome, including dysarthria and gait ataxia. One patient had prominent downbeat nystagmus. Two patients had metastatic malignant melanoma, detected 5 and 19 months before onset of neurological symptoms, and one patient had metastatic small cell lung cancer, detected 3 months after onset of neurological symptoms. An MRI scan performed 9 months after onset of ataxia revealed mild cerebellar atrophy in patient 2, while for the other two patients cerebral MRIs were normal 3 and 17 months (patient 1) or 2 months (patient 3) after neurological onset. CSF findings indicated mild pleocytosis in 2 patients and total protein elevation in all 3. Importantly, all three patients showed evidence of an intrathecal IgG production, consistent with an autoimmune process. Immunotherapies comprised pulsed high-dose intravenous methylprednisolone in all patients and plasma exchange, intravenous immunoglobulins and rituximab in one patient; they were associated with no further progression, or transient improvement, but no sustained improvement of neurological symptoms (Table 1). One patient died at home with the direct cause of his death being unknown. Table 1Clinical and paraclinical findings, tumor associations, therapies, and outcome of three patients with septin-3 IgG antibodiesPatient #, sex, age at neurological onsetClinical summaryCancerCancer therapy (start before/after onset of neurological symptoms)Septin-3 IgGCoexisting antibodiesBrain MRICSF findingsImmunotherapy (response)Follow-up time (months)Outcome#1male, 64aSubacute progressive cerebellar syndrome with dysarthria, bilateral limb and gait ataxiaMalignant melanoma (Clark level IV) right groin with inguinal lymph node metastasesAdjuvant immunotherapy with interferon-alpha-2b(5 months before)Serum 1:32.000Antinuclear Abs (1:320), GAD 65 Abs (28 units/ml, reference range 10 units/ml), GABAB receptor Abs 1:100Few small unspecific microangiopathic frontal lesions3 cells/µl, TPb 718 mg/l, QAlb mildly elevated, intrathecal IgG synthesis ($56\%$)c3 × 500 mg IVMP (none),PE (transient), IVIG (transient), rituximab (transient)29Death#2male, 69Subacute progressive cerebellar syndrome with dysarthria and marked gait ataxiaMalignant melanoma (Clark level IV) back with lung, liver and axillary lymph node metastasesIrradiation; adjuvant immunotherapy with nivolumab; dabrafenib and trametinib(19 months before)Serum 1:10.000, CSF 1:1000NoneMild cerebellar atrophy, moderate microangiopathy13 cells/µl, TPb 611 mg/l, normal QAlb, CSF-specific OCB5 × 1000 mg IVMP (partial improvement)32Cerebellar syndrome stable, but worsening (epileptic seizures) due to cerebral metastases#3male, 60Subacute progressive cerebellar syndrome with dysarthria, ataxia left leg, down beat nystagmusSmall-cell lung cancer with mediastinal lymph node metastasesCisplatin/etoposide and atezolizumab(3 months after)Serum1:100.000Antinuclear Abs (1:160)Normal20 cells/µl, TPb 889 mg/l, CSF-specific OCB5 × 250 mg IVMP, oral prednisolone (no further worsening)15Neurological symptoms stableAbs, antibodies; CSF, cerebrospinal fluid; IgG, immunoglobulin G; IVMP, intravenous methylprednisolone; IgG, immunoglobulin G; IVIG, intravenous immunoglobulins; MRI, magnetic resonance imaging; OCB, oligoclonal bands; PE, plasma exchange; TP, total proteinaPatient 1 was previously reported in [7]bUpper limit of normal of CSF total protein: 450 mg/lcNormal value for intrathecal IgG production: $0\%$ ## Patient sera show a similar pattern in indirect immunofluorescence assays with neuronal tissues In tissue IIFA, sera of the three patients revealed a characteristic, similar staining pattern on rat hippocampus, with more intense staining of the outer than the inner molecular layer (Fig. 1A). Furthermore, the sera stained the granular layer and molecular layer of rat and primate cerebellum, while Purkinje cells were mostly spared (Fig. 1B). On murine encephalon sections, the strongest reactivity was detected in the molecular layer of the cerebellum (Fig. 1C, D). Interestingly, the characteristic staining pattern on rat hippocampus was likewise observed with anti-septin-5 or anti-septin-7 positive control sera (Additional file 1: Fig. S1), suggesting that the three patient sera might also contain anti-septin autoantibodies. Serum endpoint titers in IIFA (as detected using rat hippocampus sections) ranged between 1:1000 and 1:10,000.Fig. 1Immunofluorescence staining of central nervous tissues with patient sera. Cryosections of rat hippocampus, primate cerebellum and murine encephalon were incubated with patient sera (A, B Patients 1–3, 1:100; C, D Patient 1, 1:100) in the first step, and with Alexa Fluor 488 labeled goat anti-human IgG in the second step (green). A granular staining of the molecular layer (ml) was obtained on hippocampus and cerebellum (A, B). Additionally, a blotchy fluorescence of the granular layer was observed on cerebellum (B). On hippocampus, staining of the outer molecular layer (oml) is more intense compared to the inner molecular layer (iml) (A). On murine encephalon (C), the strongest reactivity was detected in the molecular layer of the cerebellum (D). Nuclei were counterstained by incubation with TO-PRO-3 iodide or DAPI (blue). ( Scale bar: A, B 100 µm, C 1000 µm, D 250 µm.) Testing of the three patient sera using IIFA with recombinant HEK293 cells separately expressing 30 different neuronal antigens (Hu, Yo, Ri, CV2, PNMA2, ITPR1, Homer 3, CARP VIII, ARHGAP26, ZIC4, DNER/Tr, GAD65, GAD67, amphiphysin, recoverin, GABAB receptor, glycine receptor, DPPX, IgLON5, NMDA receptor, AMPA receptor, mGluR1 receptor, mGluR5 receptor, GLURD2 receptor, LGI1, CASPR2, M1-AQP4, M23-AQP4, MOG, ATP1A3, NCDN) revealed no positive results except for serum of patient 1, which showed a positive reaction with GABAB receptor-transfected HEK293 cells, as previously reported [7], with an endpoint titer of 1:100. At the same time, co-existing anti-glutamate decarboxylase antibodies were detected at low levels by ELISA (28 units/ml, upper reference limit 10 units/ml) in serum of patient 1. However, because of the particularly strong brain tissue IIFA pattern caused by this patient’s serum (endpoint titer of 1:1000 on rat hippocampus at Laboratory Stöcker, Lübeck; 1:20,000 on cerebellum at the University of Heidelberg using different methodology [7]) the presence of an additional autoantibody was suspected. ## Patient sera immunoprecipitate septin-3, -5, -6, -7 and -11 In immunoprecipitates of all three patient sera and cerebellar lysates, four bands were revealed by SDS-PAGE/Coomassie staining in the 35–55 kDa range (Fig. 2). Mass spectrometry identified five members of the septin family (septin-3, -5, -6, -7 and -11) in gel fragments picked at the position of these bands. Fig. 2Immunoprecipitation and antigen identification with patient serum. A SDS-PAGE of the immunoprecipitates of patient serum 1 (PS1) or control serum (CS) with cerebellar lysates stained with colloidal Coomassie revealed four specific bands (1–4) between 40 and 50 kDa in the eluate fraction of PS1 but not CS. Mass spectrometry analysis identified the following proteins: band 1 septin-6,-7,-11; band 2 septin-6,-7,-11; band 3 septin-3,-5; band 4 septin-3,-5 in the eluate fraction of PS1 ## Recombinant septin-3, -5, -6, -7 and -11 build complexes in HEK293 cells Immobilized metal chelate affinity chromatography purification of recombinant septin-3-His together with non-tagged septin-5, -6, -7, and -11 in HEK293 cells was performed to prove that these five septin proteins form a complex. By mass spectrometry analysis, all transfected septins were detected clearly, indicating that septin heterocomplexes have been formed in the recombinant HEK293 cells (Additional file 2: Fig. S2). ## Patient sera recognize septin-3 in recombinant indirect immunofluorescence assays As the exact epitope of the patients’ antibodies was unclear, septin-3, -5, -6, -7 and -11 were expressed either individually or together in HEK239 cells and tested using RC-IIFA. All three patient sera showed a positive reaction with septin-3 expressing HEK293 cells (IgG end titers 1:32,000, 1:10,000 and 1:100,000; no IgA/IgM) and the HEK293 cells expressing the septin-$\frac{3}{5}$/$\frac{6}{7}$/11 complex (IgG end titers 1:32,000, 1:10,000 and 1:100,000; no IgA/IgM) (Fig. 3). The serum anti-septin-3 autoantibodies belonged to the IgG1 and IgG2 subclass (patient 1: IgG1 and IgG2; patient 2: IgG2; patient 3: IgG2 > IgG1). CSF was available from patient 2 and reacted positively with the HEK293-septin-3 cells and the HEK293-septin-$\frac{3}{5}$/$\frac{6}{7}$/11 cells (IgG titer 1:1,000 with each substrate; IgG2 subclass). As no blood–CSF barrier dysfunction was present in this patient (as indicated by a normal CSF/serum albumin quotient), intrathecal synthesis of septin-3 IgG is likely based on a CSF/serum ratio of 1:10 (compared to a normal mean CSF/serum ratio for total IgG of ~ 1:300). In contrast, sera with known anti-septin-5- or anti-septin-7-reactivity, respectively, used as controls, reacted with the septin-5-transfected cells (end titer 1:1,000) or septin-7-transfected cells (end titer 1:3200), respectively, and with the septin-$\frac{3}{5}$/$\frac{6}{7}$/11 complex cells (end titer 1:3200 and 1:1000, respectively) but not with the septin-3-expressing HEK293 cells (Fig. 3).Fig. 3Indirect immunofluorescence analysis of single septin or septin-$\frac{3}{5}$/$\frac{6}{7}$/11 complex transfected HEK293 cells with patient serum. Acetone-fixed recombinant HEK293 cells expressing septin-3, septin-5, septin-6, septin-7 or septin-11 individually or co-expressing septin-$\frac{3}{5}$/$\frac{6}{7}$/11 or an empty vector-transfected control were incubated with the patient serum (PS1) or an anti-septin-5 or anti-septin-7 positive serum (1:100). All anti-septin positive sera showed a positive reaction with the recombinant septin-$\frac{3}{5}$/$\frac{6}{7}$/11 expressing HEK293 cells. Additionally, PS1 recognized cells expressing recombinant septin-3 but none of the other single septin expressing HEK293 cells. Recombinant septin-5 or septin-7 expressing HEK293 cells were recognized by the anti-septin-5 or anti-septin-7 positive control serum, respectively. ( Scale bar 100 µm.) Experiments with HEK293 cells expressing different combinations of only four of the five septins confirmed anti-septin-3 specificity of the three patient sera. Indeed, the combination of septin-$\frac{5}{6}$/$\frac{7}{11}$ missing septin-3 was the only one not recognized by the patient sera (Additional file 3: Fig. S3). None of 149 healthy control sera showed a positive reaction with the HEK293-septin-$\frac{3}{5}$/$\frac{6}{7}$/11 complex cells. However, four of 149 healthy control sera were positive in RC-IIFA with HEK239 cells expressing recombinant septin-3 in a 1:1000 dilution (end titer CS7, CS24, CS74 1:1,000; CS74 1:3,200), but importantly, none of these sera showed the characteristic staining pattern in tissue IIFA at a dilution of 1:10 and 1:100 (data not shown), suggesting that the antibodies present in the four controls were different from those in the three patients. This was confirmed by immunoprecipitation experiments, in which none of the tissue IIFA-negative control sera that reacted with the septin-3-expressing HEK293 cells immunoprecipitated septin proteins from hippocampal lysates (Additional file 4: Fig. S4). Therefore, we suggest that the septin-$\frac{3}{5}$/$\frac{6}{7}$/11 complex should be used as a first-line tool for the work-up of samples suspected to contain anti-septin-3 autoantibodies because of the tissue pattern in IIFA. Furthermore, 59 sera of patients with multiple sclerosis and 50 sera of patients positive for different anti-neuronal autoantibodies (10 × anti-Yo, 10 × anti-Ri, 10 × anti-GAD65, 10 × anti-ITPR1, 10 × Sez6l2) were analyzed using RC-IIFA with HEK293-septin-$\frac{3}{5}$/$\frac{6}{7}$/11 complex cells as controls. Only $\frac{1}{59}$ sera of patients with multiple sclerosis and $\frac{1}{50}$ sera of patients with anti-neuronal autoantibodies reacted positive in RC-IIFA with HEK293-septin-$\frac{3}{5}$/$\frac{6}{7}$/11 complex cells at a titer of 1:320 in both cases. However, similar to the RC-IIFA-positive healthy controls, none of these two control sera showed the distinct staining pattern observed with the three patient sera in the tissue IIFA (data not shown). ## Tissue reactivity of patient sera is caused by anti-septin-3 autoantibodies To determine whether the tissue IIFA pattern was caused by anti-septin-3 autoantibodies, we performed neutralization assays. Pre-incubation of patient serum with HEK293-septin-3 or HEK293-septin-$\frac{3}{5}$/$\frac{6}{7}$/11 extract abolished the tissue reactivity of serum of patient 1, while incubation with empty vector-transfected HEK293 cell extract had no effect (Fig. 4). By contrast, pre-incubation with the HEK293-septin-5 extract had no effect on the tissue reactivity of the anti-septin-3-positive patient serum (Fig. 4), but clearly reduced the reactivity of an anti-septin-5-positive serum (Additional file 5: Fig. S5).Fig. 4Neutralization of indirect immunofluorescence reaction on neuronal tissues with patient serum. Patient serum 1 (PS1, 1:100) was pre-incubated with extracts of HEK293 cells transfected with empty control vector (A) or with septin-$\frac{3}{5}$/$\frac{6}{7}$/11 (B), septin-3 (C) or septin-5 (D) before an indirect immunofluorescence assay with neuronal cryosections and Alexa Fluor 488 labeled goat anti-human IgG as secondary antibody was performed. The extract containing the septin-$\frac{3}{5}$/$\frac{6}{7}$/11 complex or septin-3 alone (B, C) abolished the immune reaction of PS1 on rat hippocampus, rat and primate cerebellum. The HEK293 control and the HEK293-septin-5 extracts (A, D) had no effect. Nuclei were counterstained with TO-PRO-3 iodide (blue). ( Scale bar: 100 µm.) ## Septin-3 is expressed in patient tumor tissue Tumor tissue was only available from patient 1. The analysis of paraffin-embedded tumor sections from patient 1 using conventional IHC with an anti-septin-3-specific commercial antibody, revealed areas with high expression of septin-3 both in the melanoma and in the lymph node metastasis (Fig. 5).Fig. 5Expression of septin-3 in tumor specimens obtained from patient 1. Immunohistochemical staining of mouse cerebellum (A1, B1) and patient’s cancer specimens (A2-3, B2-3) incubated with anti-septin-3 rabbit commercial antibody diluted 1:1000 (A1) or 1:500 (A2-3) and control rabbit serum (B1-3). ( A2, B2) Melanoma, patient 1; (A3, B3) lymph node metastasis, patient 1. ( Magnification: 200-fold.) IHC of mouse cerebellum sections with the anti-septin-3 antibody showed staining of the granular layer and molecular layer (Fig. 5) similar to the pattern observed with patient sera in IIFA with rat cerebellum (Fig. 1). In contrast, IHC using a commercial anti-septin-11 antibody or a commercial anti-GABAB2 receptor antibody showed no staining of the tumor tissues of patient 1, though antibody functionality was demonstrated by IHC with mouse neuronal tissue sections (data not shown). ## Discussion In this study we identified septin-3 as a new target antigen in autoimmune cerebellar ataxia. All three patients analyzed had cancer (2 × melanoma, with a lymph node metastasis in one of them; 1 × small cell lung cancer with lymph node metastases), suggesting that septin-3 IgG-associated autoimmune cerebellar ataxia may be a novel paraneoplastic neurological syndrome (PNS). Indeed, septin-3, which is usually absent outside the CNS [9, 10], was ectopically and strongly expressed in two tumor samples from one of our patients, corroborating this notion. However, as no tumor material was available of patient 2 and 3, we could not investigate septin-3 expression in these cases. Human septin-3 is expressed at high level in the human cerebellum, the cerebral cortex, including the temporal cortex, and in the hippocampus [9, 11]. In the neocortex, it was found in association with neuropil and punctate structures suggestive of synaptic junctions [11]. For human septin-3, a role in synaptic vesicle recycling [10], synaptogenesis and neuronal development has been suggested [11]. A role of septins in tumorigenesis has been discussed previously, as septin mutations or changes in expression levels have been observed in a variety of cancers [12–14]. Mutations of septin-3 have most frequently been observed in lung, skin and intestinal tumors [12]. Previously, autoantibodies against another neuron-specific septin, septin-5, have been reported in patients with cerebellar ataxia, which was associated with eye movement disorders in most of them. However, no associated tumor was found in any of the published cases associated with anti-septin-5 autoantibodies [3–5]. Most recently, we and others identified antibodies against septin-7, which is ubiquitously expressed throughout the body. The clinical phenotypes of septin-7 IgG-positive patients were more diverse [5]. The majority developed encephalopathy, partially accompanied by neuropsychiatric symptoms. A tumor was detected in $\frac{4}{15}$ anti-septin-7-positive patients. Importantly, in the present study none of the septin-3-positive patient sera cross-reacted with septin-5 or septin-7, as demonstrated by RC-IIFA. In line with this finding, neither the serum nor the CSF of a septin-3-positive patient (patient #1 in the present study) reacted significantly with isolated recombinant septin-5, septin-7, septin-1, septin-2, septin-4 (transcript variants 1 and 3), septin-6 (transcript variant II and V), septin-9, septin-11 or septin-12 included in a microarray assay independently performed at the University of Heidelberg (data not shown; see ref. [ 15] for methodology); recombinant septin-3 was not available at that time and thus not included in this commercial protein array by the manufacturer. Altogether, the different clinical phenotypes reported in patients with anti-septin-3, anti-septin-5 or anti-septin-7 autoimmunity so far suggest that these autoantibodies may be markers for different clinical presentations. Eight out of ten anti-septin-5 or -7 patients with immunotherapy data available improved after treatment. [ 3–5]. In the present study, only transient improvement (patient 1 and 2) or stabilization of symptoms but no improvement (patient 3) was observed in the anti-septin-3-positive patients. However, little is known about how different treatments and tumor associations influence short-term and long-term outcome. Although septins are reported as intracellular proteins, a pathogenic role of anti-septin-5 and -7 autoantibodies was suggested because these autoantibodies react in IIFA with living hippocampal neurons and had electrophysiological effects on cortical neurons [5]. However, the pathogenic mechanism of autoantibodies targeting intracellular septin proteins is not understood so far and requires further studies. It is currently unknown whether septin-3 can reach the cell surface during exocytosis, which would make it accessible to extracellular IgG. Notably, the onset of cerebellar ataxia in patient 1 and patient 2 was preceded by tumor therapy with interferon-alpha (INF-alpha-2b) or the checkpoint inhibitor nivolumab, respectively. Induction of autoimmune diseases has been previously described as a relatively frequent side effect of interferon-alpha treatment [16–18]. In a clinical trial, production of autoantibodies (including anticardiolipin, antithyroglobulin, and antinuclear antibodies) was observed in as much as $52\%$ of patients with malignant melanoma treated with pegylated IFN-alpha-2b [19]. It thus appears conceivable that IFN-alpha treatment may have contributed to the development of autoimmunity in patient 1. Similarly, a substantial proportion of cancer patients treated with immune checkpoint inhibitors (ICI) develop immune-related adverse events (irAE), with a reported incidence of ~ $20\%$ of high grade irAE for anti-PD1 treatment and of ~ $60\%$ for the combination of anti-PD1 and anti-CTLA4 [20]. Neurological manifestations with grade 3 or 4, including autoimmune encephalitis, were reported with an incidence of $1.5\%$ after ICI treatment [21]. Melanomas are very rare among the tumors usually associated with PNS [22]. However, ICI could induce such complications also in patients with tumors not commonly associated with paraneoplastic neurological syndromes [23, 24]. In particular, Sechi et al. reported that among 63 patients with ICI-related neurologic autoimmunity 27 had melanoma [23]. In patient 3, PD1-inhibition was only initiated 3 months after onset of neurological symptoms. In serum of patient 1, co-existing antibodies to GABAB receptor (titer RC-IIFA 1:100) and low-titer antibodies to GAD65 (28 units/ml, reference range 10 units/ml; negative pancreas tissue IIFA) were detected in serum but not in CSF [7] in addition to septin-3 antibodies (end titer RC-IIFA 1:32.000), which is in line with a more widespread effect of interferon-alpha on (auto)antibody production. While the tissue IIFA neutralization experiments indicate that the tissue pattern was caused by anti-septin-3 autoantibodies (Fig. 4), it cannot be ruled out that the patient’s clinical symptoms were in part also related to autoimmunity to GABAB receptor and/or GAD65. Patient sera applied in this work precipitated a septin-$\frac{3}{5}$/$\frac{6}{7}$/11 complex from rat cerebellar lysates. Tsang et al. described septin complexes including these five septins which contain one member of each septin group and septin-6 and -11 from the same group in rat hippocampal neurons and suggested a functional role of these complexes in neurotransmitter release [26]. The immunoprecipitation of a complex with two septins from the same group might indicate that septin filaments bound by the patients’ autoantibodies contain two distinct septin octamers. In addition, Fujishima et al. demonstrated that in mature nerve terminals of mice, septin-3 directly binds to septin-5 and septin-7, which form a heteromeric complex [27]. RC-IIFA with HEK293-cells expressing septin-3,-5,-6,-7 or -11 separately demonstrated that our patients’ autoantibodies target septin-3 but none of the other septin proteins, indicating that the other septin proteins were co-immunoprecipitated. Complex formation of recombinant septin-3, -5, -6, -7, and -11 was demonstrated in this work by co-purification of septin-5, -6, -7, and -11 with septin-3-His from HEK293 cells (Additional file 2: Fig. S2). The observation that the anti-septin-3-positive patient sera described in this study did not bind recombinant septin-5,-6,-7 or -11 suggests that the epitopes recognized by anti-septin-3 autoantibodies are located in the heterogeneous N- or C-terminal region of septin-3, rather than the conserved central GTPase and polybasic domains [6]. Control sera reacted more frequently with the HEK293-septin-3 RC-IIFA substrate compared to the HEK293-septin-$\frac{3}{5}$/$\frac{6}{7}$/11 complex-expressing cells. Several studies indicate that single septins that are missing their partners become unstable and aggregate into amyloid-like structures [28, 29]. It is therefore conceivable that septin heterocomplexes are the dominant physiological species. Testing only with cells overexpressing septin-3 alone, as done for RC-IIFA, might give false-positive results; as such overexpression might create unphysiological filaments exposing neoepitopes prone to antibody binding. As a strategy for the detection of anti-septin-3 autoantibodies in patient samples with a characteristic pattern using IIFA with hippocampal or cerebellar tissue sections, we would thus recommend an IIFA with HEK293 cells expressing the recombinant septin-$\frac{3}{5}$/$\frac{6}{7}$/11 complex as screening assay. Positive samples should then be analyzed further in an RC-IIFA with single septin expressing HEK293-cells including HEK293-septin-3 cells to confirm specificity for the individual septin. In contrast, positive results obtained only by use of septin-3-transfected HEK293-based assays and without confirmation in tissue-based IIFA and heterocomplex-based assays should be regarded with caution and may be non-specific. Septin-3 antibodies form part of a broader spectrum of novel autoantibodies associated with cerebellar ataxia that have been discovered over the past two decades (e.g., [3–5, 15, 30–40]), some of which are of paraneoplastic origin. Revealing an underlying autoimmune pathogenesis and/or paraneoplastic etiology in patients presenting with cerebellar ataxia of unknown cause may substantially help to guide treatment decisions and hopefully lead to better outcomes in the future. ## Conclusions Together, our data indicate that autoantibodies against the septin-heterocomplex-integrated form of septin-3 may represent a novel biomarker in a paraneoplastic form of autoimmune cerebellar ataxia. After septin-5 and septin-7, septin-3 is the third member of the septin protein family which is linked to neuronal autoimmunity and many undetected cases might possibly exist. Antibodies against all three septins can be detected by RC-IIFA using HEK293 cells expressing septin-$\frac{3}{5}$/$\frac{6}{7}$/11. Thus we suggest inclusion of the septin-$\frac{3}{5}$/$\frac{6}{7}$/11 complex in neuronal autoimmunity routine testing. ## Supplementary Information Additional file 1: Figure S1. Immunofluorescence staining of rat hippocampus with different anti-septin-positive sera. Cryosections of rat hippocampus were incubated with patient serum 1 (PS1, anti-septin-3 positive), an anti-septin-5-positive control serum, and an anti-septin-7-positive control serum (1:100), respectively, in the first step, and with Alexa Fluor 488 labeled goat anti-human IgG in the second step (green). A more intense staining of the outer molecular layer (oml) compared to the inner molecular layer (iml) was observed with these three different anti-septin positive sera. Nuclei were counterstained by incubation with TO-PRO-3 iodide (blue). ( Scale bar: 100 µm).Additional file 2: Figure S2. Purification of septin-3/-5/-6/-7/-11 complex by immobilized metal ion affinity chromatography (IMAC). His-tagged septin-3 and non-tagged septin-5, -6, -7, and -11 were coexpressed in HEK293 cells. Septin-3-His was enriched by IMAC combined with anion exchange chromatography. The fraction was analyzed by ESI-TOF mass spectrometry and visualized by SDS-PAGE stained with Coomassie. Identified recombinant septins are shown in the table (numbers of identified peptides in parentheses).Additional file 3: Figure S3. Indirect immunofluorescence analysis of HEK293 cells expressing different combinations of septin proteins with patient serum. Acetone-fixed recombinant HEK293 cells expressing the septin-$\frac{3}{5}$/$\frac{6}{7}$/11 complex or different combinations of only four of the five septins were incubated with the patient serum (PS1) or an anti-septin-5 or anti-septin-7-positive control serum (1:100). PS1 did not react with the combination lacking septin-3, but with all other combinations. In contrast, the sera comprising an autoantibody to septin-5 or septin-7 did not show a positive reaction with the combinations lacking septin-5 or septin-7, respectively. ( Scale bar: 100 µm).Additional file 4: Figure S4. Immunoprecipitation and antigen identification with patient serum and control sera. SDS-PAGE of the immunoprecipitates of patient serum 1 (PS1) or control sera (CS) with cerebellar lysates stained with colloidal Coomassie. Mass spectrometry analysis of the 35–55 kDa range was performed with every sample. Septins were identified above cutoff only in the immunoprecipitate of PS1 but not in any of the four control sera. Additional file 5: Figure S5. Neutralization of indirect immunofluorescence reaction on neuronal tissues with anti-septin-5 positive serum. An anti-septin-5 positive serum was pre-incubated with extracts of HEK293 cells transfected with empty control vector or with septin-5 before an indirect immunofluorescence assay with neuronal cryosections and Alexa Fluor 488 labeled goat anti-human IgG as secondary antibody was performed. The extract containing overexpressed septin-5 abolished the immune reaction of the anti-septin-5 positive serum on rat hippocampus, rat and primate cerebellum. Nuclei were counterstained by incubation with TO-PRO-3 iodide (blue). Please note that vascular staining on primate tissue is not septin-3-specific but was caused by cross-reaction of the goat anti-human IgG secondary antibody with primate IgG and is thus regularly seen with this type of assay (Scale bar: 100 µm).Additional file 6: Table S1. Additional information on cDNA, oligonucleotide primers, and vectors used for recombinant expression of septin proteins in HEK293 cells. cDNA, complementary deoxyribonucleic acid; HEK293, human embryonic kidney 293. ## References 1. McKeon A. **Paraneoplastic and other autoimmune disorders of the central nervous system**. *Neurohospitalist* (2013) **3** 53-64. DOI: 10.1177/1941874412453339 2. Rosenfeld MR, Dalmau J. **Paraneoplastic neurologic syndromes**. *Neurol Clin* (2018) **36** 675-685. DOI: 10.1016/j.ncl.2018.04.015 3. Herrero San Martin A, Amarante Cuadrado C, Gonzalez Arbizu M, Rabano-Suarez P, Ostos-Moliz F, Naranjo L, Sabater L, Martinez Hernandez E, Ruiz Garcia R, Toledo Alfocea D. **Autoimmune septin-5 disease presenting as spinocerebellar ataxia and nystagmus**. *Neurology* (2021) **97** 291-292. DOI: 10.1212/WNL.0000000000012240 4. Honorat JA, Lopez-Chiriboga AS, Kryzer TJ, Fryer JP, Devine M, Flores A, Lennon VA, Pittock SJ, McKeon A. **Autoimmune septin-5 cerebellar ataxia**. *Neurol Neuroimmunol Neuroinflamm* (2018) **5** e474. DOI: 10.1212/NXI.0000000000000474 5. Hinson SR, Honorat JA, Grund EM, Clarkson BD, Miske R, Scharf M, Zivelonghi C, Al-Lozi MT, Bucelli RC, Budhram A. **Septin-5 and -7-IgGs: neurologic, serologic and pathophysiologic characteristics**. *Ann Neurol* (2022). DOI: 10.1002/ana.26482 6. Cavini IA, Leonardo DA, Rosa HVD, Castro D, D'Muniz Pereira H, Valadares NF, Araujo APU, Garratt RC. **The structural biology of septins and their filaments: an update**. *Front Cell Dev Biol* (2021) **9** 765085. DOI: 10.3389/fcell.2021.765085 7. Jarius S, Steinmeyer F, Knobel A, Streitberger K, Hotter B, Horn S, Heuer H, Schreiber SJ, Wilhelm T, Trefzer U. **GABAB receptor antibodies in paraneoplastic cerebellar ataxia**. *J Neuroimmunol* (2013) **256** 94-96. DOI: 10.1016/j.jneuroim.2012.12.006 8. Scharf M, Miske R, Kade S, Hahn S, Denno Y, Begemann N, Rochow N, Radzimski C, Brakopp S, Probst C. **A spectrum of neural autoantigens, newly identified by histo-immunoprecipitation, mass spectrometry, and recombinant cell-based indirect immunofluorescence**. *Front Immunol* (2018) **9** 1447. DOI: 10.3389/fimmu.2018.01447 9. Uhlen M, Fagerberg L, Hallstrom BM, Lindskog C, Oksvold P, Mardinoglu A, Sivertsson A, Kampf C, Sjostedt E, Asplund A. **Proteomics tissue-based map of the human proteome**. *Science* (2015) **347** 1260419. DOI: 10.1126/science.1260419 10. Xue J, Tsang CW, Gai WP, Malladi CS, Trimble WS, Rostas JA, Robinson PJ. **Septin 3 (G-septin) is a developmentally regulated phosphoprotein enriched in presynaptic nerve terminals**. *J Neurochem* (2004) **91** 579-590. DOI: 10.1111/j.1471-4159.2004.02755.x 11. Takehashi M, Tanaka S, Stedeford T, Banasik M, Tsukagoshi-Nagai H, Kinoshita N, Kawamata T, Ueda K. **Expression of septin 3 isoforms in human brain**. *Gene Expr* (2004) **11** 271-278. DOI: 10.3727/000000003783992270 12. Angelis D, Spiliotis ET. **Septin mutations in human cancers**. *Front Cell Dev Biol* (2016) **4** 122. DOI: 10.3389/fcell.2016.00122 13. Peterson EA, Petty EM. **Conquering the complex world of human septins: implications for health and disease**. *Clin Genet* (2010) **77** 511-524. DOI: 10.1111/j.1399-0004.2010.01392.x 14. Russell SE, Hall PA. **Do septins have a role in cancer?**. *Br J Cancer* (2005) **93** 499-503. DOI: 10.1038/sj.bjc.6602753 15. Jarius S, Wandinger KP, Horn S, Heuer H, Wildemann B. **A new Purkinje cell antibody (anti-Ca) associated with subacute cerebellar ataxia: immunological characterization**. *J Neuroinflammation* (2010) **7** 21. DOI: 10.1186/1742-2094-7-21 16. Tanaka J, Sugimoto K, Shiraki K, Beppu T, Yoneda K, Fuke H, Yamamoto N, Ito K, Takei Y. **Type 1 diabetes mellitus provoked by peginterferon alpha-2b plus ribavirin treatment for chronic hepatitis C**. *Intern Med* (2008) **47** 747-749. DOI: 10.2169/internalmedicine.47.0653 17. Onishi S, Nagashima T, Kimura H, Matsuyama Y, Yoshio T, Minota S. **Systemic lupus erythematosus and Sjogren's syndrome induced in a case by interferon-alpha used for the treatment of hepatitis C**. *Lupus* (2010) **19** 753-755. DOI: 10.1177/0961203309353172 18. Tomer Y. **Hepatitis C and interferon induced thyroiditis**. *J Autoimmun* (2010) **34** J322-326. DOI: 10.1016/j.jaut.2009.11.008 19. Bouwhuis MG, Suciu S, Testori A, Kruit WH, Sales F, Patel P, Punt CJ, Santinami M, Spatz A, Ten Hagen TL, Eggermont AM. **Phase III trial comparing adjuvant treatment with pegylated interferon Alfa-2b versus observation: prognostic significance of autoantibodies–EORTC 18991**. *J Clin Oncol* (2010) **28** 2460-2466. DOI: 10.1200/JCO.2009.24.6264 20. Khan S, Gerber DE. **Autoimmunity, checkpoint inhibitor therapy and immune-related adverse events: a review**. *Semin Cancer Biol* (2020) **64** 93-101. DOI: 10.1016/j.semcancer.2019.06.012 21. Dubey D, David WS, Reynolds KL, Chute DF, Clement NF, Cohen JV, Lawrence DP, Mooradian MJ, Sullivan RJ, Guidon AC. **Severe neurological toxicity of immune checkpoint inhibitors: growing spectrum**. *Ann Neurol* (2020) **87** 659-669. DOI: 10.1002/ana.25708 22. Devine MF, Kothapalli N, Elkhooly M, Dubey D. **Paraneoplastic neurological syndromes: clinical presentations and management**. *Ther Adv Neurol Disord.* (2021) **14** 1756286420985323. DOI: 10.1177/1756286420985323 23. Sechi E, Markovic SN, McKeon A, Dubey D, Liewluck T, Lennon VA, Lopez-Chiriboga AS, Klein CJ, Mauermann M, Pittock SJ. **Neurologic autoimmunity and immune checkpoint inhibitors: autoantibody profiles and outcomes**. *Neurology* (2020) **95** e2442-e2452. DOI: 10.1212/WNL.0000000000010632 24. Valencia-Sanchez C, Zekeridou A. **Paraneoplastic neurological syndromes and beyond emerging with the introduction of immune checkpoint inhibitor cancer immunotherapy**. *Front Neurol* (2021) **12** 642800. DOI: 10.3389/fneur.2021.642800 25. Triggianese P, Novelli L, Galdiero MR, Chimenti MS, Conigliaro P, Perricone R, Perricone C, Gerli R. **Immune checkpoint inhibitors-induced autoimmunity: the impact of gender**. *Autoimmun Rev* (2020) **19** 102590. DOI: 10.1016/j.autrev.2020.102590 26. Tsang CW, Estey MP, DiCiccio JE, Xie H, Patterson D, Trimble WS. **Characterization of presynaptic septin complexes in mammalian hippocampal neurons**. *Biol Chem* (2011) **392** 739-749. DOI: 10.1515/BC.2011.077 27. Fujishima K, Kiyonari H, Kurisu J, Hirano T, Kengaku M. **Targeted disruption of Sept3, a heteromeric assembly partner of Sept5 and Sept7 in axons, has no effect on developing CNS neurons**. *J Neurochem* (2007) **102** 77-92. DOI: 10.1111/j.1471-4159.2007.04478.x 28. Pissuti Damalio JC, Garcia W, Alves Macedo JN, de Almeida MI, Andreu JM, Giraldo R, Garratt RC, Ulian Araujo AP. **Self assembly of human septin 2 into amyloid filaments**. *Biochimie* (2012) **94** 628-636. DOI: 10.1016/j.biochi.2011.09.014 29. Kumagai PS, Martins CS, Sales EM, Rosa HVD, Mendonca DC, Damalio JCP, Spinozzi F, Itri R, Araujo APU. **Correct partner makes the difference: Septin G-interface plays a critical role in amyloid formation**. *Int J Biol Macromol* (2019) **133** 428-435. DOI: 10.1016/j.ijbiomac.2019.04.105 30. Jarius S, Wildemann B. **'Medusa head ataxia': the expanding spectrum of Purkinje cell antibodies in autoimmune cerebellar ataxia. Part 3: Anti-Yo/CDR2, anti-Nb/AP3B2, PCA-2, anti-Tr/DNER, other antibodies, diagnostic pitfalls, summary and outlook**. *J Neuroinflammation* (2015) **12** 168. DOI: 10.1186/s12974-015-0358-9 31. Jarius S, Wildemann B. **'Medusa head ataxia': the expanding spectrum of Purkinje cell antibodies in autoimmune cerebellar ataxia. Part 2: anti-PKC-gamma, anti-GluR-delta2, anti-Ca/ARHGAP26 and anti-VGCC**. *J Neuroinflammation* (2015) **12** 167. DOI: 10.1186/s12974-015-0357-x 32. Jarius S, Wildemann B. **'Medusa-head ataxia': the expanding spectrum of Purkinje cell antibodies in autoimmune cerebellar ataxia. Part 1: anti-mGluR1, anti-Homer-3, anti-Sj/ITPR1 and anti-CARP VIII**. *J Neuroinflammation* (2015) **12** 166. DOI: 10.1186/s12974-015-0356-y 33. Jarius S, Scharf M, Begemann N, Stocker W, Probst C, Serysheva II, Nagel S, Graus F, Psimaras D, Wildemann B, Komorowski L. **Antibodies to the inositol 1,4,5-trisphosphate receptor type 1 (ITPR1) in cerebellar ataxia**. *J Neuroinflammation* (2014) **11** 206. DOI: 10.1186/s12974-014-0206-3 34. Jarius S, Brauninger S, Chung HY, Geis C, Haas J, Komorowski L, Wildemann B, Roth C. **Inositol 1,4,5-trisphosphate receptor type 1 autoantibody (ITPR1-IgG/anti-Sj)-associated autoimmune cerebellar ataxia, encephalitis and peripheral neuropathy: review of the literature**. *J Neuroinflammation* (2022) **19** 196. DOI: 10.1186/s12974-022-02545-4 35. Muniz-Castrillo S, Vogrig A, Ciano-Petersen NL, Villagran-Garcia M, Joubert B, Honnorat J. **Novelties in autoimmune and paraneoplastic cerebellar ataxias: twenty years of progresses**. *Cerebellum* (2022) **21** 573-591. DOI: 10.1007/s12311-021-01363-3 36. Bataller L, Sabater L, Saiz A, Serra C, Claramonte B, Graus F. **Carbonic anhydrase-related protein VIII: autoantigen in paraneoplastic cerebellar degeneration**. *Ann Neurol* (2004) **56** 575-579. DOI: 10.1002/ana.20238 37. Zuliani L, Sabater L, Saiz A, Baiges JJ, Giometto B, Graus F. **Homer 3 autoimmunity in subacute idiopathic cerebellar ataxia**. *Neurology* (2007) **68** 239-240. DOI: 10.1212/01.wnl.0000251308.79366.f9 38. Miske R, Scharf M, Stark P, Dietzel H, Bien CI, Borchers C, Kermer P, Ott A, Denno Y, Rochow N. **Autoantibodies against the Purkinje cell protein RGS8 in paraneoplastic cerebellar syndrome**. *Neurol Neuroimmunol Neuroinflamm* (2021) **8** e987. DOI: 10.1212/NXI.0000000000000987 39. Sillevis Smitt P, Kinoshita A, De Leeuw B, Moll W, Coesmans M, Jaarsma D, Henzen-Logmans S, Vecht C, De Zeeuw C, Sekiyama N. **Paraneoplastic cerebellar ataxia due to autoantibodies against a glutamate receptor**. *N Engl J Med* (2000) **342** 21-27. DOI: 10.1056/NEJM200001063420104 40. Sabater L, Bataller L, Carpentier AF, Aguirre-Cruz ML, Saiz A, Benyahia B, Dalmau J, Graus F. **Protein kinase C gamma autoimmunity in paraneoplastic cerebellar degeneration and non-small-cell lung cancer**. *J Neurol Neurosurg Psychiatry* (2006) **77** 1359-1362. DOI: 10.1136/jnnp.2006.097188
--- title: Study on the relationship between obesity and complications of Pediatric Epilepsy surgery authors: - Lei Shen - Mengyang Wang - Jingwei Zhao - Yuanyuan Ruan - Jingyi Yang - Songshan Chai - Xuan Dai - Bangkun Yang - Yuankun Cai - Yixuan Zhou - Zhimin Mei - Zhixin Zheng - Dongyuan Xu - Hantao Guo - Yu Lei - Runqi Cheng - Chuqiao Yue - Tiansheng Wang - Yunchang Zhao - Xinyu Liu - Yibo Chai - Jingcao Chen - Hao Du - Nanxiang Xiong journal: BMC Pediatrics year: 2023 pmcid: PMC10061988 doi: 10.1186/s12887-023-03948-9 license: CC BY 4.0 --- # Study on the relationship between obesity and complications of Pediatric Epilepsy surgery ## Abstract ### Objective Studies have shown that obesity has a significant impact on poor surgical outcomes. However, the relationship between obesity and pediatric epilepsy surgery has not been reported. This study aimed to explore the relationship between obesity and complications of pediatric epilepsy surgery and the effect of obesity on the outcome of pediatric epilepsy surgery, and to provide a reference for weight management of children with epilepsy. ### Methods A single-center retrospective analysis of complications in children undergoing epilepsy surgery was conducted. Body mass index (BMI) percentiles were adjusted by age and used as a criterion for assessing obesity in children. According to the adjusted BMI value, the children were divided into the obese group ($$n = 16$$) and nonobese group ($$n = 20$$). The intraoperative blood loss, operation time, and postoperative fever were compared between the two groups. ### Results A total of 36 children were included in the study, including 20 girls and 16 boys. The mean age of the children was 8.0 years old, ranging from 0.8 to 16.9 years old. The mean BMI was 18.1 kg/m2, ranging from 12.4 kg/m2 to 28.3 kg/m2. Sixteen of them were overweight or obese ($44.4\%$). Obesity was associated with higher intraoperative blood loss in children with epilepsy ($$p \leq 0.04$$), and there was no correlation between obesity and operation time ($$p \leq 0.21$$). Obese children had a greater risk of postoperative fever ($56.3\%$) than nonobese children ($55.0\%$), but this was statistically nonsignificant ($$p \leq 0.61$$). The long-term follow-up outcomes showed that 23 patients ($63.9\%$) were seizure-free (Engel grade I), 6 patients ($16.7\%$) had Engel grade II, and 7 patients ($19.4\%$) had Engel grade III. There was no difference in long-term seizure control outcomes between obese and nonobese groups ($$p \leq 0.682$$). There were no permanent neurological complications after surgery. ### Conclusion Compared with nonobese children with epilepsy, obese children with epilepsy had a higher intraoperative blood loss. It is necessary to conduct early weight management of children with epilepsy as long as possible. ## Background Epilepsy is one of the most common disabling chronic neurological diseases [1]. Despite the availability of over 20 antiepileptic drugs (AEDs) for the symptomatic treatment of epilepsy, approximately one-third of patients with epilepsy have epilepsy refractory to AEDs [2]. It is generally acknowledged that epilepsy surgery and neuromodulation surgery are effective therapies to treat refractory epilepsy [3, 4]. Recent studies have shown that neurostimulation has also become one of the effective therapies for refractory epilepsy [5, 6]. Childhood obesity is one of the primary public health problems faced by children [7]. Recent surveys show that $17.1\%$ of children have obesity, with an increasing obesity rate of children [8, 9]. Obesity is particularly common in children with epilepsy due to the side effects of AEDs [10, 11]. It has been reported that $38.6\%$ of children with epilepsy are overweight or obese, of which $19.9\%$ are obese and $18.7\%$ are overweight [12]. Studies have shown that obesity is one of the important risk factors for poor surgical outcomes, which may be related to prolonged operation time, poor wound healing, and comorbidities in obese patients [13–16]. However, the relationship between obesity and pediatric epilepsy surgery has not been reported. By reviewing the cases of children with refractory epilepsy, this study discussed the relationship between obesity and complications of pediatric epilepsy surgery with refractory epilepsy and the effect of obesity on the outcome of pediatric epilepsy surgery, and provided a reference for weight management of children with epilepsy. ## Case selection Data from patients with refractory focal epilepsy who underwent epilepsy surgery in Wuhan Children’s Hospital from January 2017 to October 2021 were collected. Epilepsy surgery included temporal lobectomy, selective amygdalohippocampectomy, selective amygdalohippocampotomy, frontal lobotomy, and hemispherotomy. Inclusion criteria included the following: [1] Patients were diagnosed with refractory focal epilepsy. [ 2] The patients were younger than 18 years old at surgery. Exclusion criteria included the following: [1] The necessary clinical data of cases were incomplete, including the height and weight of patients. [ 2] The presence of intraoperative blood loss and postoperative fever could not be determined or recorded. ## Extraction of clinical data The clinical data, including demographic characteristics, operation time, intraoperative blood loss, and postoperative fever, were extracted from electronic health records. The amount of intraoperative blood loss was measured by the suction device: the container of the suction device had a scale to measure the amount of imbibition. Meanwhile, the amount of saline used for intraoperative irrigation was also recorded. Then the difference between the amount of imbibition in the container of the suction device and the amount of saline was considered as the amount of intraoperative blood loss. The measurements of height and weight were as follows. For children up to 2 years old, a length measuring device was used to measure length as height and a horizontal baby electronic scale was used to measure weight. For children over 2 years old, a standing height meter was used to measure height and a vertical weight scale was used to measure weight. The formula for calculating body mass index (BMI) was BMI = weight/height2. BMI percentiles adjusted for age were used as a criterion for assessing obesity in children [17]. As a secondary response, BMI percentiles were classified into the following categories: [1] obese: The BMI was ≥ the 85th percentile for age, [2] overweight: The BMI was more than 85th percentile and less than the 95th percentile for age, [3] nonobese: The BMI was<the 85th percentile for age. In order to facilitate the comparison and analysis, cases were divided into an obese group (obese or overweight cases) and a nonobese group (nonobese cases) by BMI percentiles. ## Follow-up Follow-up methods mainly included outpatient follow-up and telephone follow-up. All patients were followed up for at least 12 months. Postoperative epilepsy control was assessed by Engel classification. The mean follow-up time of the patients was 20 months, ranging from 12 to 48 months. The long-term follow-up outcomes showed that 23 patients ($63.9\%$) were seizure-free (Engel grade I), 6 patients ($16.7\%$) had Engel grade II, and 7 patients ($19.4\%$) had Engel grade III. In the obese group, 10 patients had Engel grade I, 2 had Engel grade II, and 4 had Engel grade III. Meanwhile, 13 patients had Engel grade I, 4 had Engel grade II, and 3 had Engel grade III in the nonobese group. There was no difference in long-term seizure control outcomes between obese and nonobese groups ($$p \leq 0.682$$). In addition, there were no long-term neurological complications after surgery, such as aphasia and hemiplegia. ## Statistical analysis Descriptive statistics were used to describe the group characteristics of children. Continuous variables included the following: The D’Agostino-Pearson normality test was used to assess whether the intraoperative blood loss followed a normal distribution, and the unpaired t-test was used to compare whether there was a difference in intraoperative blood loss between obese and nonobese groups. The mean ± standard deviation and $95\%$ confidence intervals (CIs) were used to measure the size of the difference between groups. Discrete variables included the following: Fisher’s exact test was used to compare whether there was a difference in the incidence of postoperative fever between obese and nonobese groups. All tests were 2-sided and used a 0.05 significance level. ## Clinical data A total of 36 cases were included in the study. The clinical data of the children are shown in Table 1, including 20 male children and 16 female children. The mean age of the children was 8.0 years old, ranging from 0.8 to 16.9 years old. The mean BMI was 18.1 kg/m2, ranging from 12.4 kg/m2 to 28.3 kg/m2. Five patients were overweight ($13.9\%$), and 11 patients were obese ($30.6\%$). The epilepsy surgeries included temporal lobectomy ($$n = 5$$), selective amygdalohippocampectomy ($$n = 6$$), selective amygdalohippocampotomy ($$n = 8$$), frontal lobotomy ($$n = 2$$) and hemispherotomy ($$n = 15$$), with no significant difference in the distribution between groups ($$p \leq 0.91$$). There were no wound complications in two groups. Table 1Clinical data of the collected cases. SAHC: Selective Amygdalohippocampectomy; SAHCo: Selective AmygdalohippocampotomyTotal($$n = 36$$)Nonobese ($$n = 20$$)Obese($$n = 16$$)P value Age(y) 8.0 ± 4.58.3 ± 4.47.6 ± 4.60.67 Male/Female $\frac{20}{1613}$/$\frac{77}{90.31}$ BMI 18.1 ± 3.815.8 ± 1.920.9 ± 3.6< 0.01 Surgical technique 0.91Temporal lobectomy523SAHC642SAHCo853Frontal lobotomy211Hemispherotomy1587 Operation time (min) 188.2 ± 59.5200.6 ± 47.4171.7 ± 69.20.22 Intraoperative blood loss(ml) 136.2 ± 85.4104.3 ± 58.6173.3 ± 96.10.04 Postoperative fever 0.61No fever16970–2 days8353–6 days4317–10 days853 Intracranial infection 000> 0.99 ## Obesity and intraoperative blood loss The mean intraoperative blood loss in the obese group was 173.3 ± 96.1 ml, ranging from 56.0 to 397.0 ml. The mean intraoperative blood loss in the nonobese group was 104.3 ± 58.6 ml, ranging from 22.0 to 208.0 ml. The D’Agostino-Pearson normality test showed that the data in both groups followed a normal distribution (obese group: $$p \leq 0.15$$; nonobese group: $$p \leq 0.44$$). The mean difference between the two groups was 69.1 ± 32.0 ml, $95\%$ CI: 3.0-135.1 ml. The results of the unpaired t-test showed that the difference was statistically significant ($$p \leq 0.04$$) (Fig. 1). Fig. 1 A violin diagram showed that the overall distribution of the obese and nonobese groups was similar, and the intraoperative blood loss in the obese group was higher than that in the nonobese group ## Obesity and operation time of children The mean operation time of the obese group was 171.7 ± 69.2 min, ranging from 63.0 to 294.0 min. The mean operation time in the nonobese group was 200.6 ± 47.4 min, ranging from 155.0 to 303.0 min. The D’Agostino-Pearson normality test showed that the two groups of data followed a normal distribution (obese group: $$p \leq 0.69$$; nonobese group: $$p \leq 0.31$$). The mean difference between the two groups was 29.0 ± 22.9 min, $95\%$ CI: 18.1–76.0 min. Unpaired t-test results showed that the difference was not statistically significant ($$p \leq 0.22$$) (Fig. 2). Fig. 2 A violin diagram showed that the overall distribution of obese and nonobese groups was similar, and there was no significant difference between the two groups ## Obesity and postoperative fever in children According to the occurrence time of postoperative fever, postoperative fever was divided into four groups: no fever, early postoperative fever (0–2 days), middle postoperative fever (3–6 days), and late postoperative fever (7–10 days). The incidence of postoperative fever of patients was shown in Table 1. A total of 20 patients ($55.6\%$) had a postoperative fever, 9 ($56.3\%$) in the obese group and 11 ($55.0\%$) in the nonobese group. Among them, 8 patients had an early postoperative fever, 5 in the obese group and 3 in the nonobese group; 4 patients had a middle postoperative fever, 1 in the obese group and 3 in the nonobese group; and 8 patients had a late postoperative fever, 3 in the obese group and 5 in the nonobese group. The obese group had a higher incidence of postoperative fever than the nonobese group, but Fisher’s exact test showed that obesity was not significantly associated with postoperative fever ($$p \leq 0.61$$) (Fig. 3). Fig. 3Interleaved bars showed that the obese group had a higher incidence of postoperative fever than the nonobese group ## Obesity and epilepsy It has been reported that $38.6\%$ of children with epilepsy are overweight or obese, of which $19.9\%$ ​​are obese and $18.7\%$ are overweight, more than double the proportion of children expected to be overweight in a normal population [12]. Due to the side effects of AEDs such as valproic acid, carbamazepine, and gabapentin, taking AEDs may lead to obesity in children. At the same time, obesity in children can also lead to a decrease in their medication compliance, which further leads to poor epilepsy control [18–20]. Among the cases included in our study, $13.9\%$ were overweight and $30.6\%$ were obese, which is consistent with Daniels’ results [12]. Regardless, it is necessary to conduct early control and treatment of epilepsy in children in time and to control the weight of children with epilepsy as long as possible. ## Obesity and intraoperative blood loss and operation time Studies have shown that obesity is a risk factor for poor surgical outcomes [13–16]. Obesity is an independent risk factor for prolonged operation time and room time [21], postoperative thrombotic complications [22], atrial arrhythmias [23, 24], and wound infection [25]. We found that the mean intraoperative blood loss in the obese group was significantly higher than that in the nonobese group ($$p \leq 0.04$$). Tjeertes found higher intraoperative blood loss in obese patients, possibly because obese patients had more difficulty in exposing and dissecting the surgical site, requiring more tissue to be cut, prolonging operation time, and increasing intraoperative blood loss [26]. The operation time may change according to surgical technique and practitioner. In our study, all epilepsy surgeries were performed by the same neurosurgeon, and there was no difference in the distribution of different epilepsy surgeries among the cases according to Table 1($$p \leq 0.91$$), so the possible influence of surgical technique and practitioner on the operation time could be excluded to some extent. However, our study found no significant difference in operation time between obese and nonobese groups ($$p \leq 0.22$$), suggesting that the length of operation time was not responsible for the difference in intraoperative blood loss between the obese and nonobese groups. Similarly, the possible influence of age and surgical technique on intraoperative blood loss could be partially excluded. Furthermore, coagulopathies, especially thrombocytopenia, are considered as the side effects of some AEDs so that patients on these AEDs might have a bleeding tendency. Gerstner found that valproate-associated coagulopathies were frequent and variable in children [27]. Another study showed that valproic acid was associated with a decreased platelet count, although thrombocytopoiesis is not affected, even in children with a reduced platelet count [28]. Carbamazepine was also thought to cause thrombocytopenia besides valproic acid through an autoimmune mechanism [29, 30]. Based on the NICE guideline [NG217] (https://www.nice.org.uk/guidance/ng217), lamotrigine and levetiracetam were considered as the first-line AEDs for focal epilepsy rather than valproic acid. For patients with refractory focal epilepsy in our study, we usually used a first-line AED combined with one of oxcarbazepine, perampanel, and nitrazepam according to the patient’s specific condition. If the patient was on valproic acid before being hospitalized, it would be stopped and be switched to oxcarbazepine for at least one week before the surgery. In addition, there may be another reason that adipose tissue in obese children mainly accumulates in the trunk and limbs, while the epilepsy surgery site is in the brain, and there is little fat accumulation at the surgery site. Therefore, for children with epilepsy, higher intraoperative blood loss in the obese group may not be associated with operation time. Some studies have found that obese patients have a hyperactive inflammatory response, increased angiogenesis in the tissue compared with nonobese patients, a more abundant blood supply in the tissue, a higher bleeding risk, and a higher amount of intraoperative blood loss [31–33]. ## Obesity and postoperative fever Fever is one of the most common postoperative complications of surgery. According to the cause of fever, postoperative fever can be divided into infectious fever and non-infectious fever. Non-infectious fevers are in turn associated with trauma and inflammation from the surgery, suture foreign body reactions, transfusion reactions, and drug-induced fevers [34, 35]. According to the time postoperative fever occurs, postoperative fever can be divided into early postoperative fever (0–2 days), middle postoperative fever (3–6 days) and late postoperative fever (7–10 days). A total of 20 patients ($55.6\%$) had a postoperative fever, 9 patients ($56.3\%$) in the obese group and 11 ($55.0\%$) in the nonobese group. The incidence in the obese group was slightly higher than that in the nonobese group, and the obese group tended to have early postoperative fever, while the nonobese group was more likely to have middle-late postoperative fever; however, the statistical results showed no significant correlation between obesity and postoperative fever ($$p \leq 0.61$$). There was no difference in postoperative fever between obese and nonobese groups in our study. The possible reason is that the type of postoperative fever in the children was mainly non-infectious fever caused by surgical trauma. It has been reported that obese patients have lower immunity and are more likely to have postoperative infectious fever [36–38]. Meanwhile, obesity has been found to be associated with altered collagen structure and resistance to leptin, leading to impaired wound healing [39]. However, all cases included in our study had good postoperative wound healing and no intracranial infection, which means that postoperative fever was not associated with surgical site infection. Intraventricular blood loss after neurosurgery is a recognized reason for aseptic meningitis and non-infectious fever [40]. There was a significant difference in intraoperative blood loss between obese and nonobese groups in our study, while no significant difference was found in the incidence of postoperative fever between the two groups. Almeida found that ventriculotomy is not an independent cause of fever after hemispherectomy [41], probably because intraventricular blood loss after ventriculotomy can be well managed and an insufficient amount of blood remains to cause a postoperative fever. ## Limitations This study still has some limitations. First, this study was a retrospective study and it was subject to inherent bias in the study design. Also, epilepsy surgery has different surgical approaches depending on the etiology and localization of epileptogenic foci, and it is difficult to determine whether different surgical approaches will affect intraoperative blood loss and postoperative fever. In addition, the sample size of cases and observation indicators included in this study were small. Considering that the immune system of children has not yet been established, they are more likely to have a fever with unknown causes. Prospective studies with larger sample sizes are still required to further explore the relationship between obesity and epilepsy surgical complications in children. ## Conclusion This study investigated the relationship between obesity and intraoperative blood loss and postoperative fever in children with refractory epilepsy. Compared with nonobese cases, obese cases had higher intraoperative blood loss during surgery. Obesity was not associated with postoperative fever or operation time. It is necessary to conduct early weight management of children with epilepsy as long as possible. Due to the small sample size of cases and limited observation indicators included in this study, the relationship between obesity and complications of pediatric epilepsy surgery still needs to be further explored. ## References 1. 1.Devinsky O, Vezzani A, O’Brien TJ, Jette N, Scheffer IE, de Curtis M et al. Epilepsy. NAT REV DIS PRIMERS. [Journal Article; Review]. 2018 2018-06-07;4(1):18024. 2. 2.Löscher W, Potschka H, Sisodiya SM, Vezzani A. Drug Resistance in Epilepsy: Clinical Impact, Potential Mechanisms, and New Innovative Treatment Options. PHARMACOL REV. [Journal Article; Research Support, Non-U.S. Gov’t; Review]. 2020 2020-07-01;72(3):606–38. 3. 3.Thijs RD, Surges R, O’Brien TJ, Sander JW. Epilepsy in adults. The Lancet. [Journal Article; Review]. 2019 2019-02-16;393(10172):689–701. 4. Huang H, Chen L, Chopp M, Young W, Robert Bach J, He X. **The 2020 yearbook of Neurorestoratology**. *J Neurorestoratology [Review]* (2021.0) **9** 1-12. DOI: 10.26599/JNR.2021.9040002 5. Qin X, Lin S, Yuan Y, Wen J, Chen Q, Lu X. **Vagus nerve stimulation for pediatric patients with drug-resistant epilepsy caused by genetic mutations: two cases**. *J Neurorestoratology [Article]* (2020.0) **8** 138-48. DOI: 10.26599/JNR.2020.9040014 6. Yang Z, Zhang C, Wang Z, Cheng T, Qin X, Deng J. **Vagal nerve stimulation is effective in pre-school children with intractable epilepsy: a report of two cases**. *J Neurorestoratology [Article]* (2020.0) **8** 149-59. DOI: 10.26599/JNR.2020.9040017 7. 7.Smith JD, Fu E, Kobayashi MA. Prevention and Management of Childhood Obesity and Its Psychological and Health Comorbidities. ANNU REV CLIN PSYCHO. [Journal Article; Research Support, N.I.H., Extramural; Research Support, U.S. Gov’t, P.H.S.; Review]. 2020 2020-05-07;16(1):351–78. 8. 8.O Connor EA, Evans CV, Burda BU, Walsh ES, Eder M, Lozano P. Screening for Obesity and Intervention for Weight Management in Children and Adolescents. JAMA. [Journal Article; Review; Systematic Review]. 2017 2017-06-20;317(23):2427. 9. 9.Bleich SN, Vercammen KA, Zatz LY, Frelier JM, Ebbeling CB, Peeters A. Interventions to prevent global childhood overweight and obesity: a systematic review.The Lancet Diabetes & Endocrinology. [Journal Article; Review; Systematic Review]. 2018 2018-04-01;6(4):332–46. 10. 10.Aaberg KM, Bakken IJ, Lossius MI, Lund Søraas C, Håberg SE, Stoltenberg C et al. Comorbidity and Childhood Epilepsy: A Nationwide Registry Study. PEDIATRICS. [Journal Article; Research Support, Non-U.S. Gov’t]. 2016 2016-09-01;138(3). 11. 11.Im DU, Kim SC, Chau GC, Um SH. Carbamazepine Enhances Adipogenesis by Inhibiting Wnt/β-catenin Expression. Cells (Basel, Switzerland). [Journal Article; Research Support, Non-U.S. Gov’t]. 2019 2019-01-01;8(11):1460. 12. 12.Daniels ZS, Nick TG, Liu C, Cassedy A, Glauser TA. Obesity is a common comorbidity for pediatric patients with untreated, newly diagnosed epilepsy. NEUROLOGY. [Journal Article; Research Support, N.I.H., Extramural]. 2009 2009-09-01;73(9):658–64. 13. 13.Abdelrahman T, Latif A, Chan DS, Jones H, Farag M, Lewis WG et al. Outcomes after laparoscopic anti-reflux surgery related to obesity: A systematic review and meta-analysis.INT J SURG. [Journal Article; Meta-Analysis; Review; Systematic Review]. 2018 2018-03-01;51:76–82. 14. 14.Byrne JJ, Smith EM, Saucedo AM, Doody KA, Holcomb DS, Spong CY. Examining the Association of Obesity With Postpartum Tubal Ligation. Obstetrics & Gynecology. [Journal Article]. 2020 2020-08-01;136(2):342–8. 15. 15.Gaulton TG, Fleisher LA, Neuman MD. The association between obesity and disability in survivors of joint surgery: analysis of the health and retirement study.BRIT J ANAESTH. [Journal Article]. 2018 2018-01-01;120(1):109–16. 16. 16.Singh S, Dulai PS, Zarrinpar A, Ramamoorthy S, Sandborn WJ. Obesity in IBD: epidemiology, pathogenesis, disease course and treatment outcomes. NAT REV GASTRO HEPAT. [Journal Article; Review; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov’t]. 2017 2017-02-01;14(2):110–21. 17. 17.CDC. Overweight & Obesity: Defining Childhood Weight Status. https://www.cdc.gov/obesity/basics/childhood-defining.html. Accessed 9 Feb 2023. 18. 18.Al-Faris EA, Abdulghani HM, Mahdi AH, Salih MA, Al-Kordi AG. Compliance with appointments and medications in a pediatric neurology clinic at a University Hospital in Riyadh, Saudi Arabia. SAUDI MED J. [Journal Article]. 2002 2002-08-01;23(8):969–74. 19. Chukwu J, Delanty N, Webb D, Cavalleri GL. **Weight change, genetics and antiepileptic drugs**. *Expert Rev Clin Pharmacol [Journal Article; Review]* (2014.0) **2014–01–01** 43-51. DOI: 10.1586/17512433.2014.857599 20. 20.Kim DW, Yoo MW, Park KS. Low serum leptin level is associated with zonisamide-induced weight loss in overweight female epilepsy patients. EPILEPSY BEHAV. [Journal Article; Research Support, Non-U.S. Gov’t]. 2012 2012-04-01;23(4):497–9. 21. 21.Gholson JJ, Shah AS, Gao Y, Noiseux NO. Morbid Obesity and Congestive Heart Failure Increase Operative Time and Room Time in Total Hip Arthroplasty.The Journal of Arthroplasty. [Journal Article]. 2016 2016-04-01;31(4):771–5. 22. 22.Sloan M, Sheth N, Lee G. Is Obesity Associated With Increased Risk of Deep Vein Thrombosis or Pulmonary Embolism After Hip and Knee Arthroplasty? A Large Database Study. Clinical Orthopaedics & Related Research. [Journal Article]. 2019 2019-03-01;477(3):523–32. 23. 23.Perrier S, Meyer N, Hoang Minh T, Announe T, Bentz J, Billaud P et al. Predictors of Atrial Fibrillation After Coronary Artery Bypass Grafting: A Bayesian Analysis. The Annals of Thoracic Surgery. [Journal Article; Observational Study]. 2017 2017-01-01;103(1):92–7. 24. 24.Lavie CJ, Pandey A, Lau DH, Alpert MA, Sanders P. Obesity and Atrial Fibrillation Prevalence, Pathogenesis, and Prognosis. J AM COLL CARDIOL. [Journal Article; Review]. 2017 2017-10-17;70(16):2022–35. 25. 25.O Byrne ML, Kim S, Hornik CP, Yerokun BA, Matsouaka RA, Jacobs JP et al. Effect of Obesity and Underweight Status on Perioperative Outcomes of Congenital Heart Operations in Children, Adolescents, and Young Adults. CIRCULATION. [Journal Article; Multicenter Study]. 2017 2017-08-22;136(8):704–18. 26. 26.Tjeertes EEKM, Hoeks SSE, Beks SSBJ, Valentijn TTM, Hoofwijk AAGM, Stolker RJRJ. Obesity – a risk factor for postoperative complications in general surgery? BMC ANESTHESIOL. [Journal Article]. 2015 2015-07-31;15(1):112. 27. 27.Gerstner T, Teich M, Bell N, Longin E, Dempfle CE, Brand J et al. Valproate-associated coagulopathies are frequent and variable in children. EPILEPSIA. [Case Reports; Comparative Study; Journal Article]. 2006 2006-07-01;47(7):1136–43. 28. 28.Kurahashi H, Takami A, Murotani K, Numoto S, Okumura A. Decreased platelet count in children with epilepsy treated with valproate and its relationship to the immature platelet fraction.INT J HEMATOL. [Journal Article]. 2018 2018-01-01;107(1):105–11. 29. 29.Kumar R, Chivukula S, Katukuri GR, Chandrasekhar UK, Shivashankar KN. Carbamazepine Induced Thrombocytopenia.J Clin Diagn Res. [Case Reports]. 2017 2017-09-01;11(9):D12-3. 30. 30.Verrotti A, Scaparrotta A, Grosso S, Chiarelli F, Coppola G. Anticonvulsant drugs and hematological disease.NEUROL SCI. [Journal Article; Review]. 2014 2014-07-01;35(7):983–93. 31. 31.Hammarstedt A, Gogg S, Hedjazifar S, Nerstedt A, Smith U. Impaired Adipogenesis and Dysfunctional Adipose Tissue in Human Hypertrophic Obesity. PHYSIOL REV. [Journal Article; Research Support, Non-U.S. Gov’t; Review]. 2018 2018-10-01;98(4):1911–41. 32. 32.Gonzalez FJ, Xie C, Jiang C. The role of hypoxia-inducible factors in metabolic diseases. NAT REV ENDOCRINOL. [Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov’t; Review]. 2019 2018-12-01;15(1):21–32. 33. 33.Saltiel AR, Olefsky JM. Inflammatory mechanisms linking obesity and metabolic disease. J CLIN INVEST. [Journal Article; Review; Research Support, N.I.H., Extramural]. 2017 2017-01-03;127(1):1–4. 34. 34.Stephenson C, Mohabbat A, Raslau D, Gilman E, Wight E, Kashiwagi D. Management of Common Postoperative Complications. MAYO CLIN PROC. [Journal Article; Review; Video-Audio Media]. 2020 2020-11-01;95(11):2540-54. 35. 35.O Mara SK. Management of Postoperative Fever in Adult Cardiac Surgical Patients. Dimensions of Critical Care Nursing. [Journal Article]. 2017 2017-05-01;36(3):182–92. 36. 36.Hotamisligil GS. Inflammation, metaflammation and immunometabolic disorders. NATURE. [Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov’t; Review]. 2017 2017-02-09;542(7640):177–85. 37. 37.Gálvez I, Martín-Cordero L, Hinchado MD, Ortega E. β2 Adrenergic Regulation of the Phagocytic and Microbicide Capacity of Circulating Monocytes: Influence of Obesity and Exercise. NUTRIENTS. [Journal Article]. 2020 2020-05-16;12(5):1438. 38. 38.Reilly SM, Saltiel AR. Adapting to obesity with adipose tissue inflammation. NAT REV ENDOCRINOL. [Journal Article; Review]. 2017 2017-11-01;13(11):633–43. 39. 39.Hirt PA, Castillo DE, Yosipovitch G, Keri JE. Skin changes in the obese patient.J AM ACAD DERMATOL. [Journal Article; Review]. 2019 2019-11-01;81(5):1037–57. 40. 40.Garibaldi RA, Brodine S, Matsumiya S, Coleman M. Evidence for the non-infectious etiology of early postoperative fever. Infect Control. [Journal Article; Research Support, U.S. Gov’t, P.H.S.]. 1985 1985-07-01;6(7):273–7. 41. 41.de Almeida AN, Marino R, Aguiar PH, Teixeira MJ. Postoperative fever after hemispherectomy: The role of non-infectious factors.Seizure. [Journal Article]. 2006 2006-07-01;15(5):340–3.
--- title: 'The low prevalence rate of vitamin E deficiency in urban adults of Wuhan from central China: findings from a single-center, cross-sectional study' authors: - Ying Shen - Ke Liu - Xia Luo - Liming Cheng journal: European Journal of Medical Research year: 2023 pmcid: PMC10062001 doi: 10.1186/s40001-023-01103-9 license: CC BY 4.0 --- # The low prevalence rate of vitamin E deficiency in urban adults of Wuhan from central China: findings from a single-center, cross-sectional study ## Abstract ### Background Vitamin E is an essential nutrient in human body famous for its antioxidant and non-antioxidant functions. However, little is known about vitamin E deficiency status in urban adults of Wuhan from central China. Our aim is to describe the distribution of both circulating and lipid-adjusted serum vitamin E concentration in urban adults of Wuhan. ### Methods We hypothesized that the prevalence rate of vitamin E deficiency would be low in Wuhan in consideration of the Chinese food composition. A cross-sectional study with 846 adults was performed in a single-center. Concentrations of vitamin E were measured by liquid chromatography coupled with tandem mass spectrometry (LC–MS/MS). ### Results The median (interquartile range, IQR) of serum vitamin E concentration was 27.40 (22.89–33.20) μmol/L while that of serum vitamin E concentration adjusted by total cholesterol or the sum of cholesterol (TC) and triglyceride (TG) (the sum of cholesterol and triglyceride, TLs) were 6.20 (5.30–7.48) and 4.86 (4.10–5.65) mmol/mol, respectively. No significant difference of the circulating and TC-adjusted vitamin E concentration was found between male and female except for vitamin E/TLs. However, concentrations of vitamin E increased significantly ($r = 0.137$, $P \leq 0.001$) with age, but lipid-adjusted concentrations of vitamin E did not. On analysis of risk factors, the subjects characterized by hypercholesterolemia are more likely to exhibit higher circulating but lower lipid-adjusted vitamin E level due to adequacy of the serum carriers for delivery of vitamin E. Only $0.47\%$ of the population were below 12 μmol/L of vitamin E defined as functional deficiency. ### Conclusion The prevalence rate of vitamin E deficiency in urban adults of *Wuhan is* low, which is important and useful to clinicians for clinical decision-making in public health practice. ### Supplementary Information The online version contains supplementary material available at 10.1186/s40001-023-01103-9. ## Background Vitamin E is an important fat-soluble nutrient in the maintenance of health famous for its antioxidant functions, beneficial for a variety of disorders including cancer, heart disease and even Parkinson’s disease [1–4]. Meanwhile vitamin E also appears to have a variety of roles depending on its non-antioxidant properties, such as modulation of monocyte function, inhibition of platelet aggregation, inhibition of smooth muscle cell proliferation and modulation of gene expression [5]. Up to now, the extensive implications of vitamin E deficiency are increasing evident, especially in developing countries whose risk for deficiency is higher due to limited intake of the vitamins from food sources and greater oxidative stressors [6]. As a result, we initiate the present study to evaluate vitamin E status in our city from China. As well known, vitamin E covers a group of eight compounds (α-, β-, γ-, δ-tocopherol, and α-, β-, γ-, δ-tocotrienol) which differ in their methyl substitution and saturation. Among them, the predominant form in the human body is α-tocopherol which demonstrates the highest vitamin E activity, comprising over $90\%$ of vitamin E [7–9]. Consequently, vitamin E status is always assessed by serum α-tocopherol concentration which provides the mostly used and direct way [10]. It is reported that vitamin E circulating in blood is transported by lipoproteins, and vitamin E partitioning out of the cellular membrane compartment would increase with the elevation of serum lipid concentrations [11]. Consequently, vitamin E deficiency may be underestimated without consideration of the lipid concentration in the case that serum lipid concentrations pathologically elevated, while may be overestimated under the situation that serum lipid concentrations are low. Many researches thus suggest that the correction of vitamin E for lipid concentrations is preferable to assess adequacy [12]. Oftentimes, serum α-tocopherol concentration adjusted for serum total cholesterol (TC) or the sum of serum levels of cholesterol and triglyceride (TG) (the sum of cholesterol and triglyceride, TLs) is considered as a reliable indicator in identifying vitamin E deficiency [13, 14]. Nonetheless, the vitamin E status of people in Wuhan from central *China is* less-known, and an understanding of the vitamin E distribution in our city should be useful to clinicians for clinical decision-making in public health practice. In this study, both the circulating serum a-tocopherol concentrations and serum a-tocopherol concentrations adjusted for lipids have been used to assess the nutritional status of vitamin E of urban adults in Wuhan. Wuhan is located on the banks of the Yangtze River in central China, and the food consumption has dominated by traditional Chinese food characterized by grains and vegetables, with increasing intake of red meat, fruits, nuts, eggs, milk, river fish and plant oil, most of which are rich in vitamin E [15, 16]. Thus, we hypothesized that the likelihood of vitamin E deficiency would be low in Wuhan in consideration of the Chinese food composition. ## Study design and subjects A single-center, cross-sectional study was performed. All subjects, residing in Wuhan, were enrolled from those who underwent physical examination program in 2019 at Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology (HUST) which is a large comprehensive hospital in Wuhan with abundant patients from local residents. The basic demographic characteristics of the participants including sex, age, BMI, blood pressure and long-term residence place were collected through medical records or face-to-face interview. This work was approved by the ethics committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology (IRB Approval Number: TJ-IRB20210807). The sample size was calculated using the formula N = [Z1-α/2]2 × P (1- P)/d2 [17]. Where N is the sample size, Z1-α/2 (1.96) is the certainty wanted expressed in the percentage point of normal distribution corresponding to the 2-sided level of significant (α = 0.05); P ($13\%$) is the global prevalence rate of vitamin E deficiency [18]; d ($3\%$) is the allowable error. Therefore, N = [(1.96)2 × 0.13 × (1–0.13)]/(0.03)2 = 483. A non-response rate of $40\%$ was added, giving a total sample of 805. In view of incomplete demographic data, a total of 850 samples were ready to be included. Finally, 846 samples were selected in real word according to the actual situations, including 471 males and 375 female aged 18 to 93 years (median age 47 years). Overnight fasting blood samples were obtained by venipuncture. Sera were obtained by centrifugation of coagulated blood samples at 3000 rpm for 5 min at room temperature. These sera were frozen and stored at − 80 °C until analysis. ## Measures The serum α-tocopherol concentrations were determined by liquid chromatography coupled with tandem mass spectrometry (LC–MS/MS) on a ABsciex Qtrap 5500 coupled to an Exion LC system (Applied Biosystems, Foster City, CA, USA) with an electrospray ionization source in positive mode, and the testing kit was gotten from Beijing Health biotech Co. Ltd. (Beijing, China). This method was well validated with linearity, precision, accuracy, analytical sensitivity and matrix effect as demonstrated in Additional file 1: Table S1. The sera were processed as follows: 0.1 ml of serum was mixed with 0.1 ml of the internal standard (α-tocopherol-d6, Sigma-Aldrich) solution in a 1.5-ml centrifuge tube; 0.6 ml of hexane was then added and mixed thoroughly for 3 min using a vortex mixer. The tube was then closed and centrifuged for 10 min at 14680 rpm. The upper hexane extracts were evaporated by nitrogen and reconstituted in acetonitrile for LC–MS/MS analysis. A symmetry C18 column (100 × 2.1 mm, 3.5 µm, Waters, USA) was used for separation. The mobile phase was consisted of solvent A (water with $0.1\%$ formic acid) and solvent B (2 mM ammonium acetate with $0.1\%$ formic acid in methanol). The flow rate was 0.7 ml/min and column temperature was 60 °C. The concentrations of serum lipids, including TC, TG, high-density lipoprotein cholesterol (HDLC) and low-density lipoprotein cholesterol (LDLC), were measured on a Cobas 8000 system (Roche, Diagnostics, Germany). During the analysis, samples were protected from light and those showing signs of hemolysis were discarded. In this study, the criterion for hypercholesterolemia is defined as TC ≥ 5.2 mmol/L [19]. The normal blood pressure recommended by WHO is < $\frac{140}{90}$ mmHg [20]. The standard weight status categories associated with BMI ranges for adults are: BMI below 18.5 is associated with ‘underweight’ weight status; BMI 18.5–24.9 is associated with ‘normal’ weight status; BMI 25.0–29.9 is associated with ‘overweight’ weight status; BMI 30.0 and above is associated with ‘obese’ weight status [21]. The vitamin E status categories for healthy adults are classified as follows [18, 22]: vitamin E serum concentrations ≤ 12 μmol/L is considered as functional deficiency; between 13 and 29 µmol/L is considered as suboptimal status; ≥ 30 μmol/L is considered as desirable status. The prevalence rate of vitamin E deficiency according to its status categories defined for healthy adults, and its comparison with other countries have been investigated. ## Statistical analysis We present the distribution of concentrations of vitamin E and the ratios of concentrations of vitamin E to serum lipids for all participants. All data were expressed as medians and interquartile ranges (IQRs). Data normality was analyzed using the Kolmogorov–Smirnov test. Student’s t test and analysis of variance (ANOVA) based on normally distributed data, or Mann–Whitney U test and Kruskal–Wallis test based on non-normally distributed data were applied to compare the means of serum concentrations of α-tocopherol across genders, age groups and strata. Pearson’s Chi-square test or Fisher’s exact test was performed to analyze the categorical data. Spearman’s correlation test was used to determine the strength of correlation between variables, such as vitamin E, age, BMI, blood pressure, TC and TG. A multivariate logistic regression was performed to assess odds ratios (ORs) regarding Vitamin E-associated factors. Variable with a global value $P \leq 0.10$ in the univariate analysis were entered into multivariate analyses. A P-value below 0.05 was considered statistically significant. Analyses were done using SPSS (version 20.0; SPSS, Isnc., Chicago, Ill, USA). The *Correlation analysis* heatmap was plotted by R language. ## Profile of the study population The basic characteristics of the study population of 846 subjects aged 18 to 93 years are demonstrated in Table 1. The median (interquartile range, IQR) of age and body mass index (BMI) were 47 (36–56) and 24.1 (21.9–26.1), respectively. The median (IQR) of blood pressures were found within normal range: 124 (113–137) mmHg for systolic blood pressure (SBP) and 76 (69–85) mmHg for diastolic blood pressure (DBP). All the subjects had measurements for concentrations of TC, HDLC, and LDLC, TG and α-tocopherol. Both levels of α-tocopherol unadjusted and adjusted for lipids were presented. Table 1Serum concentrations of α-tocopherol per sex in adults ($$n = 846$$)aTotalMaleFemalePN (%)846471 ($55.67\%$)375 ($44.33\%$)Age (years)47 (36–56)47 (36–56)47 (34–56)0.422BMI (kg/m2)24.1 (21.9–26.1)25.1 (23.3–27.0)22.6 (20.7–24.8) < 0.001SBP (mm Hg)124 (113–137)127 (116–140)119 (108–132) < 0.001DBP(mm Hg)76 (69–85)80 (73–87)72 (65–81) < 0.001TC (mmol/L)4.41 (3.89–4.99)4.41 (3.92–4.99)4.40 (3.88–5.00)0.788TG (mmol/L)1.16 (0.82–1.70)1.39 (1.00–1.96)0.97 (0.68–1.40) < 0.001HDLC (mmol/L)1.25 (1.07–1.49)1.15 (1.00–1.29)1.43 (1.25–1.65) < 0.001LDLC (mmol/L)2.77 (2.29–3.32)2.84 (2.38–3.40)2.69 (2.23–3.26)0.030TLs (mmol/L)5.65 (4.97–6.64)5.88 (5.14–6.86)5.43 (4.81–6.31) < 0.001Unadjusted Vitamin E (μmol/L)27.40 (22.89–33.20)27.63 (22.96–32.97)27.16 (22.82–33.20)0.683Adjusted for TC (mmol/mol)6.20 (5.30–7.48)6.28 (5.36–7.55)6.16 (5.24–7.27)0.369 TLs (mmol/mol)4.86 (4.10–5.65)4.70 (3.97–5.53)4.98 (4.31–5.86) < 0.001aData are presented with median (IQR) for continuous variablesSBP systolic blood pressure, DBP diastolic blood pressure, TLs total lipids (cholesterol + triglyceride) ## The association of vitamin E level with the physiological conditions The association of vitamin E level with sex and age was investigated. Analysis of the data, noted as a function of sex (Table 1), shown that men had significant higher BMI ($P \leq 0.001$), SBP ($P \leq 0.001$), DBP ($P \leq 0.001$), TG ($P \leq 0.001$) and LDLC ($$P \leq 0.030$$) than women, and inversely had lower HDLC ($P \leq 0.001$) and vitamin E/TLs ($P \leq 0.001$) than women with no difference found in age, TC, vitamin E and vitamin E/TC. Likewise, the median levels of all indicators in Table 2 varied significantly with age except for vitamin E/TC ($$P \leq 0.573$$) and vitamin E/TLs ($$P \leq 0.131$$). This was also confirmed by the positive correlation between age and BMI ($r = 0.183$, $P \leq 0.001$), SBP ($r = 0.409$, $P \leq 0.001$), DBP ($r = 0.214$, $P \leq 0.001$), TC ($r = 0.152$, $P \leq 0.001$), TG ($r = 0.126$, $P \leq 0.001$), LDLC ($r = 0.073$, $P \leq 0.05$), TLs ($r = 0.168$, $P \leq 0.001$), vitamin E ($r = 0.137$, $P \leq 0.001$) (Fig. 1). Nonetheless, only HDLC, vitamin E/TC and vitamin E/TLs did not vary in trend, resulting in low Spearman correlation coefficients or insignificant associations between different age subgroups. Table 2Serum concentrations of α-tocopherol per age group in adults ($$n = 846$$)18–29 years30–39 years40–49 years50–59 years60–69 years ≥ 70 yearsPN (%)107 ($12.65\%$)174 ($20.57\%$)201 ($23.76\%$)206 ($24.35\%$)79 ($9.34\%$)79 ($9.34\%$)Age (years)26.00 (24.00–28.00)35.00 (32.00–37.00)45.00 (42.00–47.00)54.00 (52.00–56.00)65.00 (62.00–67.00)78.00 (73.00–84.00) < 0.001BMI (kg/m2)21.20 (19.10–24.70)23.80 (21.20–26.10)24.50 (22.30–26.05)24.65 (22.68–26.9)24.20 (22.30–26.00)24.70 (22.30–27.30) < 0.001SBP (mm Hg)116 (104–129)118.5 (109.0–130.0)119 (111–130)127.5 (117–141)129 (120–144)147 (133–158) < 0.001DBP (mm Hg)71 (64–78)74 (67–80)76 (67–83.5)82 (71.75–89)79 (72–87)82 (74–86) < 0.001TC (mmol/L)4.03 (3.60–4.49)4.20 (3.78–4.77)4.48 (3.96–5.03)4.72 (4.22–5.20)4.61 (4.05–5.22)4.38 (3.78–4.94) < 0.001TG (mmol/L)0.82 (0.61–1.19)1.12 (0.80–1.62)1.13 (0.82–1.68)1.40 (0.92–2.06)1.28 (0.99–1.77)1.44 (1.03–1.83) < 0.001HDLC (mmol/L)1.36 (1.20–1.57)1.20 (1.05–1.43)1.26 (1.06–1.50)1.24 (1.03–1.50)1.26 (1.10–1.49)1.22 (1.01–1.42)0.009LDLC (mmol/L)2.45 (2.11–3.02)2.66 (2.23–3.23)2.83 (2.33–3.32)3.00 (2.60–3.48)2.85 (2.33–3.57)2.60 (1.99–3.33) < 0.001TLs (mmol/L)4.95 (4.30–5.70)5.33 (4.73–6.24)5.65 (5.07–6.66)6.13 (5.38–7.11)5.84 (5.36–6.85)5.93 (4.93–6.69) < 0.001Unadjusted Vitamin E (μmol/L)24.84 (21.41–30.88)25.88 (21.85–30.65)27.63 (23.10–34.59)28.32 (23.85–34.19)28.32 (23.91–35.29)28.32 (22.33–35.52) < 0.001Adjusted for TC (mmol/mol)6.15 (5.42–7.25)6.16 (5.34–7.13)6.36 (5.34–7.41)6.14 (5.21–7.44)6.46 (5.22–7.62)6.71 (5.35–8.21)0.573 TLs (mmol/mol)5.08 (4.31–6.03)4.82 (4.18–5.49)4.86 (4.10–5.72)4.68 (3.94–5.39)4.73 (4.10–5.72)5.01 (4.02–6.26)0.131Fig. 1Spearman’s correlation coefficients between parameters in adults ($$n = 846$$)1. 1***$P \leq 0.001$, **$P \leq 0.01$, *$P \leq 0.05.$ The color bar on the right side represents Spearman’s correlation coefficients in the range of − 1.0 (blue color)-1.0 (red color). The redder the color and the larger the circle, the stronger the positive correlation; the bluer the color and the larger the circle, the stronger the negative correlation. BMI body mass index, SBP systolic blood pressure, DBP diastolic blood pressure, TC total cholesterol, TG triglyceride, HDLC high-density lipoprotein cholesterol, LDLC low-density lipoprotein cholesterol, TLs total lipids (the sum of cholesterol and triglyceride), VE vitamin E, VE/TC vitamin E/total cholesterol, VE/TLs vitamin E/total lipids ## The association of vitamin E level with risk factors Many people suffer from risk factors as critical links to the development of diseases, such as TC level, blood pressure or BMI. Thus, the α-tocopherol levels on these risk factors were also analyzed. No difference was found in BMI and DBP as presented in Additional file 1: Table S2. However, the people with hypercholesterolemia have significantly older age ($P \leq 0.001$), higher SDP ($$P \leq 0.036$$), TC ($P \leq 0.001$), TG ($P \leq 0.001$), HDLC ($$P \leq 0.007$$), LDLC ($P \leq 0.001$), TLs ($P \leq 0.001$) and vitamin E levels ($P \leq 0.001$) while significantly lower vitamin E/TC ($P \leq 0.001$) and vitamin E/TLs levels($P \leq 0.001$) (Table 3 and Additional file 1: Table S2). These parameters were further confirmed by Spearman’s correlation coefficients between TC and these indicators (Fig. 1). The people were divided into two groups. The population with high blood pressure exhibited older age ($P \leq 0.001$), higher BMI ($P \leq 0.001$), higher TC ($$P \leq 0.005$$), TG ($P \leq 0.001$) and TLs ($P \leq 0.001$) levels with inversely lower HDLC ($P \leq 0.001$) and vitamin E/TLs levels ($$P \leq 0.020$$). Nonetheless, no differences were found in LDLC ($$P \leq 0.058$$), vitamin E ($$P \leq 0.105$$) and vitamin E/TC ($$P \leq 0.560$$) levels, which were also evidenced by the insignificant correlation between SBP, DBP and vitamin E or vitamin E/TC except for LDLC (Table 3, Additional file 1: Table S3 and Fig. 1).Table 3The analysis of α-tocopherol levels on risk factorsNMale (n %)AgeVE (μmol/L)VE/TC (mmol/mol)VE/TLs (mmol/mol)TC level Hypercholesterolemia (total cholesterol ≥ 5.2 mmol/L)155 ($18.32\%$)84 ($54.19\%$)51 (43–57)32.5 (26.00–39.74)5.65 (4.58–6.93)4.36 (3.60–5.16) Normal (total cholesterol < 5.2 mmol/L)691 ($81.68\%$)387 ($56.01\%$)45 (34–56)26.7 (22.45–31.58)6.35 (5.46–7.55)4.91 (4.22–5.73) P0.681 < 0.001 < 0.001 < 0.001 < 0.001Blood pressure High blood pressure (≥ $\frac{140}{90}$ mmHg)210 ($24.82\%$)145 ($69.05\%$)56 (46–70)28.32 (22.92–34.48)6.36 (5.29–7.80)4.72 (3.90–5.46) Normal (< $\frac{140}{90}$ mmHg)636 ($75.18\%$)326 ($51.25\%$)44 (33–53)27.16 (22.89–32.50)6.16 (5.32–7.37)4.90 (4.16–5.70) $P \leq 0.001$ < 0.0010.1050.5600.020BMI < 18.5 (under weight)23 ($2.72\%$)4 ($17.39\%$)27 (21–29)24.61 (21.41–28.32)6.48 (5.42–8.12)5.35 (4.51–6.13) 18.5–24.9 (normal weight)488 ($57.68\%$)223 ($45.70\%$)47 (35–56)27.40 (22.62–33.206.18 (5.37–7.42)4.96 (4.22–5.87) 25.0–29.9 (over weight)303 ($35.82\%$)222 ($73.27\%$)49 (40–57)27.40 (23.45–33.20)6.26 (5.26–7.61)4.64 (3.88–5.45) ≥ 30.0 (obese)32 ($3.78\%$)22 ($68.75\%$)41 (32–62)28.32 (22.54–32.97)6.26 (4.89–7.02)4.48 (3.78–5.30) $P \leq 0.001$ < 0.0010.1280.5760.094 In order to evaluate whether there were differences in vitamin E, lipid-adjusted vitamin E or other indicators between BMI subgroups, we could only perform Kruskal–Wallis test in consideration of abnormal distribution of theses variables. As demonstrated in Table 3, with the increase of age, the people were apt to be overweight or obese (the median of age was above 41 years old). The people in group with higher BMI exhibited significantly increased levels of blood pressure, TC, TG, LDLC and TLs (all P values below 0.008), but decreased HDLC concentration ($P \leq 0.001$). Nevertheless, there were no differences in vitamin E, vitamin E/TC and vitamin E/TLs (Table 3 and Additional file 1: Table S4). These results were in agreement with the Spearman’s correlation analysis between BMI and other indicators as revealed in Fig. 1 except for vitamin E/TLs which presented strengthened correlation with BMI. ## The distribution of vitamin E The classification of subjects according to their α-tocopherol levels is presented in Table 4. In this study, $0.47\%$ of the population were below 12 μmol/L (functional deficiency), $61.47\%$ between 12 and 30 μmol/L (suboptimal concentration), and $38.06\%$ above 30 μmol/L (desirable concentration). As a stratification by sex, the males and females exhibited similar distributions as the total did. As described above, a significant positive association was revealed between age and α-tocopherol. Therefore, as a stratification by age, the higher proportion of people above the desirable concentration was found in older age group with a maximal value of $45.57\%$, but the proportion was slightly gone down again in people above 70 years old. Table 4Classification of the subjects according to their serum concentrations of α-tocopherol in adults ≤ 12 μmol/L (Deficient)13–29 μmol/L (Suboptimal) ≥ 30 μmol/L (Desirable)Total ($$n = 846$$)4 ($0.47\%$)520 ($61.47\%$)322 ($38.06\%$)Sex Male ($$n = 471$$)2 ($0.42\%$)289 ($61.36\%$)180 ($38.22\%$) Female ($$n = 375$$)2 ($0.53\%$)231 ($61.60\%$)142 ($37.87\%$)Age 18–29 ($$n = 107$$)077 ($71.96\%$)30 ($28.04\%$) 30–39 ($$n = 174$$)0123 ($70.69\%$)51 ($29.31\%$) 40–49 ($$n = 201$$)3 ($1.49\%$)117 ($58.21\%$)81 ($40.30\%$) 50–59 ($$n = 206$$)0116 ($56.31\%$)90 ($43.69\%$) 60–69 ($$n = 79$$)043 ($54.43\%$)36 ($45.57\%$) ≥ 70 ($$n = 79$$)1 ($1.27\%$)44 ($55.70\%$)34 ($43.03\%$) Regarding that there are only four cases with vitamin E deficiency, subjects with insufficiency and deficiency (< 30 μmol/L) were grouped together to further assess factors associated with vitamin E status, which referred the subjects with vitamin E sufficiency (≥ 30 μmol/L) as a comparison group. The factors determining vitamin E insufficiency and deficiency derived from multivariate logistic regression analyses are shown in Table 5. The analysis confirmed the significant association between vitamin E status and age (OR: 0.986; $95\%$ CI 0.976–0.996), and vitamin E status and TC level (OR: 0.305; $95\%$ CI 0.212–0.439).Table 5Association between serum vitamin E status and factorsVitamin EaPInsufficiency and deficiency, OR ($95\%$ CI)Participates, n524Age0.986 (0.976–0.996)0.007TC level < 0.0001 Normal1 Hypercholesterolemia0.305 (0.212–0.439)Blood pressure0.838 Normal1 High blood pressure0.964 (0.678–1.371)aDesirable vitamin E levels: α-tocopherol ≥ 30 μmol/L (comparison group) ## Discussion As the diverse role of vitamin E, especially function as a potent antioxidant in the maintenance of health and prevention of disease, the extensive implications of its deficiency are increasingly evident. As usual, serum vitamin E should be transported to tissues for exerting their functions by using lipoproteins as the major carries [23]. In humans, vitamin E is mostly transported in LDL and HDL at similar proportions with less carried in VLDL and other lipoproteins. As a result, vitamin E homeostasis is intimately connected to lipoprotein metabolism in vivo. Assessment of vitamin E status not only depends on its concentration, but also on the concentrations of the circulating lipoproteins. In fact, vitamin E deficiency is rare in humans. When it occurs, it is a result of lipoprotein deficiencies or lipid malabsorption syndromes. It is important to identify patients who have real vitamin E deficiency or other abnormalities known to cause vitamin E deficiency [24]. Thus, concentration of vitamin E, both unadjusted and adjusted for cholesterol or total lipids, should be used to evaluate vitamin E adequacy [25]. This study confirmed that the prevalence rate of vitamin E deficiency is low in urban adults of Wuhan based on both unadjusted and lipid-adjusted vitamin E levels, which provided valuable information about the distribution of vitamin E in central China. In this study, we found that the vitamin E level was not associated with sex as revealed in Table 1 (27.63 vs 27.16 μmol/L, $$P \leq 0.683$$), whereas the vitamin E level slightly increased with age in Table 2. The results were consistent with the previous report [26]. Meanwhile, the levels of BMI, SBP, DBP, TG and LDLC were significantly higher in men, mostly in consistent with Sun’s work [27]. However, an opposite direction of association in lipoprotein between men and women was found in Qi’s study that TC was significantly lower in men with no TG difference [28], which might be due to dietary intake or life habit in different regions. Likewise, it is well understood that aging process affect BMI, blood pressure and lipoprotein which are risk factors for cardiovascular diseases as Yao’s or Mendes’s report [29, 30]. In order to learn about the vitamin E level in these subjects with risk factors for cardiovascular diseases potentially imposing a great threat to human health, the subjects were divided into subgroups according to TC level, blood pressure and BMI. The transport of vitamin E is closely related to lipoprotein [24]. Therefore, hypercholesterolemia might affect the vitamin E level. As demonstrated in Fig. 1, the level of vitamin E is positively correlated with TC, TG, LDLC and TLs ($P \leq 0.05$), and much higher level of the vitamin E is found in subjects with hypercholesterolemia. *In* general, the participates who have increased blood lipid concentration also have increased serum vitamin E level, accompanied by evidently decreased lipid-adjusted vitamin E levels [25, 31]. This might be ascertained to the increased serum carriers for delivery of vitamin E in tissues [11]. Although TC level, blood pressure and BMI are highly correlated with each other as displayed in the *Spearman analysis* (Fig. 1) and stratification analysis (Additional file 1: Tables S2, S3, S4) [32], no significant difference of vitamin E, vitamin E/TC or vitamin E/TLs is found between subgroups based on blood pressure or BMI except that vitamin E/TLs is lower in subjects with hypertension. Obesity might be the most important factor associated with blood pressure, followed by hyperlipidemia [33], which contributes to the consistent result between blood pressure and BMI. In this study, a considerable proportion of population presents suboptimal vitamin E status ($61.47\%$), while a very small population exhibits vitamin E deficiency ($0.47\%$). Both age and TC level are significantly correlated with vitamin E status, which are indirectly evidenced by associations of vitamin E concentrations and variables (Table 2 and Table 3). It is found that almost equal proportions of the men and women in distribution of vitamin E. However, the young adults (18–39 years old) exhibited lower proportion of desirable vitamin E level than that of older, which was not consistent with Oldewage-Theron’s study [34]. Perhaps, the older adults in urban China have paid more attention to vitamin/trace element supplements in daily life due to their enhanced awareness of health. However, poor food intake as well as a monotonous diet in the elderly above 70 years old made the prevalence of vitamin E deficiency suffered. In addition, the vitamin E level in different countries is summarized in Table 6 [18, 26, 34–38]. Compared to the results from other countries, the serum vitamin E level in our subjects is higher than that of most countries and in parallel with that of US, even above the global level. Given that the cut-off point of vitamin E is 12 μmol/L, the prevalence of vitamin E inadequacy for urban adults in Wuhan from central *China is* $0.47\%$, much lower than that of other countries. Meanwhile, if 11.6 μmol/L was adopted as the cut-off value [39], the deficiency rate was $0.24\%$. It is noted that there is a great variation in blood lipids across nations and vitamin E is transported in lipoprotein fraction in the blood which is determinant for its concentration. However, a few studies provide lipid-adjusted vitamin E, thus it is not clear that whether the prevalence defined is comparable in various countries. If vitamin E deficiency was expressed as < 2.5 mmol/mol total cholesterol or expressed as < 1.59 mmol/mmol (total cholesterol + triacylglycerols) which was too uncommon to report [36], the prevalence of deficiency was $0.47\%$ or $0.35\%$ in Wuhan, respectively. Inspiringly, no matter what methods were used to adjust or evaluate for vitamin E concentration, the subjects in this study were identified as being vitamin E sufficient in comparison with other countries or regions. The low prevalence of deficiency in present study might due to the fact that the Chinese diet depends on vegetable oils, meat, green leafy vegetables, cereals, wheat germ and egg yolk, which are known to be the principal dietary sources of vitamin E [40].Table 6Comparison of the vitamin E status between Wuhan in central China and other countriesReferenceLocationNoAgeDefinition of deficiency% DeficiencyMedian or mean of vitamin E (μmol/L)Median or mean of vitamin E/TC (mmol/mol)This study, 2020Wuhan, central China84618–93Serum α-tocopherol ≤ 12.0 μmol/L (I) OR < 11.6 (II) OR serumα-tocopherol: cholesterol < 2.5 mmol/mol (III)OR α-tocopherol:(cholesterol + triacylglycerols) < 1.59 mmol/mol (IV)0.47(I)0.24(II)0.47(III)0.35(IV)27.406.20Péter et al., 2016 [18]Global132 studiesNASerum α-tocopherol ≤ 12.0 μmol/L1322.1NAOldewage-Theron et al., 2009 [34]Sharpeville, South Africa23560–93 yrSerum α-tocopherol < 2.8 μmol/L (I) OR < 3.7 μmol/L (II)20.9 (I)16.2 (II)4.8NAAssantachai et al. ,2007 [35]Thailand2336 ≥ 60 yrPlasma α-tocopherol < 14 μmol/L55.5NANAFord et al. ,2006 [36]US4087≥ 20 yrSerum α-tocopherol < 11.6 μmol/L0.5027.394.93Obeid et al., 2006 [37]Beirut, Lebanon85725–64 yrPlasma α-tocopherol < 5.8 μmol/L (I) OR < 11.6 (II) OR plasmaα-tocopherol: cholesterol < 2.5 μmol/mmol (III)0.7 (I)3.7 (II)4.1 (III)24.54.67Gouado et al., 2005 [26]Northern Cameroon813–61 yrSerum α-tocopherol < 5.8 μmol/L (I) OR < 11.6 μmol/L (II)12.3 (I)33.3 (II)12.2NAKang et al., 2004 [38]Taiwan1841 ≥ 19 yrSerum α-tocopherol < 11.6 μmol/L7.220.0NA Certain limitations should be noted when interprets the result of this study. Firstly, the results are based on cross-sectional data and the sample size is small. Secondly, this a single-center study, and thus its explanatory power is limited. Thirdly, the information for dietary supplements is deficient to know about the relationship between food sources and the serum vitamin E level. ## Conclusion The prevalence of vitamin E deficiency was lower in Wuhan from central China than other countries. The vitamin E was closely associated with lipids in comparison with BMI or blood pressure. We hope that the information in this study would prove useful to learn about vitamin E status of urban adults in China. ## Supplementary Information Additional file 1: Table S1 Method validation. Table S2 Classification of the subjects according to their TC levels in adults ($$n = 846$$). Table S3 Classification of the subjects according to their blood pressures in adults ($$n = 846$$). Table S4 Classification of the subjects according to their BMI in adults ($$n = 846$$). ## References 1. Lodge JK. **Mass spectrometry approaches for vitamin E research**. *Biochem Soc Trans* (2008.0) **36** 1066-1070. DOI: 10.1042/BST0361066 2. Zhang XH, Feng MH, Liu F, Qin L, Qu RJ, Li DL. **Subacute oral toxicity of BDE-15, CDE-15, and HODE-15 in ICR male mice: assessing effects on hepatic oxidative stress and metals status and ascertaining the protective role of vitamin E**. *Environ Sci Pollut Res* (2014.0) **21** 1924-1935. DOI: 10.1007/s11356-013-2084-0 3. Manosso LM, Camargo A, Dafre AL, Rodrigues ALS. **Vitamin E for the management of major depressive disorder: possible role of the anti-inflammatory and antioxidant systems**. *Nutr Neurosci* (2020.0) **14** 1-15 4. Matsura T. **Protective effect of tocotrienol on in vitro and in vivo models of Parkinson’s disease**. *J Nutr Sci Vitaminol* (2019.0) **65** S51-53. DOI: 10.3177/jnsv.65.S51 5. Zingg JM, Azzi A. **Non-antioxidant activities of vitamin E**. *Curr Med Chem* (2004.0) **11** 1113-1133. DOI: 10.2174/0929867043365332 6. Dror DK, Allen LH. **Vitamin E deficiency in developing countries**. *Food Nutr Bull* (2011.0) **32** 124-143. DOI: 10.1177/156482651103200206 7. Jiang Q. **Natural forms of vitamin E: metabolism, antioxidant, and anti-inflammatory activities and their role in disease prevention and therapy**. *Free Radic Biol Med* (2014.0) **72** 76-90. DOI: 10.1016/j.freeradbiomed.2014.03.035 8. Hall WL, Jeanes MY, Lodge JK. **Hyperlipidemic subjects have reduced uptake of newly absorbed vitamin E into their plasma lipoproteins, erythrocytes, platelets, and lymphocytes, as studied by deuterium-labeled alpha-tocopherol biokinetics**. *J Nutr* (2005.0) **135** 58-63. DOI: 10.1093/jn/135.1.58 9. Jensen SK, Lauridsen C. **Alpha-tocopherol stereoisomers**. *Vitam Horm* (2007.0) **76** 281-308. DOI: 10.1016/S0083-6729(07)76010-7 10. Azzini E, Polito A, Fumagall A, Intorre F, Venneria E, Durazzo A. **Mediterranean diet effect: an Italian picture**. *Nutr J* (2011.0) **10** 125. DOI: 10.1186/1475-2891-10-125 11. Ford L, Farr J, Morris P, Berg J. **The value of measuring serum cholesterol-adjusted vitamin E in routine practice**. *Ann Clin Biochem* (2006.0) **43** 130-134. DOI: 10.1258/000456306776021526 12. Traber MG. **Vitamin E inadequancy in humans causes and consequences**. *Adv Nutr* (2014.0) **5** 503-514. DOI: 10.3945/an.114.006254 13. Thurnham DI, Davies JA, Crump BJ, Situnayake RD, Davis M. **The use of different lipids to express serum tocopherol: lipid ratios for the measurement of vitamin E status**. *Ann Clin Biochem* (1986.0) **23** 514-520. DOI: 10.1177/000456328602300505 14. Gunanti IR, Marks GC, Al-Mamun A, Long KZ. **Low serum concentrations of carotenoids and vitamin E are associated with high adiposity in Mexican-American children**. *J Nutr* (2014.0) **1444** 489-495. DOI: 10.3945/jn.113.183137 15. Wang JQ, Lin X, Bloomgarden ZT, Ning G. **The Jiangnan diet, a healthy diet pattern for Chinese**. *J Diabetes* (2020.0) **12** 365-371. DOI: 10.1111/1753-0407.13015 16. Zaaboul F, Liu YF. **Vitamin E in foodstuff: nutritional, analytical, and food technology aspects**. *Com Rev Food Saf* (2022.0) **21** 964-998. DOI: 10.1111/1541-4337.12924 17. Lwanga SK, Lamshhow S. *Sample determination in health studies; a practical manual* (1991.0) 18. Péter S, Eggersdorfer M, Weber P, Weber P, Birringer M, Blumberg JB, Eggersdorfer M, Frank J. **Vitamin E intake and serum levels in the general population: a global perspective**. *Vitamin E in human health* (2019.0) 19. Rosada A, Kassner U, Weidemann F, König M, Buchmann N, Steinhagen-Thiessen E. **Hyperlipidemias in elderly patients: results from the berlin aging study II (BASEII), a cross-sectional study**. *Lipids Health Dis* (2020.0) **19** 92. DOI: 10.1186/s12944-020-01277-9 20. Lu JP, Lu Y, Wang XC, Li XY, Linderman GC, Wu CQ. **Prevalence, awareness, treatment, and control of hypertension in China: data from 1·7 million adults in a population-based screening study (China PEACE million persons project)**. *Lancet* (2017.0) **390** 2549-2558. DOI: 10.1016/S0140-6736(17)32478-9 21. Kitsantas P, Wu H. **Body mass index, smoking, age and cancer mortality among women: a classification tree analysis**. *J Obstet Gynaecol Res* (2013.0) **39** 1330-1338. DOI: 10.1111/jog.12065 22. Péter S, Friedel A, Roos FF, Wyss A, Eggersdorfer M, Hoffmann K. **A systematic review of global alpha-tocopherol status as assessed by nutritional intake levels and blood serum concentrations**. *Int J Vitam Nutr Res* (2015.0) **85** 261-281. DOI: 10.1024/0300-9831/a000281 23. Rigotti A. **Absorption, transport, and tissue delivery of vitamin E**. *Mol Aspects Med* (2007.0) **28** 423-436. DOI: 10.1016/j.mam.2007.01.002 24. Kayden HJ, Traber MG. **Absorption, lipoprotein transport, and regulation of plasma concentrations of vitamin E in humans**. *J Lipid Res* (1993.0) **34** 343-358. DOI: 10.1016/S0022-2275(20)40727-8 25. Traber MG, Jialal I. **Measurement of lipid-soluble vitamins–further adjustment needed?**. *Lancet* (2000.0) **355** 2013-2014. DOI: 10.1016/S0140-6736(00)02345-X 26. Gouado I, Ejoh RA, Kenne M, Ndifor F, Mbiapo FT. **Serum concentration of vitamins A and E and lipid in a rural population of north Cameroon**. *Ann Nutr Metab* (2005.0) **49** 26-32. DOI: 10.1159/000084174 27. Sun GZ, Li Z, Guo L, Zhou Y, Yang HM, Sun YX. **High prevalence of dyslipidemia and associated risk factors among rural Chinese adults**. *Lipids Health Dis* (2014.0) **13** 189. DOI: 10.1186/1476-511X-13-189 28. Qi L, Ding X, Tang W, Li Q, Mao D, Wang Y. **Prevalence and risk factors associated with dyslipidemia in Chongqing, China**. *Int J Environ Res Public Health* (2015.0) **12** 13455-13465. DOI: 10.3390/ijerph121013455 29. Yao XG, Frommlet F, Zhou L, Zu F, Wang HM, Yan ZT. **The prevalence of hypertension, obesity and dyslipidemia in individuals of over 30 years of age belonging to minorities from the pasture area of Xinjiang**. *BMC Public Health* (2010.0) **10** 91. DOI: 10.1186/1471-2458-10-91 30. Mendes R, Themudo Barata JL. **Aging and blood pressure**. *Acta Med Port* (2008.0) **21** 193-198. PMID: 18625098 31. Gross M, Yu X, Hannan P, Prouty C, Jacobs DR. **Lipid standardization of serum fat-soluble antioxidant concentrations: the YALTA study**. *Am J Clin Nutr* (2003.0) **77** 458-466. DOI: 10.1093/ajcn/77.2.458 32. Khoo KL, Tan H, Liew YM, Sambhi JS, Aljafri AM, Hatijah A. **Blood pressure, body mass index, heart rate and levels of blood cholesterol and glucose of volunteers during national heart weeks, 1995–1997**. *Med J Malaysia* (2000.0) **55** 439-450. PMID: 11221155 33. Liao CC, Su TC, Chien KL, Wang JK, Chiang CC, Lin CC. **Elevated blood pressure, obesity, and hyperlipidemia**. *J Pediatr* (2009.0) **155** 79-83. DOI: 10.1016/j.jpeds.2009.01.036 34. Oldewage-Theron WH, Samuel FO, Djoulde RD. **Serum concentration and dietary intake of vitamins A and E in low-income South**. *Clin Nutr* (2010.0) **29** 119-123. DOI: 10.1016/j.clnu.2009.08.001 35. Assantachai P, Lekhakula S. **Epidemiological survey of vitamin deficiencies in older Thai adults: implications for national policy planning**. *Public Health Nutr* (2007.0) **10** 65-70. DOI: 10.1017/S136898000720494X 36. Ford ES, Schleicher RL, Mokdad AH, Ajani UA, Liu S. **Distribution of serum concentrations of alpha-tocopherol and gamma-tocopherol in the US population**. *Am J Clin Nutr* (2006.0) **84** 375-383. DOI: 10.1093/ajcn/84.2.375 37. Obeid OA, Al-Ghali RM, Khogali M, Hwalla N. **Vitamins A and E status in an urban Lebanese population: a case study at Dar al-fatwa area, Beirut**. *Int J Vitam Nutr Res* (2006.0) **76** 3-8. DOI: 10.1024/0300-9831.76.1.3 38. Kang MJ, Lin YC, Yeh WH, Pan WH. **Vitamin E status and its dietary determinants in Taiwanese–results of the nutrition and health survey in Taiwan 1993–1996**. *Eur J Nutr* (2004.0) **43** 86-92. PMID: 15083315 39. Sauberlich HE, Dowdy RP, Skala JH. **Laboratory tests for the assessment of nutritional status**. *Crit Rev Clin Lab Sci* (1973.0) **4** 215-340. DOI: 10.3109/10408367309151557 40. 40.Food and Agriculture Organization of the United Nations. Agriculture food and nutrition for Africa-A resource book for teachers of agriculture, http://www.fao.org/3/W0078E/W0078E00.html.; 1997. Accessed 21 Jan 2021.
--- title: 'Cholesterol: An Important Determinant of Muscle Atrophy in Astronauts' authors: - Hoangvi Le - Vikrant Rai - Devendra K Agrawal journal: Journal of biotechnology and biomedicine year: 2023 pmcid: PMC10062007 doi: 10.26502/jbb.2642-91280072 license: CC BY 4.0 --- # Cholesterol: An Important Determinant of Muscle Atrophy in Astronauts ## Abstract Since cholesterol is not routinely measured in astronauts before and after their return from space, there is no data on the role of blood cholesterol level in muscle atrophy and microgravity. Since the first moon landing, aerospace medicine became outdated and has not pushed boundaries like its rocket engineering counterpart. Since the 2019 astronaut twin study, there has yet to be another scientific breakthrough for aerospace medicine. Microgravity-induced muscle atrophy is the most known consequence of spaceflight. Yet, so far, there is no therapeutic solution to prevent it or any real efforts in understanding it on a cellular or molecular level. The most obvious reason to this unprecedented level of research is due to the small cohort of astronauts. With the establishment of private space industries and exponential recruitment of astronauts, there is more reason to push forward spaceflight-related health guidelines and ensure the safety of the brave humans who risk their lives for the progression of mankind. Spaceflight is considered the most challenging job and the failure to prevent injury or harm should be considered reckless negligence by the institutions that actively prevented sophistication of aerospace medicine. In this critical review, role of cholesterol is analyzed across the NASA-established parameters of microgravity-induced muscle atrophy with a focus on potential therapeutic targets for research. ## Introduction Microgravity induced skeletal muscle atrophy is well established in aerospace medicine. In 1999, it was first demonstrated that skeletal muscle cells were directly affected by space travel [1, 2]. However, the mechanism of muscle atrophy in astronauts lacks foundation other than microgravity-induced unloading. Skeletal muscle atrophy is characterized by reduced muscle fiber size and wasting. Skeletal muscle wasting is a predominate indicator of terminal illness because skeletal muscle is a homeostatic-sensitive organ. Any slight shift in balance causes skeletal muscle to dramatically reduce itself to maintain homeostasis. Currently, medical experiments on the International Space Station (ISS) have not revealed any substantial leads in how to prevent muscle atrophy or have investigated other reasons for muscle atrophy other than microgravity. This is described in one of NASA’s human research gaps M23: “Determine if factors other than unloading contribute to muscle atrophy during space flight” and lists potential risk factors as “inflammation, redox balance, energy balance, hydration, etc.” [ 3]. The potential risk factors in the NASA list are all associated with cholesterol and will be the focus of discussion in this article. Cholesterol is a fundamental and modifiable biochemical component in phospholipid bilayer. Hypercholesterolemia (HC) is characterized by high cholesterol in the blood with increased levels of low-density lipoproteins (LDLs) and low levels of high-density lipoproteins (HDLs). One study has demonstrated significant skeletal muscle fiber wasting as a result of ApoE inactivation and increased non-HDL cholesterol in LGMD2B mice. It was postulated that damaged vascular barriers allow the chronic leakage of plasma lipids into muscle tissue, resulting in muscle inflammation [4]. Microgravity shortened the body length and increased fat accumulation in microgravity-cultured worms compared to normal gravity cultured worms [5]. Impaired lipolysis in adipose tissues and skeletal muscles due to reduced mRNA expression levels of lipoprotein lipase (LPL) in adipose tissue and reduced LPL activity in skeletal muscle [6]. Impaired function of LPL in skeletal muscle mediates HC which ultimately leads to increased LDLs and muscle atrophy due to decreased blood supply caused by lumen obstruction [7]. This notion is supported by the fact that lower skeletal muscle mass index is associated with dyslipidemia and increased muscle mass is a must to prevent hypercholesterolemia [8]. These results suggest an association of skeletal muscle weakness with hypercholesterolemia and increased muscle mass and lowering LDLs as a therapeutic strategy to prevent muscle atrophy. Cholesterol (in the context of space) has become a significant health marker in heart disease, which NASA also aims to mitigate in astronauts [9]. However, the current collection of space-related medical research on cholesterol is insufficient and does not reveal much about the effect of microgravity on cholesterol. In fact, cholesterol is not a routine measurement for astronauts when landing [10]. According to NASA, hypercholesterolemia, obesity, diabetes, and hypertension could develop in astronauts [11]. Growing interest in hypercholesterolemia-induced muscle atrophy may provide novel insight to protect astronauts from such health risks [4, 12]. This review aims to critically evaluate the cholesterol-related microgravity research and emphasizes the detrimental consequences of undervalued cholesterol as a target in treating muscle atrophy in astronauts. ## NASA-defined Spaceflight Health Risks It is important to recognize that astronauts perform tasks inside and outside the International Space Station (ISS), and few researchers have considered that not all astronauts are tasked with spacewalks. Astronauts who participate in spacewalks could be potential outliers in medical experiments because of the physical impact from the severely outdated spacesuit. According to the audit of April 2017, only 11 of the original extravehicular mobility unit (EMU) spacesuits are still in use, 4 of which are available on the ISS and the rest are on Earth for maintenance [13]. This is prompted by the need to develop a next-generation spacesuit to address the limitations of the current spacesuit. In August 2021, NASA Office of Inspector General performed another audit to examine the current progress of the next-generation spacesuits for the ISS and Artemis missions [14]. This audit also revealed the health-related risks associated with the current spacesuit and examples of negative health outcome from specific missions. Spacesuit-related risks include decompression sickness, thermal regulation, shoulder injuries, hand injuries, malnutrition, and dehydration. In addition to this, astronauts are not completely protected by the current spacesuit from cosmic radiation during spacewalks or on the Moon. Compared to activities done safely inside the ISS, spacewalks are considered one of the most dangerous jobs in mankind and can certainly contribute to the physical and psychological stress of astronauts. However, similar health risks are associated within the ISS as described in Appendix B of NASA’s November 2021 audit [IG-22–005] [15]. Some notable mentions are injury due to the operations of extravehicular activity (EVA), altered immune response, inadequate food and nutrition, spaceflight-induced cardiovascular disease (CVD), and space radiation exposure. The same audit highlights how mitigation of human health risks requiring ISS microgravity testing will not be complete by 2030, the retirement date of the ISS, and 8 out the 12 critical human health risks will not be mitigated at an acceptable level for long-term spaceflight [15]. Although this schedule is based on an approximation, it can be delayed even further if research is not productive. This warrants critical evaluation of the current aerospace research efforts and what shortcomings they share to pinpoint the reason behind the slow progress. ## Role of Cholesterol in Space Health Risks Microgravity is undoubtedly a main contender for muscle atrophy, but research in microgravity has an astronomical amount of confounding variables, leaving inconsistent and often uninterpretable results. The main concerns NASA described in their audits have one common variable: cholesterol. According to the history, cholesterol was first described as a perpetrator of heart disease in 1968, only 7 years after the first manned spaceflight in 1961 and around the same time as the Apollo moon landing in 1969. Whether or not the first biomedical researchers of NASA have considered these factors and how far to investigate them is up to debate. Since the discovery on the role of cholesterol role in heart disease, the recommended consumption of cholesterol is less than 300mg/d for astronauts, which has not changed since. The behavior of cholesterol in microgravity was demonstrated in a basic study where simulated microgravity enhanced lipid accumulation along imitation-vessels due to random particle direction [16]. In an environment of potent solar radiation, oxidation of cholesterol may exacerbate the health of astronauts. In each relevant health risk, cholesterol will be described in context of clinical scenarios that mimic health outcomes in microgravity (Figure 1). ## Cholesterol and Decompression Sickness Decompression sickness occurs when an astronaut’s body experiences sudden drop in surrounding pressure, such as their spacesuit. Decompression sickness is associated with terrestrial activities such as deep sea diving and non-pressurized aerial flying [17]. Hypercholesterolemia after high-fat meal increases risk for decompression sickness in divers [18]. Astronauts tend to eat before spacewalk to avoid eating in spacesuit. If preparing for an 8-hour spacewalk, it can be assumed that they are eating enough to sustain themselves for that period. Binge eating beforehand can spike serum cholesterol concentrations of an astronaut right before preparing for a spacewalk, as seen in diving. Decompression sickness is associated with oxygen toxicity because deep-sea divers and astronauts both breathe pure oxygen. However, it has long been known that concentrations of oxygen greater than normal breathing air has slow but detrimental effects. One of these effects include oxidative stress-induced inflammation and lipid peroxidation [19]. On the other hand, divers or astronauts returning from breathing pure oxygen results in decreased blood oxygen levels and results in hypoxia. This is also associated with high altitude sickness where there is a drop in oxygen pressure [20]. Acclimatization to high altitudes show significant positive correlation with increasing serum cholesterol [21]. High-altitude induced hypoxia are directly associated with increased non-HDL cholesterol [22]. Hypoxia can lead to chronic inflammation and oxidative stress, another common characteristic in astronauts, and will be discussed further in the following sections [23, 24]. ## Cholesterol and Thermoregulation Astronauts have a water-cooling system in their spacesuits to help regulate their increased temperature during ISS spacewalks. Thermal regulation is challenging because astronauts may spend up to 8 hours in their spacesuits in direct solar radiation while also performing tasks. Cholesterol is a fundamental component of animal cell membrane homeostasis that can act as a buffer molecule to stabilize the phospholipid bilayer over a range of temperatures. Cholesterol lowers membrane fluidity in high temperatures and increases membrane fluidity in lower temperatures [25]. Astronauts experience increased core body temperature and are at risk for heat stress and hyperthermia [26]. On earth, increased ambient temperature is associated with increased levels of LDL and decreased levels in HDL [27]. Heat stress from a heat wave has shown to increase plasma cholesterol in a British population [28]. Hyperthermia is correlated with increased cellular cholesterol contents studied in mammalian cell lines [29]. Long-term heat exposure influenced cholesterol metabolism in pigs and temporarily increased serum and LDL cholesterol levels. However, inflammation or tissue damage was not present [30]. In hibernating brown bears, dyslipidemia and muscles were protected against lipid-specific oxidative damage due to higher plasma-antioxidants reserves [31]. Increased temperature may only be relevant to current space activities, whereas decreased temperatures on the Moon or Mars may pose a new threat in space exploration. ## Cholesterol and Spacesuit Injuries Extravehicular activity (EVA) injuries of hands and shoulders are usually sustained with the EMU spacesuit during spacewalks and EVA training. In zero gravity, astronauts tend to use their upper extremities for movement and physical tasks, versus their lower extremities, resulting in the overuse of shoulders and hands. The spacesuit hardware of the upper torso was the main cause of injury during active duty [32]. Although, shoulder injury has only increased recently in NASA perhaps due to changes in the space suit design or changes in the spaceflight requirements, such as all astronauts must be EVA certified in order to fly starting in 2000 [33]. Astronauts selected in the 1990’s have higher incidences in shoulder surgery. Astronauts who have performed more than five spacewalks were twice as likely to sustain shoulder injuries than astronauts who performed one spacewalk [34]. Shoulder injury is a broad term that encompasses many different types of myopathies or tendinopathies relating to the shoulder rotator cuff. Rotator cuff injury could manifest from hypercholesterolemia due to higher levels of total cholesterol, triglycerides, and LDL cholesterol [35]. Dyslipidemia and lipid deposition is associated with the failure of rotator cuff repairs and increased risk of retear [36]. NASA is fully aware of the detrimental effects of the spacesuit to cause shoulder injury in astronauts and have opened this issue for public collaboration. Texas Women’s University and Wichita State University are currently in the works for a mechanical sensor detection system to avoid shoulder injury [37, 38]. On the other hand, astronauts also experience handgrip fatigue due to pressurized gloves and limited mobility [39]. Those with smaller hands are more likely to sustain hand injuries, such as blisters, cuts, or joint pain [13]. From 1993 to 2010, NASA reports that 76 percent of astronauts sustained injuries of the fingernail, finger crotch, metacarpophalangeal joint, or fingertip [40]. Cholesterol deposits were found in extensor tendons of the hands in hypercholesterolemia patients [41, 42]. This could possibly increase the risk of astronauts developing hypercholesterolemia-induced arthritis [43]. Efforts are underway to improve pressurized gloves to mitigate hand injury, such as the next-generation high performance EVA gloves and the second-generation Space Suit Robot Glove (SSRG) [44, 45]. ## Cholesterol and Spaceflight Malnutrition As mentioned before, astronauts may spend several hours performing an EVA without eating or drinking fluids. Single food bars were previously installed in EMU spacesuits, but were discontinued due to inconvenience (low calorie, extra weight, smearing on visor, limited storage space, etc.) [ 13, 46]. Astronauts prefer to eat a solid nutrient-dense meal before an EVA to remain satiated for as long as possible [47]. It is not clear if all astronauts are eating enough calories for their spacewalk or if astronauts over-eat to mitigate potential fatigue and hunger caused by the physical demands of an EVA. The food prepared for astronauts are heavily processed (freeze-dried) to meet sanitary and package requirements for long-term storage. Although fresh foods are more nutritional, they are prohibited due to shorter shelf-life and potential microbial contamination. Freeze-dried food and menu fatigue contribute to decreased appetite in astronauts [48]. Underconsumption of required nutritional intake may increase the risk of malnutrition in astronaut and subsequently increase oxidative stress in muscles. One study demonstrated that depriving nutrients in C2C12 muscle cells significantly increased the levels of reactive oxygen species (ROS) [49]. Food prepared in the ISS are higher in cholesterol (300 mg as of year 2020) than the standard recommendation of 200 mg [50]. If astronauts eat big meals, this may suddenly cause serum cholesterol to spike, such as consuming high amounts of sugar or eggs [51, 52]. It would be beneficial to study the body metabolism of astronauts before and after an EVA to investigate metabolic efficiency. Because most seasoned astronauts are middled aged, they are at higher risk for cardiovascular disease. Age-related metabolic alterations may affect how nutritional intake impacts endocrine functions, muscle mass homeostasis, and lipid profile [53]. Aging is correlated with dyslipidemia and cardiovascular disease [54, 55]. Aging also impacts muscle mass through sarcopenia and chronic inflammation [56, 57]. Striated muscle atrophy has been observed in cardiovascular disease [58]. Cardiovascular disease risk may be due to hypercholesterolemia. High LDL-cholesterol has been associated with cardiovascular mortality and lowering LDL-C levels are beneficial for preventing cardiovascular disease in men [59, 60]. Moreover, high levels of oxidized-low density lipoprotein (ox-LDL) exacerbate the progression of atherosclerosis [61]. Although NASA aims to mitigate cardiovascular disease in astronauts, it is concerning that cholesterol is not routinely measured, which leads to the lack of cholesterol-specific research [10]. On the other spectrum, decreased vitamin D was the most striking nutritional changes during spaceflight. Vitamin D shares the same precursor as cholesterol, 7-dehydrocholesterol (7-DHC) [62]. UV light is completely shielded on the ISS and spacesuits have built in UV-blocking material. Therefore astronauts are unable to endogenously synthesize vitamin D for long period of time. Chronic vitamin D deficiency is a critical concern for long-term space exploration because of the decreased calcium absorption and subsequent bone wasting [63]. Dyslipidemia, increased total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), decreased in high-density lipoprotein cholesterol (HDL-C) levels can manifest from vitamin D deficiency as well [64]. Astronauts also experience prolonged states of dehydration both in the ISS and during EVA spacewalks. Dehydration occurs after microgravity-induced fluid shift during spaceflight [65]. The recommended daily intake of water for astronauts is eight ounces per hour, but the disposable in-suit drink bag only contains 32 ounces of water, only sufficient for 4 hour spacewalks [66]. The average period of an EVA spacewalk in 2021–2022 is about 7–8 hours, which suggests that one drink bag is not enough for the entire mission [67]. Dehydration can lead to hypertension, muscle fatigue, and dizziness [68–70]. Cholesterol and hydration play a critical role in cell membrane fluidity and are directly correlated with each other [71]. This is demonstrated by increased serum cholesterol concentration in blood tests in dehydrated patients [72]. Therefore, it begs the question of whether the reported cholesterol levels in all previous human astronaut research is consistent or not. ## Cholesterol and Space Radiation In addition to UV radiation, astronauts experience high exposure to space radiation. Space radiation, or cosmic radiation, consists of high-charged energy particles, X-rays, and gamma rays. It is known that lipid peroxidation produced from space radiation [73]. LDL-cholesterol in particular is a vulnerable target of radiation-mediated oxidation [74]. Six hour exposure to UV radiation causes cholesterol to increase by 21.6-fold and direct cell membrane modification [75]. The effects of long-term exposure to dangerous levels of gamma radiation were significantly associated with hypercholesterolemia in Japanese atomic bomb survivors [76, 77]. For these reasons, cardiovascular disease and cancer are negative outcomes of space radiation that need to be mitigated before considering deep-space exploration [78]. ## Cholesterol and Space-induced Altered Immune Response Astronauts experienced allergy-like symptoms, such as prolonged congestion, rhinitis, sneezing, and skin rashes, while traveling in space for long periods of time, which prompted investigation of how microgravity affects immune response [79]. Chronic inflammation was observed in astronauts due to many confounding factors, such as oxidative stress, but the mechanism behind microgravity-induced immune hyperactivity is not clear [80]. Since oxidative stress is a major contender of space risk, oxidation of LDL-cholesterol should not be ignored as a predictor of chronic inflammation. Hypercholesterolemia is often characterized by chronic inflammation response to LDL-cholesterol dominated macrophages, or foam cells [81]. Macrophages studies in microgravity are very diverse, making it more difficult to come to a significant conclusion [82]. Hyperlipidemia enhances neutrophil activity and are also elevated during microgravity as a response to oxidative stress [83, 84]. Response to dendritic cells diminished in microgravity as demonstrated by the suppression of T-cell reactivity [85, 86]. Microgravity studies have also shown hypoxia-inducible factor 1α (HIF-1α) as a potential therapeutic target for its role in macrophage and T-cell activation [87]. ## Cholesterol and Muscle Atrophy in Astronauts As mentioned above, NASA lists potentially causative factors for space-induced skeletal muscle atrophy, including inflammation, redox balance, energy balance, hydration, and others [3]. Evidence provided so far support the role of cholesterol in all these potential causes/factors of muscle atrophy. Understanding cholesterol on its very basic level is extremely important to its homeostatic functions in cell anatomy. Too much cholesterol can cause negative effects on important skeletal muscle membrane-protein species, such as those found in the transverse tubule (t-tubule). The t-tubule is considerably more cholesterol-rich than the sarcolemma and increasing this level further impedes the trafficking of intracellular glucose transporter, GLUT4, to the t-tubule and other surface membranes [88, 89]. Increased t-tubule cholesterol concentration may also may negatively alter voltage-gated Ca2+ channels [90]. In microgravity (where cholesterol may increase through many variables), astronauts may experience diabetic symptoms and dysfunctional metabolism, rendering carbohydrate/glucose intake as potential waste. Depletion of this cholesterol impairs excitation and contraction coupling. [ 91] In addition, high cholesterol causes inhibition of ATPases by overloading cholesterol in the striated muscle sarcolemma [92, 93]. Cholesterol depletion induced by statin drugs, used by some astronauts, may exacerbate muscle atrophy [94]. Astronauts who need rotator cuff surgery may be at risk for poor repair outcomes due to fatty infiltration and rotator cuff muscle atrophy [95]. Re-analysis of the twin study landing data show that sharp increase of cytokines and chemokines, and other inflammatory markers, such as IL-6, IL-10, IL-1β, IL-1Ra, CRP, CCL2, and TNF-α, suggest regenerative response to atrophy rather than inflammatory response [96]. In fact, chronic inflammation can disrupt proteins in the skeletal muscle fiber triggering atrophy [97]. It was demonstrated in rats flown in space for 12.5 days that muscle atrophy occurred and it was characterized by the dysfunctional microcirculation, denervation, infiltration and phagocytosis of cellular debris by macrophages and neutrophils in necrotized skeletal muscle fibers [98]. Chronic inflammation in skeletal muscle is dependent on macrophage kinetics and disturbance in cell signaling can lead to muscle fiber degeneration [99]. In chronic inflammation, both M1 and M2 macrophages increase and compete for arginine metabolism as it is a shared substrate for iNOS and arginase. However, M2c macrophages reduce the activity of M1 macrophages, leading to a shift in metabolism from iNOS to arginase, resulting in a pro-fibrotic environment. M2 macrophages also increase myogenic factors, such as MYOG and MYOD [100]. Inflammation-induced striated muscle atrophy has been observed in the dysregulation of muscle fibers such as actin, myosin, and titin [101–103]. Actin fragments were generated by caspase-3 and further degraded by a ubiquitin-proteasome [104]. Muscle-specific E3 ubiquitin-ligase, TRIM63 (or MuRF1), has been shown to degrade myosin light chains 1 and 2, myosin heavy chains, and myosin-binding protein C [105]. TRIM63 has also been observed in titin degradation in cardiomyopathies [106]. In hindlimb unloading studies, proteolytic titin fragments increased significantly after 7-days of gravitational unloading [107]. Current spaceflight-related muscle research suggests that studies should focus on catabolic state markers, such as FOXO1 and muscle-specific TRIM63 (MuRF1) [108]. However, a recent transcriptome study done in 2021 has shown that deletion of MuRF1 in mice did not prevent muscle atrophy during spaceflight and suggested Cacng1 as a new target for microgravity-induced atrophy. The study also demonstrated that transfecting myotubes with active mutant of FOXO3, FOXO3a, decreased average myotube diameter by $27.5\%$ [109, 110]. Mitochondrial function and mitochondrial DNA (mtDNA) can also be another relevant target for microgravity-mediated muscle atrophy caused by oxidative stress, DNA damage, and inflammation [111, 112]. Reactive oxygen species (ROS) can activate NF-κB pathways and subsequent release of cytokines like TNF-α, as well as damaging the sarcolemma and contractile proteins, exacerbating muscle dysfunction in dystrophic muscle cells[49]. The plasticity of skeletal muscle is often challenged by hypercholesterolemia and dyslipidemia. Skeletal muscle can fluctuate between hypertrophy, as seen in bodybuilders, and atrophy, as seen with disuse. Lipotoxicity can trigger inflammation and tissue damage as pro-inflammatory cytokines and adipokines are released from resident macrophages and adipocytes [113]. Increased cholesterol levels have been shown to intensify muscle wasting in the Duchenne muscular dystrophy (DMD) mouse model [114]. This suggests that hypercholesterolemia, through inflammation and fatty infiltration, can promote skeletal muscle atrophy. Age-related muscle atrophy, sarcopenia, reveal how low muscle mass and muscle strength are risk factors for disability and mortality [57]. Research on sarcopenia has suggested OPA1 as a potential therapeutic target for age-related muscle loss and chronic inflammation. Skeletal muscle sacrifices its own reserves to regulate metabolic dysfunction in lipid and glucose homeostasis in any state of health, which is why skeletal muscle atrophy is often observed in a range of diseases, such as infections to cancer [115]. Altered lipid metabolism plays a role in skeletal muscle weakness, as shown in obesity-like disease models. In rats, short-term high fat diet impaired function in oxidative-type skeletal muscles [116]. For patients with spinal muscular atrophy (SMA), malnutrition is a major concern because of defects in fatty acid transport and mitochondrial β-oxidation [117, 118]. In addition to altered lipid metabolism, glucose metabolism alteration may cause skeletal muscle atrophy, as observed in diabetes [119]. Glucose is essential for skeletal muscle function because it is responsible for maintaining blood glucose homeostasis by metabolizing $80\%$ of consumed glucose. It has been shown in mice, knock out for skeletal-muscle-specific endogenous circadian clock, Bmal1, leads to glucose metabolism and systemic glucose homeostasis disruption [120]. A metabolic organ such as skeletal muscle is sensitive to homeostatic disturbances and may lead to imbalances of protein synthesis and degradation. Therefore, insulin-resistance may trigger skeletal muscle protein degradation through mTOR complex 1 signaling pathway (mTORC1) [121]. Hyperglycemia may also trigger muscle atrophy by WWP1/KLF15 pathway in diabetes [122]. Metabolic reprogramming may induce muscle mass wasting through impaired skeletal muscle specific mitochondrial pyruvate carrier (MPC), and upregulating the hepatic gluconeogenesis in the Cory cycle and fatty acid oxidation [123]. It has been hypothesized that microgravity-mediated chronic inflammation can develop into dysmetabolic conditions that include imbalance of lipid and glucose metabolism [124]. ## Future Prospective and Potential Targets The main factors precipitating space-induced skeletal muscle atrophy are cholesterol and chronic inflammation (Figure 2). Based on the scientific literature available about inflammatory mediators, oxidative stress, hypercholesterolemia, altered metabolism, and muscular protein degradation, there is an obvious link between muscle atrophy and cholesterol. However, it is gravely important to point out the potential contraindications of implementing statins as a treatment for hypercholesterolemia because statins may also cause myopathies, such as myalgia and rhabdomyolysis [125, 126]. Additionally, glucocorticoids should not be considered as a potential therapy for chronic-inflammation-induced muscle atrophy because it increases the rate of the ubiquitin-proteasome system [127]. Hypercholesterolemic LGMD2B mice with muscle atrophy have developed fatty infiltration and inflammation in limb and girdle muscles histology, which suggests chronic cytokine release from adipocytes and macrophages [4]. Macrophage cytokine overexpression of IL-6, TNF-α, and IL-1 have been demonstrated to increase myogenic factors and FOXO transcription factors leading to the subsequent increase titin degradation through MuRF1 (TRIM63) [99, 128, 129]. With the acknowledgment of microgravity-induced atrophy occurring even with MuRF1 deletion in mice, the mechanism between hypercholesterolemia and chronic inflammation in skeletal muscle must be investigated further to elucidate potential therapeutic targets. Studies investigating the interaction between skeletal muscle cytokines, also known as myokines, and other key organs, such as adipose tissue and the brain, have highlighted the multifaceted role that upregulated levels of IL-6 play in chronic inflammation and the decrease in appetite after exercise. Depending on the signaling origin, such as canonical myeloid cells or non-canonical adipocytes and muscle, IL-6 could play an pro-inflammatory or anti-inflammatory role in skeletal muscle, respectively [130]. However, IL-6 is a gravity-sensitive myokine and may contribute to homeostatic dysregulation of muscle [131]. Elucidating the mechanisms of IL-6 and other myokines in microgravity would provide potential targets for developing anti-inflammatory therapies for astronauts. Space travel leads to an increase in production of reactive oxygen species (ROS), which causes cellular stress and damage to astronauts, including muscle atrophy. The development of an antioxidant cocktail was proposed to maintain the health of astronauts and requires consideration of factors such as the physiological effects of ROS on the body, genetic predisposition of astronauts to damage, and the efficacy of antioxidants. [ 132]. For ROS-related muscle atrophy, it was demonstrated that two antioxidants, N-acetyl-L-cysteine (NAC) and pyrroloquinoline quinone (PQQ), significantly decreased the development of skeletal muscle atrophy induced by fasting [49]. Exposure to the microgravity and radiation leads to destroyed red blood cells and excessive iron stores, which results in an imbalance in redox homeostasis, leading to oxidative damage of cells and injuries in the musculoskeletal system. Therefore, antioxidants and exogenous iron chelators were suggested as potential therapies [133]. An article reviewed the significance of certain nutrients in combating the harm brought about by microgravity during space missions. To mitigate oxidative stress, it was suggested to increase the consumption of antioxidants such as vitamins A, C, and E, omega-3 fatty acids, and minerals like copper, zinc, manganese, selenium, and iron through the diet. The combination of dietary defenses and the production of endogenous antioxidants could potentially play a significant role in guarding against oxidative damage. To address cardiovascular problems, it was advised to follow low-glycemic index diets. Vitamin D3 was deemed important for preventing bone damage, but high doses of it could lead to hypercalcemia, kidney stones, and the calcification of soft tissues [134]. The TCA cycle could be another target for microgravity-induced atrophy as all participating enzymes were reported to have low gene and protein expression, such as citrate synthase, aconitase, isocitrate dehydrogenase, succinate dehydrogenase, fumarase, and malate dehydrogenase [5]. In muscle atrophy, there is a reduction in energy production, which leads to decreased muscle function. By targeting the TCA cycle, it may be possible to increase energy production in the muscle and thus, counteract or prevent muscle wasting. Metabolomics and epigenomics are promising bioinformatic studies that focus on the shifts in environments and how that impacts gene expression. Fundamental areas of space biology research include oxidative stress, DNA damage, mitochondrial dysregulation, epigenetics, telomere length alterations, and microbiome shifts [135]. The Gene Lab database of NASA is useful for researchers interested in bioinformatic analyses of experiments performed in space. However, it limits human data due to privacy concerns and access must be authorized by the NASA Human Research Program. Processing of the bioinformatic data also takes time and requires interdisciplinary understanding of multiple statistical methods. ## Conclusions The area of hyperlipidemia-associated muscle atrophy in astronauts is important but is poorly investigated. Skeletal muscle and cholesterol share an evolutionary-conserved function in energy metabolism and storage. Overall, this review critically evaluated the efforts of NASA, or lack thereof, in the coordination of their life sciences departments. This was also highlighted in the October 2015 audit of NASA [136]. A critical description is also presented as to how the twin study is an example of the small sample size that does not represent the population of astronauts and could lead to vague conclusions about the effects of spaceflight. The basis of scientific research requires larger population and decrease the workload off the few astronauts who are in high demand. Despite the recent renewal of TRISH (Translational Research Institute of Space Health) by the NASA Human Research Program in December 2020, more biomedical researchers and physicians with aerospace background are needed to facilitate meaningful investigations. Aerospace medical programs need to be marketed as much as aerospace engineering programs. Besides military medical residencies, only one civilian residency program for aerospace medicine exists at the University of Texas Medical Branch (UTMB) and one civilian fellowship program at the Mayo Clinic [137]. Recently, UCLA medical school opened their new aerospace medicine fellowship program starting July 2022 [138]. Whether cholesterol levels increase or decrease due to microgravity, better protocols and experimental methods must be established to thoroughly investigate the role of cholesterol in astronaut health. Because cholesterol is an important biomarker for many co-morbidities of cardiovascular disease, it cannot be ignored in aerospace biomedical research. ## Data Availability Statement Not applicable since the information is gathered from published articles. ## References 1. Droppert PM. **A review of muscle atrophy in microgravity and during prolonged bed rest**. *J Br Interplanet Soc* **46** 83-86. PMID: 11539498 2. Vandenburgh H, Chromiak J, Shansky J. **Space travel directly induces skeletal muscle atrophy**. *Faseb j* **13** 1031-1038. PMID: 10336885 3. 3.NASA. Gap - M23: Determine if factors other than unloading contribute to muscle atrophy during space flight (2022).. *Gap - M23: Determine if factors other than unloading contribute to muscle atrophy during space flight* (2022.0) 4. Sellers SL, Milad N, White Z. **Increased nonHDL cholesterol levels cause muscle wasting and ambulatory dysfunction in the mouse model of LGMD2B**. *J Lipid Res* **59** 261-272. PMID: 29175948 5. Higashibata A. **Microgravity elicits reproducible alterations in cytoskeletal and metabolic gene and protein expression in space-flown Caenorhabditis elegans**. *npj Microgravity* (2016.0) **2** 15022. PMID: 28725720 6. Ward ZJ. **Projected U.S. State-Level Prevalence of Adult Obesity and Severe Obesity**. *New England Journal of Medicine* **381** 2440-2450. PMID: 31851800 7. Sellers SL. **Increased nonHDL cholesterol levels cause muscle wasting and ambulatory dysfunction in the mouse model of LGMD2B**. *Journal of Lipid Research* **59** 261-272. PMID: 29175948 8. Lee JH. **Relationship between muscle mass index and LDL cholesterol target levels: Analysis of two studies of the Korean population**. *Atherosclerosis* **325** 1-7. PMID: 33857762 9. 9.NASA. Risk of Cardiovascular Adaptations Contributing to Adverse Mission Performance and Health Outcomes (2022).. *Risk of Cardiovascular Adaptations Contributing to Adverse Mission Performance and Health Outcomes* (2022.0) 10. Smith SM, Zwart SR. **Nutritional biochemistry of spaceflight**. *Adv Clin Chem* **46** 87-130. PMID: 19004188 11. Rainey K. *Biological rhythms in space and on Earth* (2015.0) 12. Lee JH. **Relationship between muscle mass index and LDL cholesterol target levels: Analysis of two studies of the Korean population**. *Atherosclerosis* **325** 1-7. PMID: 33857762 13. General N.O.o.I.. *Final Report - IG-17–018 - NASA’s Management and Development of Spacesuits* (2017.0) 14. General N.O.o.I.. *Final Report - IG-21–025 – NASA’s Development of Next-Generation Spacesuits* (2021.0) 15. General N.O.o.I.. *IG-22–005-NASA’s Management of the International Space Station and Efforts to Commercialize Low Earth Orbit* (2021.0) 16. Hanpanich O. *Lipid Accumulation in Vessel-imitating Tubes under Microgravity Condition* (2012.0) 17. Savioli G. **Dysbarism: An Overview of an Unusual Medical Emergency**. *Medicina* **58** 104. PMID: 35056412 18. Kaczerska D. **The influence of high-fat diets on the occurrence of decompression stress after air dives**. *Undersea Hyperb Med* **40** 487-497. PMID: 24377191 19. Halliwell B, Gutteridge JM. **Oxygen toxicity, oxygen radicals, transition metals and disease**. *Biochem J* **219** 1-14. PMID: 6326753 20. Luks AM, Swenson ER, Bärtsch P. **Acute high-altitude sickness**. *European Respiratory Review* **26** 160096. PMID: 28143879 21. Temte JL. **Elevation of serum cholesterol at high altitude and its relationship to hematocrit**. *Wilderness Environ Med* **7** 216-224. PMID: 11990116 22. Gonzales GF, Tapia V. **Association of high altitude-induced hypoxemia to lipid profile and glycemia in men and women living at 4,100m in the Peruvian Central Andes**. *Endocrinol Nutr* **60** 79-86. PMID: 22925953 23. Møller P. **Acute hypoxia and hypoxic exercise induce DNA strand breaks and oxidative DNA damage in humans**. *Faseb j* **15** 1181-1186. PMID: 11344086 24. Pena E. **Oxidative Stress and Diseases Associated with High-Altitude Exposure**. *Antioxidants (Basel)* (2022.0) **11** 25. 25.Harvard University Department of M. and B. Cellular, 5. Cholesterol Modulates Membrane Fluidity (2020).. *Cholesterol Modulates Membrane Fluidity* (2020.0) **5** 26. Stahn AC. **Increased core body temperature in astronauts during long-duration space missions**. *Scientific Reports* (2017.0) **7** 27. Halonen JI. **Outdoor temperature is associated with serum HDL and LDL**. *Environ Res* **111** 281-287. PMID: 21172696 28. Keatinge WR. **Increased platelet and red cell counts, blood viscosity, and plasma cholesterol levels during heat stress, and mortality from coronary and cerebral thrombosis**. *Am J Med* **81** 795-800. PMID: 3776986 29. Soejima S. **Hyperthermic sensitivity and cholesterol levels of mammalian cell lines in culture**. *Cancer Lett* **60** 159-167. PMID: 1933839 30. Wen X. **Effects of long-term heat exposure on cholesterol metabolism and immune responses in growing pigs**. *Livestock Science* **230** 103857 31. Giroud S. **Hibernating brown bears are protected against atherogenic dyslipidemia**. *Scientific Reports* (2021.0) **11** 32. Anderson AP, Newman DJ, Welsch RE. **Statistical Evaluation of Causal Factors Associated with Astronaut Shoulder Injury in Space Suits**. *Aerosp Med Hum Perform* **86** 606-613. PMID: 26102140 33. Laughlin MS. *Shoulder Injury Incidence Rates In Nasa Astronauts* (2014.0) 34. Murray JD. *RATE OF SHOULDER SURGERY AMONG NASA ASTRONAUTS* (2014.0) 35. Abboud JA, Kim JS. **The effect of hypercholesterolemia on rotator cuff disease**. *Clin Orthop Relat Res* **468** 1493-1497. PMID: 19885710 36. Gatto AP. **Dyslipidemia is associated with risk for rotator cuff repair failure: a systematic review and meta-analysis**. *JSES Reviews, Reports, and Techniques* **2** 302-309 37. Boutwell M. **A Wearable Sensor to Mitigate Shoulder Injury in Astronauts**. *International Journal of Exercise Science: Conference Proceedings* (2020.0) **2** 38. Loflin B. **Identification of shoulder joint clearance in space suit using electromagnetic resonant spiral proximity sensor for injury prevention**. *Acta Astronautica* **170** 46-54 39. Rajulu SL, Klute GK. **A Comparison Of Hand Grasp Breakaway Strengths And Bare-Handed Grip Strengths Of The Astronauts, SML 3 Test Subjects, And The Subjects From The General Population**. *NASA Technical Paper* (1993.0) **3286** 40. McFarland S. **Analysis Of Potential Glove-Induced Hand Injury Metrics During Typical Neutral Buoyancy Training Operations**. *NASA Technical Reports Server* (2016.0) 41. Kruth HS. **Lipid deposition in human tendon xanthoma**. *Am J Pathol* **121** 311-315. PMID: 4061567 42. Yang Y, Lu H, Qu J. **Tendon pathology in hypercholesterolaemia patients: Epidemiology, pathogenesis and management**. *J Orthop Translat* **16** 14-22. PMID: 30723677 43. Li D. **Xanthomatosis in bilateral hands mimicking rheumatoid arthritis: A case report**. *Medicine (Baltimore)* **96** e9399. PMID: 29390551 44. 44.NASA. Space Suit RoboGlove (SSRG) | T2 Portal. technology.nasa.gov (2018).. *Space Suit RoboGlove (SSRG) | T2 Portal* (2018.0) 45. Walsh SK. *Next Generation Life Support: High Performance Eva Glove* (2015.0) 46. Dunbar B. *NASA - Food for Space Flight* (2004.0) 47. 47.Quora. How Do Astronauts Eat During A Spacewalk? Forbes (2017).. *How Do Astronauts Eat During A Spacewalk?* (2017.0) 48. Tang H. **Long-Term Space Nutrition: A Scoping Review**. *Nutrients* (2021.0) **14** 49. Qiu J. **Mechanistic Role of Reactive Oxygen Species and Therapeutic Potential of Antioxidants in Denervation- or Fasting-Induced Skeletal Muscle Atrophy**. *Frontiers in Physiology* (2018.0) **9** 50. Smith S. *Human Adaptation to Spaceflight: The Role of Food and Nutrition Second Edition (NP-2021–03-003-JSC)* (2021.0) 51. Silbernagel G. **Cholesterol synthesis is associated with hepatic lipid content and dependent on fructose/glucose intake in healthy humans**. *Exp Diabetes Res* (2012.0) 361863. PMID: 22203835 52. Spence JD, Jenkins DJ, Davignon J. **Dietary cholesterol and egg yolks: not for patients at risk of vascular disease**. *Can J Cardiol* **26** e336-339. PMID: 21076725 53. Volpi E, Nazemi R, Fujita S. **Muscle tissue changes with aging**. *Curr Opin Clin Nutr Metab Care* **7** 405-410. PMID: 15192443 54. Liu HH, Li JJ. **Aging and dyslipidemia: a review of potential mechanisms**. *Ageing Res Rev* **19** 43-52. PMID: 25500366 55. Rosada A. **Hyperlipidemias in elderly patients: results from the Berlin Aging Study II (BASEII), a cross-sectional study**. *Lipids Health Dis* **19** 92. PMID: 32410691 56. Tezze C. **Age-Associated Loss of OPA1 in Muscle Impacts Muscle Mass, Metabolic Homeostasis, Systemic Inflammation, and Epithelial Senescence**. *Cell Metabolism* **25** 1374-1389. PMID: 28552492 57. Kalyani RR, Corriere M, Ferrucci L. **Age-related and disease-related muscle loss: the effect of diabetes, obesity, and other diseases**. *Lancet Diabetes Endocrinol* **2** 819-829. PMID: 24731660 58. Sisto IR, Hauck M, Plentz RDM. **Muscular Atrophy in Cardiovascular Disease**. *Adv Exp Med Biol* **1088** 369-391 59. Abdullah SM. **Long-Term Association of Low-Density Lipoprotein Cholesterol With Cardiovascular Mortality in Individuals at Low 10-Year Risk of Atherosclerotic Cardiovascular Disease**. *Circulation* **138** 2315-2325. PMID: 30571575 60. Vallejo-Vaz AJ. **Low-Density Lipoprotein Cholesterol Lowering for the Primary Prevention of Cardiovascular Disease Among Men With Primary Elevations of Low-Density Lipoprotein Cholesterol Levels of 190 mg/dL or Above: Analyses From the WOSCOPS (West of Scotland Coronary Prevention Study) 5-Year Randomized Trial and 20-Year Observational Follow-Up**. *Circulation* **136** 1878-1891. PMID: 28877913 61. Gao S, Liu J. **Association between circulating oxidized low-density lipoprotein and atherosclerotic cardiovascular disease**. *Chronic Diseases and Translational Medicine* **3** 89-94. PMID: 29063061 62. Prabhu AV. **Cholesterol-mediated Degradation of 7-Dehydrocholesterol Reductase Switches the Balance from Cholesterol to Vitamin D Synthesis**. *Journal of Biological Chemistry* **291** 8363-8373. PMID: 26887953 63. Smith SM, Zwart SR, Heer M. *Risk Factor of Inadequate Nutrition Human Research Program Human Health Countermeasures Element* (2015.0) 64. Kim MR, Jeong SJ. **Relationship between Vitamin D Level and Lipid Profile in Non-Obese Children**. *Metabolites* (2019.0) **9** 65. Iwase S, Mano T. **Microgravity and autonomic nervous system**. *Nihon Rinsho* **58** 1604-1612. PMID: 10944920 66. 66.NASA. NASA SPACEFLIGHT HUMAN-SYSTEM STANDARD VOLUME 2: HUMAN FACTORS, HABITABILITY, AND ENVIRONMENTAL HEALTH (2019).. *NASA SPACEFLIGHT HUMAN-SYSTEM STANDARD VOLUME 2: HUMAN FACTORS, HABITABILITY, AND ENVIRONMENTAL HEALTH* (2019.0) 67. 67.NASA. Space Station Spacewalks (2022).. *Space Station Spacewalks* (2022.0) 68. Watso JC, Farquhar WB. **Hydration Status and Cardiovascular Function**. *Nutrients* (2019.0) **11** 69. Cleary MA, Sitler MR, Kendrick ZV. **Dehydration and symptoms of delayed-onset muscle soreness in normothermic men**. *J Athl Train* **41** 36-45. PMID: 16619093 70. Shaheen NA. **Public knowledge of dehydration and fluid intake practices: variation by participants’ characteristics**. *BMC Public Health* **18** 1346. PMID: 30518346 71. Maiti A, Daschakraborty S. **How Do Urea and Trimethylamine N-Oxide Influence the Dehydration-Induced Phase Transition of a Lipid Membrane?**. *The Journal of Physical Chemistry B* **125** 10149-10165. PMID: 34486370 72. Campbell NR. **Dehydration during fasting increases serum lipids and lipoproteins**. *Clin Invest Med* **17** 570-576. PMID: 7895421 73. Huff J. **Risk of Radiation Carcinogenesis Human Research Program Space Radiation Element**. *Human Research Roadmap* (2016.0) 74. Khouw AS, Parthasarathy S, Witztum JL. **Radioiodination of low density lipoprotein initiates lipid peroxidation: protection by use of antioxidants**. *Journal of Lipid Research* **34** 1483-1496. PMID: 8228633 75. Kuebodeaux RE, Bernazzani P, Nguyen TTM. **Cytotoxic and Membrane Cholesterol Effects of Ultraviolet Irradiation and Zinc Oxide Nanoparticles on Chinese Hamster Ovary Cells**. *Molecules* (2018.0) **23** 76. Wong FL. **Effects of Radiation on the Longitudinal Trends of Total Serum Cholesterol Levels in the Atomic Bomb Survivors**. *Radiation Research* **151** 736-746. PMID: 10360794 77. Multhoff G, Radons J. **Radiation, Inflammation, and Immune Responses in Cancer**. *Frontiers in Oncology* (2012.0) **2** 78. Boerma M. **Space radiation and cardiovascular disease risk**. *World J Cardiol* **7** 882-888. PMID: 26730293 79. Crucian B. **Incidence of clinical symptoms during long-duration orbital spaceflight**. *Int J Gen Med* **9** 383-391. PMID: 27843335 80. Crucian BE. **Immune System Dysregulation During Spaceflight: Potential Countermeasures for Deep Space Exploration Missions**. *Front Immunol* **9** 1437. PMID: 30018614 81. Tall AR, Yvan-Charvet L. **Cholesterol, inflammation and innate immunity**. *Nat Rev Immunol* **15** 104-116. PMID: 25614320 82. Ludtka C. **Macrophages in microgravity: the impact of space on immune cells**. *NPJ Microgravity* **7** 13. PMID: 33790288 83. Drechsler M. **Hyperlipidemia-triggered neutrophilia promotes early atherosclerosis**. *Circulation* **122** 1837-1845. PMID: 20956207 84. Paul AM. **Neutrophil-to-Lymphocyte Ratio: A Biomarker to Monitor the Immune Status of Astronauts**. *Front Immunol* **11** 564950. PMID: 33224136 85. Tackett N. **Prolonged exposure to simulated microgravity diminishes dendritic cell immunogenicity**. *Scientific Reports* (2019.0) **9** 86. Kalinski P. **Exhaustion of Human Dendritic Cells Results in a Switch from the IL-15R/IL-15- to IL-2R/IL-2-driven Expansion of Antigen-specific CD8+ T Cells**. *The Journal of Immunology* **196** 196.16-196.16. PMID: 26621863 87. Vogel J. **Expression of Hypoxia-Inducible Factor 1α (HIF-1α) and Genes of Related Pathways in Altered Gravity**. *Int J Mol Sci* (2019.0) **20** 88. Czech MP, Corvera S. **Signaling mechanisms that regulate glucose transport**. *J Biol Chem* **274** 1865-1868. PMID: 9890935 89. Grice BA. **Excess membrane cholesterol is an early contributing reversible aspect of skeletal muscle insulin resistance in C57BL/6NJ mice fed a Western-style high-fat diet**. *American Journal of Physiology-Endocrinology and Metabolism* **317** E362-E373. PMID: 31237447 90. Barrientos G. **Membrane Cholesterol in Skeletal Muscle: A Novel Player in Excitation-Contraction Coupling and Insulin Resistance**. *Journal of Diabetes Research* (2017.0) 3941898. PMID: 28367451 91. Barrientos G. **Cholesterol removal from adult skeletal muscle impairs excitation–contraction coupling and aging reduces caveolin-3 and alters the expression of other triadic proteins**. *Frontiers in Physiology* (2015.0) **6** 92. Ortega A, Mas-Oliva J. **Cholesterol effect on enzyme activity of the sarcolemmal (Ca2+ + Mg2+)-ATPase from cardiac muscle**. *Biochim Biophys Acta* **773** 231-236. PMID: 6145444 93. Bastiaanse EML, Höld KM, Van der Laarse A. **effect of membrane cholesterol content on ion transport processes in plasma membranes**. *Cardiovascular Research* **33** 272-283. PMID: 9074689 94. 94.NASA. HRR - Gap - CV-203: Test countermeasures on the ISS against the spaceflight-induced changes in the cardiovascular system of importance for development of disease, in humanresearchroadmap.nasa.gov. (2022).. *HRR - Gap - CV-203: Test countermeasures on the ISS against the spaceflight-induced changes in the cardiovascular system of importance for development of disease* (2022.0) 95. Gladstone JN. **Fatty Infiltration and Atrophy of the Rotator Cuff do not Improve after Rotator Cuff Repair and Correlate with Poor Functional Outcome**. *The American Journal of Sports Medicine* **35** 719-728. PMID: 17337727 96. Gertz ML. **Multi-omic, Single-Cell, and Biochemical Profiles of Astronauts Guide Pharmacological Strategies for Returning to Gravity**. *Cell Rep* **33** 108429. PMID: 33242408 97. Tuttle CSL, Thang LAN, Maier AB. **Markers of inflammation and their association with muscle strength and mass: A systematic review and meta-analysis**. *Ageing Research Reviews* **64** 101185. PMID: 32992047 98. Riley DA. **Skeletal muscle fiber, nerve, and blood vessel breakdown in space-flown rats**. *Faseb j* **4** 84-91. PMID: 2153085 99. Perandini LA. **Chronic inflammation in skeletal muscle impairs satellite cells function during regeneration: can physical exercise restore the satellite cell niche?**. *The FEBS Journal* **285** 1973-1984. PMID: 29473995 100. Satriano J. **Arginine pathways and the inflammatory response: Interregulation of nitric oxide and polyamines: Review article**. *Amino Acids* **26** 321-329. PMID: 15290337 101. Nakanishi N. **Urinary Titin N-Fragment as a Biomarker of Muscle Atrophy, Intensive Care Unit-Acquired Weakness, and Possible Application for Post-Intensive Care Syndrome**. *Journal of Clinical Medicine* **10** 614. PMID: 33561946 102. Reid MB, Moylan JS. **Beyond atrophy: redox mechanisms of muscle dysfunction in chronic inflammatory disease**. *J Physiol* **589** 2171-2179. PMID: 21320886 103. Zhang YG. **Actin-Binding Proteins as Potential Biomarkers for Chronic Inflammation-Induced Cancer Diagnosis and Therapy**. *Anal Cell Pathol (Amst)* (2021.0) 6692811. PMID: 34194957 104. Du J. **Activation of caspase-3 is an initial step triggering accelerated muscle proteolysis in catabolic conditions**. *Journal of Clinical Investigation* **113** 115-123. PMID: 14702115 105. Huang Z. **Effect of mammalian target of rapamycin signaling pathway on nerve regeneration**. *Biotarget* **2** 18-18 106. Peng J. **Muscle atrophy in Titin M-line deficient mice**. *Journal of Muscle Research and Cell Motility* **26** 381-388 107. Ulanova A. **Effect of L-Arginine on Titin Expression in Rat Soleus Muscle After Hindlimb Unloading**. *Frontiers in Physiology* (2019.0) **10** 108. Ploutz-Snyder L. *HRP-47072 Evidence Report: Risk of Impaired Performance Due to Reduced Muscle Mass, Strength, and Endurance Human Research Program Human Health Countermeasures Element* (2015.0) 109. Okada R. **Transcriptome analysis of gravitational effects on mouse skeletal muscles under microgravity and artificial 1 g onboard environment**. *Scientific Reports* **11** 9168. PMID: 33911096 110. Cadena SM. **Skeletal muscle in MuRF1 null mice is not spared in low-gravity conditions, indicating atrophy proceeds by unique mechanisms in space**. *Scientific Reports* **9** 9397. PMID: 31253821 111. Bisserier M. **Cell-Free Mitochondrial DNA as a Potential Biomarker for Astronauts’ Health**. *J Am Heart Assoc* **10** e022055. PMID: 34666498 112. Nguyen HP. **The effects of real and simulated microgravity on cellular mitochondrial function**. *npj Microgravity* (2021.0) **7** 113. Ferraro E. **Exercise-induced skeletal muscle remodeling and metabolic adaptation: redox signaling and role of autophagy**. *Antioxid Redox Signal* **21** 154-176. PMID: 24450966 114. Milad N. **Increased plasma lipid levels exacerbate muscle pathology in the mdx mouse model of Duchenne muscular dystrophy**. *Skeletal Muscle* (2017.0) **7** 115. Sartori R, Romanello V, Sandri M. **Mechanisms of muscle atrophy and hypertrophy: implications in health and disease**. *Nature Communications* (2021.0) **12** 116. Andrich DE. **Altered Lipid Metabolism Impairs Skeletal Muscle Force in Young Rats Submitted to a Short-Term High-Fat Diet**. *Front Physiol* **9** 1327. PMID: 30356919 117. Li YJ. **Metabolic and Nutritional Issues Associated with Spinal Muscular Atrophy**. *Nutrients* (2020.0) **12** 118. Deguise MO. **Abnormal fatty acid metabolism is a core component of spinal muscular atrophy**. *Ann Clin Transl Neurol* **6** 1519-1532. PMID: 31402618 119. Rudrappa SS. **Human Skeletal Muscle Disuse Atrophy: Effects on Muscle Protein Synthesis, Breakdown, and Insulin Resistance—A Qualitative Review**. *Frontiers in Physiology* (2016.0) **7** 120. Harfmann BD. **Muscle-specific loss of Bmal1 leads to disrupted tissue glucose metabolism and systemic glucose homeostasis**. *Skeletal Muscle* (2016.0) **6** 121. Koopman R, Ly CH, Ryall JG. **A metabolic link to skeletal muscle wasting and regeneration**. *Front Physiol* **5** 32. PMID: 24567722 122. Hirata Y. **Hyperglycemia induces skeletal muscle atrophy via a WWP1/KLF15 axis**. *JCI Insight* (2019.0) **4** 123. Sharma A. **Impaired skeletal muscle mitochondrial pyruvate uptake rewires glucose metabolism to drive whole-body leanness**. *eLife* (2019.0) **8** 124. Strollo F. **Space Flight-Promoted Insulin Resistance as a Possible Disruptor of Wound Healing**. *Frontiers in Bioengineering and Biotechnology* (2022.0) **10** 125. Tournadre A. **Statins, myalgia, and rhabdomyolysis**. *Joint Bone Spine* **87** 37-42. PMID: 30735805 126. Hansen KE. **Outcomes in 45 Patients With Statin-Associated Myopathy**. *Archives of Internal Medicine* **165** 2671-2676. PMID: 16344427 127. Braun TP, Marks DL. **The regulation of muscle mass by endogenous glucocorticoids**. *Frontiers in Physiology* (2015.0) **6** 128. Peris-Moreno D, Taillandier D, Polge C. **MuRF1/TRIM63, Master Regulator of Muscle Mass**. *Int J Mol Sci* (2020.0) **21** 129. Zanders L. **Sepsis induces interleukin 6, gp130/JAK2/STAT3, and muscle wasting**. *J Cachexia Sarcopenia Muscle* **13** 713-727. PMID: 34821076 130. Severinsen MCK, Pedersen BK. **Muscle-Organ Crosstalk: The Emerging Roles of Myokines**. *Endocr Rev* **41** 594-609. PMID: 32393961 131. Smith JK. **IL-6 and the dysregulation of immune, bone, muscle, and metabolic homeostasis during spaceflight**. *NPJ Microgravity* **4** 24. PMID: 30534586 132. Gómez X. **Key points for the development of antioxidant cocktails to prevent cellular stress and damage caused by reactive oxygen species (ROS) during manned space missions**. *npj Microgravity* **7** 35. PMID: 34556658 133. Yang J. **Effects of Iron Overload and Oxidative Damage on the Musculoskeletal System in the Space Environment: Data from Spaceflights and Ground-Based Simulation Models**. *Int J Mol Sci* (2018.0) **19** 134. Costa F. **Spaceflight Induced Disorders: Potential Nutritional Countermeasures**. *Frontiers in Bioengineering and Biotechnology* (2021.0) **9** 135. Afshinnekoo E. **Fundamental Biological Features of Spaceflight: Advancing the Field to Enable Deep-Space Exploration**. *Cell* **183** 1162-1184. PMID: 33242416 136. 136.NASA. NASA Office of Inspector General Office of Audits NASA’S EFFORTS TO MANAGE HEALTH AND HUMAN PERFORMANCE RISKS FOR SPACE EXPLORATION National Aeronautics and Space Administration (2015).. *NASA Office of Inspector General Office of Audits NASA’S EFFORTS TO MANAGE HEALTH AND HUMAN PERFORMANCE RISKS FOR SPACE EXPLORATION National Aeronautics and Space Administration* (2015.0) 137. 137.ASMA. Aerospace Medical Association | Residency Programs & Related Courses. www.asma.org.. *Aerospace Medical Association | Residency Programs & Related Courses* 138. 138.UCLA Space Medicine Fellowship program aims to prepare next generation of flight surgeons | UCLA Health. www.uclahealth.org (2022).. *UCLA Space Medicine Fellowship program aims to prepare next generation of flight surgeons* (2022.0)
--- title: Computed tomography body composition and clinical outcomes following lung transplantation in cystic fibrosis authors: - Ann L Jennerich - Lois Downey - Christopher H Goss - Siddhartha G Kapnadak - Joseph B Pryor - Kathleen J Ramos journal: BMC Pulmonary Medicine year: 2023 pmcid: PMC10062009 doi: 10.1186/s12890-023-02398-4 license: CC BY 4.0 --- # Computed tomography body composition and clinical outcomes following lung transplantation in cystic fibrosis ## Abstract ### Background Low muscle mass is common in patients approaching lung transplantation and may be linked to worse post-transplant outcomes. Existing studies assessing muscle mass and post-transplant outcomes include few patients with cystic fibrosis (CF). ### Methods Between May 1993 and December 2018, 152 adults with CF received lung transplants at our institution. Of these, 83 met inclusion criteria and had usable computed tomography (CT) scans. Using Cox proportional hazards regression, we evaluated the association between pre-transplant thoracic skeletal muscle index (SMI) and our primary outcome of death after lung transplantation. Secondary outcomes, including days to post-transplant extubation and post-transplant hospital and intensive care unit (ICU) length of stay, were assessed using linear regression. We also examined associations between thoracic SMI and pre-transplant pulmonary function and 6-min walk distance. ### Results Median thoracic SMI was 26.95 cm2/m2 (IQR 23.97, 31.32) for men and 22.83 cm2/m2 (IQR 21.27, 26.92) for women. There was no association between pre-transplant thoracic SMI and death after transplant (HR 1.03; $95\%$ CI 0.95, 1.11), days to post-transplant extubation, or post-transplant hospital or ICU length of stay. There was an association between pre-transplant thoracic SMI and pre-transplant FEV$1\%$ predicted ($b = 0.39$; $95\%$ CI 0.14, 0.63), with higher SMI associated with higher FEV$1\%$ predicted. ### Conclusions Skeletal muscle index was low for men and women. We did not identify a significant relationship between pre-transplant thoracic SMI and post-transplant outcomes. There was an association between thoracic SMI and pre-transplant pulmonary function, confirming the potential value of sarcopenia as a marker of disease severity. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12890-023-02398-4. ## Introduction Cystic fibrosis (CF) is associated with significant pulmonary disease, and progressive respiratory failure is a common cause of death [1, 2]. Outcomes have improved significantly, but lung transplantation remains an important treatment option that can improve survival and quality of life for many people with advanced CF lung disease [3–5]. Selecting optimal candidates for lung transplantation includes evaluating risk factors for adverse post-transplant outcomes [6], but this assessment can be imprecise, particularly related to pre-transplant malnutrition, which is known to be common among people with advanced CF lung disease. Body mass index (BMI) is often used as an indicator of nutritional status in the pre-transplant population, but its relationship to post-transplant outcomes is inconsistent, particularly in patients who are underweight [7–15]. A potential explanation for inconsistencies in the relationship between BMI and clinical outcomes following lung transplantation is the inability of BMI to discriminate between fat mass and muscle mass. Loss of skeletal muscle mass and function, also referred to as sarcopenia, is a key component of frailty, a syndrome characterized by the accumulation of physiologic deficits that increase vulnerability to adverse events [16]. Sarcopenia is common and has been linked to mortality in advanced lung disease [17], along with worse outcomes following lung transplantation [18, 19]; however, these studies include limited numbers of patients with CF, leaving much to be understood about sarcopenia and lung transplantation in this patient population. There are many potential mechanisms that could contribute to sarcopenia in CF including physical inactivity, inflammation, malnutrition, and cystic fibrosis transmembrane conductance regulator (CFTR) specific muscle dysfunction [20–22]. With the advent of CFTR modulator therapies patients have experienced significant improvements in weight and BMI [23]; however, the impact on lean body mass is unknown and the study of muscle mass in CF remains relevant to current clinical care. Including a measure of muscle mass in the assessment of nutritional status may provide a clearer picture of a patient’s pre-transplant risk than BMI alone. Computed tomography (CT) body composition analysis is an alternative approach that has been utilized among patients undergoing liver transplantation [24–28], but there is insufficient evidence to inform risk assessments of patients with CF who are preparing for lung transplantation [18, 19, 29]. Our primary objective was to determine whether muscle mass measured by CT is associated with post-transplant outcomes after lung transplantation for CF. Our primary outcome was survival, with secondary outcomes of days to post-transplant extubation and post-transplant hospital and intensive care unit (ICU) length of stay. Additional aims included an evaluation of the association between muscle mass measured by CT and markers of functional reserve among patients with CF, including pre-transplant 6-min walk distance and forced expiratory volume in 1 s (FEV1) % predicted. We hypothesized that lower muscle mass would be associated with poor pre-transplant functional reserve and higher mortality following lung transplantation. ## Study design and participants Our transplant program’s candidate selection process has evolved over the past 25 years, and like the greater lung transplant community, we have gradually shifted towards transplanting slightly sicker and older patients. Despite this gradual evolution, there were no major changes to our official listing criteria over the study period. Between May 1993 and December 2018, 152 adult CF patients received lung transplants at the University of Washington Medical Center (UWMC). Of these patients, 93 had accessible CT scans of the chest completed within one year prior to transplant. From this group we excluded individuals who underwent multi-organ transplant (e.g., heart and lung) ($$n = 2$$) or were re-transplant patients ($$n = 2$$) and those for whom no post-transplant follow-up records were available ($$n = 2$$). Of the remaining patients with accessible pre-transplant CT scans, 4 scans were unusable because of anatomical distortion (e.g., subcutaneous emphysema), leaving 83 patients for our total sample. This project was approved by the institutional review board at the University of Washington (STUDY00012301). ## Measures CT scans were available in a centralized, secure picture archiving and communication system (PACS) at UWMC. For our predictor of interest, we used muscle mass measured by CT scan of the chest obtained closest in time to transplant. Using a standardized image selection protocol, we selected a single axial slice nearest the inferior aspect of the T12 vertebral body. We selected the inferior aspect to obtain measurements as close as possible to the region associated with peak skeletal muscle area [30]. The direct measure was skeletal muscle cross sectional area (cm2) at the T12 vertebrae, adjusted for patient stature by dividing by height in m2, rendering a thoracic skeletal muscle index (SMI) (cm2/m2) [30]. Tissue cross-sectional area (cm2) in slices was computed automatically by summing appropriate pixels using the CT Hounsfield unit (HU) range − 29 HU to 150 HU for skeletal muscle. The following muscles in the axial slice were included in our measurements: rectus abdominus, external and internal intercostals, external obliques and internal obliques (if present), latissimus dorsi, erector spinae (iliocostalis thoracis, longissimus dorsi, spinalis), and transversospinalis (if present). We did not incorporate the diaphragm or transverse abdominis in our measurements. The diaphragm, when present in T12 slices, is difficult to differentiate from adjacent solid organs. To maintain consistency the transverse abdominis was excluded as well, as it may interdigitate with the diaphragm in the T12 area, making it difficult to differentiate between these muscles. All scans were analyzed using Slice-O-Matic software (Tomovision, Magog, QC, Canada). One reviewer (ALJ) independently assessed muscle mass on all scans, and a second reviewer (KJR) assessed a random selection of $10\%$ of scans for assessment of inter-rater reliability. Chart abstraction was used to obtain demographics including CF genotype (F508del homozygous vs not), and additional pre-transplant patient characteristics: body mass index (BMI), positive respiratory cultures in the year preceding transplant, pretransplant lung allocation score, CF-related diabetes requiring insulin, supplemental oxygen use at rest, forced expiratory volume in 1 s (FEV1) % predicted, 6-min walk distance (6MWD), receipt of mechanical ventilation immediately prior to transplant, and use of extracorporeal life support immediately prior to transplantation. For pre-transplant BMI, pre-transplant lung allocation score, 6-min walk distance, and FEV$1\%$ predicted, we used values obtained closest in time to the CT scan used for muscle mass measurement. Post-transplant outcomes included days to extubation, hospital and ICU length of stay, and survival status (last updated January 2022). Days to extubation included days on mechanical ventilation from transplant to initial extubation. For patients discharged to acute care who required ICU readmission, ICU length of stay was calculated using the discharge date from the patient’s second admission. ## Analysis Our assessment of interrater reliability between reviewers was measured with the intraclass correlation coefficient for a two-way mixed effects model (patients as random effect and raters as fixed effect [same two raters for all patients]) in which absolute agreement was assessed. We evaluated the association between thoracic SMI and post-transplant survival, using multivariable Cox proportional hazards regression with robust standard errors. Due to our small sample size and limited number of deaths, we were required to be parsimonious in our inclusion of a priori potential confounders and included sex (to account for differences in muscle mass across sexes and potential relationships between sex and post-transplant outcomes) and calendar year of transplant (to account for changes over time in nutritional management in CF and transplant care) in our main model. We used sensitivity analyses to evaluate additional confounders, including CF genotype or BMI, in separate Cox proportional hazards regression models. Multivariable linear regression was used to evaluate associations between thoracic SMI and our secondary outcomes, including days to extubation and hospital and ICU length of stay. These models were adjusted for sex, calendar year of transplant, CF genotype, and BMI. Associations of pre-transplant FEV$1\%$ predicted and 6-min walk distance with thoracic SMI were examined using separate multivariable linear regression with robust standard errors, adjusting for sex and calendar year of transplant. Additional analyses included: 1) assessment of the hazard for death by thoracic SMI using bivariate Cox proportional hazard regression stratified by sex; 2) an assessment of the probability for survival based on being above or below the median SMI, using Cox proportional hazard regression adjusted for sex; 3) an assessment of the correlation between thoracic SMI and BMI; and 4) an assessment of sex-specific median thoracic skeletal muscle index by year of transplant All Cox models were evaluated for violation of the proportional-hazards assumption, based on Schoenfeld residuals. Two-sided p values of ≤ 0.05 were deemed significant. Analyses were done with IBM SPSS Statistics (Version 27) and Stata/IC (Version 16.1). ## Results Our final cohort included 83 adults with CF. For the 83 patients included in our analyses, median survival time was 6.17 years. At the time of the last survival status update, 45 were survivors ($54\%$) and 38 were decedents ($46\%$). The sample included similar proportions of male and female patients and had a median age at transplant of 29 years (IQR 24, 34). The median elapsed time from pre-transplant CT to transplant was 122 days (IQR 57, 188). Median skeletal muscle cross sectional area at T12 was 68.53 cm2 (IQR 59.02, 86.27), and median thoracic SMI was 25.62 cm2/m2 (IQR 22.10, 29.03). Interrater reliability for review of muscle mass measurements was excellent, with an intraclass correlation of 0.95 for skeletal muscle cross sectional area. Comparing men to woman, median thoracic SMI was 26.95 cm2/m2 (IQR 23.97, 31.32) for men and 22.83 cm2/m2 (IQR 21.27, 26.92) for women. Although values depend on a variety of measurement-specific factors, for healthy individuals mean SMI at this vertebral level has been reported as 44.1 (± standard deviation 7.7) for men and 34.0 (± standard deviation 6.6) for women [30]. Sarcopenia cutoffs at T12, using sex-specific cutoffs for ‘low’ values set at two standard deviations below the mean of healthy adults, have been reported as 20.8 cm2/m2 for women and 28.8 cm2/m2 for men [30]. Sex-specific median SMI by year is detailed in our supplementary material (Table E1, Figure E1). Median pre-transplant BMI was 20 kg/m2 (IQR 18.44, 21.44), and the Pearson correlation between thoracic SMI and BMI was 0.61 ($p \leq 0.001$). A complete description of patient characteristics by survival status is included in Table 1, and information about positive respiratory cultures in the year preceding transplant is available in online supplement Table E2.Table 1Patient characteristics by survival statusaCharacteristicSurvivorsDecedentsTotalnDescriptiveanDescriptiveanDescriptiveaSex453883 Male15 (33.3)25 (65.8)40 (48.2) Female30 (66.7)13 (34.2)43 (51.8)Age at transplant, median (IQR)4531 (25, 38.5)3827.5 (23.75, 32.25)8329 [24, 34]Transplant year, median (range)452013 [2001, 2018]382010 [2003, 2017]832011 [2001, 2018]Genotype: F508del homozygote423173 No18 (42.9)8 (25.8)26 (35.6) Yes24 (57.1)23 (74.2)47 (64.4)Pre-transplant FEV$1\%$ predicted, median (IQR)b4522 (18.5, 26)3821 (18, 27.25)8322 [18, 26]Pre-transplant 6MWD in feet, median (IQR)b441082.5 [780, 1333]351070 [857, 1205]791070 [826, 1275]Pre-transplant lung allocation score, median (IQR)b3641.0 (36.2, 43.1))2237.3 (35.0, 42.0)5839.6 (36.0, 43.0)CF-related diabetes requiring insulin453883 No20 (44.4)22 (57.9)42 (50.6) Yes25 (55.6)16 (42.1)41 (49.4)Pre-transplant oxygen at rest453782 No15 (33.3)7 (18.9)22 (26.8) Yes30 (66.7)30 (81.1)60 (73.2)Pre-transplant mechanical ventilationc453883 No41 (91.1)35 (92.1)76 (91.6) Yes4 (8.9)3 (7.9)7 (8.4)Pre-transplant extracorporeal life supportc453883 No43 (95.6)36 (94.7)79 (95.2) Yes2 (4.4)2 (5.3)4 (4.8)Pretransplant body dimensions, median (IQR)453883Height, metersb1.66 (1.58, 1.73)1.675 (1.61, 1.73)1.67 (1.60, 1.73)Skeletal muscle cross sectional area at T12, cm266.94 (56.78, 85.44)70.74 (66.07, 89.46)68.53 (59.02, 86.27)Thoracic skeletal muscle index, cm2/m224.17 (21.51, 28.57)26.01 (22.75, 29.36)25.62 (22.10, 29.03)Weight in kilogramsb55.60 (50.65, 61.65)53.85 (49, 63.5)55.20 (50.40, 62.00)Body mass indexb20.03 (18.44, 21.78)20.10 (18.51, 21.27)20.03 (18.44, 21.44)a Unless otherwise specified, each cell contains the number of cases and percentage of valid cases for the columnb Values obtained closest in time to the pre-transplant CT scan used for muscle mass measurementc Used immediately prior to transplantationFig. 1Post-transplant survival probability by SMI group, based on Cox proportional hazards regression model, adjusted for sex (two groups plotted at the mean value for sex)Table 2Hazard for post-transplant deathaPredictorsHRp$95\%$ CIThoracic skeletal muscle index1.030.530.95, 1.11Female0.530.090.25, 1.09Transplant year1.000.930.92, 1.09a Sample size = 83. Results are based on a multi-predictor Cox proportional hazards regression model with robust standard errors. Test of proportional hazards assumption: χ2 = 1.18, 3 df, $$p \leq 0.757.$$ Thirty-eight deaths; 45 patients censored at end of follow-up on $\frac{1}{26}$/22 ## Thoracic skeletal muscle index and post-transplant outcomes Using multivariable Cox proportional hazards regression, we found no significant association between thoracic SMI and death adjusting for sex and calendar year of transplant (HR = 1.03; $95\%$ CI 0.95, 1.11) (Table 2). Additional analyses adjusting for CF genotype and BMI in separate models also demonstrated a non-significant relationship between thoracic SMI and death (online supplement Tables E3-E4). A sensitivity analysis assessing the bivariate association between thoracic SMI and death, stratified by sex, similarly showed no significant association (males HR 1.03; $95\%$ CI 0.93, 1.14 and females HR 1.00; $95\%$ CI 0.90, 1.11). Nor were there differences in survival between two groups divided at the median SMI, using Cox regression adjusted for sex, although the $95\%$ CI was large (HR 1.22; $95\%$ CI 0.61, 2.43) (Fig. 1). Multivariable linear regression models examining the relationship between thoracic SMI and days to extubation, and hospital and ICU length of stay showed no significant associations. Complete results with covariate adjustments are included in online supplement Tables E5-E7. ## Thoracic skeletal muscle index and markers of pre-transplant functional reserve We found a significant association between thoracic SMI and pre-transplant FEV$1\%$ predicted, where higher SMI was associated with higher FEV$1\%$ predicted (b 0.39; $$p \leq 0.002$$; $95\%$ CI 0.14, 0.63) (Table 3). The association between thoracic SMI and pre-transplant 6MWD was not statistically significant (online supplement Table E8).Table 3Association between thoracic SMI and pre-transplant FEV$1\%$ predictedaPredictorsbp$95\%$ CIThoracic skeletal muscle index0.390.0020.14, 0.63Female1.630.23-1.03, 4.28Transplant year-0.040.81-0.34, 0.27a. Sample size = 83. Results based on a multi-predictor linear regression model with robust standard errors ## Discussion In this study we were able to successfully quantify skeletal muscle mass in a cohort of patients with advanced CF lung disease who underwent lung transplantation. Measurements had very high interrater reliability, and compared to population norms, both men and women in our population had low SMI values [30]. However, it is unclear if cutoffs specified for healthy adults can be applied to patients with chronic illness or used to define sarcopenia in CF [30]. We did not identify an association between pre-transplant thoracic SMI and death following lung transplantation, nor did we identify associations between pre-transplant thoracic SMI and days to post-transplant extubation or hospital or ICU length of stay. Given the $95\%$ CI we observed, our primary and sensitivity analyses ruled out a large association. We did identify an association between pre-transplant thoracic SMI and pre-transplant FEV$1\%$ predicted, a marker of lung disease severity. The evaluation for lung transplantation involves assessing a patient’s indication for transplant and identifying potential factors that could increase the risk of transplant [31]. The most recent guidelines on selection of lung transplantation candidates recommend using BMI as a component of the selection process [6], and BMI is the only variable in the lung allocation score addressing a patient’s body composition [32]. However, there is debate about the importance of BMI in the selection process, with new evidence challenging existing notions about the relationship between BMI and post-transplant outcomes particularly as it relates to underweight CF patients [33, 34]. Because BMI does not discriminate between fat mass and fat-free body mass, it is not an ideal measure of body composition. The presence of low muscle mass, or sarcopenia, may be a better indicator of nutritional health. Low muscle mass has been identified in normal, overweight, and obese adults [35–38], underscoring its potential to outperform BMI as a measure of body composition. Sarcopenia is also closely linked to the concept of frailty [39–41]. Frailty has been associated with morbidity and mortality in kidney [42–44] and liver transplant [45] populations, but less is known about frailty in lung transplantation [18]. Existing data suggest that frailty is common in lung transplant candidates and associated with a higher rate of delisting or death before lung transplant [46]. Likewise, sarcopenia has been associated with worse post-transplant outcomes in other solid organ transplant populations [25–28, 47], but few studies have addressed sarcopenia in lung transplant recipients [18, 19, 29]. It is important to note that existing studies include very few patients with CF [18, 19, 29], a population where mechanisms for muscle loss may be quite different compared to patients with other forms of advanced lung disease, like chronic obstructive pulmonary disease (COPD) or idiopathic pulmonary fibrosis (IPF). Differences tied to the etiology of advanced lung disease may provide a potential explanation for our results, which suggest the absence of a significant relationship between thoracic SMI and post-transplant survival in patients with CF. Both IPF and COPD occur later in life and result in transplantation at an older age. It may be that, compared to patients with other forms of advanced lung disease, the younger age of patients with CF offers a greater level of reserve that allows one to tolerate lower pre-transplant muscle mass. Younger patients have also not accrued the cumulative years of chronic illness born by those with COPD or IPF, particularly cardiovascular disease. Cardiovascular disease is extremely rare in CF thus potentially protecting this patient group in the post-transplant period. Also, despite a low muscle mass prior to transplantation, the ability to rehabilitate after transplant may be more robust among younger patients. Future research is needed to understand the relationship between muscle mass and long-term post-transplant outcomes like FEV1 and 6WMD, as well as patient-reported outcomes related to quality of life. Although there was no association between thoracic SMI and our post-transplant outcomes, the identified relationship between pre-transplant thoracic SMI and pre-transplant FEV$1\%$ predicted suggests the potential role of muscle mass measurement as an additional indicator of severity of illness in CF. Given our sample size, we were unable to assess whether there was an interaction between SMI and FEV$1\%$ predicted on our primary outcome. Additionally, for patients who are normal weight or overweight, the presence of sarcopenia may prompt nutritional interventions for severity of illness which may not have been indicated based on BMI alone. We found a significant correlation between BMI and thoracic SMI; however, the strength of correlation (0.61) suggests that both measurements may provide important information, independently of one another. Moreover, in a patient population where a narrow range of BMI is expected, other measures may be necessary to develop an accurate impression of body composition. Additional research is needed to further elucidate the relationship between muscle mass, measurements of functional reserve, and BMI to better inform our understanding of frailty in patients with CF. Our study has several important limitations. First, although our cohort included a relatively large number of patients transplanted for CF, the use of data from a single center limits generalizability, and our sample size may have affected our ability to detect associations. Of the 59 patients transplanted at our center during the study period who were not included, 35 had no available CT scans and 24 had scans done more than one year prior to transplant. Among the 35 patients without available scans, $89\%$ were transplanted in the pre-lung allocation score era (i.e., prior to 2005). The ability to include only those with available imaging, within our specified time frame, may have contributed to some degree of selection bias for the following reasons: changes in clinical practice and the health of transplant recipients changed over time, including not only CFTR modulators but also those related to early diagnosis via newborn screening, nutritional supplementation, and consistent implementation of a CF care model that have improved clinical outcomes; and individuals without available scans in the year preceding transplant may have differed from those who did have available scans. We did adjust for calendar year of transplant in our analyses, to account for changes over time in CF management and transplant care, and we felt it important to maintain the 1-year time frame for scan inclusion, given potential changes in muscle mass over time. Second, the techniques used to measure muscle mass in patients with pulmonary disease are highly variable [19, 29, 48–50]. It is possible that the methods used in other studies provide a better representation of total body muscle mass than measurements performed at T12. Muscle mass measured at the 3rd lumbar vertebrae (L3) by CT is considered the reference standard for estimating total body skeletal muscle mass [51]. However, CT scans of the chest do not include L3, necessitating measurement elsewhere. Evidence suggests T12 is an acceptable position, and based on its presence on CT scans of the chest and its correlation with muscle area measured in the lumbar region, it was the most acceptable choice for our purposes [52]. Third, because our study was limited to patients who underwent lung transplantation, our cohort may have been a “healthier” or less frail population than if it had included all candidates with CF who were evaluated for transplantation. The number of candidates with CF at our institution who were declined for transplant or who died on the waiting list was quite small during the study period, therefore precluding this analysis, but it should be noted that the relationship between thoracic SMI and clinical outcomes may be different in those who are deemed to be poor candidates for transplant by clinical teams. Fourth, for our outcome of days to extubation, we included days on mechanical ventilation from transplant to initial extubation and thus did not account for instances of reintubation. Lastly, our study includes patients cared for before highly effective CFTR modulator therapies became available for most of the CF population. While existing data suggests improvements in BMI with these therapies [23], and we expect improvements in BMI to correlate with increases in muscle mass, sarcopenia in CF remains relevant until this relationship is better understood. In summary, despite a growing body of evidence highlighting sarcopenia as a potentially valuable measure for transplant assessments, we did not find evidence of a relationship between thoracic SMI and post-transplant outcomes for people with CF. We did, however, find a relationship between muscle measurements and pre-transplant pulmonary function, which confirms the potential value of examining sarcopenia as a marker of disease severity in CF. Additional research is necessary to understand the prognostic potential of sarcopenia for patients with CF approaching lung transplantation. Finally, to advance our understanding of sarcopenia in patients with CF, uniformity in the methods used to obtain muscle mass measurement will be essential, along with concerted efforts to establish a consistent approach to defining sarcopenia in patients with advanced lung disease [53]. ## Supplementary Information Additional file 1: Table E1. Sex-specific median thoracic SMI by year of transplant. Figure E1. Sex-specific median thoracic SMI by year of transplant. Table E2. Positive respiratory cultures in the year preceding transplant by survival status. Table E3. Hazard for post-transplant death, addition of genotype as a potential confoundera. Table E4. Hazard for post-transplant death, addition of BMI as a potential confoundera. Table E5. Days from Transplant to First Extubationa. Table E6. Days from Transplant to Hospital Dischargea. Table E7. Days from Transplant to ICU Dischargea. Table E8. Pre-transplant 6MWDa. ## References 1. O'Sullivan BP, Freedman SD. **Cystic fibrosis**. *Lancet* (2009.0) **373** 1891-1904. DOI: 10.1016/S0140-6736(09)60327-5 2. 2.Cystic Fibrosis Foundation Patient Registry Annual Data Report, Bethesda, Maryland; 2020. 3. Chambers DC, Cherikh WS, Harhay MO, Hayes D, Hsich E, Khush KK. **The International Thoracic Organ Transplant Registry of the International Society for Heart and Lung Transplantation: Thirty-sixth adult lung and heart-lung transplantation Report-2019; Focus theme: Donor and recipient size match**. *J Heart Lung Transplant* (2019.0) **38** 1042-1055. DOI: 10.1016/j.healun.2019.08.001 4. Ramos KJ, Smith PJ, McKone EF, Pilewski JM, Lucy A, Hempstead SE. **Lung transplant referral for individuals with cystic fibrosis: Cystic fibrosis foundation consensus guidelines**. *J Cystic Fibros : Official J European Cystic Fib Soc* (2019.0) **18** 321-333. DOI: 10.1016/j.jcf.2019.03.002 5. Kapnadak SG, Dimango E, Hadjiliadis D, Hempstead SE, Tallarico E, Pilewski JM. **Cystic Fibrosis Foundation consensus guidelines for the care of individuals with advanced cystic fibrosis lung disease**. *J Cyst Fibros : official journal of the European Cystic Fibrosis Society* (2020.0) **19** 344-354. DOI: 10.1016/j.jcf.2020.02.015 6. 6.Leard LE, Holm AM, Valapour M, Glanville AR, Attawar S, Aversa M, et al. Consensus document for the selection of lung transplant candidates: An update from the International Society for Heart and Lung Transplantation. The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation. 2021. 7. Lederer DJ, Wilt JS, D'Ovidio F, Bacchetta MD, Shah L, Ravichandran S. **Obesity and underweight are associated with an increased risk of death after lung transplantation**. *Am J Respir Crit Care Med* (2009.0) **180** 887-895. DOI: 10.1164/rccm.200903-0425OC 8. Allen JG, Arnaoutakis GJ, Weiss ES, Merlo CA, Conte JV, Shah AS. **The impact of recipient body mass index on survival after lung transplantation**. *J Heart Lung Transplant* (2010.0) **29** 1026-1033. DOI: 10.1016/j.healun.2010.05.005 9. Singer JP, Peterson ER, Snyder ME, Katz PP, Golden JA, D'Ovidio F. **Body composition and mortality after adult lung transplantation in the United States**. *Am J Respir Crit Care Med* (2014.0) **190** 1012-1021. DOI: 10.1164/rccm.201405-0973OC 10. Hollander FM, van Pierre DD, de Roos NM, van de Graaf EA, Iestra JA. **Effects of nutritional status and dietetic interventions on survival in Cystic Fibrosis patients before and after lung transplantation**. *J Cyst Fibros : Official J European Cyst Fibros Soc* (2014.0) **13** 212-218. DOI: 10.1016/j.jcf.2013.08.009 11. Chaikriangkrai K, Jhun HY, Graviss EA, Jyothula S. **Overweight-mortality paradox and impact of six-minute walk distance in lung transplantation**. *Ann Thorac Med* (2015.0) **10** 169-175. DOI: 10.4103/1817-1737.160835 12. Kanasky WF, Anton SD, Rodrigue JR, Perri MG, Szwed T, Baz MA. **Impact of body weight on long-term survival after lung transplantation**. *Chest* (2002.0) **121** 401-406. DOI: 10.1378/chest.121.2.401 13. Culver DA, Mazzone PJ, Khandwala F, Blazey HC, Decamp MM, Chapman JT. **Discordant utility of ideal body weight and body mass index as predictors of mortality in lung transplant recipients**. *J Heart Lung Transplant* (2005.0) **24** 137-144. DOI: 10.1016/j.healun.2003.09.040 14. de la Torre MM, Delgado M, Paradela M, Gonzalez D, Fernandez R, Garcia JA. **Influence of body mass index in the postoperative evolution after lung transplantation**. *Transpl Proc* (2010.0) **42** 3026-3028. DOI: 10.1016/j.transproceed.2010.07.078 15. Ruttens D, Verleden SE, Vandermeulen E, Vos R, van Raemdonck DE, Vanaudenaerde BM. **Body mass index in lung transplant candidates: a contra-indication to transplant or not?**. *Transpl Proc* (2014.0) **46** 1506-1510. DOI: 10.1016/j.transproceed.2014.04.004 16. Bagshaw SM, McDermid RC. **The role of frailty in outcomes from critical illness**. *Curr Opin Crit Care* (2013.0) **19** 496-503. DOI: 10.1097/MCC.0b013e328364d570 17. Rozenberg D, Wickerson L, Singer LG, Mathur S. **Sarcopenia in lung transplantation: a systematic review**. *J Heart Lung Transplant* (2014.0) **33** 1203-1212. DOI: 10.1016/j.healun.2014.06.003 18. Kelm DJ, Bonnes SL, Jensen MD, Eiken PW, Hathcock MA, Kremers WK. **Pre-transplant wasting (as measured by muscle index) is a novel prognostic indicator in lung transplantation**. *Clin Transplant* (2016.0) **30** 247-255. DOI: 10.1111/ctr.12683 19. Rozenberg D, Mathur S, Herridge M, Goldstein R, Schmidt H, Chowdhury NA. **Thoracic muscle cross-sectional area is associated with hospital length of stay post lung transplantation: a retrospective cohort study**. *Transpl Int* (2017.0) **30** 713-724. DOI: 10.1111/tri.12961 20. Troosters T, Langer D, Vrijsen B, Segers J, Wouters K, Janssens W. **Skeletal muscle weakness, exercise tolerance and physical activity in adults with cystic fibrosis**. *Eur Respir J* (2009.0) **33** 99-106. DOI: 10.1183/09031936.00091607 21. King SJ, Nyulasi IB, Bailey M, Kotsimbos T, Wilson JW. **Loss of fat-free mass over four years in adult cystic fibrosis is associated with high serum interleukin-6 levels but not tumour necrosis factor-alpha**. *Clin Nutr (Edinburgh, Scotland)* (2014.0) **33** 150-155. DOI: 10.1016/j.clnu.2013.04.012 22. Gruet M, Troosters T, Verges S. **Peripheral muscle abnormalities in cystic fibrosis: Etiology, clinical implications and response to therapeutic interventions**. *J Cyst Fibros : Official J European Cyst Fibros Soc* (2017.0) **16** 538-552. DOI: 10.1016/j.jcf.2017.02.007 23. Petersen MC, Begnel L, Wallendorf M, Litvin M. **Effect of elexacaftor-tezacaftor-ivacaftor on body weight and metabolic parameters in adults with cystic fibrosis**. *J Cyst Fibros : official journal of the European Cystic Fibrosis Society* (2022.0) **21** 265-271. DOI: 10.1016/j.jcf.2021.11.012 24. Englesbe MJ, Patel SP, He K, Lynch RJ, Schaubel DE, Harbaugh C. **Sarcopenia and mortality after liver transplantation**. *J Am Coll Surg* (2010.0) **211** 271-278. DOI: 10.1016/j.jamcollsurg.2010.03.039 25. Izumi T, Watanabe J, Tohyama T, Takada Y. **Impact of psoas muscle index on short-term outcome after living donor liver transplantation**. *Turk J Gastroenterol : the official journal of Turkish Society of Gastroenterology* (2016.0) **27** 382-388. DOI: 10.5152/tjg.2016.16201 26. 26.Kaido T, Tamai Y, Hamaguchi Y, Okumura S, Kobayashi A, Shirai H, et al. Effects of pretransplant sarcopenia and sequential changes in sarcopenic parameters after living donor liver transplantation. Nutrition (Burbank, Los Angeles County, Calif). 2016. 27. Kalafateli M, Mantzoukis K, Choi Yau Y, Mohammad AO, Arora S, Rodrigues S. **Malnutrition and sarcopenia predict post-liver transplantation outcomes independently of the Model for End-stage Liver Disease score**. *J Cachexia Sarcopenia Muscle* (2016.0) **8** 113-121. DOI: 10.1002/jcsm.12095 28. van Vugt JL, Levolger S, de Bruin RW, van Rosmalen J, Metselaar HJ, JN IJ. **Systematic review and meta-analysis of the impact of computed tomography-assessed skeletal muscle mass on outcome in patients awaiting or undergoing liver transplantation**. *Am Soc Transplant Am Soc Transpl Surg* (2016.0) **16** 2277-92. DOI: 10.1111/ajt.13732 29. Lee S, Paik HC, Haam SJ, Lee CY, Nam KS, Jung HS. **Sarcopenia of thoracic muscle mass is not a risk factor for survival in lung transplant recipients**. *J Thorac Dis* (2016.0) **8** 2011-2017. DOI: 10.21037/jtd.2016.07.06 30. Derstine BA, Holcombe SA, Ross BE, Wang NC, Su GL, Wang SC. **Skeletal muscle cutoff values for sarcopenia diagnosis using T10 to L5 measurements in a healthy US population**. *Sci Rep* (2018.0) **8** 11369. DOI: 10.1038/s41598-018-29825-5 31. Thabut G, Christie JD, Mal H, Fournier M, Brugière O, Leseche G. **Survival benefit of lung transplant for cystic fibrosis since lung allocation score implementation**. *Am J Respir Crit Care Med* (2013.0) **187** 1335-1340. DOI: 10.1164/rccm.201303-0429OC 32. Adler FR, Aurora P, Barker DH, Barr ML, Blackwell LS, Bosma OH. **Lung transplantation for cystic fibrosis**. *Proc Am Thorac Soc* (2009.0) **6** 619-633. DOI: 10.1513/pats.2009008-088TL 33. Upala S, Panichsillapakit T, Wijarnpreecha K, Jaruvongvanich V, Sanguankeo A. **Underweight and obesity increase the risk of mortality after lung transplantation: a systematic review and meta-analysis**. *Transpl Int : official journal of the European Society for Organ Transplantation* (2016.0) **29** 285-296. DOI: 10.1111/tri.12721 34. Ramos KJ, Kapnadak SG, Bradford MC, Somayaji R, Morrell ED, Pilewski JM. **Underweight patients with cystic fibrosis have acceptable survival following lung transplantation: a united network for organ sharing registry study**. *Chest* (2020.0) **157** 898-906. DOI: 10.1016/j.chest.2019.11.043 35. Prado CM, Lieffers JR, McCargar LJ, Reiman T, Sawyer MB, Martin L. **Prevalence and clinical implications of sarcopenic obesity in patients with solid tumours of the respiratory and gastrointestinal tracts: a population-based study**. *Lancet Oncol* (2008.0) **9** 629-635. DOI: 10.1016/S1470-2045(08)70153-0 36. Bouchonville MF, Villareal DT. **Sarcopenic obesity: how do we treat it?**. *Curr Opin Endocrinol Diabetes Obes* (2013.0) **20** 412-419. DOI: 10.1097/01.med.0000433071.11466.7f 37. Cauley JA. **An Overview of Sarcopenic Obesity**. *J Clin Densitom : the official journal of the International Society for Clinical Densitometry* (2015.0) **18** 499-505. DOI: 10.1016/j.jocd.2015.04.013 38. Carneiro IP, Mazurak VC, Prado CM. **Clinical Implications of sarcopenic obesity in cancer**. *Curr Oncol Rep* (2016.0) **18** 62. DOI: 10.1007/s11912-016-0546-5 39. Bernabei R, Martone AM, Vetrano DL, Calvani R, Landi F, Marzetti E. **Frailty, physical frailty, sarcopenia: a new conceptual model**. *Stud Health Technol Inform* (2014.0) **203** 78-84. PMID: 26630514 40. Angulo J, El Assar M, Rodriguez-Manas L. **Frailty and sarcopenia as the basis for the phenotypic manifestation of chronic diseases in older adults**. *Mol Aspects Med* (2016.0) **50** 1-32. DOI: 10.1016/j.mam.2016.06.001 41. Cesari M, Nobili A, Vitale G. **Frailty and sarcopenia: From theory to clinical implementation and public health relevance**. *Eur J Int Med* (2016.0) **35** 1-9. DOI: 10.1016/j.ejim.2016.07.021 42. Garonzik-Wang JM, Govindan P, Grinnan JW, Liu M, Ali HM, Chakraborty A. **Frailty and delayed graft function in kidney transplant recipients**. *Arch Surg (Chicago, Ill : 1960)* (2012.0) **147** 190-3. DOI: 10.1001/archsurg.2011.1229 43. McAdams-DeMarco MA, Law A, King E, Orandi B, Salter M, Gupta N. **Frailty and mortality in kidney transplant recipients**. *Am J Transplant Off J Am Soc Transplant Am Soc Transplant Surg* (2015.0) **15** 149-154. DOI: 10.1111/ajt.12992 44. 44.McAdams-DeMarco MA, King EA, Luo X, Haugen C, DiBrito S, Shaffer A, et al. Frailty, Length of Stay, and Mortality in Kidney Transplant Recipients: A National Registry and Prospective Cohort Study. Annals of surgery. 2016. 45. Lai JC, Feng S, Terrault NA, Lizaola B, Hayssen H, Covinsky K. **Frailty predicts waitlist mortality in liver transplant candidates**. *Am J Transplant Off J Am Soc Transplant Am Soc Transplant Surg* (2014.0) **14** 1870-1879. DOI: 10.1111/ajt.12762 46. Singer JP, Diamond JM, Gries CJ, McDonnough J, Blanc PD, Shah R. **Frailty phenotypes, disability, and outcomes in adult candidates for lung transplantation**. *Am J Respir Crit Care Med* (2015.0) **192** 1325-1334. DOI: 10.1164/rccm.201506-1150OC 47. Streja E, Molnar MZ, Kovesdy CP, Bunnapradist S, Jing J, Nissenson AR. **Associations of pretransplant weight and muscle mass with mortality in renal transplant recipients**. *Clin J Am Soc Nephrol : CJASN* (2011.0) **6** 1463-1473. DOI: 10.2215/CJN.09131010 48. Mathur S, Rodrigues N, Mendes P, Rozenberg D, Singer LG. **Computed tomography-derived thoracic muscle size as an indicator of sarcopenia in people with advanced lung disease**. *Cardiopulm Phys Ther J* (2017.0) **28** 99-105. DOI: 10.1097/CPT.0000000000000054 49. McClellan T, Allen BC, Kappus M, Bhatti L, Dafalla RA, Snyder LD. **Repeatability of computerized tomography-based anthropomorphic measurements of frailty in patients with pulmonary fibrosis undergoing lung transplantation**. *Curr Probl Diagn Radiol* (2017.0) **46** 300-304. DOI: 10.1067/j.cpradiol.2016.12.009 50. Fintelmann FJ, Troschel FM, Mario J, Chretien YR, Knoll SJ, Muniappan A. **Thoracic skeletal muscle is associated with adverse outcomes after lobectomy for lung cancer**. *Ann Thorac Surg* (2018.0) **105** 1507-1515. DOI: 10.1016/j.athoracsur.2018.01.013 51. Shen W, Punyanitya M, Wang Z, Gallagher D, St-Onge MP, Albu J. **Total body skeletal muscle and adipose tissue volumes: estimation from a single abdominal cross-sectional image**. *J Applied Physiol* (2004.0) **97** 2333-8. DOI: 10.1152/japplphysiol.00744.2004 52. Nemec U, Heidinger B, Sokas C, Chu L, Eisenberg RL. **Diagnosing sarcopenia on thoracic computed tomography: quantitative assessment of skeletal muscle mass in patients undergoing transcatheter aortic valve replacement**. *Acad Radiol* (2017.0) **24** 1154-1161. DOI: 10.1016/j.acra.2017.02.008 53. Maheshwari JA, Kolaitis NA, Anderson MR, Benvenuto L, Gao Y, Katz PP. **Construct and predictive validity of sarcopenia in lung transplant candidates**. *Ann Am Thorac Soc* (2021.0) **18** 1464-1474. DOI: 10.1513/AnnalsATS.202007-796OC
--- title: Perivascular adipose tissue promotes vascular dysfunction in murine lupus authors: - Hong Shi - Brandee Goo - David Kim - Taylor C. Kress - Mourad Ogbi - James Mintz - Hanping Wu - Eric J. Belin de Chantemèle - David Stepp - Xiaochun Long - Avirup Guha - Richard Lee - Laura Carbone - Brian H. Annex - David Y. Hui - Ha Won Kim - Neal L. Weintraub journal: Frontiers in Immunology year: 2023 pmcid: PMC10062185 doi: 10.3389/fimmu.2023.1095034 license: CC BY 4.0 --- # Perivascular adipose tissue promotes vascular dysfunction in murine lupus ## Abstract ### Introduction Patients with systemic lupus erythematosus (SLE) are at elevated risk for Q10 cardiovascular disease (CVD) due to accelerated atherosclerosis. Compared to heathy control subjects, lupus patients have higher volumes and densities of thoracic aortic perivascular adipose tissue (PVAT), which independently associates with vascular calcification, a marker of subclinical atherosclerosis. However, the biological and functional role of PVAT in SLE has not been directly investigated. ### Methods Using mouse models of lupus, we studied the phenotype and function of PVAT, and the mechanisms linking PVAT and vascular dysfunction in lupus disease. ### Results and discussion Lupus mice were hypermetabolic and exhibited partial lipodystrophy, with sparing of thoracic aortic PVAT. Using wire myography, we found that mice with active lupus exhibited impaired endothelium-dependent relaxation of thoracic aorta, which was further exacerbated in the presence of thoracic aortic PVAT. Interestingly, PVAT from lupus mice exhibited phenotypic switching, as evidenced by “whitening” and hypertrophy of perivascular adipocytes along with immune cell infiltration, in association with adventitial hyperplasia. In addition, expression of UCP1, a brown/beige adipose marker, was dramatically decreased, while CD45-positive leukocyte infiltration was increased, in PVAT from lupus mice. Furthermore, PVAT from lupus mice exhibited a marked decrease in adipogenic gene expression, concomitant with increased pro-inflammatory adipocytokine and leukocyte marker expression. Taken together, these results suggest that dysfunctional, inflamed PVAT may contribute to vascular disease in lupus. ## Introduction Systemic lupus erythematosus (SLE) is a heterogeneous systemic inflammatory autoimmune disorder that primarily affects women of childbearing age, characterized by profound dysregulation of immune responses and multiorgan involvement with high morbidity and mortality compared to the general population (1–3). Despite the reduction in SLE-associated mortality over the last several decades due to improvements in diagnosis and therapy, mortality due to cardiovascular disease (CVD) remains strikingly elevated [4]. This is especially the case for young females with SLE, where the CVD risk can be up to 50-fold higher than aged-matched controls [5, 6]. The CVD disease in patients with SLE is mainly characterized by premature and accelerated atherosclerosis [7]. In addition, outcomes after coronary artery stenting are worse in patients with SLE [8], suggesting an enhanced response to vascular injury. While traditional Framingham risk factors (such as hypertension, hyperlipidemia, diabetes, and smoking) likely contribute to CVD in SLE, they cannot fully account for the increased risk. Thus, the pathogenesis of premature CVD in SLE may rely on factors unique to SLE itself [7, 9, 10]. While several theories have been proposed [11, 12], the underlying mechanisms have not been defined, nor have effective treatment strategies been developed. Although prior studies suggest that adipose tissues are a source of chronic inflammation that may play an active role in atherosclerosis in SLE (13–16), the specific adipose tissue depots contributing to vascular disease in SLE have not been identified. Perivascular adipose tissue (PVAT) is a unique adipose tissue depot which surrounds most vessels except the cerebral vasculature. While it was initially thought to simply provide structural support to blood vessels, PVAT is now recognized to possess distinct endocrine/paracrine functions that regulate vascular homeostasis. In healthy states, PVAT may resemble thermogenic brown or beige adipose tissue and play a protective role in vascular metabolism and function. Conversely, in the setting of high fat diet and other cardiovascular risk factors, PVAT becomes dysfunctional, exhibiting a white-like phenotype, associated with loss of thermogenic capacity, enhanced oxidative stress, and increased immune cell infiltration and expression of inflammatory cytokines/adipokines, thus promoting endothelial dysfunction and atherosclerosis (17–21). Notably, computed tomography (CT) scanning in women with SLE has demonstrated higher volume and density (a marker of inflammation) of PVAT surrounding the thoracic aorta compared to heathy control subjects [22, 23]. In addition, PVAT density was strongly associated with aortic calcification score in SLE patients independent of age, circulating inflammatory markers, CVD risk factors and body mass index (BMI) [23]. Moreover, SLE patients demonstrated increased thoracic aortic adventitial thickness, which was associated with aortic atherosclerosis, abnormal stiffness, and eccentric vessel remodeling [24]. These findings suggest that PVAT may be dysfunctional in patients with SLE, thus contributing to adventitial remodeling and CVD. However, virtually nothing is known regarding the biology or function of PVAT, or how dysfunctional PVAT might contribute to vascular disease, in SLE. Using mouse lupus models (NZBWF1/J and MRL/lpr lines), which are prone to developing endothelial and vascular dysfunction compared to control mice (12, 25–27), we investigated the phenotype and function of PVAT in the context of active lupus. ## Animals Breeding pairs of two lupus-prone mouse lines, lupus MRL/lpr (#000485) and control MRL/MpJ (#000486), and lupus NZBWF1/J (#100008) and control NZW/LacJ (#001058), were purchased from The Jackson Laboratory. Mice were bred and maintained in specific pathogen-free conditions in the animal facilities. They were housed in a controlled environment at 20-22oC with a 12 hr light/12 hr dark cycle. Food and water were provided ad libitum to all animals. Only female mice were used in the experiments described here. MRL and MRL/lpr mice were harvested at age of 7 weeks (baseline, before production of autoantibodies and proteinuria) and 14 weeks (active disease with proteinuria). NZW/LacJ and NZBWF1/J mice were harvested between 36 to 40 weeks of age (active disease with proteinuria). All animal care and experimental protocols complied with the National Institutes of Health Guide for the Care and Use of Laboratory Animals and relevant ethical regulations, and were approved by the institutional Animal Care & Use Committee of Augusta University (AU). ## Measurements of body composition, energy homeostasis, body temperature and blood pressure Body composition of fat and lean mass was measured by nuclear magnetic resonance (NMR) spectroscopy (Bruker Minispec LF90II, Bruker, Billerica, USA) as previously reported and normalized to total body weight [28]. The volume of oxygen consumption (VO2), carbon dioxide production (VCO2), spontaneous motor activity and food intake were measured using the Comprehensive Laboratory Monitoring System (CLAMS) (Columbus Instruments, Columbus, USA) [28]. The respiratory exchange ratio (RER) was calculated from the ratio of VCO2 to VO2. Mice were individually placed into the sealed chambers with free access to food and water. The study was carried out in a room set at 22oC with 12-12 hr (6:00 am ~ 6:00 pm) light-dark cycles, and the measurements were carried out continuously for 72 hours after a 24 h acclimatization period. The data were averaged over 72 hr. Rectal body temperatures were measured by a BAT-12 thermometer (Physitemp, USA). Blood pressure was recorded in conscious, restrained mice at age of 7 weeks and 14 weeks using the CODA tail cuff blood pressure monitoring system (Kent Scientific, USA). ## Biochemical analyses At age of 14 weeks, the mice were fasted for 5 hours and blood collected from the inferior vena cava. The fasting whole blood glucose was measured using an Alpha TRAK glucometer. The fasting plasma insulin, leptin and resistin levels were assessed by Luminex Assay using MILIIPLEX Metabolic Hormones Expanded Panel (Cat. # MMHE-44K-07, Millipore, Burlington, USA). Plasma adiponectin was assessed by Luminex Bio-Plex Pro Mouse Diabetes Adiponectin Assay (Cat. # 171F7002M, Biorad, Hercules, USA), according to manufacturer’s instructions. For detection of adiponectin, plasma samples were diluted 6400 times. The chemocytokine levels were measured by Flow Based Bead Array using mouse inflammation panel (Cat. # 740446, BioLegend, USA). Levels of the mast cell-specific protease MCPT-6 in the plasma were measured using the Mouse Mast Cell Protease-6/Mcpt6 ELISA Kit according to the manufacturer’s instructions (Thermofisher, USA). The plasma total cholesterol and triglyceride levels were measured with LabAssayTM Cholesterol and LabAssayTM Triglyceride Kits (FujiFilm Healthcare, Lexington, MA, USA) as previously described [29]. Plasma lipoprotein profiles were determined as described previously [29]. Briefly, pooled plasma from 4 MRL/lpr mice and 5 MRL control mice were subjected to FPLC gel filtration on two Superose 6 columns connected in series. For assessment of the production of autoantibodies in lupus mice, serum anti-double stranded DNA (anti-dsDNA) antibodies were quantified by ELISA according to the manufacturer’s instruction (Alpha Diagnostics). Mice were screened for proteinuria weekly with Uristix-4 (Siemens), and elevations confirmed by measuring urinary albumin and creatinine concentrations using a mouse albumin enzyme-linked immunosorbent assay (ELISA; Bethyl Laboratories) and a creatinine Assay Kit (BioAssay Systems) following the manufacturer’s protocols. Urinary albumin-to-creatinine ratios (UACR) were then calculated. ## Glucose tolerance test Mice were fasted for 5 hr followed by intraperitoneal injection of glucose at 1g/kg body weight. Glucose levels were measured via tail vein by an Alpha TRAK glucometer at baseline and every 15 min up to 2 hr following glucose injection as previously described [28]. ## Assessment of vascular function Thoracic aortas were isolated and dissected into rings of 2-3 mm in length, with PVAT either removed or left intact. Isometric tension was recorded using a wire myograph system 620 (Danish Myo Technology A.S, Denmark). The rings with or without PVAT were equilibrated for 60 min and contracted two times with 120 mM KCl. Concentration-response curves were obtained after contracting vessels with phenylephrine (PE; 10-6 M), followed by acetylcholine (Ach; 10-7 M) to test endothelial integrity. The rings with or without PVAT were submaximally pre-contracted with PE at EC75 concentration and allowed to reach a stable tension. To examine endothelium-dependent relaxation, Ach (10−9 M to 10−4 M) was added cumulatively to the bath and a curve was generated. Finally, endothelium-independent relaxation (vascular smooth muscle response) was assessed by washing out PE and Ach and then repeating the experiment with PE contraction and cumulative addition of sodium nitroprusside (SNP) (10−10 M to 10−5 M) to the bath. Ach and SNP relaxation were expressed as percentage of PE contraction. ## Tissue harvesting At age of 14 weeks, retroperitoneal (posterior to the kidneys), subcutaneous, gonadal, retroperitoneal, and brown adipose tissues, as well as mesenteric and thoracic perivascular adipose tissues from MRL and MRL/lpr mice, were carefully harvested after perfusion with phosphate buffered saline. The adipose tissues were gently blotted and individually weighed. ## Histology and immunohistochemistry Thoracic aortas (with PVAT) and kidneys were fixed in $10\%$ neutral formalin prior to being embedded in paraffin and sectioned into 5 μm thick serial cross sections by Augusta University Histopathology Laboratory. Tissues were stained with hematoxylin and eosin (H&E) and images captured using DP74 microscope camera (Olympus). To quantify adipocyte size and size distribution, uncompressed tif files were analyzed for adipocyte area by the Adiposoft plugin (v.1.16) from National Institutes of Health (NIH) ImageJ software, with each cell being individually identified by lipid inclusion (empty fields) in the tissue. Adipocytes ranging from 25-5000 μm2 in area were included. Three slides were prepared from each mouse and ~ 300-450 adipocytes were analyzed. Relative frequency of adipocyte area was calculated. To evaluate vascular remodeling, aortic sections were stained using Masson’s Trichrome (MT) and Verhoeff-Van Gieson (VVG). Three sections from each mouse at 300-μm intervals were analyzed. The lumen, internal elastic lamina (IEL), external elastic lamina (EEL) and adventitia were defined, and the medial area was calculated (tissue between IEL and EEL) using NIH ImageJ. Immunohistochemical (IHC) staining was performed on paraffin-embedded sections of thoracic aortas (with PVAT) following standard procedures by incubating the sections with a primary antibody against uncoupling protein 1 (UCP1, 1:800; Abcam, ab234430, MA) or CD45 (1:1000; Abcam, ab281586, MA) at 4oC overnight. After washing, the sections were incubated with ImmPRESS-AP Horse Anti-Rabbit IgG Polymer kit (Vector Laboratories, Burlingame, CA) and developed. To examine mast cell distribution in the PVAT, the slides were stained with $0.1\%$ Toluidine Blue solution (PH 2.3) (Sigma; 89640-5G) for 2 minutes, followed by rinsing in distilled water for 3 exchanges and then dehydrated rapidly by applying $95\%$ and $100\%$ alcohol. Images were captured using DP74 (Olympus). Areas of positive staining were quantified using NIH ImageJ. ## RNA extraction and quantitative reverse transcription PCR Total RNA was isolated from PVAT using the RNeasy Lipid Tissue Mini Kit (QIAGEN) according to the manufacturer’s instruction. Purity of total RNA was determined as $\frac{260}{280}$ nm absorbance ratio with expected values between 1.8 to 2.2 using Biodrop Duo (Biochrom, Holliston, MA). cDNA was synthesized from 500 ng total RNA using the OneScript® Hot cDNA Synthesis Kit (abm G594, Richmond, Canada). Quantitative reverse transcription PCR (RT-PCR) was performed with BlasTaq™ 2x qPCR Master Mix (abm G892, Richmond, Canada) and the StepOne Plus™ Real-Time PCR System (Applied Biosystems, Waltham, MA) following the manufacturer’s instructions. Relative mRNA levels were determined using acidic ribosomal phosphoprotein P0 (Arbp) as an endogenous control gene for adipogenic/thermogenic and inflammatory markers, and Gapdh an endogenous control gene for immune cell markers. Sequences of the primers used for PCR amplification are listed in Supplementary Material Table S1. ## Statistical analysis All data were presented as mean ± standard error of the mean (SEM). To calculate statistical significance, a nonparametric Mann-Whitney test was used after determining the distribution and variance of the data. One-way analysis of variance (ANOVA) followed by Tukey’s multiple comparisons test was used when more than two independent groups were compared. All t-tests were two-tailed, and a value of $P \leq 0.05$ was considered statistically significant. All statistical analyses were conducted with Prism 9 software (Graphpad, La Jolla, CA). ## Partial lipodystrophy in MRL/lpr mice MRL/lpr mice recapitulate clinical manifestations and immune dysregulation observed in human lupus, including skin lesions, lymphadenopathy, splenomegaly, elevated autoantibodies such as anti-dsDNA antibodies, and renal disease at age of 14 to 20 weeks (Supplementary Material Figure S1). Body weight was measured weekly throughout the experiment, and body fat composition was determined using NMR spectroscopy. While both MRL/lpr and control MRL mice had similar body weights throughout the study (Figures 1A, B), MRL/lpr mice with active lupus displayed reduced fat mass (Figure 1B), characterized by decreased weights of subcutaneous, gonadal, and retroperitoneal (back of kidney) adipose tissues, as well as mesenteric PVAT (Figures 1C–E, G, H). There were no differences, however, in weights of brown adipose tissue or thoracic aortic PVAT between lupus and control mice (Figures 1F, G). Interestingly, in contrast to control mice, fat mass and percentage did not change over time in lupus mice (Figure 1B). **Figure 1:** *MRL/lpr mice with active disease exhibited partial lipodystrophy. (A) Body weights were monitored weekly. (B) Total weight, fat mass and fat percentage at baseline (7 weeks) and with active disease (14 weeks) measured by NMR. (C-G) Representative images and weights of adipose tissue depots. Subcutaneous (SQAT, (C), gonadal (gAT, (D), retroperitoneal (E), brown (BAT, (F), thoracic aortic PVAT (tPVAT, red arrows, (G) and abdominal aortic PVAT (aPVAT, yellow arrows, (G) and mesenteric arterial PVAT (mPVAT, yellow arrows, (H). In all images, MRL tissues are depicted on the left and MRL/lpr on the right. n=10-15. ****p<0.0001 vs MRL (control). ns, not significant.* ## MRL/lpr mice were hypermetabolic In order to investigate metabolic phenotype in our lupus model, we performed indirect calorimetry test using comprehensive laboratory animal monitoring system (CLAMS). Oxygen consumption was increased in MRL/lpr mice (Figure 2A), while CO2 production (Figure 2B) and RER (calculated as the ratio of CO2 production to O2 consumption) (Figure 2C) were similar to that observed in control mice. The increased oxygen consumption in MRL/lpr mice was accompanied by increases in heat production/energy expenditure (Figure 2D) and locomotor activity (Figure 2E), while the rate of food intake (Figure 2F) was not significantly different between lupus and control mice. These results suggest that lupus mice are hypermetabolic, without alterations in energy substrate utilization. The increased heat generation occurring throughout the measurement period, even during times of relative inactivity (Figure 2D), implies that the increased energy expenditure in MRL/lpr mice is in part metabolically-driven, and not solely due to increased locomotor activity. Interestingly, despite the hypermetabolic state, MRL/lpr mice had lower core temperature compared to control mice in ambient environment (20-22°C) (Supplementary Material Figure S2), which may reflect insufficient insulating capacity due to loss of subcutaneous fat. **Figure 2:** *MRL/lpr mice were hypermetabolic. Comprehensive laboratory animal monitoring system (CLAMS) was performed at age of 14 weeks. VO2 (A), CO2 (B), heat production (D), activity (E), and food consumption (F) were measured, and respiratory exchange ratio (C) was calculated. n=4. *p<0.05, ***p<0.001, ****p<0.0001 vs MRL (control). ns, not significant.* ## MRL/lpr mice displayed normal blood pressure, insulin sensitivity, glucose tolerance and dyslipidemia Although systolic blood pressure, measured using tail cuff, was increased over time (up to 14 weeks), there was no significant difference in blood pressure between lupus and control mice (Figure 3A). Next, we measured fasting glucose and insulin levels and estimated insulin sensitivity using the insulin resistance index (HOMA-IR), calculated using the University of Oxford HOMA calculator software as: [fasting glucose (mmol/L) × fasting insulin (µIU/mL)] ÷ 22.5. No significant differences were detected between lupus and control mice in fasting glucose (Figure 3B), fasting insulin (Figure 3C), or HOMA-IR (Figure 3D). Moreover, we performed glucose tolerance tests (GTTs), which showed similar results in lupus and control mice (FigureS 3E, F). In addition, there were no significant differences in levels of plasma total triglycerides (Figure 3I) or cholesterol (Figure 3J) between lupus and control mice. However, MRL/lpr mice displayed higher levels of very-low-density lipoprotein (VLDL)-triglyceride [MRL/lpr (21.9 mg/dL) vs MRL (4.6 mg/dL)], but not VLDL-cholesterol, compared with control mice (Figures 3G, H). Moreover, MRL/lpr mice showed increased levels of intermediate-density lipoprotein (IDL)/low-density lipoprotein (LDL)-cholesterol [(MRL/lpr (5.0 mg/dL) vs MRL (0.9 mg/dL)] and reduced high-density lipoprotein (HDL)-cholesterol [(MRL/lpr (20.6 mg/dL) vs MRL (28.3 mg/dL)] (Figure 3H). No significant differences in lipid contents of other plasma lipoprotein species, including IDL/LDL-triglyceride, HDL-triglyceride and VLDL-cholesterol, were noted in MRL/lpr mice (Figures 3G, H). These findings suggest that MRL/lpr mice exhibit selective abnormalities in lipoprotein profile while maintaining normal baseline parameters of other systemic cardiovascular and metabolic health, such as blood pressure, glucose and insulin tolerance. **Figure 3:** *MRL/lpr mice with active disease displayed normal blood pressure and metabolic health. Systolic blood pressure (A) was measured using tail cuff (n=8). Fasting blood glucose (B) and insulin (C) levels were measured, and estimated insulin sensitivity as insulin resistance index (HOMA-IR) (D) was calculated using the University of Oxford HOMA calculator software (n=15). Glucose tolerance tests (GTT, (E) were performed and the area under the curve (AUC, (F) between MRL/lpr and MRL was compared (n=5). (G) and (H) representative plasma lipoprotein profiles measured via FPLC using pooled plasma (0.22 ml) from 4 MRL/lpr mice and 5 MRL mice. Plasma levels of triglyceride (I) and cholesterol (J) were also measured using biochemical assays (n=10). VLDL, very low-density lipoprotein; IDL, intermediate-density lipoprotein; LDL, low-density lipoprotein; HDL, high-density lipoprotein. ns, not significant.* ## Vascular endothelial function was impaired in MRL/lpr mice and exacerbated by PVAT To evaluate vascular function, thoracic aortas were isolated with or without PVAT from MRL/lpr and control mice at age of 14 weeks, and isometric tension was recorded using a wire myograph system. Consistent with previous reports, MRL/lpr mice exhibited impaired endothelium-dependent vasorelaxation to acetylcholine (Ach) in thoracic aortas (Figures 4B, D-F). To investigate the role of PVAT in regulating endothelial function, we tested aortas with or without associated PVAT. Interestingly, the endothelial dysfunction in MRL/lpr mice was further augmented by the presence of PVAT (Figure 4) as evidenced by decreased Emax (D), pEC50 (E), and AUC (F), while the presence of PVAT tended to enhance endothelium-dependent relaxation in control mice (Figures 4A-F). In contrast, endothelium-independent vasorelaxation to sodium nitroprusside (SNP) was unaffected by presence of PVAT in MRL/lpr or control mice (Supplementary Material Figure S3). We further evaluated endothelial function in a different lupus-prone mouse model (NZBWF1/J mice). As was observed in MRL/lpr mice, endothelial dysfunction was exacerbated by the presence of PVAT in lupus NZB/W mice. Note that the NZB/W mouse study was conducted at 40 weeks of age, at which time PVAT appeared to lose its anti-contractile function in the control mice (Supplementary Material Figure S4). These findings suggest that the detrimental effects of PVAT on endothelial function in MRL/lpr mice were not related to Fas-Fasl signaling associated with the MRL/lpr background. Collectively, these findings suggest that PVAT exacerbates endothelial dysfunction in the setting of active lupus. **Figure 4:** *MRL/lpr mice exhibited impaired endothelium-dependent relaxation, which was exacerbated by PVAT. (A) Concentration-response curves to Ach in all groups. (B) Concentration-response curves to Ach in the absence of PVAT. (C) Concentration-response curves to Ach in the presence of PVAT. Gray shaded areas indicate reductions in Ach-induced relaxation in MRL/lpr mice. (D) Maximum responses (Emax) to Ach from all concentration-response curves. (E) Negative logarithm of EC50 (pEC50) for all concentration-response curves. (F) Area under the curve (AUC) for all concentration-response curves. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. Ach, acetylcholine; PVAT, perivascular adipose tissue. ns, not significant.* ## PVAT “whitening” and vascular remodeling in MRL/lpr mice PVAT is brown-like in healthy mice, and whitening of PVAT is associated with abnormal vascular homeostasis. To characterize the morphological features of PVAT from lupus mice, histology and immunohistochemical analysis was performed. We analyzed adipocyte size distribution in PVAT. Adipocytes were significantly hypertrophic in the PVAT of MRL/lpr mice compared to control mice (1601 ± 91.03 μm2 vs 396.1 ± 27.14 um2, respectively, Figures 5A, B). In addition, while control mice exhibited a beige adipocyte dominant pattern, characterized by the majority of adipocytes being < 250 μm2, lupus mice exhibited a flattening and a shift to the right of the adipocyte size distribution curves, indicating an increased proportion of large adipocytes (Figure 5C). Furthermore, collagen deposition (Figures 5D, H, Masson Trichrome staining – blue color) was increased in PVAT from lupus mice, which was most evident at the adventitial border zone. Additionally, adventitial hyperplasia (Figures 5E, I) was also observed, whereas no difference was seen in elastin staining or media-lumen ratio (Figures 5E, J). Notably, as compared to control mice, expression of UCP1, a beige adipocyte marker, (Figures 5F, K), was reduced, while leukocyte recruitment as examined by CD45 immunostaining (Figures 5G, L) was increased, in PVAT from lupus mice. While mast cells play an important role in the pathogenesis of SLE, there was no difference in mast cell recruitment to PVAT of lupus mice compared with control mice, as determined by toluidine blue staining (Supplementary material Figure S5A) or mast cell-specific gene expression (CD117, Supplementary material Figure S5B). Furthermore, plasma levels of mast cell specific protease-6 (Mcpt6) were similar in lupus mice and control mice (Supplementary Material Figure S5D), further suggesting that mast cells are most likely not responsible for the phenotypic changes in PVAT observed in lupus mice. **Figure 5:** *PVAT exhibited whitening and vascular remodeling in MRL/lpr mice. (A) Hematoxylin and eosin (H&E), scale bar=100 μm. (B) Quantitative analysis of average adipocyte size (area per adipocyte, μm2); (C) the relative distribution of adipocytes calculated as frequency of adipocyte area (%) over total area. (D) Masson Trichrome (MT) staining for collagen, scale bar=100 μm; (E) Verhoeff-Van Gieson (VVG) staining for elastin, scale bar=200 μm; (F) UCP1 staining for beige adipocytes, scale bar=100 μm; (G) CD45 staining for leukocytes, scale bar=20 μm; and (H-L) quantification of adventitial areas, adventitia-media ratio, media-lumen ratio, percentage of positive UCP1 and CD45 areas. n=10. ****p < 0.0001 vs MRL (control). ns, not significant.* ## PVAT from MRL/lpr mice exhibited increased expression of pro-inflammatory genes, and decreased expression of adipogenic and beige genes Next, we quantified expression of inflammatory, adipogenic, and metabolic genes in PVAT from lupus mice. Interestingly, adiponectin expression (Figure 6A) was significantly reduced in PVAT from lupus mice, concordant with decreased plasma levels of adiponectin (Supplementary Material Figure S4A). While mRNA expression of pro-inflammatory leptin in PVAT was similar in lupus mice versus control mice (Figure 6A), plasma leptin levels were decreased in lupus mice (Supplementary Material Figure S4A). Adipogenic (PPARγ) and thermogenic (UCP1) marker gene expression was significantly decreased in PVAT from lupus mice (Figure 6A). Importantly, expression of pro-inflammatory cytokines (IL-1β, IL-6, TNFα and IFNγ) (Figure 6B) was increased in PVAT from lupus mice, concomitant with elevated plasma levels of TNFα, IFNγ and IL-6 (Supplementary Material Figure S3B). In addition, multiple chemokines, including CCL2, CCL5, and CXCL10, were expressed at higher levels in PVAT and plasma from lupus mice (Figure 6C, Supplementary Figure S3C). The expression of markers of T cells (CD4, CD8α), B cells (CD19) and macrophages (CD68) was elevated in PVAT from lupus mice (Figure 6D). These findings suggest that inflammation originating in PVAT could induce whitening, impair thermogenic and metabolic activities, and promote the local release of factors that perturb endothelial function in lupus mice. **Figure 6:** *PVAT from MRL/lpr mice exhibited decreased adipogenic and thermogenic marker genes (A), and increased proinflammatory genes including cytokines (B), chemokines (C), and inflammatory marker (D) genes. n=6. *p<0.05, **p < 0.01, vs MRL (control). ns, not significant.* ## Discussion Inflammation promotes endothelial dysfunction and formation of intimal lesions in atherosclerosis. Increasing evidence suggests that the outer layers of the arterial wall, including the adventitia and PVAT, may promote vascular inflammation and endothelial dysfunction through an “outside-in” mechanism. Here, we used lupus-prone mice to examine the role of PVAT in regulating vascular function in lupus, a disease associated with chronic systemic inflammation and increased risk of atherosclerotic CVD [30]. We report for the first time that thoracic aortic PVAT of lupus mice exhibits: [1] dysfunctional features, as evidenced by “whitening” and hypertrophy of perivascular adipocytes, and immune cell infiltration; [2] reduced expression of adipogenic and beige adipocyte genes, including adiponectin, PPARγ, and UCP1; and [3] increased expression of pro-inflammatory cytokines and chemokines. This dysfunctional and inflamed PVAT in lupus mice was associated with impaired endothelium-dependent relaxation and adventitial remodeling. Collectively, these data suggest that dysfunctional PVAT contributes to CVD risk in the context of lupus. Generalized loss of adipose tissues in subcutaneous and visceral compartments, a condition known as lipodystrophy, is associated with insulin resistance, dyslipidemia, endothelial dysfunction, and predisposition to atherosclerosis [31, 32]. In our study, female MRL/lpr lupus mice with active disease were partially lipodystrophic and dyslipidemic but exhibited normal blood pressure, insulin sensitivity, and glucose tolerance. Nevertheless, the lupus mice exhibited endothelial dysfunction of thoracic aorta, which was aggravated by the presence of PVAT. Taken together, these findings suggest that lipodystrophy per se is not the sole cause of endothelial dysfunction in lupus mice and imply a distinct pathogenic role for PVAT in this process. We did not systematically investigate the biology or function of other adipose depots in our mice, nor did we examine the impact of PVAT on resistance microvasculature responsible for blood pressure control. However, Choi et al. demonstrated that male MRL/lpr mice, which likewise harbor a Fas mutation but do not typically develop lupus at early age, exhibited reduced subcutaneous, visceral, and brown adipose tissue mass, decreased adipocyte size in subcutaneous adipose tissues, and enhanced glucose tolerance. Moreover, these mice exhibited increased white (epididymal and inguinal subcutaneous) adipose tissue UCP1 expression and browning after a cold challenge and were resistant to high-fat diet induced obesity [33]. This latter finding is consistent with the hypermetabolic state noted in our female MRL/lpr mice housed at ambient temperature. Furthermore, Wueest et al. reported that Fas expression was increased in adipocytes isolated from insulin-resistant mice and in adipose tissues of obese and diabetic patients, and that deletion of Fas in adipocytes decreased adipose tissue inflammation, hepatic steatosis, and insulin resistance induced by a high-fat diet [34]. In order to exclude an effect of Fas-FasL signaling associated with the MRL/lpr background, we evaluated endothelial function in another lupus-prone mouse model (NZB/W mice) in the absence or presence of PVAT. Consistent with MRL/lpr mice, lupus-prone NZB mice exhibited exacerbation of endothelial dysfunction in the presence of PVAT. Taken together, these findings suggest that PVAT itself may uniquely drive endothelial dysfunction in lupus. Like adipose depots in other anatomic locations, PVAT contains both preadipocytes and mature adipocytes with distinct gene expression and functional profiles [19, 35]. The PVAT surrounding thoracic aorta in healthy rodents and humans was reported to be enriched in beige adipocytes [36]. Similarly to brown adipocytes, beige adipocytes exhibit increased mitochondrial biogenesis, multilocular lipid droplets, and elevated expression of UCP1 [37]. Thermogenic activity of healthy perivascular adipocytes can also increase vascular lipid clearance. Smooth muscle cell selective PPARγ (SMPG) knockout mice, which lack PVAT, exhibited impaired thermogenic activity and endothelial dysfunction. Moreover, cold exposure inhibited atherosclerosis in mice with intact PVAT, but not in SMPG knockout mice, suggesting a potentially protective role of thermogenic PVAT in atherosclerosis [38]. Interestingly, mice with active lupus exhibit loss of thermogenic activity in PVAT, as evidenced by reduced expression of UCP1 and phenotypic “whitening,” characterized by reduced multilocular lipid droplets and increased adipocyte size. These findings suggest that active lupus is associated with conversion of perivascular adipocytes from beige to white, in conjunction with loss of healthy PVAT’s atheroprotective functions. Under homeostatic conditions, PVAT inflammation is low, and perivascular adipocytes primarily secrete relaxing factors and anti-inflammatory adipokines, such as adiponectin, which inhibits vascular inflammation and plaque formation [39] and improves endothelial function through endothelial NO synthase phosphorylation [40]. Moreover, adiponectin may reduce vascular smooth muscle cell proliferation and migration [41, 42]. By contrast, in atherosclerosis and following vascular injury, PVAT is heavily infiltrated by immune cells [21, 43, 44] and predominantly produces contracting factors such as norepinephrine and reactive oxygen species, as well as pro-inflammatory cytokines and chemokines such as IL-1β, IL-6, TNFα, CCL2 [19, 20, 45, 46]. This imbalance in relaxing and contractile factors, and anti- and pro-inflammatory factors is strongly linked to vascular inflammation and disease [39]. The PVAT in lupus mice exhibited increased infiltration of immune cells and expression of pro-inflammatory cytokines (e.g., IL-1β, IL-6, TNFα, IFNγ), along with decreased expression of adiponectin, which likely promotes endothelial dysfunction and vascular wall remodeling. The subtypes of immune cells, and the mechanisms responsible for their recruitment to PVAT, remain to be determined. Also, the mechanism of dyslipidemia, the impact of high fat diet on PVAT, and the subsequent influence on endothelial dysfunction and atherosclerotic lesion formation in lupus mice will require further investigations. Finally, our data suggest that the increased collagen content and adventitial remodeling detected in lupus mice might impose an anatomical barrier to outside-in signaling from PVAT to the vascular wall. Whether and how this might impact lupus-related vascular disease is unclear. Although MRL/lpr mice have been widely accepted and used as a mouse model of lupus disease, they do not perfectly mimic the clinical parameters of lupus patients. Our MRL/lpr mice were lean and exhibited reduced PVAT mass, whereas many patients with lupus have obesity, metabolic syndrome, and/or increased volume of thoracic PVAT. Medications (such as steroids), high caloric “western” diets, and sedentary behavior likely contribute to these differences between mice and humans with lupus. Additionally, humans typically live in comfortable, temperature-controlled environments, whereas the mice used in this study were kept at 20-22°C, well below their thermoneutral zone (28-32°C). This causes murine brown/beige adipocytes to utilize energy for heat production, thus diminishing the size of fat mass depots. Further studies are required to determine how changes in diet, activity and environmental temperature modulate PVAT function in lupus mice, and the subsequent impact on CVD. In conclusion, our findings suggest that active lupus is associated with dysfunctional, inflamed PVAT, which may lead to impaired endothelial-dependent vasorelaxation and aberrant vascular remodeling. These findings may have important clinical implications for lupus-related CVD. ## Data availability statement The original contributions presented in the study are included in the article/ Supplementary Material. Further inquiries can be directed to the corresponding author. ## Ethics statement The animal study was reviewed and approved by Institutional Animal Care & Use Committee of Augusta University. ## Author contributions HS, HWK and NLW developed the conception and design of the study, HS, BG, DK, TCK, MO, DYH and JM performed experiments and collected data, HW, EJB, DS, XL, AG, RL, LC, BHA, HWK and NLW analyzed, interpreted and discussed data, HS, HWK and NLW wrote the manuscript. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1095034/full#supplementary-material ## References 1. Tsokos GC. **Systemic lupus erythematosus**. *N Engl J Med* (2011) **365**. DOI: 10.1056/NEJMra1100359 2. Alamanos Y, Voulgari PV, Parassava M, Tsamandouraki K, Drosos AA. **Survival and mortality rates of systemic lupus erythematosus patients in northwest greece. study of a 21-year incidence cohort**. *Rheumatology* (2003) **42**. DOI: 10.1093/rheumatology/keg291 3. Thomas G, Mancini J, Jourde-Chiche N, Sarlon G, Amoura Z, Harlé JR. **Mortality associated with systemic lupus erythematosus in France assessed by multiple-cause-of-death analysis**. *Arthritis Rheum* (2014) **66**. DOI: 10.1002/art.38731 4. Bartels CM, Buhr KA, Goldberg JW, Bell CL, Visekruna M, Nekkanti S. **Mortality and cardiovascular burden of systemic lupus erythematosus in a US population-based cohort**. *J Rheumatol* (2014) **41**. DOI: 10.3899/jrheum.13087410.1016/j.phrs.2022.106354 5. Manzi S, Meilahn EN, Rairie JE, Conte CG, Medsger TA, Jansen-McWilliams L. **Age-specific incidence rates of myocardial infarction and angina in women with systemic lupus erythematosus: Comparison with the framingham study**. *Am J Epidemiol* (1997) **145**. DOI: 10.1093/oxfordjournals.aje.a009122 6. Hak AE, Karlson EW, Feskanich D, Stampfer MJ, Costenbader KH. **Systemic lupus erythematosus and risk of cardiovascular disease results from the nurses’ health study**. *Arthritis Rheum* (2009) **61**. DOI: 10.1002/art.24537 7. Esdaile JM, Abrahamowicz M, Grodzicky T, Li Y, Panaritis C, du Berger R. **Traditional framingham risk factors fail to fully account for accelerated atherosclerosis in systemic lupus erythematosus**. *Arthritis Rheum* (2001) **44**. DOI: 10.1002/1529-0131(200110)44:10<2331::AID-ART395>3.0.CO;2-I 8. Bundhun PK, Boodhoo KD, Long MY, Chen MH. **Impact of antiphospholipid syndrome and/or systemic lupus erythematosus on the long-term adverse cardiovascular outcomes in patients after percutaneous coronary intervention: A systematic review and meta-analysis**. *Medicine* (2016) **95**. DOI: 10.1097/MD.0000000000003200 9. Magder LS, Petri M. **Incidence of and risk factors for adverse cardiovascular events among patients with systemic lupus erythematosus**. *Am J Epidemiol* (2012) **176**. DOI: 10.1093/aje/kws130 10. Tselios K, Gladman DD, Su J, Ace O, Urowitz MB. **Evolution of risk factors for atherosclerotic cardiovascular events in systemic lupus erythematosus: A long-term prospective study**. *J Rheumatol* (2017) **44**. DOI: 10.3899/jrheum.161121 11. Liu Y, Kaplan MJ. **Cardiovascular disease in systemic lupus erythematosus: An update**. *Curr Opin Rheumatol* (2018) **30**. DOI: 10.1097/BOR.0000000000000528 12. Knight JS, Zhao W, Luo W, Subramanian V, O’Dell AA, Yalavarthi S. **Peptidylarginine deiminase inhibition is immunomodulatory and vasculoprotective in murine lupus**. *J Clin Invest* (2013) **123**. DOI: 10.1172/JCI67390 13. Mok CC, Tse SM, Chan KL, Ho LY. **Effect of the metabolic syndrome on organ damage and mortality in patients with systemic lupus erythematosus: A longitudinal analysis**. *Clin Exp Rheumatol* (2018) **36** 14. Chung CP, Long AG, Solus JF, Rho YH, Oeser A, Raggi P. **Adipocytokines in systemic lupus erythematosus: relationship to inflammation, insulin resistance and coronary atherosclerosis**. *Lupus* (2009) **18** 799-806. DOI: 10.1177/0961203309103582 15. McMahon M, Skaggs BJ, Sahakian L, Grossman J, FitzGerald J, Ragavendra N. **High plasma leptin levels confer increased risk of atherosclerosis in women with systemic lupus erythematosus, and are associated with inflammatory oxidised lipids**. *Ann Rheum Dis* (2011) **70**. DOI: 10.1136/ard.2010.142737 16. Hahn BH, Lourenço EV, McMahon M, Skaggs B, Le E, Anderson M. **Pro-inflammatory high-density lipoproteins and atherosclerosis are induced in lupus-prone mice by a high-fat diet and leptin**. *Lupus* (2010) **19**. DOI: 10.1177/0961203310364397 17. Fernández-Alfonso MS, Gil-Ortega M, Aranguez I, Souza D, Dreifaldt M, Somoza B. **Role of PVAT in coronary atherosclerosis and vein graft patency: friend or foe**. *Br J Pharmacol* (2017) **174**. DOI: 10.1111/bph.13734 18. Kim HW, Belin de Chantemèle EJ, Weintraub NL. **Perivascular adipocytes in vascular disease**. *Arterioscler Thromb Vasc Biol* (2019) **39**. DOI: 10.1161/ATVBAHA.119.312304 19. Kim HW, Shi H, Winkler MA, Lee R, Weintraub NL. **Perivascular adipose tissue and vascular Perturbation/Atherosclerosis**. *Arterioscler Thromb Vasc Biol* (2020) **40**. DOI: 10.1161/ATVBAHA.120.312470 20. Mancio J, Oikonomou EK, Antoniades C. **Perivascular adipose tissue and coronary atherosclerosis**. *Heart* (2018) **104**. DOI: 10.1136/heartjnl-2017-312324 21. Horimatsu T, Patel AS, Prasad R, Reid LE, Benson TW, Zarzour A. **Remote effects of transplanted perivascular adipose tissue on endothelial function and atherosclerosis**. *Cardiovasc Drugs Ther* (2018) **32**. DOI: 10.1007/s10557-018-6821-y 22. Shields KJ, Barinas-Mitchell E, Gingo MR, Tepper P, Goodpaster BH, Kao AH. **Perivascular adipose tissue of the descending thoracic aorta is associated with systemic lupus erythematosus and vascular calcification in women**. *Atherosclerosis* (2013) **231**. DOI: 10.1016/j.atherosclerosis.2013.09.004 23. Shields KJ, el Khoudary SR, Ahearn JM, Manzi S. **Association of aortic perivascular adipose tissue density with aortic calcification in women with systemic lupus erythematosus**. *Atherosclerosis* (2017) **262** 55-61. DOI: 10.1016/j.atherosclerosis.2017.04.021 24. Roldan LP, Roldan PC, Sibbitt WL, Qualls CR, Ratliff MD, Roldan CA. **Aortic adventitial thickness as a marker of aortic atherosclerosis, vascular stiffness, and vessel remodeling in systemic lupus erythematosus**. *Clin Rheumatol* (2021) **40**. DOI: 10.1007/s10067-020-05431-7 25. Thacker SG, Duquaine D, Park J, Kaplan MJ. **Lupus-prone new Zealand Black/New Zealand white F1 mice display endothelial dysfunction and abnormal phenotype and function of endothelial progenitor cells**. *Lupus* (2010) **19**. DOI: 10.1177/0961203309353773 26. Furumoto Y, Smith CK, Blanco L, Zhao W, Brooks SR, Thacker SG. **Tofacitinib ameliorates murine lupus and its associated vascular dysfunction**. *Arthritis Rheumatol* (2017) **69**. DOI: 10.1002/art.39818 27. Zhao W, Thacker SG, Hodgin JB, Zhang H, Wang JH, Park JL. **The peroxisome proliferator-activated receptor γ agonist pioglitazone improves cardiometabolic risk and renal inflammation in murine lupus**. *J Immunol* (2009) **183**. DOI: 10.4049/jimmunol.0804341 28. Goo B, Ahmadieh S, Zarzour A, Yiew NKH, Kim D, Shi H. **Sex-dependent role of adipose tissue HDAC9 in diet-induced obesity and metabolic dysfunction**. *Cells* (2022) **11**. DOI: 10.3390/cells11172698 29. Yiew NKH, Greenway C, Zarzour A, Ahmadieh S, Goo B, Kim D. **Enhancer of zeste homolog 2 (EZH2) regulates adipocyte lipid metabolism independent of adipogenic differentiation: Role of apolipoprotein e**. *J Biol Chem* (2019) **294**. DOI: 10.1074/jbc.RA118.006871 30. Kahlenberg JM, Kaplan MJ. **Mechanisms of premature atherosclerosis in rheumatoid arthritis and lupus**. *Annu Rev Med* (2013) **64**. DOI: 10.1146/annurev-med-060911-090007 31. Kinzer AB, Shamburek RD, Lightbourne M, Muniyappa R, Brown RJ. **Advanced lipoprotein analysis shows atherogenic lipid profile that improves after metreleptin in patients with lipodystrophy**. *J Endocr Soc* (2019) **3**. DOI: 10.1210/js.2019-00103 32. Hussain I, Patni N, Garg A. **Lipodystrophies, dyslipidaemias and atherosclerotic vascular disease**. *Pathology* (2019) **51**. DOI: 10.1016/j.pathol.2018.11.004 33. Choi EW, Lee M, Song JW, Kim K, Lee J, Yang J. **Fas mutation reduces obesity by increasing IL-4 and IL-10 expression and promoting white adipose tissue browning**. *Sci Rep* (2020) **10** 12001. DOI: 10.1038/s41598-020-68971-7 34. Wueest S, Rapold RA, Schumann DM, Rytka JM, Schildknecht A, Nov O. **Deletion of fas in adipocytes relieves adipose tissue inflammation and hepatic manifestations of obesity in mice**. *J Clin Invest* (2010) **120** 191-202. DOI: 10.1172/JCI38388 35. Shi H, Wu H, Winkler MA, Belin de Chantemèle EJ, Lee R, Kim HW. **Perivascular adipose tissue in autoimmune rheumatic diseases**. *Pharmacol Res* (2022) **182**. DOI: 10.1016/j.phrs.2022.106354 36. Chang L, Xiong W, Zhao X, Fan Y, Guo Y, Garcia-Barrio M. **Bmal1 in perivascular adipose tissue regulates resting-phase blood pressure through transcriptional regulation of angiotensinogen**. *Circulation* (2018) **138** 67-79. DOI: 10.1161/CIRCULATIONAHA.117.029972 37. Cannon B, Nedergaard J. **Brown adipose tissue: Function and physiological significance**. *Physiol Rev* (2004) **84** 277-359. DOI: 10.1152/physrev.00015.2003 38. Chang L, Villacorta L, Li R, Hamblin M, Xu W, Dou C. **Loss of perivascular adipose tissue on peroxisome proliferator-activated receptor-γ deletion in smooth muscle cells impairs intravascular thermoregulation and enhances atherosclerosis**. *Circulation* (2012) **126**. DOI: 10.1161/CIRCULATIONAHA.112.104489 39. Zhou Y, Wei Y, Wang L, Wang X, Du X, Sun Z. **Decreased adiponectin and increased inflammation expression in epicardial adipose tissue in coronary artery disease**. *Cardiovasc Diabetol* (2011) **10**. DOI: 10.1186/1475-2840-10-2 40. Chen H, Montagnani M, Funahashi T, Shimomura I, Quon MJ. **Adiponectin stimulates production of nitric oxide in vascular endothelial cells**. *J Biol Chem* (2003) **278**. DOI: 10.1074/jbc.M307878200 41. Takaoka M, Nagata D, Kihara S, Shimomura I, Kimura Y, Tabata Y. **Periadventitial adipose tissue plays a critical role in vascular remodeling**. *Circ Res* (2009) **105**. DOI: 10.1161/CIRCRESAHA.109.199653 42. Pan XX, Ruan CC, Liu XY, Kong LR, Ma Y, Wu QH. **Perivascular adipose tissue-derived stromal cells contribute to vascular remodeling during aging**. *Aging Cell* (2019) **18**. DOI: 10.1111/acel.12969 43. Takaoka M, Suzuki H, Shioda S, Sekikawa K, Saito Y, Nagai R. **Endovascular injury induces rapid phenotypic changes in perivascular adipose tissue**. *Arterioscler Thromb Vasc Biol* (2010) **30**. DOI: 10.1161/ATVBAHA.110.207175 44. Piacentini L, Vavassori C, Colombo GI. **Trained immunity in perivascular adipose tissue of abdominal aortic aneurysm-a novel concept for a still elusive disease**. *Front Cell Dev Biol* (2022) **10**. DOI: 10.3389/fcell.2022.886086 45. Nosalski R, Guzik TJ. **Perivascular adipose tissue inflammation in vascular disease**. *Br J Pharmacol* (2017) **174**. DOI: 10.1111/bph.13705 46. Krawczyńska A, Herman AP, Antushevich H, Bochenek J, Wojtulewicz K, Ziba DA. **The influence of photoperiod on the action of exogenous leptin on gene expression of proinflammatory cytokines and their receptors in the thoracic perivascular adipose tissue (PVAT) in ewes**. *Mediators Inflammation* (2019) **2019**. DOI: 10.1155/2019/7129476
--- title: High-fat intake reshapes the circadian transcriptome profile and metabolism in murine meibomian glands authors: - Sen Zou - Jiangman Liu - Hongli Si - Duliurui Huang - Di Qi - Xiaoting Pei - Dingli Lu - Shenzhen Huang - Zhijie Li journal: Frontiers in Nutrition year: 2023 pmcid: PMC10062204 doi: 10.3389/fnut.2023.1146916 license: CC BY 4.0 --- # High-fat intake reshapes the circadian transcriptome profile and metabolism in murine meibomian glands ## Abstract ### Background Nutritional and food components reshape the peripheral clock and metabolism. However, whether food challenges affect the circadian clock and metabolism of meibomian glands (MGs) has not been fully explored. This study was designed to analyze alterations in the rhythmic transcriptome and metabolism of MGs of murine fed a balanced diet or a high-fat diet (HFD). ### Methods Male C57BL/6J mice were maintained on a $\frac{12}{12}$ h light/dark cycle and fed ad libitum on normal chow (NC) or HFD for 4 weeks. MGs were collected from sacrificed animals at 3-h intervals throughout a 24-h circadian cycle. The circadian transcriptome of MGs was analyzed via bioinformatics approaches using high-throughput RNA sequencing (RNA-seq). In addition, circadian oscillations of lipid components in MGs were analyzed. ### Results Meibomian glands displayed robust transcriptome rhythmicity. HFD feeding significantly altered the circadian transcriptome profile of MGs—including composition and phase—and spatiotemporally affected the enriched signaling pathways. In addition, HFD feeding significantly altered the normal rhythmic oscillations of lipid components in MGs. ### Conclusion Our data show that HFD significantly affects MGs’ rhythmicity, which reveals a high sensitivity of MGs’ clocks to lipid composition in food. ## 1. Introduction Meibomian glands (MGs) are sebaceous glands located in the palpebral plate opening at the edge of the eyelid. They provide specialized lipids to the tear film to avoid tear evaporation and overflow and maintain tears between the oily margin and the eyeball to maintain the structural and functional integrity of the ocular surface [1, 2]. When the lipids secreted by this gland are altered qualitatively and quantitatively for various reasons, it can result in increased tear evaporation, hyperosmolarity, tear film instability, and bacterial growth at the lid margin, ultimately leading to damage to the ocular surface [3]. Currently, MGs dysfunction of various causes is becoming one of the most common diseases in the clinical setting of ophthalmology (3–5). It seriously affects the quality of life of patients. However, our understanding of the structure and physiological function of MGs and the factors affecting them remains extremely limited to date [6]. Given the biological evolutionary drive, the organs, tissues, and physiological processes of any mammalian species can be predicted to undergo significant rhythmic changes accompanying the daily light–dark cycle of the Earth (7–9). Similarly, ocular tissues and their physiological activities undergo synchronous rhythmic changes [10]. Published studies, including our team’s series of work, suggest that the cornea (11–13), lacrimal gland [14, 15], retinal pigment epithelium, and retina [16, 17] all exhibit robust rhythmic changes in the phase of the lighting cycle. However, little attention has been paid so far to the circadian rhythmical pattern of MGs and their underlying mechanisms [18, 19]. Considering the importance of MGs in maintaining tear film stability through lipid secretion, understanding their circadian rhythmic activity pattern and their associated mechanisms is of clinical importance. Circadian rhythmicity in mammals shows different patterns, depending on the organ, tissue, and physiological function [7, 20, 21]. This rhythmicity is closely coordinated between various organs of the body [22]. Many factors, such as high-calorie diets [22] and hypoxia [23], can significantly alter these circadian rhythms and the interconnections between the respective systems. Because of the acceleration of human economic and social activities, a Western diet characterized by high fat content has become prevalent in every corner of the world. Such diets increase the risk of developing many systemic diseases, such as metabolic syndrome, diabetes, and cardiovascular diseases [24]. Similarly, the altered composition of high-calorie diets poses a challenge to the physiological function of ocular tissues and the development of disease [25]. Metabolic stress from a high-calorie diet can remarkably alter the circadian activity of the cornea and lacrimal gland and the composition of the transcriptome that controls these activities [26]. Furthermore, preliminary data suggest that a high-fat diet (HFD) promotes the onset and development of dry eye disease through the induction of an inflammatory response in the lacrimal gland [27, 28]. However, the detailed mechanisms are unclear. Therefore, new tools are needed to revisit the HFD-induced dysfunction of MGs and their underlying mechanisms. Here, we compared the altered transcriptomes of MGs in mice fed a balanced diet and an HFD. Then, the effect of metabolic stress generated by HFD on the circadian clock of MGs and its possible underlying mechanisms were explored by bioinformatics analysis and the detection of diurnal oscillations of lipid droplets in MGs. We found that increased lipid content in food drastically altered the characteristics of the circadian transcriptome of MGs and produced previously unobserved effects on the transcriptome of MGs. This might provide a pathophysiological basis for explaining how food components affect the physiological function of MGs and bring about certain diseases. ## 2.1. Animals and dietary interventions Six-week-old male C57BL/6J mice were obtained from Nanjing University in China and housed in light-tight circadian chambers ($\frac{12}{12}$ h light/dark daily cycle) (Longer-Biotech Co., Ltd, Guangzhou, China) [29]. The Zeitgeber time (ZT) scale was used here to record the time: ZT0 referred to time of lights on (7 a.m.), and ZT12 referred to lights off (7 p.m.) [30]. Mice were provided with ad libitum access to their respective diets throughout the study. After 2 weeks of adaption in light-tight circadian chambers, all mice (8 weeks old now) were divided randomly into two groups. The normal chow (NC) group mice were provided with standard NC with $9\%$ kcal fat (Trophic Animal Feed High-Tech Co., Ltd., Nantong, China) for 4 weeks. The HFD group mice were provided with HFD with $60\%$ kcal fat (Trophic Animal Feed High-Tech Co., Ltd., Nantong, China) for 4 weeks (Figure 1A), as previously described [26]. RNA-*Seq data* for circadian analysis were collected at eight time points throughout the circadian cycle (3-h intervals) (Figure 1B). *Circadian* gene identification and circadian transcriptomic analysis (phase and amplitude) were performed by the Jonckheere–Terpstra–Kendall (JTK) cycling algorithm. The biological processes and molecular function of genes were annotated by the Kyoto Encyclopedia of Genes and Genomes (KEGG), gene ontology (GO), phase set enhanced analysis (PSEA), time-series clustering analysis, and gene set enriched analysis (GSEA) (Figure 1C). Circadian changes in lipid droplets in MGs were studied by Oil Red O (ORO) staining. All mice were euthanized by cervical dislocation after inhalation of ether. **FIGURE 1:** *Experimental design. (A) After adaption, all mice were divided randomly into two groups. Mice in the NC- and HFD-fed groups were provided with standard normal chow and a high-fat diet for 4 weeks, respectively. (B) MGs were obtained from euthanized mice at 3-h intervals (for transcriptomic profiling analysis) or 6-h intervals (for ORO staining) over a 24-h circadian cycle. (C) High-throughput sequencing (RNA-Seq) was performed after MG collection. Circadian gene identification and circadian transcriptomic analysis were performed using the Jonckheere–Terpstra–Kendall (JTK) cycling algorithm. The biological processes and molecular function of genes were annotated by the Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), phase set enhanced analysis (PSEA), time-series clustering analysis, and gene set enriched analysis (GSEA).* ## 2.2. MG collection, total RNA extraction, and RNA-seq After exposure to NC or HFD dietary regimens, the upper and lower MGs from the left eyelid were collected and combined from euthanized animals at 3-h intervals over the circadian cycle from NC- and HFD-fed mice, as previously described [27, 31]. Total RNA was isolated from the MGs using an RNAeasy spin column kit (Qiagen). For each ZT point, RNA-*Seq analysis* was performed using three biological replicates [15, 30]. Library preparation and sequencing for the total RNA of MGs were performed according to our previous report [26, 30, 32]. In brief, total RNA was quantified using a NanoDrop spectrophotometer (Thermo Fisher Scientific, MA, USA). The cDNA was amplified by PCR, and raw reads were filtered by SOAPnuke (Version v1.5.2) [33]. HISAT2 [34] and Bowtie2 were used to align the clean reads (reference: Mus_musculus, GCF_000001635.26_GRCm38. p6) [35]. Differentially expressed genes (DEGs) between the NC- and HFD-fed groups were identified using the R software edgeR package.1 ## 2.3. Analysis of rhythmic genes The circadian genes of MGs were identified using the JTK_CYCLE algorithm in R software, as previously described [26, 30, 32]. The time-ordered fragments per kilobase of exon model per million mapped fragments (FPKM) of all MG genes were imported into the algorithm. *Rhythmic* genes with a period of 24 h were identified, and the phases with amplitudes of the rhythmic genes were also determined. All MG genes were composed of low expression genes (FPKM < 0.1), rhythmic genes (FPKM ≥ 0.1 and Bonferroni-adjusted $P \leq 0.05$), and non-rhythmic genes (FPKM ≥ 0.1 and Bonferroni-adjusted P ≥ 0.05). ## 2.4. Functional annotation by KEGG, GO, PSEA, and GSEA Biological processes and molecular function annotations of MG genes were performed using KEGG, GO, PSEA, and GSEA, as previously described [26, 30, 32]. KEGG and GO enrichment analysis were performed by online bioinformatic platform Dr. Tom,2 an online software developed by Beijing Genomic Institute (BGI) [36]. PSEA software (v1.1) was used to annotate rhythmic genes at the oscillating phase’s level with the reference gene set (c2.cp.kegg.v7.2.symbols.gmt) downloaded from MSigDB3 [30]. GSEA software (v3.0) was used to characterize the biological pathways of MG genes by reference gene sets c5.go.bp.v7.2.symbols.gmt and c2.cp.v7.2.symbols.gmt. The significance threshold of the Q- or P-value for the analysis was 0.05 [26, 30, 32]. ## 2.5. Time-series clustering analysis and protein–Protein association networks To reveal dynamic expression trends in the rhythmic genes of the MGs, the fuzzy c-means clustering algorithm in the Mfuzz package was adopted, as previously described [30]. In this paper, the number of clusters in the rhythmic genes of NC- and HFD-fed mice was set as 4 on the basis of gene expression trends, with default values for other parameters. To visualize the gene--gene interaction of lipid--metabolic genes in the NC- and HFD-fed mice, protein--protein association network (PPAN) analysis was performed via STRING analysis.4 The parameters in the full STRING network were as follows: meaning of network edges, evidence; active interaction sources, experiments and databases, kmeans clustering method with 3 as the number of clusters. ## 2.6. Immunohistochemistry of MGs After dietary intervention, eyelid tissues with eyeballs were collected from the right side of the NC- and HFD-fed mice at 6-h intervals throughout a 24-h circadian cycle (ZT0, 6, 12, 18), as previously described [15, 26]. In brief, paraffin tissues were collected for hematoxylin and eosin staining to visualize the morphology of the MG tissues, and frozen sections were prepared for ORO staining (G1016, Servicebio Company). Eyelid tissues were cut into sagittal sections (5 μm thick). MG sections were immersed in ORO solution for 10 min in the dark. ORO staining was analyzed by mean optical density using ImageJ software (version 1.42q; National Institutes of Health, USA). Representative ORO staining images of the NC- and HFD-fed MGs were selected using CaseViewer software (3DHISTECH Ltd., Budapest, Hungary). ## 2.7. Statistical analysis and software Statistical analysis and figure preparation were processed using GraphPad Prism 9.3.1. A heatmap of circadian genes was prepared using the “pheatmap” package in R software. Data with normal distribution were statistically analyzed using the Student’s t-test to compare the differences between the NC- and HFD-fed mice. A value of $P \leq 0.05$ indicated a statistically significant difference. ## 3.1. HFD alters the characteristics of circadian transcriptome in murine MGs To visualize the transcriptomic differences between NC- and HFD-fed MGs, we performed a comparative expression analysis of RNA-seq data using a volcano plot (Figure 2A). We identified 1,397 and 1,722 circadian genes (Supplementary Table 1, JTK_adj $P \leq 0.05$) from all the MG genes of the NC- and HFD-fed mice, respectively (Figures 2B, C). In total, 338 cycling genes were shared between the two diet interventions; 1,059 were unique to the NC-fed MGs, and 1,384 were unique to the HFD-fed MGs (Figure 2C and Supplementary Table 2). HFD intervention did not significantly alter the oscillation patterns of shared rhythm genes in MGs within 24 h at 3-h intervals (Figure 2D). The peak expression of NC-unique cycling genes in MGs was throughout the circadian cycle, but they did not show a circadian rhythmic expression pattern in HFD-fed MGs (Figure 2E). In contrast, HFD-unique cycling genes were mainly expressed in the light phase, whereas they did not show a circadian rhythmic pattern in NC MGs (Figure 2F). **FIGURE 2:** *High-fat diet (HFD) reprograms the characteristics of the circadian transcriptome in murine MGs. (A) Volcano plot of RNA-seq data for NC- and HFD-fed MG genes. The red and blue dots denote ≥1.2-fold or ≤0.83-fold changes in expression between the NC-fed and HFD-fed MGs, respectively. (B) The number of rhythmic genes in NC- and HFD-fed MGs under different threshold conditions in the JTK_ algorithm. (C) Venn diagram showing the gene sets of the MGs of NC- and HFD-fed mice (JTK algorithm, adjusted P < 0.05 and expression ≥ 0.1). n = 3 mice per group per time point at 3-h intervals. n = 24 mice per group. (D) Heatmaps visualizing the expression levels of 338 shared rhythmic genes in the MGs of NC- (left) and HFD-fed (right) mice at different ZT points at 3-h intervals throughout the circadian cycle. The expression levels were indicated by a color bar ranging from blue to red, with the expression range normalized to ± 2. (E) Heatmaps visualizing the expression levels of 1,059 rhythmic genes unique in the MGs of NC-fed mice at various ZT points at 3-h intervals throughout the circadian cycle. (F) Heatmaps visualizing the expression levels of 1,384 rhythmic genes unique in the MGs of HFD-fed mice at various ZT points at 3-h intervals throughout the circadian cycle. (G–J) Phase analysis of rhythmic genes in the MGs of NC- and HFD-fed mice. Gray shading: dark phase. (K) Phase analysis of 338 shared rhythmic genes in the MGs of NC- and HFD-fed mice. Phase distribution plot for phase-delayed (L) and phase-advanced genes (M) in shared cycling genes. Gray shading: dark phase.* The expression phase of NC-unique rhythmic genes was mainly in ZT6 to ZT10.5 and ZT18 to ZT22.5 and throughout the circadian cycle (Figure 2G). Importantly, the phase of HFD-unique rhythmic genes peaked in ZT6 to ZT9 (Figure 2H). In contrast, the shared rhythmic genes were mainly from ZT0 to ZT1.5 in the NC-treated MGs (Figure 2I) and from ZT22.5 to ZT1.5 in the HFD-treated MGs (Figure 2J). For the shared cycling genes, $63.6\%$ were phase shifted, whereas $36.4\%$ were in phase (Figure 2K). Of the phase-shifted cycling genes, $30.2\%$ were advanced in phase, and $69.8\%$ were delayed (Figures 2K–M). There was no significant difference in the amplitude of cycling genes in shared or unique cycling genes between the MGs of NC- and HFD-fed mice (Supplementary Figure 1). Collectively, these data suggest that, under homeostatic conditions, HFD intervention dramatically altered the composition, number, and oscillation phase of rhythm genes in murine MGs. ## 3.2. HFD alters the functional characteristics of cycling genes in mouse MGs To evaluate the effect of HFD feeding on the biological processes of cycling genes, we performed GO annotations for MG genes in NC- and HFD-fed mice. The NC- and HFD-specific cycling genes were enriched in various biological processes, especially in the immune, metabolic, and nervous systems, as shown in Figure 3A. PSEA analyzes were performed to characterize the effect of HFD intervention on the spatiotemporal distribution of the signaling pathways of cycling genes. The pathways enriched in the NC-fed MGs were distributed throughout the circadian cycle, whereas those in the HFD-fed group were mainly located in the light phase (Figures 3B, C). Importantly, more immune-related pathways were enriched in the MGs of NC- fed mice, and more important signaling pathways were enriched in the light phase of the MGs of HFD-fed mice (Figures 3B, C). In summary, our results indicate that HFD intervention significantly rewired the rhythmic activity in the GO and PSEA levels, which may result in changes in the potential functions of these rhythmic genes in the MGs of HFD-fed mice. **FIGURE 3:** *High-fat diet (HFD) alters the oscillatory characteristics of circadian transcriptomic profiling in murine MGs. (A) Functional annotations for NC-unique (up) and HFD-unique (down) cycling genes by GO Biological Process analysis (Q < 0.05). Phase distribution of significantly enriched KEGG pathways (Q < 0.05) in the MGs of NC-fed (B) and HFD-fed (C) mice. The blue (B) and yellow (C) lines on the outside circle indicate the enriched pathways at various phases. Gray shading: dark phase.* ## 3.3. HFD alters the cluster-dependent transcriptomic map To reveal the dynamic expression trends in the rhythmic genes of the MGs after HFD intervention, we analyzed the time series clustering analysis of cycling genes in the MGs of NC- and HFD-fed mice. Four oscillating patterns were determined on the basis of the positions of the peaks and troughs in the NC or HFD groups. The peaks of Cluster 1 were located at ZT6 and the troughs at ZT18, and the 298 and 459 cycling genes were enriched in the MGs of NC- and HFD-fed mice, respectively (Figures 4A, B). The peaks of Cluster 2 were located at ZT18 and the troughs at ZT6, and 337 and 325 cycling genes were enriched in the MGs of NC- and HFD-fed mice, respectively (Figures 4C, D). The peaks of Cluster 3 were located at ZT12 and the troughs at ZT0, and 161 and 198 cycling genes were enriched in the MGs of NC- and HFD-fed mice, respectively (Figures 4E, F). The peaks of Cluster 4 were located at ZT3 and the troughs at ZT15, and 263 and 402 cycling genes were clustered in the MGs of NC- and HFD-fed mice, respectively (Figures 4G, H). *Cycling* genes in each cluster of the MGs of NC- and HFD-fed mice are listed in Supplementary Table 3. **FIGURE 4:** *High-fat diet (HFD) alters the cluster-dependent transcriptomic map. (A,C,E,G) Oscillating patterns of normalized expression for rhythmic genes from four enriched clusters for the MGs of NC-fed mice (left). The enriched KEGG pathways for genes in each cluster (P < 0.05) are shown in the (right) panel. Gray shading: dark phase. (B,D,F,H) Oscillating patterns of normalized expression for rhythmic genes from four enriched clusters for the MGs of HFD-fed mice (left). The enriched KEGG pathways for genes in each cluster (P < 0.05) are shown in the (right) panel. Gray shading: dark phase.* The KEGG annotation functions for cycling genes with similar temporal patterns between the MGs of NC- and HFD-fed mice had significantly different annotation pathways (Figures 4A–H, right of each panel). *Rhythmic* genes in cluster 2 of the MGs of NC-fed mice were enriched mainly in immune function (Figure 4C), whereas the cluster 4 genes of the MGs of HFD-fed mice were associated with immune pathways (Figure 4H). The cycling genes of cluster 1 in the MGs of NC-fed mice were mainly related to important signaling pathways (Figure 4A), whereas similar pathways in the MGs of HFD-fed mice were concentrated in Cluster 4 (Figure 4H). Cluster 3 genes in the MGs of HFD-fed mice were related to metabolism pathways, especially fat metabolism (Figure 4F), whereas a few pathways were associated with metabolism function in the MGs of NC-fed mice (Figure 4). Collectively, these results suggest that HFD intervention reshapes the oscillating patterns and corresponding functional pathways of rhythmic genes. ## 3.4. HFD does not elicit core clock desynchrony of MGs To determine the effect of HFD intervention on the oscillatory pattern of core clock machinery genes in the mouse MG, we compared the expression levels and oscillation amplitudes of the core clock genes, including Arntl (Bmal1), Npas2, Clock, Per1, Per2, Per3, Nr1d1, Nr1d2, Cry1, and Cry2, between the MGs of NC- and HFD-fed mice at 3-h intervals over a 24- h circadian cycle. The results showed that the expression of all these core clock genes exhibited significant diurnal rhythmicity in MGs from NC- and HFD-fed mice (Figure 5). However, the phase distribution and oscillation amplitude of core clock gene expression were not significantly altered in the MGs of HFD-fed mice compared to those of NC-fed mice (Figure 5). Thus, these data suggest that HFD intervention does not interfere with the synchronization of the core clock machinery in MGs. **FIGURE 5:** *Expression and oscillation patterns of core clock machinery genes in the MGs of NC- and HFD-fed mice. n = 3 mice per group for each sampling time point. Student’s t-test was performed for each ZT time point for the NC- and HFD-fed mice. Gray shading: dark phase.* ## 3.5. HFD-induced lipid metabolism disorder in MGs To verify the effects of HFD intervention on the lipid metabolism-related genes and their potential functions in murine MGs, we compared the differential expression level of genes between the MGs of NC- and HFD-fed mice (fold change ≥1.2 or ≤0.83, adjust $P \leq 0.05$). As shown in Figure 6A and Supplementary Table 4, 98 DEGs related to lipid metabolism were found, of which 61 were upregulated in the MGs of HFD-fed mice, and 37 genes were downregulated. The top 20 up- and down-regulated DEGs at various ZT points are shown in Figure 6B. The DEGs related to lipid metabolism were enriched in some lipid metabolism pathways (Q < 0.05), as shown in Figure 6C. Analyzes by PPANs (Figure 6D) and GSEA (Figures 6E–H) were performed to investigate the enrichment of genes in specific molecular functions. These data revealed that significantly enriched signaling pathways were related to specific lipid metabolism, including glycerolipid/glycerophospholipid/ether lipid metabolism, response to lipid/fatty acid, regulation of lipid storage, and lipid catabolic/metabolic process (Figure 6D). The GSEA results revealed that triglyceride metabolism/catabolism, PPAR signaling pathway, and fatty acid metabolic process were enriched specifically in the MGs of HFD-fed mice (Figures 6E–H). **FIGURE 6:** *High-fat diet (HFD)-induced lipid metabolism disorder in MGs. (A) Heatmaps visualizing the expression levels of the differentially expressed lipid-associated genes (fold change ≥1.2 or ≤0.83, adjust P < 0.05) in MGs between the NC- and HFD-fed mice at various ZT points at 3-h intervals throughout the circadian cycle. The expression levels were indicated by a color bar ranging from blue to red, with the expression range normalized to ± 3. (B) Heatmaps visualizing the expression levels of the top 20 up- and down-regulated DEGs of lipid metabolism-related genes in the MGs between the NC- and HFD-fed mice at various ZT points. (C) The top 10 significant KEGG annotations of 98 DGEs associated with lipid metabolism in the MGs between the NC- and HFD-fed mice (Q < 0.05). (D) The protein–protein association networks (PPANs) and functional clusters with specific KEGG annotations of lipid metabolism-related DEGs in the MGs between the NC- and HFD-fed mice (Q < 0.05). (E–H) Enrichment plots for triglyceride metabolism/catabolism, PPAR signaling pathway, and fatty acid metabolic process were enriched specifically in the MGs of HFD-fed mice by GSEA analysis. (I) Temporal changes in lipid droplets in the MGs of NC- and HFD-fed mice at 6 -h intervals. Three to five right-sided MGs were randomly selected from each NC- and HFD-fed mouse. n = 6 mice per group per sampling time point. Student’s t-test was performed for each ZT point in the NC- and HFD-fed mice. ***P < 0.001. The gray shading indicates the dark phase. (J) Average lipid droplet accumulation in the MGs of NC- and HFD-fed mice. n = 24 mice per group. Student’s t-test between the NC- and HFD-fed mice. ***P < 0.001. (K) Representative ORO staining images of lipid deposition in the MGs of NC- (left) and HFD-fed (right) mice at ZT18. Scale bar: 50 μm.* To determine the effect of HFD feeding on lipid metabolism in MGs, we performed ORO staining to observe the differences in lipid droplets between the MGs of NC- and HFD-fed mice. The results showed that lipid amounts showed a strong rhythm in the MGs of NC-fed mice, with lipid droplets peaking at ZT12 and troughing at ZT0 (Figure 6I). In contrast, in the MGs of HFD-fed mice, lipid amounts peaked at ZT18 and trough at ZT12 (Figure 6I). In addition, the amount of lipids in the MGs of HFD-fed mice was significantly higher than that in the MGs of NC-fed mice (Figures 6J, K). These results suggest that HFD intervention alters lipid metabolism in murine MGs and causes lipid accumulation in MGs. ## 4. Discussion To the best of our knowledge, this is the first study to show that high-fat nutritional stress uniquely affects the circadian transcriptome of murine MGs. We found that a 4 weeks high-fat dietary regimen significantly altered the circadian characteristics of MGs, including their cycling transcriptome profiles and content of lipid droplets. Notably, high-fat intake shifts cycling genes and their enriched functional signaling pathways that occur throughout the light–dark cycle in the MGs of balanced diet-fed mice to only the light phase of HFD-fed mice. These data suggest that the nutritional challenges posed by short-term, high-fat dietary intake reorganize the circadian rhythms of MGs. In mammals, circadian physiology is generated or controlled by the suprachiasmatic nucleus (SCN), a central pacemaker in the hypothalamus [37, 38]. The SCN generates or controls output circadian physiology through diffusible signals, including hormonal rhythms, sympathetic/parasympathetic systems, core body temperature, and feeding patterns, to control the molecular clock in peripheral tissue cells, thereby generating output circadian physiological activity [8]. However, many exogenic zeitgebers (39–41), including nutritional alternations (42–44), feeding timing [29, 45], and altered sleep/wakefulness [46] can disrupt the normal circadian rhythm and promote the occurrence of some diseases, such as metabolic syndrome and type 2 diabetes [47, 48]. Previous studies by us and other teams have found that interventions, such as short-term HFD [26], high fructose intake [32], jet lag [15], and gut dysbiosis accompanying aging [14], can reformat the rhythmic profile of the murine lacrimal gland. Similarly, a high fructose intake significantly alters the rhythmic pattern of the murine corneal transcriptome and its associated physiological activities. Consistent with these studies, the present study confirms that a 4 weeks high-fat dietary regimen reshapes the composition of rhythmic transcriptome and their functional signaling pathways enriched in mouse MGs at a spatiotemporal level. These results suggest that the nutritional challenge from an HFD alters the circadian rhythmicity of MGs in a tissue-specific manner. Therefore, further exploration of the underlying mechanisms will likely be of high significance. Each mammalian cell contains a machinery of core clock genes that generate rhythmic oscillatory gene expression and its associated physiological activities in a 24-h cycle by binding thousands of pathways to the entire genome and driving a feedback regulatory system [49]. Core clock genes are the central controllers of the biological clock system. The available data suggest that the core clock system of the cell is a relatively stable system. If the retino-hypothalamic tract (RHT) system is not disturbed, the core clock system maintains a steady state [50, 51]. This stability is not only present in metabolic stress but also in aging organs and tissues (51–53). Similarly, the same stabilization phenomenon has been observed in ocular tissues subjected to nutritional challenges, such as the cornea [11] and the lacrimal gland [26] subjected to high fructose intake and the lacrimal gland subjected to a high-fat diet [26]. Similarly, this asynchrony is present in the nutritionally challenged liver [54], as well as in several aging tissues (51–53). Recently, we found that the core clock of the lacrimal gland was not significantly altered, even in sleep deprivation-treated mice, without altering the light/dark cycle, although the output gene fraction was drastically altered [55]. These studies further confirm the strong stability of the core clock without altering the day/night cycle. However, the cause of the altered output genes under the aforementioned nutritional stress and other factors has thus far been unclear. Recently, Deota et al. speculated that the rhythmicity of output gene expression in most tissues is not exclusively driven by the circadian clock. Systemic signals generated by other factors (e.g., feeding-fasting cycles) combined with endogenous clock modulated signals may play a dominant role in regulating the rhythmicity of gene expression in peripheral organs [56]. Therefore, further exploration of the mechanisms by which HFD leads to decoupling the core clock from the downstream core clock-controlled output system is potentially valuable for addressing the pathophysiological alterations in the structure and function of MGs caused by HFD. High-throughput RNA-seq data-based bioinformatics analysis is currently one of the main tools used to elucidate the complex molecular mechanisms behind circadian rhythm alterations. Time series clustering methods provide an effective approach for assessing the accompanying temporal features for big data analysis [57]. The present study provides another dimensional analysis of the pattern of altered physiological activity of MGs due to excessive lipid intake. Consistent with previously studied ocular tissues, such as the cornea [11] and lacrimal gland [26], nutritional challenges dramatically altered the output transcriptome of circadian rhythms in both gene composition and the oscillations of their enriched signaling pathways. MGs play an important role in maintaining the stability of the tear film mainly through the lipid layer of the tear film [3, 58, 59]. Therefore, we specifically analyzed the effect of HFD on the lipid metabolism-related transcriptome of MGs and the content of lipid droplets accompanying temporal oscillations. As predicted, this study confirms that HFD has a profound effect on lipid metabolism-related pathways and oscillations of lipid droplets in MGs. These data provide new insights into HFD-induced MG dysfunction. However, further exact mechanisms would require further in-depth analysis by lipidomics, proteomics, and metabolomics. This study has several limitations. First, the C57BL/6 mice used in this study were nocturnal animals. The sleep–wake cycle in mice is opposite to that in humans [60]. Therefore, certain facts about human MGs must be interpreted with caution. Second, the present study only provided changes in the transcriptomic profile of MGs in male mice, and further observations in female mice may provide more information, especially regarding sex-specific differences [61, 62]. Third, the C57BL/6 mice used in this study are melatonin-deficient mouse models [63], but melatonin plays an important role in regulating circadian rhythms in humans [64]. Therefore, not all phenomena that occur in humans can be addressed. Fourth, this paper focuses on the bioinformatic interpretation of the effect of HFD on the transcriptomic rhythmicity of MGs, and we will attempt to focus more on the cellular and molecular mechanisms in future studies. Finally, this project provides only the effect of HFD on the bulk transcriptome rhythmicity of MGs. Considering the complexity of the different cell types of MGs and their existence of different oscillatory cycles, the use of single-cell RNA-seq sequencing technology in the future will offer solutions to this problem [65, 66]. ## 5. Conclusion In conclusion, our observations support the concept that an HFD alters the output component of the circadian rhythm of the MGs, rather than the core clock machinery (Figure 7). These data emphasize the importance of nutritional interventions in maintaining the health of MGs. Exploring or targeting the loss-of-coupling mechanism between the core clock and the output component has the potential to ameliorate MG dysfunction induced by HFD. **FIGURE 7:** *Summary displaying the effects of an HFD on the cyclical transcriptomic profile of MGs. In mice receiving a high-fat dietary regimen, the light-regulated central clock pacemaker (SCN) functions normally and expresses normal sleep/wake and fasting/feeding rhythms. However, a high-fat diet alters the normal circadian rhythmicity transcriptome profiles and lipid droplet oscillation of MGs.* ## Data availability statement The original contributions presented in this study are publicly available. This data can be found here: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA924579. ## Ethics statement All animal experiments in this study were approved by the Animal Ethics Committee of Henan Provincial People’s Hospital and followed the guidelines described in the ARVO Statement for the Use of Animals in Vision and Ophthalmic Research. ## Author contributions ZL and SZ designed the study and wrote the manuscript. SZ, ZL, JL, HS, DH, and DQ collected and prepared the samples. SZ performed RNA-seq and bioinformatics analysis with help from XP, DL, and SH. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2023.1146916/full#supplementary-material ## References 1. Knop E, Knop N, Millar T, Obata H, Sullivan D. **The international workshop on meibomian gland dysfunction: report of the subcommittee on anatomy, physiology, and pathophysiology of the meibomian gland.**. (2011) **52** 1938-78. DOI: 10.1167/iovs.10-6997c 2. Sun M, Moreno I, Dang M, Coulson-Thomas V. **Meibomian gland dysfunction: What have animal models taught us?**. (2020) **21**. DOI: 10.3390/ijms21228822 3. Chhadva P, Goldhardt R, Galor A. **Meibomian gland disease: the role of gland dysfunction in dry eye disease.**. (2017) **124** 20-6. DOI: 10.1016/j.ophtha.2017.05.031 4. Sabeti S, Kheirkhah A, Yin J, Dana R. **Management of meibomian gland dysfunction: a review.**. (2020) **65** 205-17. DOI: 10.1016/j.survophthal.2019.08.007 5. Nelson J, Shimazaki J, Benitez-del-Castillo J, Craig J, McCulley J, Den S. **The international workshop on meibomian gland dysfunction: report of the definition and classification subcommittee.**. (2011) **52** 1930-7. DOI: 10.1167/iovs.10-6997b 6. Butovich I. **The meibomian puzzle: combining pieces together.**. (2009) **28** 483-98. DOI: 10.1016/j.preteyeres.2009.07.002 7. Patke A, Young M, Axelrod S. **Molecular mechanisms and physiological importance of circadian rhythms.**. (2020) **21** 67-84. DOI: 10.1038/s41580-019-0179-2 8. Mohawk J, Green C, Takahashi J. **Central and peripheral circadian clocks in mammals.**. (2012) **35**. DOI: 10.1146/annurev-neuro-060909-153128 9. Takahashi J. **Transcriptional architecture of the mammalian circadian clock.**. (2017) **18** 164-79. DOI: 10.1038/nrg.2016.150 10. Felder-Schmittbuhl M, Buhr ED, Dkhissi-Benyahya O, Hicks D, Peirson S, Ribelayga C. **Ocular clocks: adapting mechanisms for eye functions and health.**. (2018) **59** 4856-70. DOI: 10.1167/iovs.18-24957 11. He J, Jiao X, Sun X, Huang Y, Xu P, Xue Y. **Short-term high fructose intake impairs diurnal oscillations in the murine cornea.**. (2021) **62** 1-22. DOI: 10.1167/iovs.62.10.22 12. Jiao X, Wu M, Lu D, Gu J, Li Z. **Transcriptional profiling of daily patterns of mrna expression in the c57bl/6j mouse cornea.**. (2019) **44** 1054-66. DOI: 10.1080/02713683.2019.1625408 13. Pal-Ghosh S, Tadvalkar G, Karpinski B, Stepp M. **Diurnal control of sensory axon growth and shedding in the mouse cornea.**. (2020) **61**. DOI: 10.1167/iovs.61.11.1 14. Jiao X, Pei X, Lu D, Qi D, Huang S, He S. **Microbial reconstitution improves aging-driven lacrimal gland circadian dysfunction.**. (2021) **191** 2091-116. DOI: 10.1016/j.ajpath.2021.08.006 15. Huang S, Jiao X, Lu D, Pei X, Qi D, Li Z. **Light cycle phase advance as a model for jet lag reprograms the circadian rhythms of murine extraorbital lacrimal glands.**. (2021) **20** 95-114. DOI: 10.1016/j.jtos.2021.02.001 16. Ruan G, Zhang D, Zhou T, Yamazaki S, McMahon D. **Circadian organization of the mammalian retina.**. (2006) **103** 9703-8. DOI: 10.1073/pnas.0601940103 17. Wang Z, Ji S, Huang Y, Liao K, Cui Z, Chu F. **The daily gene transcription cycle in mouse retina.**. (2021) **207**. DOI: 10.1016/j.exer.2021.108565 18. Blackie C, Korb D. **The diurnal secretory characteristics of individual meibomian glands.**. (2010) **29** 34-8. DOI: 10.1097/ICO.0b013e3181ac9fd0 19. Sasaki L, Hamada Y, Yarimizu D, Suzuki T, Nakamura H, Shimada A. **Intracrine activity involving nad-dependent circadian steroidogenic activity governs age-associated meibomian gland dysfunction.**. (2022) **2** 105-14. DOI: 10.1038/s43587-021-00167-8 20. Kinouchi K, Mikami Y, Kanai T, Itoh H. **Circadian rhythms in the tissue-specificity from metabolism to immunity: insights from omics studies.**. (2021) **80**. DOI: 10.1016/j.mam.2021.100984 21. Honma S. **Development of the mammalian circadian clock.**. (2020) **51** 182-93. DOI: 10.1111/ejn.14318 22. Dyar K, Lutter D, Artati A, Ceglia N, Liu Y, Armenta D. **Atlas of circadian metabolism reveals system-wide coordination and communication between clocks.**. (2018) **174** 1571-85. DOI: 10.1016/j.cell.2018.08.042 23. Wu G, Lee Y, Gulla E, Potter A, Kitzmiller J, Ruben M. **Short-term exposure to intermittent hypoxia leads to changes in gene expression seen in chronic pulmonary disease.**. (2021) **10**. DOI: 10.7554/eLife.63003 24. Wali J, Jarzebska N, Raubenheimer D, Simpson S, Rodionov R, O’Sullivan J. **Cardio-metabolic effects of high-fat diets and their underlying mechanisms–a narrative review.**. (2020) **12**. DOI: 10.3390/nu12051505 25. Clarkson-Townsend D, Douglass A, Singh A, Allen R, Uwaifo I, Pardue M. **Impacts of high fat diet on ocular outcomes in rodent models of visual disease.**. (2021) **204**. DOI: 10.1016/j.exer.2021.108440 26. Zou S, Jiao X, Liu J, Qi D, Pei X, Lu D. **High-fat nutritional challenge reshapes circadian signatures in murine extraorbital lacrimal glands.**. (2022) **63** 1-23. DOI: 10.1167/iovs.63.5.23 27. Bu J, Wu Y, Cai X, Jiang N, Jeyalatha M, Yu J. **Hyperlipidemia induces meibomian gland dysfunction.**. (2019) **17** 777-86. DOI: 10.1016/j.jtos.2019.06.002 28. Osae E, Bullock T, Chintapalati M, Brodesser S, Hanlon S, Redfern R. **Obese mice with dyslipidemia exhibit meibomian gland hypertrophy and alterations in meibum composition and aqueous tear production.**. (2020) **21**. DOI: 10.3390/ijms21228772 29. Sherman H, Genzer Y, Cohen R, Chapnik N, Madar Z, Froy O. **Timed high-fat diet resets circadian metabolism and prevents obesity.**. (2012) **26** 3493-502. DOI: 10.1096/fj.12-208868 30. Jiao X, Lu D, Pei X, Qi D, Huang S, Song Z. **Type 1 diabetes mellitus impairs diurnal oscillations in murine extraorbital lacrimal glands.**. (2020) **18** 438-52. DOI: 10.1016/j.jtos.2020.04.013 31. Bu J, Zhang M, Wu Y, Jiang N, Guo Y, He X. **High-fat diet induces inflammation of meibomian gland.**. (2021) **62** 1-13. DOI: 10.1167/iovs.62.10.13 32. Lu D, Lin C, Jiao X, Song Z, Wang L, Gu J. **Short-term high fructose intake reprograms the transcriptional clock rhythm of the murine extraorbital lacrimal gland.**. (2019) **60** 2038-48. DOI: 10.1167/iovs.18-26030 33. Cock P, Fields C, Goto N, Heuer M, Rice P. **The sanger fastq file format for sequences with quality scores, and the solexa/illumina fastq variants.**. (2010) **38** 1767-71. DOI: 10.1093/nar/gkp1137 34. Kim D, Langmead B, Salzberg S. **Hisat: a fast spliced aligner with low memory requirements.**. (2015) **12** 357-60. DOI: 10.1038/nmeth.3317 35. Langmead B, Salzberg S. **Fast gapped-read alignment with bowtie 2.**. (2012) **9** 357-9. DOI: 10.1038/nmeth.1923 36. Ma Z, Liu D, Li W, Di S, Zhang Z, Zhang J. **Styk1 promotes tumor growth and metastasis by reducing spint2/hai-2 expression in non-small cell lung cancer.**. (2019) **10** 1-14. DOI: 10.1038/s41419-019-1659-1 37. Hastings M, Maywood E, Brancaccio M. **Generation of circadian rhythms in the suprachiasmatic nucleus.**. (2018) **19** 453-69. DOI: 10.1038/s41583-018-0026-z 38. Lee H, Nelms J, Nguyen M, Silver R, Lehman M. **The eye is necessary for a circadian rhythm in the suprachiasmatic nucleus.**. (2003) **6** 111-2. DOI: 10.1038/nn1006 39. Grandin L, Alloy L, Abramson L. **The social zeitgeber theory, circadian rhythms, and mood disorders: review and evaluation.**. (2006) **26** 679-94. DOI: 10.1016/j.cpr.2006.07.001 40. López-Olmeda J, Madrid J, Sánchez-Vázquez F. **Light and temperature cycles as zeitgebers of zebrafish (danio rerio) circadian activity rhythms.**. (2006) **23** 537-50. DOI: 10.1080/07420520600651065 41. Ehlers C, Kupfer D, Frank E, Monk T. **Biological rhythms and depression: the role of zeitgebers and zeitstorers.**. (1993) **1** 285-93. DOI: 10.1002/depr.3050010602 42. Barnea M, Madar Z, Froy O. **High-fat diet delays and fasting advances the circadian expression of adiponectin signaling components in mouse liver.**. (2009) **150** 161-8. DOI: 10.1210/en.2008-0944 43. Kohsaka A, Laposky A, Ramsey K, Estrada C, Joshu C, Kobayashi Y. **High-fat diet disrupts behavioral and molecular circadian rhythms in mice.**. (2007) **6** 414-21. DOI: 10.1016/j.cmet.2007.09.006 44. Tognini P, Samad M, Kinouchi K, Liu Y, Helbling J, Moisan M. **Reshaping circadian metabolism in the suprachiasmatic nucleus and prefrontal cortex by nutritional challenge.**. (2020) **117** 29904-13. DOI: 10.1073/pnas.2016589117 45. Lundell L, Parr E, Devlin B, Ingerslev L, Altıntaş A, Sato S. **Time-restricted feeding alters lipid and amino acid metabolite rhythmicity without perturbing clock gene expression.**. (2020) **11** 1-11. DOI: 10.1038/s41467-020-18412-w 46. Steele T, St Louis E, Videnovic A, Auger R. **Circadian rhythm sleep–wake disorders: a contemporary review of neurobiology, treatment, and dysregulation in neurodegenerative disease.**. (2021) **18** 53-74. DOI: 10.1007/s13311-021-01031-8 47. Gallou-Kabani C, Vigé A, Gross M, Rabès J, Boileau C, Larue-Achagiotis C. **C57bl/6j and a/j mice fed a high-fat diet delineate components of metabolic syndrome.**. (2007) **15** 1996-2005. DOI: 10.1038/oby.2007.238 48. Skovsø S. **Modeling type 2 diabetes in rats using high fat diet and streptozotocin.**. (2014) **5** 349-58. DOI: 10.1111/jdi.12235 49. Curtis A, Bellet M, Sassone-Corsi P, O’Neill L. **Circadian clock proteins and immunity.**. (2014) **40** 178-86. DOI: 10.1016/j.immuni.2014.02.002 50. Damiola F, Le Minh N, Preitner N, Kornmann B, Fleury-Olela F, Schibler U. **Restricted feeding uncouples circadian oscillators in peripheral tissues from the central pacemaker in the suprachiasmatic nucleus.**. (2000) **14** 2950-61. DOI: 10.1101/gad.183500 51. Sato S, Solanas G, Peixoto F, Bee L, Symeonidi A, Schmidt M. **Circadian reprogramming in the liver identifies metabolic pathways of aging.**. (2017) **170** 664.-677. DOI: 10.1016/j.cell.2017.07.042 52. Solanas G, Peixoto F, Perdiguero E, Jardí M, Ruiz-Bonilla V, Datta D. **Aged stem cells reprogram their daily rhythmic functions to adapt to stress.**. (2017) **170** 678-92. DOI: 10.1016/j.cell.2017.07.035 53. Oishi K, Koyanagi S, Ohkura N. **Circadian mrna expression of coagulation and fibrinolytic factors is organ-dependently disrupted in aged mice.**. (2011) **46** 994-9. DOI: 10.1016/j.exger.2011.09.003 54. Eckel-Mahan K, Patel V, De Mateo S, Orozco-Solis R, Ceglia N, Sahar S. **Reprogramming of the circadian clock by nutritional challenge.**. (2013) **155** 1464-78. DOI: 10.1016/j.cell.2013.11.034 55. Huang S, Si H, Liu J, Qi D, Pei X, Lu D. **Sleep loss causes dysfunction in murine extraorbital lacrimal glands.**. (2022) **63** 1-19. DOI: 10.1167/iovs.63.6.19 56. Deota S, Lin T, Chaix A, Williams A, Le H, Calligaro H. **Diurnal transcriptome landscape of a multi-tissue response to time-restricted feeding in mammals.**. (2023) **35** 150-65. DOI: 10.1016/j.cmet.2022.12.006 57. Kumar L, Futschik M. **Mfuzz: a software package for soft clustering of microarray data.**. (2007) **2** 5-7. DOI: 10.6026/97320630002005 58. Dietrich J, Garreis F, Paulsen F. **Pathophysiology of meibomian glands–an overview.**. (2021) **29** 803-10. DOI: 10.1080/09273948.2021.1905856 59. Green-Church K, Butovich I, Willcox M, Borchman D, Paulsen F, Barabino S. **The international workshop on meibomian gland dysfunction: report of the subcommittee on tear film lipids and lipid–protein interactions in health and disease.**. (2011) **52** 1979-93. DOI: 10.1167/iovs.10-6997d 60. Mure L, Le H, Benegiamo G, Chang M, Rios L, Jillani N. **Diurnal transcriptome atlas of a primate across major neural and peripheral tissues.**. (2018) **359**. DOI: 10.1126/science.aao0318 61. Anderson S, FitzGerald G. **Sexual dimorphism in body clocks.**. (2020) **369** 1164-5. DOI: 10.1126/science.abd4964 62. Gómez-Abellán P, Madrid J, Luján J, Frutos M, González R, Martínez-Augustín O. **Sexual dimorphism in clock genes expression in human adipose tissue.**. (2012) **22** 105-12. DOI: 10.1007/s11695-011-0539-2 63. Roseboom P, Namboodiri M, Zimonjic D, Popescu N, Rodriguez I, Gastel J. **Natural melatoninknockdown’in c57bl/6j mice: rare mechanism truncates serotonin n-acetyltransferase.**. (1998) **63** 189-97. DOI: 10.1016/S0169-328X(98)00273-3 64. Macchi M, Bruce J. **Human pineal physiology and functional significance of melatonin.**. (2004) **25** 177-95. DOI: 10.1016/j.yfrne.2004.08.001 65. Xu Z, Liao X, Li N, Zhou H, Li H, Zhang Q. **A single-cell transcriptome atlas of the human retinal pigment epithelium.**. (2021) **9**. DOI: 10.3389/fcell.2021.802457 66. Kan H, Zhang K, Mao A, Geng L, Gao M, Feng L. **Single-cell transcriptome analysis reveals cellular heterogeneity in the ascending aortas of normal and high-fat diet-fed mice.**. (2021) **53** 1379-89. DOI: 10.1038/S12276-021-00671-2
--- title: Influence of mental health medication on microbiota in the elderly population in the Valencian region authors: - Nicole Pesantes - Ana Barberá - Benjamí Pérez-Rocher - Alejandro Artacho - Sergio Luís Vargas - Andrés Moya - Susana Ruiz-Ruiz journal: Frontiers in Microbiology year: 2023 pmcid: PMC10062206 doi: 10.3389/fmicb.2023.1094071 license: CC BY 4.0 --- # Influence of mental health medication on microbiota in the elderly population in the Valencian region ## Abstract Spain has an aging population; $19.93\%$ of the Spanish population is over 65. Aging is accompanied by several health issues, including mental health disorders and changes in the gut microbiota. The gut-brain axis is a bidirectional network linking the central nervous system with gastrointestinal tract functions, and therefore, the gut microbiota can influence an individual’s mental health. Furthermore, aging-related physiological changes affect the gut microbiota, with differences in taxa and their associated metabolic functions between younger and older people. Here, we took a case–control approach to study the interplay between gut microbiota and mental health of elderly people. Fecal and saliva samples from 101 healthy volunteers over 65 were collected, of which 28 (EE|MH group) reported using antidepressants or medication for anxiety or insomnia at the time of sampling. The rest of the volunteers (EE|NOMH group) were the control group. 16S rRNA gene sequencing and metagenomic sequencing were applied to determine the differences between intestinal and oral microbiota. Significant differences in genera were found, specifically eight in the gut microbiota, and five in the oral microbiota. Functional analysis of fecal samples showed differences in five orthologous genes related to tryptophan metabolism, the precursor of serotonin and melatonin, and in six categories related to serine metabolism, a precursor of tryptophan. Moreover, we found 29 metabolic pathways with significant inter-group differences, including pathways regulating longevity, the dopaminergic synapse, the serotoninergic synapse, and two amino acids. ## Introduction Spain has an aging population. In 2000, according to data from INE, the Spanish national statistics office (Instituto Nacional de Estadistica, 2021a)1, the population over 65 years of age was $16.53\%$ and in 2022 that percentage will rise to $20.22\%$. In fact, demographic projections made by INE suggest that this trend is accelerating, and by 2,068 people, over 65 years of age could represent $29.4\%$ of the population (García et al., 2021). The increase in elderly population over recent years, and the aging rate (i.e., ratio of people over 65 vs. those under 16) is currently $129.11\%$ in Spain and the Comunidad Valenciana (Instituto Nacional de Estadistica, 2021b). This circumstance is a clear indicator of the improvement in the quality of life in post-industrial countries, but we cannot ignore the fact that the quantity of life alone is not a sufficient indicator of quality of life. According to the World Health Organization, over $20\%$ of adults aged 60 and over suffer from a mental or neurological disorder. Mental disorders are defined as “health conditions characterized by alterations in thinking, mood, or behavior (or a combination thereof) associated with distress and impaired functioning.” Mental health disorders affect mood, thinking, and behavior. These also include depression, anxiety, insomnia, eating disorders, and addictive behaviors. In the Comunidad Valenciana (Spain), there is a $24.6\%$ risk of mental health disorders in adulthood, which can rise to $50\%$ in people over 84 years old (Conselleria de Sanitat Universal i Salut Pública, 2020). Geriatric depression often remains undiagnosed and untreated and its symptoms are commonly attributed to normal aging; however, the lack of treatment has important consequences for both the patients’ quality of life and the primary care system (Park and Unützer, 2011). The elderly may experience life stressors common to all people, but also other stressors that are more common in later life, like a significant ongoing loss in capacities and a decline in functional ability. For example, older adults may experience reduced mobility, chronic pain, frailty, or other health problems, for which they require some form of long-term care (Chen et al., 2020). In addition, older people are more likely to experience events such as bereavement, or a decline in socioeconomic status with retirement (Venkatapuram et al., 2017). All of these stressors can result in isolation, loneliness, or psychological distress in the elderly, for which they may require long-term care (Harman, 2006). There is growing evidence that the gut-brain axis, a bidirectional communication network that links the emotional and cognitive centers of the brain with peripheral intestinal functions, plays a role in promoting mental health or disorders (Richards et al., 2021). It regulates, for instance, appetite and feeding, glucose and metabolite homeostasis, and gut motility (Cryan and O’Mahony, 2011). Several factors can influence the bidirectional interplay between the gut and the brain, including: (i) neurological diseases like Parkinson, autism spectrum disorder or Alzheimer; (ii) psychological disorders, including depression, anxiety and insomnia; and (iii) gastrointestinal (GI) disorders such as irritable bowel syndrome and obesity (Liang et al., 2018; Suganya and Koo, 2020; Richards et al., 2021). The transmission of sensory information from the gut to the brain is mediated by hormonal and neural circuits (Suganya and Koo, 2020). After a stimulus such as ingestion, the passage of nutrients from the duodenum and jejunum produces chemical and mechanical stimuli that are detected by enteroendocrine cells (EECs). These cells will then secrete signaling peptides detected by sensory cells from the enteric nervous system (ENS) or the central nervous system (CNS) (Liang et al., 2018). There are intestinal microorganisms with the ability to produce metabolites, such as serotonin and Gamma-aminobutyric acid (GABA), which are active neurotransmitters in the human nervous system (Mazzoli and Pessione, 2016). These metabolites, once secreted by the microbiota, induce intestinal epithelial cells to release neural modulating molecules that signal the ENS, which, in turn, signals the brain function and therefore influences the hosts’ demeanor. GABA is the most abundant inhibitory neurotransmitter in the mammalian CNS. It is produced by microorganisms, plants, and animals and plays an important role in regulating blood pressure, sleep, cognition, and obesity, among other physiological functions. Therefore, it has been used as an antidepressant, hypotensive, insulin secretagogue, and as insomnia medication (Kalueff and Nutt, 2007). It is also interesting to mention the functional role of essential amino acids produced by gut microbes, in particular tryptophan. The majority of tryptophan in the human body circulates in the blood attached to albumin, while only 10–$20\%$ can be found circulating freely (Gao et al., 2020). Studies have shown that changes in the gut microbiota affect the gut-brain axis by modulating the tryptophan metabolism and that metabolic products of tryptophan metabolism can interact with the gut-brain axis and the CNS. These metabolites include 5-hydroxytryptamine (5-HT or serotonin), indolic compounds, and kynurenines (KYN) (Gao et al., 2020). Only 1–$2\%$ of available ingested tryptophan goes through the 5-HT pathway. This has important implications as 5-HT is the neurotransmitter mainly responsible for regulating mood and anxiety. Low serotonin levels in the CNS contribute to significantly increased depression and anxiety (Lindseth et al., 2015). The 5-HT pathway is involved in modulating emotions, food intake, sleep, sexual behavior, and pain management. Indeed, $8.95\%$ of serotonin is synthesized in the GI tract by enterochromaffin cells (EC), which are the most common type of EECs, and help regulate intestine permeability, motility, secretion, epithelial development, mucosal inflammation, and the development and neurogenesis of the enteric nervous system (Liu et al., 2021). It is estimated that $95\%$ of the produced serotonin is found in the GI tract (Richard et al., 2009). The biosynthesis of 5-HT is entirely dependent on the enzyme tryptophan hydroxylase (TPH), which converts tryptophan into 5-hydroxytryptophan (5-HTP) (Gao et al., 2020). TPH is a rate-limiting enzyme that exists in TPH1 and TPH2. TPH1 is expressed in the EC cells in the GI tract and the pineal gland while TPH2 is mainly expressed in the myenteric plexus of the ENS and the serotonergic neurons of the brainstem (Pelosi et al., 2015). Dysregulation in TPH expression is believed to play a role in psychiatric disorders such as anxiety and GI diseases such as irritable bowel syndrome (Gao et al., 2020). More than $90\%$ of tryptophan is metabolized through the kynurenine pathway (KP). Indolamine 2, 3-dioxygenase (IDO), expressed in various organs such as the brain, the GI tract, and the liver, and tryptophan 2, 3-dioxygenase (TDO), mainly expressed in the liver, are the enzymes that catalyze the first step of tryptophan metabolism on KP (Gao et al., 2020). These enzymes transform tryptophan into N-formylkynurenine, which is subsequently metabolized into KYN. Of these enzymes, TDO mediates the metabolism of KP at a basal level, while IDO is activated in an immune-activated environment (Chen et al., 2021). After KYN biosynthesis, it will continue to form other KYN such as kynurenic acid (KYNA) and quinolinic acid (QUIN). These compounds can cross the Blood–Brain Barrier (BBB) and reach the CNS, where they can act as neuromodulators and exert either neuroprotective or neurotoxic effects (Gao et al., 2020). Ruiz-Ruiz et al. [ 2020] identified a link between aging and the microbial pathway associated with tryptophan and indole (tryptophan degradation product) production and metabolism by the commensal microbiota. The key proteins involved in tryptophan-to-indole metabolism, tryptophanase (TnaA), and tryptophan synthase (TrpB) are more abundant and expressed at higher levels in the gut microbiota of infants, whereas they are expressed at significantly lower levels in adults and even lower levels or below the detection limit in the elderly. From the age of 11 years, the human gut microbiota may exhibit a decreased capacity to produce these metabolites, and from the age of 34 years, this capacity may drop by over $90\%$ compared to childhood (Ruiz-Ruiz et al., 2020). Tryptophan deficiency from a certain age could be associated with a high risk of mental health disorders in adulthood. Oral health is also influenced by aging, with an increased prevalence of periodontal disease (Clark et al., 2021). There are studies that have shown that the composition and diversity of the oral microbiota are related to the general health state and frailty in aging (Ogawa et al., 2018; Singh et al., 2019). Furthermore, there is strong evidence that elderly people who have a relatively high number of missing teeth are more likely to develop dementia and mild cognitive impairment (Batty et al., 2013). Also, it has been suggested that transition of bacteria from the oral mucosa to the gut is more frequent in the elderly than in adults (Iwauchi et al., 2019), which increases when volunteers suffer from inflammatory oral or intestinal diseases (Kitamoto et al., 2020). Another studies demonstrated the significance of the oral microbiome in the development or progression of a number of systemic disorders, including type 2 diabetes (Arimatsu et al., 2014) and colorectal cancer Komiya et al., 2019, which might suggest a possible effect of the oral microbiota over other disorders including mental health disorders. In the present study, we carried out 16S rRNA gene and metagenomic sequencing to determine differences in the taxa, functions, and metabolic pathways of intestinal and oral microbiota in a cohort of over 65-year-olds in the Comunidad Valenciana (Spain). The study included individuals treated with medication for anxiety, depression, and/or insomnia and those who were not diagnosed with any mental health disorders. ## Study participants A case–control study was performed. Fecal and saliva samples from 101 volunteers over 65 were collected (EE cohort). All participants were residents of the Comunidad Valenciana (Spain) and filled out a questionnaire about their diet, general health, habits, weight and height (with which the body mass index (BMI) has been calculated), employment situation, medical history, and vaccinations. Some of this information is collected in Supplementary Table 1. The EE cohort was composed of 37 males and 63 females (average age 71.29 ± 5.83), 28 of whom ($27.72\%$) reported being treated with antidepressants, anxiety, or insomnia medication (EE|MH group). Of these, 24 were women corresponding to $85.7\%$ of the group ($23.8\%$ of the complete EE cohort), and 4 were men corresponding to $14.3\%$ of the group ($4\%$ of the complete EE cohort). The remaining 73 were controls (EE|NOMH group). All procedures were reviewed and approved by the Ethics Committee (Reference: $\frac{20210305}{07}$) of Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana (FISABIO). All the volunteers provided written informed consent before their participation. ## Sample preparation Fecal samples were collected from each volunteer in sterile tubes, containing 10 mL of RNAlater Solution (Ambion) to stabilize and preserve the integrity of nucleic acids prior analysis. Samples were homogenized by adding 10 mL phosphate-buffered saline (PBS) (containing, per liter, 8 g of NaCl, 0.2 g of KCl, 1.44 g of Na2HPO4, and 0.24 g of KH2PO4 [pH 7.2]) and then centrifuged to eliminate solid waste. The obtained fecal microbial suspension was aliquoted and stored at −80°C until further processing. With respect to saliva samples, 3 mL was collected from each volunteer in sterile containers, aliquoted, and stored at −80°C until further processing. ## DNA extraction of fecal samples A total of 500 μL of fecal suspension was pelleted and weighted and the total genomic DNA was extracted using the QIAamp DNA mini stool kit (Qiagen). The fecal suspension pellet was resuspended in 1 mL of inhibitEX Buffer from the extraction kit and then 20 μL of lysozyme (10 mg/mL) was added for cellular lysis, followed by 30 min incubation at 37°C. The lysate was subjected to mechanical treatment with 200 μL of 150–212 μm diameter Glass Beads (Sigma) and heated to 95°C for 5 min. The samples were then centrifuged and 600 μL of the supernatant was treated with 45 μL of proteinase K. The following steps were carried out according to the manufacturers’ recommendations. ## DNA extraction of saliva samples A total of 250 μL of saliva was pelleted at 4°C, weighted, and total genomic DNA was extracted using the QIAamp DNA mini kit (Qiagen) with a few preliminary steps. The pellet was resuspended in the leftover supernatant and incubated for 45 s in a 37°C ultrasonic cleaner (Raypa). Then, 130 μL of AL Buffer from the extraction kit was added to each sample and then 10 μL of “enzyme mix” containing 2.5 μL of lysozyme (100 mg/mL), 2.5 μL of lysostaphin (5 mg/mL), 2.5 μL of mutanolysin, and 2.5 μL of nuclease-free water was also added and incubated for one-hour at 37°C. Subsequently, 20 μL of proteinase K from the extraction kit was added to the lysate and the samples were incubated for 10 min at 56°C, followed by 10 min at 70°C, and 3 min at 95°C incubation. The lysate was then mixed with 200 μL of $100\%$ ethanol and placed on the kit mini-column. Finally, the washing steps were performed according to the manufacturers’ recommendations. ## 16S rRNA gene amplification, library, and sequencing For fecal and saliva samples, V3-V4 hypervariable regions of the 16S rRNA gene were amplified by PCR using primers: 5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG-3′ (forward); and 5′-GTCTCGTGGGCTCGGAGATG TGTATAAGAGACAGGACTACHVGGTATCTAATCC-3′ (reverse). Amplicons were purified using NucleoMag NGS Clean-up and Size Select magnetic beads (Macherey-Nagel) and then Illumina sequencing adapters using the Nextera XT Index Kit (Illumina) were attached. Quantification of DNA was performed with a Qubit 3.0 fluorometer using the Qubit dsDNA HS assay kit (Thermo Fisher Scientific). Amplicon libraries were pooled in equimolar ratios for sequencing on a MiSeq platform of Illumina (2 × 300 bp paired-end reads) following the manufacturers’ recommendations. ## Metagenome library and sequencing For fecal samples, whole-genome sequencing was also performed from total DNA. Metagenome libraries were obtained with Illumina’s Nextera XT DNA Library Preparation Kit. Short fragments were eliminated using NucleoMag NGS Clean-up and Size Select magnetic beads (Macherey-Nagel) and the obtained purified libraries were sequenced in a MiSeq platform of Illumina (2 × 150 bp paired-end reads) following the manufacturers’ recommendations. ## Bioinformatics and statistical analysis In-house bioinformatic analysis pipelines were applied. For 16S rDNA gene analysis, we obtained the amplicon sequence variant (ASV) data with the DADA2 pipeline (Callahan et al., 2016), which removed the forward and reverse primers, filtered low-quality reads, and trimmed reads by length. Paired reads were merged to obtain the full denoised sequences, combined and abundance matrices were obtained. Chimeric ASVs as well as host (human) ASVs were removed. Finally, taxonomy was assigned to each variant by comparing them against the SILVA database (Quast et al., 2012) (naive Bayesian classifier to assign up to genus level and $97\%$ blast matching for species level). For metagenomic analysis, once the raw sequencing data were obtained, the sequencing adaptors were removed by Cutadapt software. Low-quality reads were eliminated using PRINSEQ, as well as short reads, and reads with a high percentage of ambiguous bases, in addition to low entropy reads. To join overlapping pairs to obtain longer sequences, the FLASH software was used. Non-overlapping forward pairs were also taken into account while non-overlapping reverse pairs were discarded. The host (human) genome and the non-coding ribosomal RNA sequences were filtered out using Bowtie2 with the SILVA database. The reads were then assembled into contigs using Megahit and mapped against the contigs using Blast. Open reading frames (ORF) were predicted using the Prodigal software and abundance tables were created. Functional annotation was performed by mapping each ORF against protein family database using the program HMMER and the KEGG Orthology database. Finally, Kaiju was used for taxonomical annotation of metagenome data. Once the functional compositional matrix was obtained, the results were grouped into functional categories and metabolic pathways. Each matrix, including the ASV, phylum, genus, and functional compositional matrices were then analyzed. The R statistics software was applied to obtain alpha (Shannon and Chao1 indexes) and beta diversity (Canonical Correspondence Analysis (CCA), Permanova test, and Wilcoxon non-parametric test). Correlation analysis between the saliva and fecal samples was obtained using the sPCA mixomics approach for a single omic (Kim-Anh et al., 2016). ## Robustness analysis: attenuation and buffering Functional capacities of microbiomes are dependent on the taxonomic structure of the microbial community, because each taxon is associated to putatively different functions and abundances. The functional metagenome could be inferred considering the taxonomic composition of the microbial community. Changes in the composition and/or abundance of one or more taxa can cause changes in functional capacities. This has recently been described as taxa-function relationships (Vieira-Silva et al., 2016; Eng and Borenstein, 2018). Two main systemic parameters can be estimated to determine the functional robustness of microbial communities: attenuation and buffering. The determination was done using the microbial community taxa-function robustness estimation pipeline developed by Eng and Borenstein [2018].2 To calculate changes in functional capacities or, more formally, to quantify the changes in gene composition induced by changes in taxonomical structure, the abovementioned work describes an approach to evaluate the taxa-function robustness and quantify the two abovementioned parameters. Attenuation measures how rapidly the functional shift increases as perturbation magnitude increases and buffering is defined as how large a taxonomic perturbation must be before noticeable functional shifts occur. These two parameters can be measured globally and for particular superpathways or pathways, thereby detecting the weakest points in the global microbiota metabolism when a stochastic change in the microbial community occurs, generating deviations in the functional profile. We have implemented some modifications in the original pipeline in order to improve sensitivity and accuracy. First, we used PICRUSt2 (Douglas et al., 2020) to derive a 16S copy number table, a genomic content annotation table, and a phylogenetic tree. Second, those files were used to replace the ones provided by the original pipeline. Manipulation of data and its graphical representation, as well as and statistical tests, was done using R scripts using libraries dplyr, ggplot, and ggpubr. Attenuation and Buffering measurements, graphical representation, and statistical tests were done using R scripts and libraries dplyr (Wickham et al., 2022) and ggplot2 (Wickham, 2016). ## Data availability statement The curated sequences from 16S rRNA gene and metagenomes were deposited in the EBI Short Read Archive under the study accession number PRJEB56919, with accession numbers ERS13596619- ERS13596719 and ERS13596821-ERS13596921 for the16S rRNA gene from fecal and saliva samples, respectively, and ERS13596720-ERS13596820 for metagenomes. The datasets presented in this study are deposited in the European Nucleotide Archive repository (https://www.ebi.ac.uk/ena), accession number PRJEB56919. ## Clinical and biochemical characteristics We obtained samples from a cohort of 101 volunteers over 65 years old (EE cohort) from the Comunidad Valenciana (Spain), 28 of whom were treated with medication for anxiety, depression, and/or insomnia (EE|NOMH group) and 73 not treated for any mental health disorders (EE|NOMH group). The medication of the EE|MH group included modulators of GABA receptors, modulators of serotonin availability, or sleep regulators (Table 1). Some participants combined more than one type of medication at the same time. In addition, some of them suffer common age-related diseases (hypercholesterolemia, hypertension, diabetes, and coronary diseases) and take medication for its treatment. Both groups had a similar representation of these most common diseases. **Table 1** | Identification number | Age | Gender | Group | | --- | --- | --- | --- | | EE13 | 67 | Female | 1 | | EE24 | 68 | Female | 1 | | EE25 | 68 | Female | 2 | | EE26 | 65 | Male | 2 | | EE27 | 68 | Female | 4 | | EE34 | 70 | Female | 3 | | EE37 | 75 | Female | 1 | | EE38 | 83 | Female | 1 | | EE40 | 69 | Female | 2 | | EE42 | 65 | Female | 1 | | EE50 | 65 | Female | 2 | | EE54 | 73 | Female | 1 | | EE62 | 66 | Female | 3 | | EE64 | 81 | Male | 1 | | EE71 | 82 | Female | 2 | | EE72 | 68 | Female | 2 | | EE74 | 88 | Female | 1 | | EE75 | 74 | Female | 3 | | EE79 | 68 | Female | 3 | | EE83 | 74 | Female | 3 | | EE89 | 71 | Female | 1 | | EE90 | 90 | Male | 1 | | EE91 | 89 | Female | 1 | | EE95 | 66 | Female | 4 | | EE102 | 65 | Female | 3 | | EE107 | 65 | Female | 1 | | EE108 | 71 | Male | 1 | | EE113 | 66 | Female | 1 | ## 16S taxonomy from fecal samples A total 7,050,645 reads were sequenced from fecal samples, $18.23\%$ of which were removed after quality check and host filtering, obtaining an average of 57,086 reads per sample (maximum length = 109,272, minimum length = 12,168, total number reads = 5,822,728). Taxonomic annotation showed two phyla with main representation in the EE cohort: Firmicutes ($48.92\%$) and Bacteroidota ($40.76\%$). Other phyla with lower representation included: Proteobacteria ($4.25\%$), Actinobacteriota ($2.82\%$), Verrucomicrobiota ($1.31\%$), Desulfobacterota ($0.70\%$), Cyanobacteria ($0.15\%$), and Synergistota ($0.12\%$). Alpha diversity analyses at genus and ASV levels showed that Shannon and Chao indexes were not statistically significant between EE|MH and EE|NOMH groups (Figures 1A,B). However, regarding beta diversity, the distribution of genera and ASV in the two groups was statistically significant (Adonis test, value of $$p \leq 0.003$$ and 0.038, respectively; Figure 1C). Because the EE/NOMH group has a clearly higher number of individuals than the EE/NOMH group (73 versus 28 volunteers), the analysis was repeated three times, each time choosing a group of 30 EE/NOMH individuals at random, in order to avoid bias due to the difference in the members of each group. In the three comparisons, the result was statistically significant with value of ps of 0.04, 0.021, and 0.002, respectively. The Wilcoxon non-parametric test also showed statistically significant differences (value of $p \leq 0.05$) between the two groups in the following eight genera (Figure 2A): Bilophila, Bacteroides, Colidextribacter, Flavonifractor, Parabacteroides, Oscillibacter, Alistipes, and Coprococcus and in five ASVs (Figure 2B), which correspond to the species Flavonifractor plautii, Bilophila wadsworthia, Lachnospira pectinoschiza, and two with Faecalibacterium prausnitzii. Of these, the genus Coprococcus and the ASVs corresponding to the species *Flavonifractor plautii* and *Bilophila wadsworthia* were more abundant in the EE|NOMH group. **Figure 1:** *16S alpha and beta diversity of fecal samples. (A) Shannon diversity index and (B) richness estimator Chao1 analysis between EE|MH and EE|NOMH groups. (C) Canonical Correspondence Analysis (CCA) of EE|MH (blue) and EE|NOMH (red) groups at genus level.* **Figure 2:** *Volcano plots showing the differential abundance of (A) genera and (B) ASVs between EE|MH (right) and EE|NOMH (left) groups in fecal samples.* ## 16S taxonomy from saliva samples A total of 9,050,887 reads were sequenced from saliva samples, $24.47\%$ of which were removed after quality check and host filtering, obtaining an average of 68,358.57 reads per sample (maximum length = 488,613, minimum length = 28,571, total number of reads = 6,835,857). Taxonomic annotation showed that the most represented phyla were Firmicutes ($30.22\%$), Bacteroidota ($28.91\%$), and Proteobacteria ($20.48\%$), and other phyla with lower representation that included Fusobacteriota ($8.15\%$), Actinobacteriota ($5.82\%$), Patescibacteria ($2.97\%$), Campilobacterota ($2.21\%$), and Spirochaetota ($0.83\%$). Similar results to those obtained with fecal samples were detected for the alpha diversity at the genus and ASV levels of saliva samples. Shannon and Chao indexes were not significantly different with p values >0.05 between EE|MH and EE|NOMH (Figures 3A,B). However, significant differences were found in the beta diversity between groups (Adonis test value of $$p \leq 0.02$$) (Figure 3C). We identified statistically significant differences (Wilcoxon test) for five genera (Figure 4A): Veillonella, Neisseria, Porphyromonas, Lactobacillus, and Treponema, and five ASV (Figure 4B), which corresponded to the species Oribacterium asaccharolyticum, Stomatobaculum longum, Fusobacterium periodonticum, Veillonella rogosae, and Porphyromonas pasteri. Only the genus Veillonella and the ASV corresponding to *Oribacterium asaccharolyticum* and *Stomatobaculum longum* were more abundant in EE|MH, while the rest were more abundant in EE|NOMH. **Figure 3:** *16S alpha and beta diversity of saliva samples. (A) Shannon diversity index and (B) richness estimator Chao1 analysis between EE|MH and EE|NOMH groups. (C) Canonical Correspondence Analysis (CCA) of EE|MH (blue) and EE|NOMH (red) groups at genus level.* **Figure 4:** *Volcano plots showing the differential abundance of (A) genera and (B) ASVs between EE|MH (right) and EE|NOMH (left) in saliva samples.* ## Correlation analysis between gut and saliva microbiota Correlation analysis between the gut and the saliva microbiota at genus level was performed using the Mixomics single omic approach. The correlation analyses showed differences between both groups. In the EE|MH group the genus Lachnospira (gut) with the genera Megasphaera and Atopobium (saliva) and the genus Subdoligranulum (gut) with the genus Lachnoanaerobaculum (saliva) showed significant negative correlations, while the genus Odoribacter (gut) with the genera Alloprevotella and Haemophilus (saliva) and the genera Lachnoclostridium, and Colidextribacter (gut) with the genus Megasphaera (saliva) showed positive correlation (Figure 5A). By contrast, the genus Alistipes (gut) had significant negative correlation with the genera Veillonella and Prevotella (saliva) in the EE|NOMH group (Figure 5B). **Figure 5:** *Heatmaps charts showing the correlations between gut and saliva microbiota in (A) EE|MH and (B) EE|NOMH groups.* ## Functional orthologs analysis from metagenome data of fecal samples A total 554,768,576 reads were sequenced, $19.09\%$ of which were removed after quality check and host filtering, obtaining an average of 4,444,094.52 reads per sample (maximum length = 15,434,500, minimum length = 924,728, number of total reads = 448,853,547); of these 270,608,848 were correctly assigned to KEGG Orthology (KO) categories (maximum number of reads assigned per sample = 10,033,384; minimum number of reads assigned per sample = 330,178). No significant differences were found in the CCA analysis between EE|MH and EE|NOMH for KO categories (Adonis test value of $$p \leq 0.24$$; Supplementary Figure 1). However, the Wilcoxon test identified 382 KO categories that showed significant differences (value of $p \leq 0.05$). It is worth mentioning that five are involved in tryptophan metabolism (K00382, K03781, K01692, K00658, and K01667) (see Supplementary Figure 2) and six in serine metabolism (K00382, K02437, K01079, K00281, K00605, and K18348/K12235). Serine is used by bacteria to convert indole into tryptophan- (see Supplementary Figure 3), which were higher in EE|MH than in EE|NOMH (Figure 6). Furthermore, 19 KO categories involved in the synthesis of metabolic products related to GABA production was higher in the EE|MH group (K13746, K03474, K00294, K00175, K01425, K03473, K09758, K05275, K00174, K17865, K05597, K01580, K00262, K01640, K00634, K01692, K13051, K01470, and K09472). Three of these KO categories correspond to the arginine and proline metabolism pathway (K00294, K01470, and, K09472), five to the alanine aspartate and glutamate metabolism (K00262, K00294, K01425, K01580, and K05597), seven to the butanoate metabolism (K00174, K00175, K00634, K01580, K01640, K01692, and K17865), and three to the vitamin B6 metabolism (K03473, K03474, and K05275) which, as a co-factor, is also involved in the biosynthesis and catabolism of amino acids and neurotransmitters like GABA (Table 2). Finally, two more KO categories were shared by the arginine and proline metabolism and the alanine, aspartate, and glutamate metabolism pathways (K00294) and by the alanine, aspartate, and glutamate metabolism, and the butanoate metabolism pathways (K01580). The genus contribution to these KOs was obtained using taxonomy information from metagenomic data through Kaiju. *The* genera Bacteroides and Alistipes were the most representative in most of the KOs analyzed. It is noteworthy that the percentage of the Bacteroides contribution was higher in the EE|MH group in all but one KO and that the genus Alistipes had a higher contribution in most of the KOs in the EE|NOMH group (Table 2). **Figure 6:** *Significant differences in KEGG categories, in bold those related to tryptophan and serine metabolism between EE|NOMH (right) and EE|MH (left) groups.* TABLE_PLACEHOLDER:Table 2 ## Analysis of KEGG metabolic pathways CCA analysis carried out between EE|MH and EE|NOMH showed no statistically significant differences in KEGG pathways (Adonis test p − value = 0.25; see Supplementary Figure 4). However, 29 KEGG pathways showed significant differences in the Wilcoxon test. Interestingly, considering that both groups consisted of individuals over the age of 65, the pathway regulating longevity was significantly higher in EE|MH than in EE|NOMH (path 04211, value of $$p \leq 0.02$$; Figure 7A). In addition, two amino acid metabolism pathways also showed higher abundance in the EE|MH group: valine, leucine, and isoleucine degradation (path 00280, value of $$p \leq 0.0084$$) and phenylalanine metabolism (path: 00360, value of $$p \leq 0.0088$$; Figure 7B). Finally, the other two significant KEGG pathways related to the CNS and tryptophan metabolism were higher in the EE|MH group, the dopaminergic synapse (path: 04728 value of $$p \leq 0.015$$) and serotoninergic synapse (path 04726, value of $$p \leq 0.019$$; Figure 7C). **Figure 7:** *(A) Differences in the longevity regulating pathway between EE|NOMH (red) and EE|MH (blue). (B) Significant differences in the metabolism of amino acids between EE|NOMH (right) and EE|MH (left). (C) Significant differences in neuronal synapses between EE|NOMH (right) and EE|MH (left) groups.* ## Robustness analysis of samples EE|MH and EE|NOMH groups showed no differences in either attenuation (Mann–Whitney test value of $$p \leq 0.072$$) or buffering (Mann–Whitney test p value = 0.15) in fecal samples. Attenuation and buffering for each individual are shown in Supplementary Table 2. In addition, values of attenuation and buffering for each individual within each group (Supplementary Figure 5A) are not correlated after applying Pearson’s correlation coefficient. In some cases, individual pathways start from a common precursor, or produce a common product, but they can also have other relationships. Superpathways can have individual reactions due to their components in addition to other pathways. Moreover, distribution curves of attenuation and buffering (Supplementary Figures 5B,C, respectively) were similar for both groups, controls and treated individuals. Similar results to those observed in fecal samples were observed for saliva. Attenuation and buffering for each individual saliva sample are shown in Supplementary Table 3. Attenuation (Mann–Whitney test, p value = 0.41) and buffering (Mann–Whitney test, p value = 0.95 for Buffering) were not significant between groups. Moreover, values of attenuation and buffering for each individual within each group (Supplementary Figure 6A) did not correlate after applying Pearson’s correlation coefficient. Furthermore, distribution curves of attenuation and buffering (Supplementary Figures 6B,C, respectively) were also similar for both groups, controls and treated. Of the 20 main superpathways, most will have an additional parent class within the pathway ontology to define their biological role. Statistical differences for attenuation were found for fecal and saliva samples in superpathways for both groups (Supplementary Figures 7A,B, respectively). In fecal samples only in superpathway cell motility (lower attenuation in treated group, value of p in Kruskal–Wallis test 0.0428) while in saliva samples, we found differences in attenuation for four superpathways (higher for treated group in superpathways for lipid metabolism and translation and lower in metabolism of terpenoids and polyketides, and cell growth and death). In case of buffering, no differences were found in fecal samples (Supplementary Figure 7C), while differences were recorded in only four superpathways in saliva samples: lipid metabolism, metabolism of other amino acids and folding, sorting and degradation (lower for treated group) and, finally, metabolism of terpenoids and polyketides (higher in treated group; Supplementary Figure 7D). ## Discussion Tryptophan is an essential amino acid for protein synthesis, and the least abundant amino acid in proteins and cells (Gao et al., 2020). Certain bacterial products of tryptophan metabolism, including serotonin, indolic compounds, and kynurenines, can interact with the gut-brain axis and the CNS of the host, thereby modulating physiology (Agus et al., 2018). Changes affecting the gut-brain axis are thought to be connected to a number of neurological disorders, such as Parkinson’s disease, *Autism spectrum* disorder, and Alzheimer’s disease, as well as some gastrointestinal (GI) disorders, such as irritable bowel syndrome and obesity, and even some psychological disorders, such as depression, anxiety, and insomnia (Liang et al., 2018; Richards et al., 2021). Other authors have focused on the role of the microbiota in the development of mental health-related conditions, discussing that conditions characterized by acute or chronic inflammation, depression, decreased quality of life or cognitive impairment are related to the metabolic alteration of amino acid precursors of neurotransmitters, such as tryptophan and phenylalanine among others Strasser et al., 2017. Around 90–$95\%$ of available tryptophan goes through the KP, 1–$2\%$ of it forms 5-HT and melatonin through the serotonin pathway, and 4–$6\%$ is metabolized into indole and other indolic derivates by bacteria (Gao et al., 2018), that can be transferred across the blood–brain barrier to reduce neuroinflammation Cox and Weiner, 2018. The microbiota plays an important role, for instance, it is crucial for the gut’s amino acid metabolism, which has an impact on neuroinflammatory illnesses. The ability of the microbiota to access gut-brain signaling pathways and modify the host’s behavior depends on bidirectional communication along the gut-brain axis. TPH2 is the protein that catalyzes the first step in serotonin biosynthesis from tryptophan in the brain. An imbalance in serotonin levels has been widely associated with neuropsychiatric disorders such as depression and anxiety (Pelosi et al., 2015). Shishkina et al. showed that TPH2 expression increases in the midbrain in animal models of depression treated with antidepressants (Shishkina et al., 2007). In our study, the volunteers with depression were also taking antidepressant medication, which might increase the abundance of genera strongly correlated with TPH2, such as Bilophila (Liu G. et al., 2020). In fact, the genus Bilophila proved significantly higher in the EE|MH group. These bacteria are reported to be significantly increased in anhedonia (loss of pleasure) in mouse models. Anhedonia is one of the two core symptoms of depression (Yang et al., 2019) and was also found to be increased in a mouse model of depression, subjected to chronic unpredictable mild stress (Zhang M. et al., 2021). Bilophila has also been described as positively correlated with tryptophan hydroxylase 2 (TPH2) gene expression (Liu G. et al., 2020). Over $90\%$ of the whole tryptophan is metabolized through the KP. IDO and tryptophan 2, 3-dioxygenase (TDO) are the enzymes that catalyze the first step of tryptophan metabolism in this pathway (Maes et al., 2011). On the one hand, TDO activation is normally stable and is regulated by tryptophan availability (Gao et al., 2020). On the other hand, IDO is induced by interferon-gamma (IFN-γ) and tumor necrosis factor-alpha (TNF-α) among other pro-inflammatory cytokines, and its activation is correlated with the intensity of depressive symptoms (Höglund et al., 2019; Gao et al., 2020; Chen et al., 2021). IDO activation by inflammation caused by bacteria such as Flavonifractor and Alistipes and promoting KYN formation through KP can decrease tryptophan availability, negatively impacting serotonin synthesis and neurotransmission. Flavonifractor and Alistipes were significantly higher in the mental-health treatment group (EE|MH). Flavonifractor has previously been reported as higher in individuals with major depressive disorder (Jiang et al., 2015; Valles-Colomer et al., 2019), generalized anxiety disorder (Jiang et al., 2015), affective disorders (Coello et al., 2019), and bipolar disorder (Lindseth et al., 2015; Wang et al., 2021). Flavonifractor has also been described as a pro-inflammatory genus and studies show a negative association between this genus and quality of life scores (Jiang et al., 2018). Parker et al. [ 2020] and Jiang et al. [ 2015] also described Alistipes to be higher in patients with depression (Jiang et al., 2018; Parker et al., 2020). This genus is believed to be associated with stress, fatigue syndrome, and depressive disorders through inflammatory pathways (Naseribafrouei et al., 2014). Bacteroides were also significantly higher in the EE/MH mental-health treatment group. The role of Bacteroides in mental health is highly controversial, with some authors observing the genus to be lower in patients suffering from mental health disorders (Jiang et al., 2015), while others find it to be higher in this group (Yang et al., 2019). This genus has previously been studied for its ability to produce cytokines and its role in inflammation, as gut inflammation has a clear association with depression (Schiepers et al., 2005; Dantzer, 2009). By contrast, *Flavonifractor is* reported to be higher in patients with remitted geriatric depression (Lee et al., 2022), which might explain why it is higher in the EE|MH group, where elderly subjects are medicated for mental health. In this case, the medication might be responsible for remission. During aging, elderly individuals suffer from systematic inflammation and, as stated above, mental illness is generally associated with an inflammatory state of the patient. Oral health is also influenced by aging and inflammation, with an increased prevalence of periodontal disease (Clark et al., 2021). Several studies suggest that some psychiatric diseases, such as Alzheimer’s or bipolar disorder, are related to leakage of pro-inflammatory oral bacteria triggering neuroinflammation (Leira et al., 2017). Furthermore, mental health issues such as anxiety and depression are related to a decrease in oral hygiene and dental check-ups (Anttila et al., 2006; Okoro et al., 2012; Simpson et al., 2020). Periodontal diseases (mainly periodontitis and gingivitis) are caused by bacterial-induced inflammation. Porphyromonas is a well-known periodontal pathogen whose virulence factors cause deregulation in inflammatory and immune responses of the host (Mysak et al., 2014; Leira et al., 2017). Studies of Alzheimer’s disease show inflammatory cytokines such as TNF-α, IL-1, IL-6, and IL-8 are released from the host cells that have been infected with Porphyromonas (Mei et al., 2020). Similarly, *Treponema denticola* is known to cause gingivitis in cases of oral dysbiosis, despite being a normal component of human oral microbiota (Simpson et al., 2020). Porphyromonas and Treponema were both higher in our EE|NOMH group saliva. Both bacteria can form synergistic biofilms and are positively associated with chronic periodontitis and severe periodontal disease (Ng et al., 2019). The genus Veillonella, which was found to be higher in the EE|MH group, was previously correlated with anti-inflammatory mediators and maintains oral pH by metabolizing lactate into weaker acids (Rosier et al., 2018). In the case of oral microbiota, we also observed the influence of mental health medicine in restoring elderly participants to a healthier state, as the medicated EE|MH group had significantly lower abundances of these pro-inflammatory genera. Correlation analysis of both oral and intestinal microbiota, showed similar results. In the EE|NOMH group, the genus Alistipes from the gut was negatively correlated with the oral genera Veillonella and Prevotella. As stated above, *Alistipes is* a pro-inflammatory genus that has previously been correlated with mental health problems while oral Veillonella and Prevotella were negatively correlated with pro-inflammatory markers, Prevotella has been even negatively associated with distress (Kohn et al., 2020). Meanwhile, in the EE|MH group the gut genus Lachnospira showed a negative correlation with the oral genera Megasphaera and Atopobium. Previous studies report Lachnospira to be lower in animal models of depression and stress (Flux and Lowry, 2020), and in a cohort of patients suffering major depressive disorder (Rosier et al., 2018). By contrast, Megasphaera and Atopobium are found to be higher in cohorts with mental health disorders (McGuinness et al., 2022). Similarly, the oral genus Lachnoanaerobaculum and the intestinal genus Subdoligranulum showed significant negative correlations in the EE|MH group. Liu R. T. et al. [ 2020] reported that the abundance of Subdoligranulum was reduced in subjects who had more severe symptoms of depression (Liu R. T. et al., 2020), while Wang et al. described an augmented abundance of Lachnoanaerobaculum in depression and anxiety (Wang et al., 2022). On the other hand, Odoribacter in gut microbiota with the oral genera Alloprevotella and Haemophilus had a positive correlation in the EE|MH group, all three of these genera are related with bad health. Odoribacter is one of the gut microbes associated with mental health issues, including major depressive disorder (Zhang M. et al., 2021). Oral *Alloprevotella is* described to be involved in periodontal disease (Sun et al., 2020) and *Haemophilus is* a well-known oral pathogen (Nørskov-Lauritsen, 2014). A similar positive correlation was obtained in the EE|MH group between Lachnoclostridium, and Colidextribacter from the intestinal microbiota with the oral genus Megasphaera that, as stated above, is elevated in mental health disorders. Lachnoclostridium has been associated with higher depressive symptoms in an induced animal model of depression (Zhang Y. et al., 2021), while Colidextribacter was associated with a positive response to antidepressant treatment in a mouse model of depression (Duan et al., 2021). Together these results again indicate that the mental-health treatment the EE|MH group may be restoring the microbiota to a healthier state, even though some genera related with mental health disorders are still present. Parabacteroides, another genus involved in tryptophan metabolism, was significantly higher in the EE|MH group. Deng et al. [ 2021] showed that the genus plays an important role in tryptophan metabolism, where it has a strong correlation between the KP and depressive-like behavioral changes in a rat model of chronic restraint stress (Wu et al., 2014; Deng et al., 2021). Moreover, Li et al. [ 2016] described that a decrease in the abundance of Parabacteroides correlated with an improvement in the mood of adults. Functional analysis of metagenome data showed five KEGG Orthology categories that are significantly higher in the EE|MH group and are related to tryptophan metabolism. Interestingly, tryptophanase (K01667) was markedly higher in the EE|MH group. This enzyme carries out the first step in the indolic pathway, transforming tryptophan into indole (Agus et al., 2018). Indole is a signaling molecule that can control bacteria antibiotic resistance, sporulation, and biofilm formation. It can also inhibit quorum sensing and modulate virulence factors (Agus et al., 2018). Indolic compounds are AhR ligands; AhR activation influences immune homeostasis via receptor anti-inflammatory effect by regulating intraepithelial lymphocytes and innate lymphoid cells (Li et al., 2011; Qiu et al., 2012; Jin et al., 2014). They are known to extend the health-span of several models of aging, such as C. elegans, D. melanogaster, and mouse (Sonowal et al., 2017). Ruiz-Ruiz et al. [ 2020] showed the loss of the tryptophanase enzyme during aging and describe how the microbiota diminishes its ability to produce indole and tryptophan in old age, compromising the health status of the elderly (Ruiz-Ruiz et al., 2020). Our EE|NOMH group, comprising over 65-year-olds who are not taking mental-health medication, had a significantly lower abundance of tryptophanase. This would indicate that medication, such as antidepressants and anti-anxiolytic drugs, restore these individuals to a healthier state, which might also explain the significant difference in the longevity regulating pathway between the EE|MH and EE|NOMH groups. GABA is the principal inhibitory neurotransmitter in the brain. It affects the control of homeostasis during stress and has been associated with mental health disorders such as anxiety and depression (Geuze et al., 2008). GABA and several other GABA analogs have been shown to have anxiolytic and hypnotic effects. Positive modulators to GABA receptors have been used to treat anxiety disorders and insomnia (Kalueff and Nutt, 2007). Classic mental-health treatments include benzodiazepines, these are positive allosteric modulators of GABA receptors (Sigel and Ernst, 2018). Oscillibacter, which we found to be enriched in the EE|MH group, has valeric acid as its main metabolic product; this metabolite mimics GABA. Valeric acid can bind with the GABAa receptor, which explains the association between valeric acid-producing bacteria and depression (Naseribafrouei et al., 2014). Rong et al. [ 2019] reported similar results, finding an increase of this genus in treated patients suffering from major depressive disorder or bipolar disorder (Rong et al., 2019). Similarly, the GABA producing genus Bacteroides (Otaru et al., 2021) was also higher in the EE|MH group. The contribution of genera to the analyzed KOs showed that Bacteroides and Alistipes were the ones contributing most to the production of these KOs in the EE|MH group. It is noteworthy that these genera were also significantly higher in the EE|MH group. The dopaminergic synapse, which includes alcoholism, the amphetamine addiction, and the cocaine addiction pathways, was higher in our EE|MH group. Dopamine is a neurotransmitter responsible for several functions in the body, including learning, memory, reward, and motor control. It has been implicated in psychiatric and psychological disorders (Ko and Strafella, 2012). Dopamine availability is higher in cocaine and amphetamine users, and the reward system in the brain was active in animal models of addiction (Di Chiara et al., 2004). The use of benzodiazepines as mental-health medication in the EE|MH group also explains the difference in synaptic pathways between both groups. Benzodiazepines are positive allosteric modulators of GABA receptors, and it has been suggested that the activation of GABA receptors enhances dopamine release (Kramer et al., 2020). The results show that treatment-related changes in taxonomic composition of microbiota modifies robustness parameters, in other words, eventual changes in taxonomic composition modify functional capacity of the bacterial community, at least in some superpathway functions. However, it is important remark that this functional capacity is based only on the content of all genes of prokaryotic organisms living in microbiota, without considering the expression levels of every gene. In the fecal microbiota, there are differences in taxonomic abundances in treated and not treated subjects, with a relevant impact on functional capacity and robustness for at least for some superpathways. For attenuation, differences were observed only in superpathway cell motility, while in buffering, no differences were found. Particularly interesting are the results for saliva samples, which show some differences in buffering. These were lower for at least three superpathways related with metabolism, which can induce variations in the metabolite landscape if alterations occur. These corresponded to the superpathway of lipid metabolism, superpathway of metabolism of terpenoids and polyketides and superpathway of metabolism of other amino acids. Also changes in attenuation were found (higher for superpathway of lipid metabolism and lower for superpathway of metabolism of terpenoids and polyketides). For the remaining superpathways not directly related with metabolism the differences observed could induce variations in cell division and growth of prokaryotic cells, without obvious consequences in community taxonomic composition dynamics, which could finally modify the values of those parameters of robustness. Therefore, at least in oral microbiota, in-depth studies should address the relationship between changes induced in functional capacity by alteration or disturbances of the microbial community, and the medication to treat pathologies. In this respect, there are numerous references on the role of medication in Xerostomia related with salivary gland dysfunction, and oral diseases associated to diazepine (De Almeida et al., 2008) and other mental-health drugs (Koller et al., 2000; Arany et al., 2021) or medication commonly used by the elderly population (Leal et al., 2010). Spain has an aging population, with $19.93\%$ of the whole population over 65 years of age, in 2021. This age range corresponds to a higher risk of suffering mental health issues, reaching around $25\%$ in 65-year-olds and up to $55\%$ in 85-year-olds (Conselleria de Sanitat Universal i Salut Pública, 2020). It is important to identify the reasons underlying this increase in mental health issues in a population that registered 1,281 suicides in people over 65 in 2020 in Spain (Instituto Nacional de Estadistica, 2020). Here, we have demonstrated that there are significant differences in the microbiome composition and function of older people in the Comunidad Valenciana being medicated for mental health issues. Our results also indicated that the medication might help to recover the microbiome to a healthier state and aid patient remission by remodeling the gut microbiota and bacterial tryptophan metabolism. ## Ethics statement The studies involving human participants were reviewed and approved by all procedures were reviewed and approved by the Ethics Committee (Reference: $\frac{20210305}{07}$) of Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana (FISABIO). All the volunteers provided written informed consent before their participation. The patients/participants provided their written informed consent to participate in this study. ## Author contributions NP and AB carried out the experiments and processed sequencing data. BP-R developed the robustness analysis. AA performed the bioinformatic analyses. SR-R and AM designed the experiments and wrote the manuscript. All authors contributed to the article and approved the submitted version. ## Funding This research was funded by the Spanish Ministry of Science and Innovation (PID2019-105969GB-I00) and supported by a grant (Programa Santiago Grisolía, GRISOLIAP/$\frac{2019}{080}$) from the Generalitat Valenciana. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2023.1094071/full#supplementary-material ## References 1. Agus A., Planchais J., Sokol H.. **Gut microbiota regulation of tryptophan metabolism in health and disease**. *Cell Host Microbe* (2018) **23** 716-724. DOI: 10.1016/j.chom.2018.05.003 2. Anttila S., Knuuttila M., Ylostalo P., Joukamaa M.. **Symptoms of depression and anxiety in relation to dental health behavior and self-perceived dental treatment need**. *Eur. J. Oral Sci.* (2006) **114** 109-114. DOI: 10.1111/j.1600-0722.2006.00334.x 3. Arany S., Kopycka-Kedzierawski D. T., Caprio T. V., Watson G. E.. **Anticholinergic medication: related dry mouth and effects on the salivary glands**. *Oral Surg. Oral Med. Oral. Pathol. Oral Radiol.* (2021) **132** 662-670. DOI: 10.1016/j.oooo.2021.08.015 4. Arimatsu K., Yamada H., Miyazawa H., Minagawa T., Nakajima M., Ryder M. I.. **Oral pathobiont induces systemic inflammation and metabolic changes associated with alteration of gut microbiota**. *Sci. Rep.* (2014) **4** 4828. DOI: 10.1038/srep04828 5. Batty G. D., Li Q., Huxley R., Zoungas S., Taylor B. A., Neal B.. **Oral disease in relation to future risk of dementia and cognitive decline: prospective cohort study based on the Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified-Release Controlled Evaluation (ADVANCE) trial**. *Eur. Psychiatry* (2013) **28** 49-52. DOI: 10.1016/j.eurpsy.2011.07.005 6. Callahan B. J., McMurdie P. J., Rosen M. J., Han A. W., Johnson A. J. A., Holmes S. P.. **DADA2: High-resolution sample inference from Illumina amplicon data**. *Nat. Methods* (2016) **13** 581-583. DOI: 10.1038/nmeth.3869 7. Chen L. M., Bao C. H., Wu Y., Liang S. H., Wang D., Wu L. Y.. **Tryptophan-kynurenine metabolism: A link between the gut and brain for depression in inflammatory bowel disease**. *J. Neuroinflammation* (2021) **18** 1-13. DOI: 10.1186/s12974-021-02175-2 8. Chen L., Zhang L., Xu X.. **Review of evolution of the public long-term care insurance (LTCI) system in different countries: influence and challenge**. *BMC Health Serv. Res.* (2020) **20** 1057. DOI: 10.1186/s12913-020-05878-z 9. Clark D., Kotronia E., Ramsay S. E.. **Frailty, aging, and periodontal disease: basic biologic considerations**. *Periodontol.* (2021) **87** 143-156. DOI: 10.1111/prd.12380 10. Coello K., Haldor T., Sørensen N., Munkholm K., Vedel L.. **Brain, Behavior, and Immunity Gut microbiota composition in patients with newly diagnosed bipolar disorder and their unaffected first-degree relatives**. *Brain Behav. Immun.* (2019) **75** 112-118. DOI: 10.1016/j.bbi.2018.09.026 11. Conselleria de Sanitat Universal i Salut Pública (Ed.). (2020). Estrategía Autonómica de Salud mental 2016-2020.pdf. Available at: http://www.san.gva.es/documents/156344/6700482/Estrateg%C3%ADa+Auton%C3%B3mica+de+Salud+mental+2016+2020+.pdf. (2020) 12. Cox L. M., Weiner H. L.. **Microbiota signaling pathways that influence neurologic disease**. *Neurotherapeutics* (2018) **15** 135-145. DOI: 10.1007/s13311-017-0598-8 13. Cryan J. F., O’Mahony S. M.. **The microbiome-gut-brain axis: from bowel to behavior: from bowel to behavior**. *Neurogastroenterol. Motil.* (2011) **23** 187-192. DOI: 10.1111/j.1365-2982.2010.01664.x 14. Dantzer R.. **Cytokine, sickness behavior, and depression**. *Immunol. Allergy Clin. N. Am.* (2009) **29** 247-264. DOI: 10.1016/j.iac.2009.02.002 15. De Almeida P. D. V., Grégio A. M., Brancher J. A., Ignácio S. A., Machado M. A., de Lima A. A.. **Effects of antidepressants and benzodiazepines on stimulated salivary flow rate and biochemistry composition of the saliva**. *Oral Surg. Oral Med. Oral Pathol. Oral Radiol.* (2008) **106** 58-65. DOI: 10.1016/j.tripleo.2007.11.008 16. Deng Y., Zhou M., Wang J., Yao J., Yu J., Liu W.. **Involvement of the microbiota-gut-brain axis in chronic restraint stress: disturbances of the kynurenine metabolic pathway in both the gut and brain**. *Gut Microbes* (2021) **13** 1-16. DOI: 10.1080/19490976.2020.1869501 17. Di Chiara G., Bassareo V., Fenu S., De Luca M. A., Spina L., Cadoni C.. **Dopamine and drug addiction: the nucleus accumbens shell connection**. *Neuropharmacology* (2004) **47** 227-241. DOI: 10.1016/j.neuropharm.2004.06.032 18. Douglas G. M., Maffei V. J., Zaneveld J. R., Yurgel S. N., Brown J. R., Taylor C. M.. **PICRUSt2 for prediction of metagenome functions**. *Nat. Biotechnol.* (2020) **38** 685-688. DOI: 10.1038/s41587-020-0548-6 19. Duan J., Huang Y., Tan X., Chai T., Wu J., Zhang H.. **Characterization of gut microbiome in mice model of depression with divergent response to escitalopram treatment**. *Transl. Psychiatry* (2021) **11** 303. DOI: 10.1038/s41398-021-01428-1 20. Eng A., Borenstein E.. **Taxa-function robustness in microbial communities**. *Microbiome* (2018) **6** 45. DOI: 10.1186/s40168-018-0425-4 21. Flux M. C., Lowry C. A.. **Finding intestinal fortitude: Integrating the microbiome into a holistic view of depression mechanisms, treatment, and resilience**. *Neurobiol. Dis.* (2020) **135** 104578. DOI: 10.1016/j.nbd.2019.104578 22. Gao K., Mu C., Farzi A., Zhu W.. **Tryptophan metabolism: a link between the gut microbiota and brain**. *Adv. Nutr.* (2020) **11** 709-723. DOI: 10.1093/advances/nmz127 23. Gao J., Xu K., Liu H., Liu G., Bai M., Peng C.. **Impact of the gut microbiota on intestinal immunity mediated by tryptophan metabolism**. *Front. Cell. Infect. Microbiol.* (2018) **8** 13. DOI: 10.3389/fcimb.2018.00013 24. García A. A., Nieto P. A., Díaz J. P., Fariñas D. R., García A. A., Rodríguez R. P.. **Indicadores estadísticos básicos**. *Informes Envejecimiento en Red* (2021) **22** 38 25. Geuze E., van Berckel B. N. M., Lammertsma A. A., Boellaard R., de Kloet C. S., Vermetten E.. **Reduced GABAA benzodiazepine receptor binding in veterans with post-traumatic stress disorder**. *Mol. Psychiatry* (2008) **13** 74-83. DOI: 10.1038/sj.mp.4002054 26. Harman D.. **Aging: overview**. *Ann. N. Y. Acad. Sci.* (2006) **928** 1-21. DOI: 10.1111/j.1749-6632.2001.tb05631.x 27. Höglund E., Øverli Ø., Winberg S.. **Tryptophan metabolic pathways and brain serotonergic activity: a comparative review**. *Front. Endocrinol.* (2019) **10** 158. DOI: 10.3389/fendo.2019.00158 28. Instituto Nacional de Estadistica (2020). Defunciones por suicidios. Available at: https://www.ine.es/dynt3/inebase/es/index.htm?padre=5453&capsel=5454. (2020) 29. Instituto Nacional de Estadistica (2021a). Indicadores de crecimiento y estructura de la población. Total Nacional. INE. Available at: https://www.ine.es/consul/serie.do?d=true&s=IDB55727&c=2&. (2021a) 30. Instituto Nacional de Estadistica (2021b). Índice de Envejecimiento por comunidad autónoma. INE. Available at: https://www.ine.es/jaxiT3/Datos.htm?t=1452. (2021b) 31. Iwauchi M., Horigome A., Ishikawa K., Mikuni A., Nakano M., Xiao J.. **Relationship between oral and gut microbiota in elderly people**. *Immun. Inflamm. Dis.* (2019) **7** 229-236. DOI: 10.1002/iid3.266 32. Jiang H., Ling Z., Zhang Y., Mao H., Ma Z., Yin Y.. **Altered fecal microbiota composition in patients with major depressive disorder**. *Brain Behav. Immun.* (2015) **48** 186-194. DOI: 10.1016/j.bbi.2015.03.016 33. Jiang H., Ruan B., Yu Z., Zhang Z., Deng M., Zhao J.. **Altered gut microbiota profile in patients with generalized anxiety disorder**. *J. Psychiatr. Res.* (2018) **104** 130-136. DOI: 10.1016/j.jpsychires.2018.07.007 34. Jin U.-H., Lee S.-O., Sridharan G., Lee K., Davidson L. A., Jayaraman A.. **Microbiome-derived tryptophan metabolites and their aryl hydrocarbon receptor-dependent agonist and antagonist activities**. *Mol. Pharmacol.* (2014) **85** 777-788. DOI: 10.1124/mol.113.091165 35. Kalueff A. V., Nutt D. J.. **Role of GABA in anxiety and depression**. *Depress. Anxiety* (2007) **24** 495-517. DOI: 10.1002/da.20262 36. Kim-Anh L. C., Rohart F., Gonzalez I., Dejean S.. (2016) 37. Kitamoto S., Nagao-Kitamoto H., Jiao Y., Gillilland M. G., Hayashi A., Imai J.. **The intermucosal connection between the mouth and gut in commensal pathobiont-driven colitis**. *Cells* (2020) **182** 447-462.e14. DOI: 10.1016/j.cell.2020.05.048 38. Ko J. H., Strafella A. P.. **Dopaminergic neurotransmission in the human brain: new lessons from perturbation and imaging**. *Neuroscientist* (2012) **18** 149-168. DOI: 10.1177/1073858411401413 39. Kohn J. N., Kosciolek T., Marotz C., Aleti G., Guay-Ross R. N., Hong S. H.. **Differing salivary microbiome diversity, community and diurnal rhythmicity in association with affective state and peripheral inflammation in adults**. *Brain Behav. Immun.* (2020) **87** 591-602. DOI: 10.1016/j.bbi.2020.02.004 40. Koller M. M., Purushotham K. R., Maeda N., Scarpace P. J., Humphreys-Beher M. G.. **Desipramine induced changes in salivary proteins, cultivable oral microbiota and gingival health in aging female NIA Fischer 344 rats**. *Life Sci.* (2000) **68** 445-455. DOI: 10.1016/s0024-3205(00)00951-6 41. Komiya Y., Shimomura Y., Higurashi T., Sugi Y., Arimoto J., Umezawa S.. **Patients with colorectal cancer have identical strains of**. *Gut* (2019) **68** 1335-1337. DOI: 10.1136/gutjnl-2018-316661 42. Kramer P. F., Twedell E. L., Shin J. H., Zhang R., Khaliq Z. M.. **Axonal mechanisms mediating γ-aminobutyric acid receptor type A (GABA-A) inhibition of striatal dopamine release**. *eLife* (2020) **9** e55729. DOI: 10.7554/eLife.55729 43. Leal S. C., Bittar J., Portugal A., Falcão D. P., Faber J., Zanotta P.. **Medication in elderly people: its influence on salivary pattern, signs and symptoms of dry mouth**. *Gerodontology* (2010) **27** 129-133. DOI: 10.1111/j.1741-2358.2009.00293.x 44. Lee S. M., Dong T. S., Krause-Sorio B., Siddarth P., Milillo M. M.. **The intestinal microbiota as a predictor for antidepressant treatment outcome in geriatric depression: a prospective pilot study**. *Int. Psychogeriatr.* (2022) **34** 33-45. DOI: 10.1017/S1041610221000120 45. Leira Y., Domínguez C., Seoane J., Seoane-Romero J., Pías-Peleteiro J. M., Takkouche B.. **Is periodontal disease associated with Alzheimer’s disease? A systematic review with meta-analysis**. *Neuroepidemiology* (2017) **48** 21-31. DOI: 10.1159/000458411 46. Li Y., Innocentin S., Withers D. R., Roberts N. A., Gallagher A. R., Grigorieva E. F.. **Exogenous stimuli maintain intraepithelial lymphocytes via aryl hydrocarbon receptor activation**. *Cells* (2011) **147** 629-640. DOI: 10.1016/j.cell.2011.09.025 47. Li L., Su Q., Xie B., Duan L., Zhao W., Hu D.. **Gut microbes in correlation with mood: case study in a closed experimental human life support system**. *Neurogastroenterol. Motil.* (2016) **28** 1233-1240. DOI: 10.1111/nmo.12822 48. Liang S., Wu X., Jin F.. **Gut-brain psychology: rethinking psychology from the microbiota–gut–brain axis**. *Front. Integr. Neurosci.* (2018) **12** 33. DOI: 10.3389/fnint.2018.00033 49. Lindseth G., Helland B., Caspers J.. **The effects of dietary tryptophan on affective disorders**. *Arch. Psychiatr. Nurs.* (2015) **29** 102-107. DOI: 10.1016/j.apnu.2014.11.008 50. Liu G., Chong H. X., Chung F. Y. L., Li Y., Liong M. T.. *Int. J. Mol. Sci.* (2020) **21** 1-16. DOI: 10.3390/ijms21134608 51. Liu R. T., Rowan-Nash A. D., Sheehan A. E., Walsh R. F. L., Sanzari C. M., Korry B. J.. **Reductions in anti-inflammatory gut bacteria are associated with depression in a sample of young adults**. *Brain Behav. Immun.* (2020) **88** 308-324. DOI: 10.1016/j.bbi.2020.03.026 52. Liu N., Sun S., Wang P., Sun Y., Hu Q., Wang X.. **The mechanism of secretion and metabolism of gut-derived 5-hydroxytryptamine**. *Int. J. Mol. Sci.* (2021) **22** 7931. DOI: 10.3390/ijms22157931 53. Maes M., Leonard B. E., Myint A. M., Kubera M., Verkerk R.. **The new ‘5-HT’ hypothesis of depression: cell-mediated immune activation induces indoleamine 2,3-dioxygenase, which leads to lower plasma tryptophan and an increased synthesis of detrimental tryptophan catabolites (TRYCATs), both of which contribute to the onset of depression**. *Prog. Neuro Psychopharmacol. Biol. Psychiatry* (2011) **35** 702-721. DOI: 10.1016/j.pnpbp.2010.12.017 54. Mazzoli R., Pessione E.. **The neuro-endocrinological role of microbial glutamate and GABA signaling**. *Front. Microbiol.* (2016) **7** 1934. DOI: 10.3389/fmicb.2016.01934 55. McGuinness A. J., Davis J. A., Dawson S. L., Loughman A., Collier F., O'Hely M.. **A systematic review of gut microbiota composition in observational studies of major depressive disorder, bipolar disorder and schizophrenia**. *Mol. Psychiatry* (2022) **27** 1920-1935. DOI: 10.1038/s41380-022-01456-3 56. Mei F., Xie M., Huang X., Long Y., Lu X., Wang X.. **Porphyromonas gingivalis and its systemic impact: current status**. *Pathogens* (2020) **9** 944. DOI: 10.3390/pathogens9110944 57. Mysak J., Podzimek S., Sommerova P., Lyuya-Mi Y., Bartova J., Janatova T.. *J. Immunol. Res.* (2014) **2014** 1-8. DOI: 10.1155/2014/476068 58. Naseribafrouei A., Hestad K., Avershina E., Sekelja M., Linløkken A., Wilson R.. **Correlation between the human fecal microbiota and depression**. *Neurogastroenterol. Motil. Neurogastroenterol Motil.* (2014) **26** 1155-1162. DOI: 10.1111/nmo.12378 59. Ng H. M., Slakeski N., Butler C. A., Veith P. D., Chen Y.-Y., Liu S. W.. **The role of**. *Front. Cell. Infect. Microbiol.* (2019) **9** 432. DOI: 10.3389/fcimb.2019.00432 60. Nørskov-Lauritsen N.. **Classification, identification, and clinical significance of**. *Clin. Microbiol. Rev.* (2014) **27** 214-240. DOI: 10.1128/CMR.00103-13 61. Ogawa T., Hirose Y., Honda-Ogawa M., Sugimoto M., Sasaki S., Kibi M.. **Composition of salivary microbiota in elderly subjects**. *Sci. Rep.* (2018) **8** 414. DOI: 10.1038/s41598-017-18677-0 62. Okoro C. A., Strine T. W., Eke P. I., Dhingra S. S., Balluz L. S.. **The association between depression and anxiety and use of oral health services and tooth loss: depression and anxiety and oral health**. *Community Dent. Oral Epidemiol.* (2012) **40** 134-144. DOI: 10.1111/j.1600-0528.2011.00637.x 63. Otaru N., Ye K., Mujezinovic D., Berchtold L., Constancias F., Cornejo F. A.. **GABA production by human intestinal**. *Front. Microbiol.* (2021) **12** 656895. DOI: 10.3389/fmicb.2021.656895 64. Park M., Unützer J.. **Geriatric depression in primary care**. *Psychiatr. Clin. North Am.* (2011) **34** 469-487. DOI: 10.1016/j.psc.2011.02.009 65. Parker B. J., Wearsch P. A., Veloo A. C. M., Rodriguez-palacios A., Rodriguez-palacios A.. **The genus**. *Front. Immunol.* (2020) **11** 906. DOI: 10.3389/fimmu.2020.00906 66. Pelosi B., Pratelli M., Migliarini S., Pacini G., Pasqualetti M.. **Generation of a Tph2 conditional knockout mouse line for time- and tissue-specific depletion of brain serotonin**. *PLoS One* (2015) **10** e0136422. DOI: 10.1371/journal.pone.0136422 67. Qiu J., Heller J. J., Guo X., Chen Z. E., Fish K., Fu Y. X.. **The aryl hydrocarbon receptor regulates gut immunity through modulation of innate lymphoid cells**. *Immunity* (2012) **36** 92-104. DOI: 10.1016/j.immuni.2011.11.011 68. Quast C., Pruesse E., Yilmaz P., Gerken J., Schweer T., Yarza P.. **The SILVA ribosomal RNA gene database project: improved data processing and web-based tools**. *Nucleic Acids Res.* (2012) **41** D590-D596. DOI: 10.1093/nar/gks1219 69. Richard D. M., Dawes M. A., Mathias C. W., Acheson A., Hill-Kapturczak N., Dougherty D. M.. **Tryptophan: basic metabolic functions, behavioral research and therapeutic indications**. *Int. J. Tryptophan Res.* (2009) **2** IJTR.S2129. DOI: 10.4137/IJTR.S2129 70. Richards P., Thornberry N. A., Pinto S.. **The gut–brain axis: identifying new therapeutic approaches for type 2 diabetes, obesity, and related disorders**. *Mol. Metabol.* (2021) **46** 101175. DOI: 10.1016/j.molmet.2021.101175 71. Rong H., Xie X. H., Zhao J., Lai W. T., Wang M. B., Xu D.. **Similarly, in depression, nuances of gut microbiota: evidences from a shotgun metagenomics sequencing study on major depressive disorder versus bipolar disorder with current major depressive episode patients**. *J. Psychiatr. Res.* (2019) **113** 90-99. DOI: 10.1016/j.jpsychires.2019.03.017 72. Rosier B. T., Marsh P. D., Mira A.. **Resilience of the oral microbiota in health: mechanisms that prevent dysbiosis**. *J. Dent. Res.* (2018) **97** 371-380. DOI: 10.1177/0022034517742139 73. Ruiz-Ruiz S., Sanchez-Carrillo S., Ciordia S., Mena M. C., Méndez-García C., Rojo D.. **Functional microbiome deficits associated with ageing: chronological age threshold**. *Aging Cell* (2020) **19** e13063-11. DOI: 10.1111/acel.13063 74. Schiepers O. J. G., Wichers M. C., Maes M.. **Cytokines and major depression**. *Prog. Neuro Psychopharmacol. Biol. Psychiatry* (2005) **29** 201-217. DOI: 10.1016/j.pnpbp.2004.11.003 75. Shishkina G. T., Kalinina T. S., Dygalo N. N.. **Up-regulation of tryptophan hydroxylase-2 mRNA in the rat brain by chronic fluoxetine treatment correlates with its antidepressant effect**. *Neuroscience* (2007) **150** 404-412. DOI: 10.1016/j.neuroscience.2007.09.017 76. Sigel E., Ernst M.. **The benzodiazepine binding sites of GABAA receptors**. *Trends Pharmacol. Sci.* (2018) **39** 659-671. DOI: 10.1016/j.tips.2018.03.006 77. Simpson C. A., Adler C., du Plessis M. R., Landau E. R., Dashper S. G., Reynolds E. C.. **Oral microbiome composition, but not diversity, is associated with adolescent anxiety and depression symptoms**. *Physiol. Behav.* (2020) **226** 113126. DOI: 10.1016/j.physbeh.2020.113126 78. Singh H., Torralba M. G., Moncera K. J., DiLello L., Petrini J., Nelson K. E.. **Gastro-intestinal and oral microbiome signatures associated with healthy aging**. *GeroScience* (2019) **41** 907-921. DOI: 10.1007/s11357-019-00098-8 79. Sonowal R., Swimm A., Sahoo A., Luo L., Matsunaga Y., Wu Z.. **Indoles from commensal bacteria extend healthspan**. *Proc. Natl. Acad. Sci. U. S. A.* (2017) **114** E7506-E7515. DOI: 10.1073/pnas.1706464114 80. Strasser B., Sperner-Unterweger B., Fuchs D., Gostner J. M.. **Mechanisms of inflammation-associated depression: immune influences on tryptophan and phenylalanine metabolisms**. *Curr. Top. Behav. Neurosci.* (2017) **31** 95-115. DOI: 10.1007/7854_2016_23 81. Suganya K., Koo B.-S.. **Gut–brain axis: role of gut microbiota on neurological disorders and how probiotics/prebiotics beneficially modulate microbial and immune pathways to improve brain functions**. *Int. J. Mol. Sci.* (2020) **21** 7551. DOI: 10.3390/ijms21207551 82. Sun X., Li M., Xia L., Fang Z., Yu S., Gao J.. **Alteration of salivary microbiome in periodontitis with or without type-2 diabetes mellitus and metformin treatment**. *Sci. Rep.* (2020) **10** 15363. DOI: 10.1038/s41598-020-72035-1 83. Valles-Colomer M., Falony G., Darzi Y., Tigchelaar E. F., Wang J., Tito R. Y.. **The neuroactive potential of the human gut microbiota in quality of life and depression**. *Nat. Microbiol.* (2019) **4** 623-632. DOI: 10.1038/s41564-018-0337-x 84. Venkatapuram S., Ehni H.-J., Saxena A.. **Equity and healthy ageing**. *Bull. World Health Organ.* (2017) **95** 791-792. DOI: 10.2471/BLT.16.187609 85. Vieira-Silva S., Falony G., Darzi Y., Lima-Mendez G., Garcia Yunta R., Okuda S.. **Species–function relationships shape ecological properties of the human gut microbiome**. *Nat. Microbiol.* (2016) **1** 16088. DOI: 10.1038/nmicrobiol.2016.88 86. Wang H., Foong J. P. P., Harris N. L., Bornstein J. C.. **Enteric neuroimmune interactions coordinate intestinal responses in health and disease**. *Mucosal Immunol.* (2021) **15** 27-39. DOI: 10.1038/s41385-021-00443-1 87. Wang Z., Liu S., Xu X., Xiao Y., Yang M., Zhao X.. **Gut microbiota associated with effectiveness and responsiveness to mindfulness-based cognitive therapy in improving trait anxiety**. *Front. Cell. Infect. Microbiol.* (2022) **12** 719829. DOI: 10.3389/fcimb.2022.719829 88. Wickham H.. *ggplot2: Elegant Graphics for Data Analysis* (2016) 89. Wickham H., François R., Henry L., Müller K.. (2022) 90. Wu H. Q., Okuyama M., Kajii Y., Pocivavsek A., Bruno J. P., Schwarcz R.. **Targeting kynurenine aminotransferase II in psychiatric diseases: promising effects of an orally active enzyme inhibitor**. *Schizophr. Bull.* (2014) **40** S152-S158. DOI: 10.1093/schbul/sbt157 91. Yang C., Fang X., Zhan G., Huang N., Li S., Bi J.. **Key role of gut microbiota in anhedonia-like phenotype in rodents with neuropathic pain**. *Transl. Psychiatry* (2019) **9** 57. DOI: 10.1038/s41398-019-0379-8 92. Zhang Y., Huang J., Xiong Y., Zhang X., Lin Y., Liu Z.. **Jasmine tea attenuates chronic unpredictable mild stress-induced depressive-like behavior in rats via the gut-brain axis**. *Nutrients* (2021) **14** 99. DOI: 10.3390/nu14010099 93. Zhang M., Li A., Yang Q., Li J., Wang L., Liu X.. **Beneficial effect of alkaloids from**. *Front. Cell. Infect. Microbiol.* (2021) **11** 1-14. DOI: 10.3389/fcimb.2021.665159
--- title: 'The associations between metabolic profiles and sexual and physical abuse in depressed adolescent psychiatric outpatients: an exploratory pilot study' authors: - Karoliina Kurkinen - Olli Kärkkäinen - Soili M. Lehto - Ilona Luoma - Siiri-Liisi Kraav - Petri Kivimäki - Anni I. Nieminen - Katriina Sarnola - Sebastian Therman - Tommi Tolmunen journal: European Journal of Psychotraumatology year: 2023 pmcid: PMC10062226 doi: 10.1080/20008066.2023.2191396 license: CC BY 4.0 --- # The associations between metabolic profiles and sexual and physical abuse in depressed adolescent psychiatric outpatients: an exploratory pilot study ## ABSTRACT Background: Sexual and physical abuse have been associated with long-term systemic alterations such as low-grade inflammation and changes in brain morphology that may be reflected in the metabolome. However, data on the metabolic consequences of sexual and physical abuse remain scarce. Objective: This pilot study sought to investigate changes in the metabolite profile related to sexual and physical abuse in depressed adolescent psychiatric outpatients. Method: The study included 76 patients aged 14–18 years, whose serum samples were analysed with a targeted metabolite profiling methodology. We estimated the associations between metabolite concentrations and the Trauma and Distress Scale (TADS) Sexual and Physical Abuse factor scores using three linear regression models (one unadjusted and two adjusted) per metabolite and trauma type pair. Additional variables in the two adjusted models were 1) the lifestyle indicators body mass index, tobacco use, and alcohol use, and 2) depression scores and the chronicity of depression. Results: TADS Sexual Abuse scores associated positively with homogentisic acid, as well as cystathionine, and negatively with choline in linear regression analysis, whereas TADS Physical Abuse scores associated negatively with AMP, choline, γ-glutamyl cysteine and succinate, and positively with D-glucuronic acid. Conclusions: This pilot study did not include a healthy control group for comparison and the cohort was relatively small. Nevertheless, we observed alterations in metabolites related to one-carbon metabolism, mitochondrial dysfunction, oxidative stress, and inflammation in depressed patients with a history of sexual or physical abuse. ## HIGHLIGHTS Metabolomic profiles associate with sexual or physical abuse. Metabolites relate to mitochondria, one-carbon, oxidative stress, and inflammation. Metabolomics a possible tool for precision psychiatry in the future. ## Introduction Sexual and physical abuse are considered traumatic life events that may lead or predispose to various psychiatric disorders, such as major depressive disorder (MDD), or post-traumatic stress disorder (PTSD; Adams et al., 2018). However, it is unclear how exposure to traumatic life events leads to the development of these disorders. The effects of trauma may be mediated or moderated by differences or changes in brain chemistry. For example, factors such as elevated low-grade inflammation (Michopoulos et al., 2017), or lowered levels of γ-aminobutyric acid (GABA) in plasma before trauma exposure have been suggested to increase the risk of or susceptibility to developing PTSD (Vaiva et al., 2006). Childhood sexual and physical abuse have been associated with a higher prevalence of MDD (Levitan et al., 1998; Mandelli et al., 2015; Rohde et al., 2008). The lifelong consequences of childhood sexual and physical abuse are often considered severe (Adams et al., 2018; Guina et al., 2018), and they are intercorrelated in their severity, onset, and duration (Adams et al., 2018). A history of sexual abuse is often associated with an even longer duration of symptoms, such as avoidance and intrusive memories of trauma, than a history of physical abuse (Müller et al., 2018). Beyond symptomatology, traumatic life events have been found to alter brain functions and metabolism (Ramage et al., 2016). For example, heightened responsivity of the amygdala and impaired functioning of the hippocampus have been observed in patients with PTSD (Shin et al., 2006). Even in clinically healthy subjects, experiences of childhood sexual abuse have been associated with neurocognitive abnormalities, such as poorer memory (Navalta et al., 2006). Furthermore, a large-scale cohort study utilising genomic and metabolomic data observed that the levels of citrate and glycoprotein acetyls affected the emotional and behavioural response to traumatic stress (Carvalho et al., 2020). A similar effect was observed for very-low-density lipoproteins (VLDL), both the level of large VLDL and total cholesterol in medium particles of VLDL (Carvalho et al., 2020). Indeed, PTSD has been suggested to associate with cardiometabolic dysfunction, changes in body mass index (BMI), and levels of creatinine, insulin, and glucose (Aliev et al., 2020). Alterations in one-carbon metabolism have been suggested in psychological trauma (De Vries et al., 2015), and elevated levels of homocysteine have been recorded in PTSD patients when compared to healthy controls (Levine et al., 2008). In addition, mitochondrial dysfunction has been observed both after traumatic stress and in PTSD (Carvalho et al., 2020; Mellon et al., 2018). Sexual abuse early in life has been specifically associated with a decrease in the levels of antioxidants (Moraes et al., 2018), and increased oxidative stress has been associated with PTSD and physical neglect (Erjavec et al., 2018; Moraes et al., 2018). Lastly, disruptions in hypothalamic–pituitary–adrenal (HPA) axis activity or inflammatory cytokines have been found in PTSD (Kim et al., 2020; Michopoulos et al., 2017). Trauma, especially when experienced in childhood, is thus associated with an extensive variety of changes in metabolism and brain function. Although sexual and physical abuse are often associated with psychiatric problems later in life, it is unclear to what degree there are observable biological changes due to these adverse experiences, or what metabolic alterations specific types of traumatic life events are associated with. However, some preliminary findings have been reported. Notably, disrupted stress regulation systems have been associated with childhood sexual abuse (Bellis et al., 2011). For example, a dysregulated HPA axis has been observed after both sexual and physical abuse (De Bellis & Zisk, 2014). To expand on these results, the present study investigated the associations between sexual and physical abuse traumatisation and a large variety of potential metabolic changes. In order to include a higher percentage of individuals with a history of trauma and thus increase the reliability of our analyses, we focused on a depressed population, in which the prevalence of traumatic events is enriched compared with the general population (Widom et al., 2007). Therefore, the aim of this pilot study was to investigate serum metabolite concentrations in depressed adolescents and young adults to improve our understanding of the associations between altered metabolic processes and sexual and physical abuse. ## Study population The current study formed part of the Systemic Metabolic Alterations Related to Different Psychiatric Disease Categories in Adolescent Outpatients (SMART) project, which recruited patients aged 14–20 years referred to the Adolescent Psychiatry Outpatient Clinic at Kuopio University Hospital (KUH) in the years 2017–2019. During this period, 445 were recruited and 192 of them gave blood samples. Of this subsample, 76 were diagnosed with MDD ($$n = 33$$; DSM-IV 296.20–296.36) or dysthymia ($$n = 12$$; DSM-IV 300.4), or both ($$n = 31$$), using the Structured Clinical Interview for DSM-IV (SCID; First et al., 2002), and were further dichotomised into MDD or chronic depression (dysthymia or double depression). All participants gave written informed consent and completed the research protocol. The SMART project complies with the Declaration of Helsinki (World Medical Association, 2013) and was approved by the Research Ethics Committee of the KUH in 2017. ## Questionnaires and clinical assessments Depressive symptoms were assessed with the Beck Depression Inventory (BDI; Beck et al., 1979). The BDI measures physical symptoms, behaviour, cognition, and feelings with 21 items scored 0–3 for a sum score range from 0 to 63. The first three questions of the Alcohol Use Disorders Identification Test (AUDIT; Saunders et al., 1993), focusing on consumption, were used to estimate the patients’ drinking habits on a scale from 0 to 12. The Alcohol, Smoking and Substance Involvement Screening Test (ASSIST 3.1; Humeniuk et al., 2010) was used to evaluate the patients’ smoking habits on a scale ranging from 0 to 31. A modified set of 16 questions from the Index of Diet Quality (IDQ; Leppälä et al., 2010) was used to estimate the health-promoting features and quality of the patients’ diet. In the short version of the IDQ, the response options were the dichotomous ‘never or nearly never’ or ‘yes, always or nearly always’ instead of a rating scale. The current medications of the patients were also inquired about and placed into the following six categories: 5. Antipsychotic medication ($$n = 13$$), 4. Selective serotonin reuptake inhibitors (SSRI; $$n = 15$$), 3. Mirtazapine ($$n = 3$$), 2. Agomelatine, tricyclic antidepressants, or vortioxetine ($$n = 4$$), 1. Other medications (melatonin, mini-pill, or oxazepam; $$n = 10$$), or 0. No medication ($$n = 31$$). The patients were grouped based on their medication to exclude bias caused by the use of different drug classes. The use of SSRI medication was evaluated also separately: 25 patients out of 76 were taking SSRI medication. The Sexual Abuse and Physical Abuse subscales of the Trauma and Distress Scale (TADS; Patterson et al., 2002; Salokangas et al., 2016) were used to assess lifetime sexual and physical abuse-related trauma, comprising recurring and nonrecurring traumatic life events as continuous scales. The effects of chronic depression and MDD were assessed with a dichotomous variable, as the patients were suffering from either episodic MDD or chronic depression, with the most recent SCID diagnosis extracted from the medical records. ## Blood sampling Blood was sampled in the morning after 12 h of fasting. Samples rested for 30 min and were then centrifuged at 2500 × g for 10 min. Centrifuged samples were prepared, and the serum was stored at −70 °C. After collecting all the samples, analyses were performed in one batch. Sample collection and storage was conducted by the laboratory unit ISLAB at KUH. ## Targeted metabolomics analysis Metabolomics analyses were conducted at the Institute for Molecular Medicine Finland. High-performance liquid chromatography coupled to mass spectrometry (HPLC-MS) was implemented for targeted metabolomics analysis. Altogether, 100 µL of serum was mixed with 10 µL of isotopically labelled internal standard, and the resulting mixtures were allowed to equilibrate. Supernatant was formed by adding 400 µL of extraction solvent (acetonitrile, $1\%$ formic acid) and collected. The supernatant of the samples was transferred to a 96-well microplate and filtered on a Hamilton On-Deck Vacuum Station (300–400 mbar, 2.5 min). Five µL of each sample was then injected into a HPLC-MS system (Xevo® TQ-S triple quadrupole mass spectrometer, Waters Corporation, Milford, MA, USA). HPLC-MS was operated with positive and negative polarisation switching every 20 milliseconds for isolation and measurement of the metabolites. The measurement was conducted in Multiple Reaction Monitoring (MRM) accession mode. MassLynx 4.1 software was employed for data collection and for handling and management of the instrument, and TargetLynx 4.1 for data processing. Aspartate, cGMP, folic acid, homoserine, 5-hydroxyindole-3-actic acid, 3-OH-DL-kynurenine, orotic acid, pyridoxine, and sorbitol were not used in the final statistical analyses due to failed linearity or poor quality of the chromatograph, and UDP-glucose was not included due to missing $99\%$ of values, resulting in 92 metabolite concentrations in total. ## Statistical analysis The SMART data set ($$n = 445$$) was used to estimate a confirmatory item factor model of the five a priori TADS factors with Mplus 8.3 software (Muthén & Muthén, 2017) using the WLSMV estimator, theta parameterisation, and default settings. The pairwise coverage between the 25 items was $97.7\%$ at its lowest and $98.6\%$ on average, making the impact of missingness minimal under the missing at random assumption. The model fit was acceptable: CFI.974, RMSEA.052, and SRMR.057. Standardised factor loadings and response thresholds are presented in Supplementary Table 3. Factor scores for the Physical Abuse and Sexual Abuse scales were calculated with the maximum a posteriori method for use in further analyses. Linear regression analyses were run to analyse the association between TADS Sexual/Physical Abuse scores ($$n = 76$$) and the individual measured metabolites, separately for each trauma type and metabolite pair. In addition to these unadjusted models, the following background variables with possible effects on metabolism were used as covariates: gender, age, BMI, eating habits (IDQ), ongoing medications, depression symptom levels (BDI), depression chronicity, smoking (ASSIST tobacco), and alcohol drinking habits (AUDIT-C). Covariates for the analyses were chosen due to being associated with Sexual or Physical Abuse factor scores, respectively, in linear regressions at the.01 significance level, and to avoid overfitting, these covariates were divided into two separate models for each metabolite. Model 1 was adjusted for the effect of the participants’ lifestyle on the metabolome, with BMI, ASSIST Tobacco, and AUDIT-C scores being taken into consideration. Model 2 was adjusted for depressive symptomology, considering BDI scores and the chronicity of depression. In addition to predicting trauma levels separately for each metabolite concentration, two multivariate regression analyses were conducted, one for each trauma type. For these multivariate analyses, metabolite concentration data were consolidated with principal component analysis (PCA) using SIMCA (version 17; Sartorius Stedim Data Analytics AB). To take multiple testing into consideration, we adjusted the level of α by dividing it with the principal component count explaining $95\%$ of the variation in the metabolomics data in the PCA. This method was used due to the correlative nature of metabolites in the targeted metabolite profiling analyses (Würtz et al., 2016). The observed differences in p-values between.05 and the adjusted α were regarded as trends. ## Associations between trauma variables and demographic and other clinical variables The demographic and clinical characteristics of the study cohort, such as age, gender, and symptom levels, are presented in Table 1, along with their unstandardised coefficients in the unadjusted regression model in which they were included. TADS Sexual Abuse factor scores were positively associated with depression levels and AUDIT-C scores, whereas Physical Abuse factor scores were positively associated with ASSIST Tobacco scores (Table 1). Both sexual and physical abuse were negatively correlated with the chronicity of depression, indicating a higher incidence of episodic depression in traumatised patients. Table 1.Characteristics of the participants with covariates in the multivariate models, and linear regression coefficients in models predicting Trauma and Distress Scale (TADS) Sexual Abuse and Physical Abuse factor scores. Demographic or clinical factorTADS Sexual AbuseTADS Physical AbuseBpBpMale n (%)12 [16].017.887-.155.182Age, mean (SD)16.43 (1.57).194.092.071.543BMI, mean (SD)23.33 (5.67).135.245.195.091IDQ scores, mean (SD)25.61 (5.69)-.199.084-.204.077Smoking, mean (SD)6.49 (8.99).182.116.258.024Alcohol consumption, mean (SD)2.70 (2.94).24.037.098.398BDI scores, mean (SD)30.18 (7.57).29.011.129.267Chronic depression n (%)43 [57]-.232.044-.329.004Medication n (%)45 [59]-.048.231-.036.346SSRI n (%)25 [33].034.845-.034.838Legend: Smoking (ASSIST Tobacco scale, Alcohol, Smoking and Substance Involvement Screening Test); Alcohol consumption (AUDIT-C, Alcohol Use Disorder Identification Test); BDI, Beck Depression Inventory; BMI, body mass index; IDQ, Index of Diet Quality subset; medication, including agomelatine, mirtazapine, SSRI, antipsychotic medication, or other medications; B, standardised regression coefficient; p, p-value from linear regression (significance); SD, standard deviation; SSRI, medication with only selective serotonin reuptake inhibitors. ## Linear regressions predicting TADS factors with metabolite concentrations Regression analyses demonstrated that some metabolites were associated with TADS Sexual and Physical Abuse. The linear regression between Sexual Abuse scores and metabolite concentrations showed a negative trend for choline ($$p \leq .004$$), and positive trends for cystathionine ($$p \leq .008$$) and homogentisic acid ($$p \leq .022$$). After implementing Model 1, adjusted for the patient’s lifestyle, choline, cystathionine, and homogentisic acid had p-values of.032,.018, and.04, respectively. After implementing Model 2, with adjustments for BDI scores and chronicity, cystathionine ($$p \leq .012$$) and homogentisic acid ($$p \leq .050$$) displayed a trend towards statistical significance, whereas choline ($$p \leq .268$$) had a stronger association with depressive symptoms and did not remain noteworthy (Supplementary Table 1). The unadjusted linear regression between TADS Physical Abuse scores and metabolites revealed negative trends for choline ($$p \leq .016$$), AMP ($$p \leq .031$$), and succinate ($$p \leq .042$$), and a positive trend for D-glucuronic acid ($$p \leq .043$$). Out of these, succinate and D-glucuronic acid did not remain significant in Models 1 and 2. Choline ($$p \leq .009$$) remained significant in Model 1, adjusted for lifestyle factors, but not in linear regression Model 2, adjusted for BDI and depression chronicity. However, AMP remained significant in Model 2 ($$p \leq .009$$) but not in Model 1. Gamma glutamyl cysteine did not show an association in the linear regression alone, but after implementing Models 1 ($$p \leq .036$$) and 2 ($$p \leq .035$$), negative trends were seen between TADS Physical Abuse scores and γ-glutamyl cysteine (Supplementary Table 2). ## Multiple testing with PCA The results from PCA demonstrated that some metabolites were associated with TADS scores for sexual abuse and physical abuse. However, it should be noted that the principal components having the strongest association with abuse history (components 3 and 5) only explained $6\%$ and $5\%$ of the variance in the data, respectively. Furthermore, 42 components were required to describe $95\%$ of the variation in the metabolomics data, so the α-level adjusted for multiple testing was set to.0012. No associations of metabolite concentrations and the abuse indicators were below this level. ## Main findings This pilot study aimed to discover metabolic alterations in sexually or physically abused depressed adolescent psychiatric outpatients. The results of the present analysis point to altered processes in one-carbon metabolism, mitochondrial function, oxidative stress, and inflammation. These processes are also interconnected (Figure 1). Both sexual and physical abuse had a negative correlation with chronicity of depression, suggesting that episodic MDD is more common in these abused patients than dysthymia or double depression is. Figure 1.Schematic illustration of metabolites correlating positively (+) or negatively [-] with the Trauma and Distress Scale (TADS) Sexual and Physical Abuse scores, and the related systems or mechanisms in which these metabolites are involved. ## One-carbon metabolism Choline, γ-glutamyl cysteine and cystathionine take part in one-carbon metabolism, which has been found disrupted in PTSD (De Vries et al., 2015) and in other psychiatric disorders such as MDD (Kurkinen et al., 2021). One-carbon metabolism is an important regulatory factor in epigenetics, involved in the synthesis of phospholipids, delivering the functional properties of various proteins, and forming part of RNA metabolism (De Vries et al., 2015). Furthermore, neurotransmitters serotonin and noradrenaline require one-carbon metabolism in their synthesis (De Vries et al., 2015). However, choline did not remain significant when background factors related to depression type and intensity were controlled for. For this reason, the negative association of choline is possibly related to MDD in these patients (Table 1; Kurkinen et al., 2021). Furthermore, negative trend of γ-glutamyl cysteine and positive trend of cystathionine suggests that instead of the methionine cycle, the transsulfuration pathway of one-carbon metabolism is more relevant to the pathophysiology of trauma itself (Figure 2). Figure 2.Illustration of the one-carbon metabolism methionine cycle, as well as the transsulfuration and phosphatidylethanolamine N-methyltransferase (PEMT) pathways. Round symbols represent the TADS Sexual Abuse scale, hexagons the TADS Physical Abuse scale, and + and – symbols represent the direction of correlation between the scale and the metabolite. aKB, α-ketobutyrate; ATP, adenosine triphosphate; BHMT, betaine-homocysteine S-methyltransferase; CBS, cystathionine β-synthase; CSE, cystathionine gamma-lyase; DMG, dimethylglycine; ETA, ethanolamine; GCL, glutamate-cysteine ligase; GS, glutathione synthetase; PC, phosphatidylcholine; PE, phosphatidylethanolamine; PEMT, phosphatidylethanolamine N-methyltransferase; Pi, phosphate; PPi, pyrophosphate; PS, phosphatidylserine; SAH, S-adenosylhomocysteine; SAM, S-adenosylmethionine. ## Mitochondrial dysfunction Mitochondrial dysfunction could explain the negative correlation of AMP and succinate with TADS Physical Abuse scores in the present study. Animal studies have linked traumatic symptoms and intergenerational trauma to mitochondrial dysfunction (Alhassen et al., 2021; Preston et al., 2020). Furthermore, low succinate levels have been shown in MDD when compared to chronically depressed patients (Kurkinen et al., 2021), and mitochondrial dysfunction has been proposed as one possible pathological mechanism in PTSD (Bersani et al., 2020; Daniels et al., 2020). In the present study, succinate concentrations appeared to be related more to MDD than a history of physical abuse, since controlling for depression length and severity reduced the strength of the association (Supplementary Table 2). In addition, AMP lost its significance when lifestyle was considered. ## Oxidative stress Both homogentisic acid and γ-glutamyl cysteine, associating with physical and sexual abuse in this pilot study, have been suggested to have roles in oxidative stress (Ribas et al., 2014; M. L. Schiavone et al., 2020). Oxidative stress is elevated by chronic stress (Miller & Sadeh, 2014; Schiavone et al., 2013), and metabolomic and genetic studies have linked oxidative stress with PTSD (Alzoubi et al., 2019; Miller et al., 2018). It has been suggested that increased levels of γ-glutamyl cysteine might act as a compensatory mechanism against oxidative stress if the glutathione pathway is compromised (Ristoff et al., 2002). The formation of γ-glutamyl cysteine is the rate-limiting step in the glutathione pathway (Lu, 2013). γ-Glutamyl cysteine formation may have been disrupted, for instance, due to reduced glutamate cysteine ligase (GCL) enzymatic activity or accumulated glutamate interrupting cysteine influx or synthesis under oxidative stress (Zhu et al., 2022). Downregulation of the catalytic part of GCL has been associated with reduced glutathione in inflammation (Zhang et al., 2020). In this respect, the negative γ-glutamyl cysteine trend with physical abuse might indicate disruptions in the pathways against cellular oxidative stress. Homogentisic acid may act as an antioxidant or a pro-oxidant, depending on its cellular concentration and the cell type (Jurič et al., 2021; Kang et al., 2005; Rosa et al., 2011). ## Inflammation The trend towards elevated D-glucuronic acid levels in physically abused patients could reflect an increased level of inflammation, since inflammation appears to increase its circulating levels (Ho et al., 2019). D-glucuronic acid has been found to increase the activity of toll-like receptor 4 (Lewis et al., 2013), which is able to initiate an inflammatory cascade and induce systemic inflammation (Buchanan et al., 2010). Increased levels of D-glucuronic acid have been observed in severely physically traumatised patients, and the levels increased over time in patients who developed chronic critical illness (Horn et al., 2021). Changes in D-glucuronic acid might be more strongly associated with lifestyle and depression than trauma per se. However, glutathione produced in transsulfuration is not only part of one-carbon metabolism but is also connected to mitochondrial functions via oxidative stress, as well as inflammation. For example, glutathione has been suggested to modify the metabolic state of inflammatory T-cells (Mak et al., 2017), and similar T-cell redox alterations have been observed in animal studies modelling psychological trauma with Social Defeat Stress (Moshfegh et al., 2019). ## Strengths and limitations Our study provides new insights regarding metabolic events associated with trauma. The participants were on average 16 years old and therefore our findings represent traumatisation at a relatively early age. In turn, the cohort was quite small, reducing statistical power to detect differences in metabolite levels. Some of the identified alterations did not remain statistically significant after adjustment for background factors, and none of the changes remained statistically significant after correction for multiple testing. Differences in the metabolome related to chronic and episodic depression have been recognised in the previous literature, and some of the metabolic alterations observed in this study might therefore also be explained by MDD (Kurkinen et al., 2021). The study also lacked a healthy control group. Nevertheless, these alterations fit well in the framework that previous research has built for trauma and should be investigated further in larger cohorts. Furthermore, it was not considered that one individual might have a history of both sexual and physical abuse. We limited our study to sexual and physical abuse to reduce the number of analyses in this pilot study. Another limitation was the lack of information regarding when these abusive events occurred and their duration, although it is often the case that traumatic life events extend over a longer time period. However, TADS has been suggested to be a reliable tool to measure the level and type of childhood traumatisation, regardless of the limited knowledge of each particular traumatic event (Salokangas et al., 2016). Furthermore, genders were unevenly represented, as only one participant in six was male, which reflects depression, treatment-seeking, and study participation being more common in females. Limited statistical power did not allow us to perform subgroup analysis by gender. However, we selected all the participants with depressive disorder in the order of appearance, and there was therefore no systematic bias in gender distribution in our sample. Rather, the difference reflects the prevalence rates of depressive disorder in different genders. There are also hormonal differences between the genders. In the future, the effect of hormones on the metabolome could be controlled for with the steroidal hormone levels, especially in youths undergoing puberty. The use of peripheral blood samples is limited from the central nervous system point of view, although the events in the CNS are suggested to reflect in the periphery to some extent (Tylee et al., 2013), especially in the case of inflammation (Cervellati et al., 2020). ## Conclusions Our pilot study suggests that metabolites related to one-carbon metabolism, mitochondrial dysfunction, oxidative stress, and inflammation may be altered in sexually and physically abused depressed adolescent patients. When depression is considered, oxidative stress and the transsulfuration pathway of one-carbon metabolism are suggested as the most relevant mechanisms for trauma in this pilot study. Further research is needed to confirm these findings in larger and more diverse cohorts. In addition, follow-up samples and alternative omics techniques could be used to better understand the impacts of traumatic life events, for example, on gene expression as well as on the metabolome. ## Disclosure statement Olli Kärkkäinen is a co-founder of a company providing global metabolomics analysis services, Afekta Technologies Ltd. (not used in this study). ## Data availability statement The data that support the findings of this study are available on request from the corresponding author, KK. The data are not publicly available due to their containing information that could compromise the privacy of research participants. The study plan approved by the ethical committee and the participant consent terms preclude public sharing of these sensitive data, even in anonymized form. ## References 1. Adams J., Mrug S., Knight D. C.. **Characteristics of child physical and sexual abuse as predictors of psychopathology**. (2018) 167-177. DOI: 10.1016/j.chiabu.2018.09.019 2. Alhassen S., Chen S., Alhassen L., Phan A., Khoudari M., De Silva A., Barhoosh H., Wang Z., Parrocha C., Shapiro E., Henrich C., Wang Z., Mutesa L., Baldi P., Abbott G. W., Alachkar A.. **Intergenerational trauma transmission is associated with brain metabotranscriptome remodeling and mitochondrial dysfunction**. (2021) 1-15. DOI: 10.1038/s42003-021-02255-2 3. Aliev G., Beeraka N. M., Nikolenko V. N., Svistunov A. A., Rozhnova T., Kostyuk S., Cherkesov I., Gavryushova L. V., Chekhonatsky A. A., Mikhaleva L. M., Somasundaram S. G., Avila-Rodriguez M. F., Kirkland C. E.. **Neurophysiology and psychopathology underlying PTSD and recent insights into the PTSD therapies—a comprehensive review**. (2020) 2951. DOI: 10.3390/jcm9092951 4. Alzoubi K. H., Al Subeh Z. Y., Khabour O. F.. **Molecular targets for the interactive effect of etazolate during post-traumatic stress disorder: Role of oxidative stress, BDNF and histones**. (2019) 111930. DOI: 10.1016/j.bbr.2019.111930 5. Beck A. T., Rush J., Shaw B. F., Emery G.. (1979) 6. Bellis D., Spratt M. D., Hooper E. G., R S.. **Neurodevelopmental biology associated with childhood sexual abuse**. (2011) 548-587. DOI: 10.1080/10538712.2011.607753 7. Bersani F. S., Mellon S. H., Lindqvist D., Kang J. I., Rampersaud R., Somvanshi P. R., Doyle F. J., Hammamieh R., Jett M., Yehuda R., Marmar C. R., Wolkowitz O. M.. **Novel pharmacological targets for combat PTSD—metabolism, inflammation, the gut microbiome, and mitochondrial dysfunction**. (2020) 311-318. DOI: 10.1093/milmed/usz260 8. Buchanan M. M., Hutchinson M., Watkins L. R., Yin H.. **Toll-like receptor 4 in CNS pathologies**. (2010) no-no. DOI: 10.1111/j.1471-4159.2010.06736.x 9. Carvalho C. M., Wendt F. R., Stein D. J., Stein M. B., Gelernter J., Belangero S. I., Polimanti R.. **Investigating causality between blood metabolites and emotional and behavioral responses to traumatic stress: A Mendelian randomization study**. (2020) 1542-1552. DOI: 10.1007/s12035-019-01823-2 10. Cervellati C., Trentini A., Pecorelli A., Valacchi G., Valacchi G., Valacchi G.. **Inflammation in neurological disorders: The thin boundary between brain and periphery**. (2020) 191-210. DOI: 10.1089/ars.2020.8076 11. Daniels T., Olsen E., Tyrka A. R.. **Stress and psychiatric disorders: The role of mitochondria**. (2020) 165-186. DOI: 10.1146/annurev-clinpsy-082719-104030 12. De Bellis M. D., Zisk A.. **The biological effects of childhood trauma**. (2014) 185-222. DOI: 10.1016/j.chc.2014.01.002 13. De Vries G. J., Lok A., Mocking R., Assies J., Schene A., Olff M.. **Altered one-carbon metabolism in posttraumatic stress disorder**. (2015) 277-285. DOI: 10.1016/j.jad.2015.05.062 14. Erjavec G. N., Konjevod M., Nikolac Perkovic M., Svob Strac D., Tudor L., Barbas C., Grune T., Zarkovic N., Pivac N.. **Short overview on metabolomic approach and redox changes in psychiatric disorders**. (2018) 178-186. DOI: 10.1016/j.redox.2017.09.002 15. First M. B., Gibbon M., Spitzer R. L., Williams J. B. W.. (2002) 16. Guina J., Nahhas R. W., Sutton P., Farnsworth S.. **The influence of trauma type and timing on PTSD symptoms**. (2018) 72-76. DOI: 10.1097/NMD.0000000000000730 17. Ho A., Sinick J., Esko T., Fischer K., Menni C., Zierer J., Matey-Hernandez M., Fortney K., Morgen E. K.. **Circulating glucuronic acid predicts healthspan and longevity in humans and mice**. (2019) 7694-7706. DOI: 10.18632/aging.102281 18. Horn D. L., Bettcher L. F., Navarro S. L., Pascua V., Neto F. C., Cuschieri J., Raftery D., O’Keefe G. E.. **Persistent metabolomic alterations characterize chronic critical illness after severe trauma**. (2021) 35-45. DOI: 10.1097/TA.0000000000002952 19. Humeniuk R., Henry-Edwards S., Ali R., Poznyak V., Monteiro M., Organization W. H.. **The Alcohol Smoking and Substance Involvement Screening Test (ASSIST): Manual for use in primary care**. (2010) 1-74 20. Jurič A., Brčić Karačonji I., Kopjar N.. **Homogentisic acid, a main phenolic constituent of strawberry tree honey, protects human peripheral blood lymphocytes against irinotecan-induced cytogenetic damage**. (2021) 1-10. DOI: 10.1016/j.cbi.2021.109672 21. Kang K. A., Chae S., Lee K. H., Zhang R., Jung M. S., You H. J., Kim J. S., Hyun J. W.. **Antioxidant effect of homogenetisic acid on hydrogen peroxide induced oxidative stress in human lung fibroblast cells**. (2005) 556-563. DOI: 10.1007/BF02932294 22. Kim T. D., Lee S., Yoon S.. **Inflammation in post-traumatic stress disorder (PTSD): A review of potential correlates of PTSD with a neurological perspective**. (2020) 107-123. DOI: 10.3390/antiox9020107 23. Kurkinen K., Kärkkäinen O., Lehto S., Luoma I., Kraav S.-L., Nieminen A., Kivimäki P., Therman S., Tolmunen T.. **One-carbon and energy metabolism in major depression compared to chronic depression in adolescent outpatients: A metabolomic pilot study**. (2021) 100261-9. DOI: 10.1016/j.jadr.2021.100261 24. Leppälä J., Lagström H., Kaljonen A., Laitinen K.. **Construction and evaluation of a self-contained index for assessment of diet quality**. (2010) 794-802. DOI: 10.1177/1403494810382476 25. Levine J., Timinsky I., Vishne T., Dwolatzky T., Roitman S., Kaplan Z., Kotler M., Sela B. A., Spivak B.. **Elevated serum homocysteine levels in male patients with PTSD**. (2008) E154-E157. DOI: 10.1002/da.20400 26. Levitan R. D., Parikh S. V., Lesage A. D., Hegadoren K. M., Adams M., Kennedy S. H., Goering P. N.. **Major depression in individuals with a history of childhood physical or sexual abuse: Relationship to neurovegetative features, mania, and gender**. (1998) 1746-1752. DOI: 10.1176/ajp.155.12.1746 27. Lewis S. S., Hutchinson M. R., Zhang Y., Hund D. K., Maier S. F., Rice K. C., Watkins L. R.. **Glucuronic acid and the ethanol metabolite ethyl-glucuronide cause toll-like receptor 4 activation and enhanced pain**. (2013) 24-32. DOI: 10.1016/j.bbi.2013.01.005 28. Lu S. C.. **Glutathione synthesis**. (2013) 3143-3153. DOI: 10.1016/j.bbagen.2012.09.008 29. Mak T. W., Grusdat M., Duncan G. S., Dostert C., Nonnenmacher Y., Cox M., Binsfeld C., Hao Z., Brüstle A., Itsumi M., Jäger C., Chen Y., Pinkenburg O., Camara B., Ollert M., Bindslev-Jensen C., Vasiliou V., Gorrini C., Lang P. A., Brenner D.. **Glutathione primes T cell metabolism for inflammation**. (2017) 675-689. DOI: 10.1016/j.immuni.2017.03.019 30. Mandelli L., Petrelli C., Serretti A.. **The role of specific early trauma in adult depression: A meta-analysis of published literature. Childhood trauma and adult depression**. (2015) 665-680. DOI: 10.1016/j.eurpsy.2015.04.007 31. Mellon S. H., Gautam A., Hammamieh R., Jett M., Wolkowitz O. M.. **Metabolism, metabolomics, and inflammation in posttraumatic stress disorder**. (2018) 866-875. DOI: 10.1016/j.biopsych.2018.02.007 32. Michopoulos V., Powers A., Gillespie C. F., Ressler K. J., Jovanovic T.. **Inflammation in fear- and anxiety-based disorders: PTSD, GAD, and beyond**. (2017) 254-270. DOI: 10.1038/npp.2016.146 33. Miller M. W., Lin A. P., Wolf E. J., Miller D. R.. **Oxidative stress, inflammation, and neuroprogression in chronic PTSD**. (2018) 57-69. DOI: 10.1097/HRP.0000000000000167 34. Miller M. W., Sadeh N.. **Traumatic stress, oxidative stress and post-traumatic stress disorder: Neurodegeneration and the accelerated-aging hypothesis**. (2014) 1156-1162. DOI: 10.1038/mp.2014.111 35. Moraes J. B., Maes M., Roomruangwong C., Bonifacio K. L., Barbosa D. S., Vargas H. O., Anderson G., Kubera M., Carvalho A. F., Nunes S. O. V.. **In major affective disorders, early life trauma predict increased nitro-oxidative stress, lipid peroxidation and protein oxidation and recurrence of major affective disorders, suicidal behaviors and a lowered quality of life**. (2018) 1081-1096. DOI: 10.1007/s11011-018-0209-3 36. Moshfegh C. M., Elkhatib S. K., Collins C. W., Kohl A. J., Case A. J.. **Autonomic and redox imbalance correlates with T-lymphocyte inflammation in a model of chronic social defeat stress**. (2019) 1-14. DOI: 10.3389/fnbeh.2019.00103 37. Muthén L., Muthén B.. (2017) 38. Müller M., Ajdacic-Gross V., Rodgers S., Kleim B., Seifritz E., Vetter S., Egger S. T., Rössler W., Castelao E., Preisig M., Vandeleur C.. **Predictors of remission from PTSD symptoms after sexual and non-sexual trauma in the community: A mediated survival-analytic approach**. (2018) 262-271. DOI: 10.1016/j.psychres.2017.11.068 39. Navalta C. P., Polcari A., Webster D. M., Boghossian A., Teicher M. H.. **Effects of childhood sexual abuse on neuropsychological and cognitive function in college women**. (2006) 45-53. DOI: 10.1176/jnp.18.1.45 40. Patterson P., Skeate A., Schultze-Lutter F., Graf von Reventlow H., Wieneke A., Ruhrmann S., Salokangas R.. (2002) 41. Preston G., Emmerzaal T., Kirdar F., Schrader L., Henckens M., Morava E., Kozicz T.. **Cerebellar mitochondrial dysfunction and concomitant multi-system fatty acid oxidation defects are sufficient to discriminate PTSD-like and resilient male mice**. (2020) 100104. DOI: 10.1016/j.bbih.2020.100104 42. Ramage A. E., Litz B. T., Resick P. A., Woolsey M. D., Dondanville K. A., Young-McCaughan S., Borah A. M., Borah E. V., Peterson A. L., Fox P. T.. **Regional cerebral glucose metabolism differentiates danger- and non-danger-based traumas in post-traumatic stress disorder**. (2016) 234-242. DOI: 10.1093/scan/nsv102 43. Ribas V., García-Ruiz C., Fernández-Checa J. C.. **Glutathione and mitochondria**. (2014) 1-19. DOI: 10.3389/fphar.2014.00151 44. Ristoff E., Hebert R., Njålsson R., Norgren S., Rooyackers O., Larsson A.. **Glutathione synthetase deficiency: Is γ-glutamylcysteine accumulation a way to cope with oxidative stress in cells with insufficient levels of glutathione?**. (2002) 577-584. DOI: 10.1023/A:1022095324407 45. Rohde P., Ichikawa L., Simon G. E., Ludman E. J., Linde J. A., Jeffery R. W., Operskalski B. H.. **Associations of child sexual and physical abuse with obesity and depression in middle-aged women**. (2008) 878-887. DOI: 10.1016/j.chiabu.2007.11.004 46. Rosa A., Tuberoso C. I. G., Atzeri A., Melis M. P., Bifulco E., Dess M. A.. **Antioxidant profile of strawberry tree honey and its marker homogentisic acid in several models of oxidative stress**. (2011) 1045-1053. DOI: 10.1016/j.foodchem.2011.05.072 47. Salokangas R. K. R., Schultze-Lutter F., Patterson P., Graf von Reventlow H., Heinimaa M., From T., Luutonen S., Hankala J., Kotimäki M., Tuominen L.. **Psychometric properties of the trauma and distress scale, TADS, in an adult community sample in Finland**. (2016) 1-13. DOI: 10.3402/ejpt.v7.30062 48. Saunders J. B., Aasland O. G., Babor T. F., De Le Fuente J. R., Grant M.. **Development of the Alcohol Use Disorders Identification Test (AUDIT): WHO Collaborative Project on Early Detection of Persons with Harmful Alcohol Consumption-II**. (1993) 791-804. DOI: 10.1111/j.1360-0443.1993.tb02093.x 49. Schiavone M. L., Millucci L., Bernardini G., Giustarini D., Rossi R., Marzocchi B., Santucci A.. **Homogentisic acid affects human osteoblastic functionality by oxidative stress and alteration of the Wnt/β-catenin signaling pathway**. (2020) 6808-6816. DOI: 10.1002/jcp.29575 50. Schiavone S., Jaquet V., Trabace L., Krause K. H.. **Severe life stress and oxidative stress in the brain: From animal models to human pathology**. (2013) 1475-1490. DOI: 10.1089/ars.2012.4720 51. Shin L. M., Rauch S. L., Pitman R. K.. **Amygdala, medial prefrontal cortex, and hippocampal function in PTSD**. (2006) 67-79. DOI: 10.1196/annals.1364.007 52. Tylee D. S., Kawaguchi D. M., Glatt S. J.. **On the outside, looking in: A review and evaluation of the comparability of blood and brain “-omes”**. (2013) 595-603. DOI: 10.1002/ajmg.b.32150 53. Vaiva G., Boss V., Ducrocq F., Fontaine M., Devos P., Ph D., Brunet A., Laffargue P., Goudemand M., Thomas P.. **Relationship between posttrauma GABA plasma levels and PTSD at 1-year follow-up**. (2006) 1446-1448. DOI: 10.1176/ajp.2006.163.8.1446 54. Widom C. S., DuMont K., Czaja S. J.. **A prospective investigation of major depressive disorder and comorbidity in abused and neglected children grown up**. (2007) 49-56. DOI: 10.1001/archpsyc.64.1.49 55. **World medical association declaration of Helsinki: Ethical principles for medical research involving human subjects**. (2013) 2191-2194. DOI: 10.1001/jama.2013.281053 56. Würtz P., Cook S., Wang Q., Tiainen M., Tynkkynen T., Kangas A. J., Soininen P., Laitinen J., Viikari J., Kahönen M., Lehtimaki T., Perola M., Blankenberg S., Zeller T., Mannistö S., Salomaa V., Jarvelin M. R., Raitakari O. T., Ala-Korpela M., Leon D. A.. **Metabolic profiling of alcohol consumption in 9778 young adults**. (2016) 1493-1506. DOI: 10.1093/ije/dyw175 57. Zhang H., Zhang S., Lyn N., Florentino A., Li A., Davies K., Forman H.. **Down regulation of glutathione and glutamate cysteine ligase in the inflammatory response of macrophages**. (2020) 53-59. DOI: 10.1016/j.freeradbiomed.2020.06.017 58. Zhu J., Zhang Y., Ren R., Sanford L. D., Tang X.. **Blood transcriptome analysis: Ferroptosis and potential inflammatory pathways in post-traumatic stress disorder**. (2022) 1-19. DOI: 10.3389/fpsyt.2022.841999
--- title: Long-COVID fatigue is not predicted by pre-pandemic plasma IL-6 levels in mild COVID-19 authors: - Maxim B. Freidin - Nathan Cheetham - Emma L. Duncan - Claire J. Steves - Katherine J. Doores - Michael H. Malim - Niccolo Rossi - Janet M. Lord - Paul W. Franks - Alessandra Borsini - Isabelle Granville Smith - Mario Falchi - Carmine Pariante - Frances M. K. Williams journal: Inflammation Research year: 2023 pmcid: PMC10062244 doi: 10.1007/s00011-023-01722-2 license: CC BY 4.0 --- # Long-COVID fatigue is not predicted by pre-pandemic plasma IL-6 levels in mild COVID-19 ## Abstract ### Objective and design Fatigue is a prominent symptom in the general population and may follow viral infection, including SARS-CoV2 infection which causes COVID-19. Chronic fatigue lasting more than three months is the major symptom of the post-COVID syndrome (known colloquially as long-COVID). The mechanisms underlying long-COVID fatigue are unknown. We hypothesized that the development of long-COVID chronic fatigue is driven by the pro-inflammatory immune status of an individual prior to COVID-19. ### Subjects and methods We analyzed pre-pandemic plasma levels of IL-6, which plays a key role in persistent fatigue, in $$n = 1274$$ community dwelling adults from TwinsUK. Subsequent COVID-19-positive and -negative participants were categorized based on SARS-CoV-2 antigen and antibody testing. Chronic fatigue was assessed using the Chalder Fatigue Scale. ### Results COVID-19-positive participants exhibited mild disease. Chronic fatigue was a prevalent symptom among this population and significantly higher in positive vs. negative participants ($17\%$ vs $11\%$, respectively; $$p \leq 0.001$$). The qualitative nature of chronic fatigue as determined by individual questionnaire responses was similar in positive and negative participants. Pre-pandemic plasma IL-6 levels were positively associated with chronic fatigue in negative, but not positive individuals. Raised BMI was associated with chronic fatigue in positive participants. ### Conclusions Pre-existing increased IL-6 levels may contribute to chronic fatigue symptoms, but there was no increased risk in individuals with mild COVID-19 compared with uninfected individuals. Elevated BMI also increased the risk of chronic fatigue in mild COVID-19, consistent with previous reports. ## Introduction Fatigue is a common symptom of COVID-19 and is one of its most pronounced acute and post-acute clinical manifestations [1]. Long-lasting manifestations—so-called long-COVID—is a subject of intense interest. Affected individuals report fatigue along with shortness of breath, headache, and loss of sense of taste and smell [2]. Mechanisms underlying the persistence of long-COVID symptoms are yet to be determined. To date, exploration of blood-borne biomarkers following COVID-19 infection has not revealed an association with inflammatory markers or white cell count [3]. The cytokine interleukin (IL)-6 has a recognized role in fatigue development in many clinical settings, including autoimmune inflammatory arthritis (reviewed in [4]) and cancer [5]. It is an established driver of acute responses to COVID-19, and treatment with anti-IL-6 monoclonal antibodies reduces mortality in severe COVID-19 [6, 7]. IL-6 is also considered a key mediator of neuropsychiatric symptoms of long-COVID, including fatigue [8]. Finally, a Mendelian randomization study of depressive symptoms has suggested that IL-6 manifests a causal influence on fatigue, as well as sleep problems and suicidality [9]. We hypothesized that long-COVID fatigue is driven at least in part by the pre-existing immune status of an individual. We have shown previously that chronic fatigue induced by treatment with interferon-alpha (IFN-α) for chronic hepatitis B viral infection is predicted by higher baseline IL-6 and IL-10 levels, as well as an exaggerated elevation of IL-6 and IL-10, in response to treatment [10]. In addition, increasing age is a major risk factor for both illness severity and longevity after SARS-CoV2 infection [2]. Aging has been associated with elevated levels of pro-inflammatory cytokines, such as TNF-α and IL-6, and reduced levels of anti-inflammatory mediator IL-10 [11, 12]. An age-related, chronic, pro-inflammatory milieu may mediate the risk for susceptibility to COVID-19 and long-COVID fatigue. To test this hypothesis, we analyzed levels of pro-inflammatory cytokine IL-6 in plasma collected prior to the SARS-CoV-2 pandemic in a longitudinal cohort sample of UK adults, who were assessed during the pandemic for chronic fatigue symptoms and SARS-CoV-2 infection. ## Sample and phenotyping Participants were selected from the UK Twin Registry (TwinsUK), an adult cohort which has been shown to be representative of the general population for lifestyle and health-related traits [13]. Ethics permission was obtained and participants have provided fully informed consent; the Declaration of Helsinki was adhered to. The registry comprises 14,500 same sex mono- and dizygotic twin volunteers from the general UK population recruited from previous twin registers and national media campaigns. The cohort is predominantly female ($83\%$), and mainly of Northern European descent [14]. Samples and data for this project were collected as part of ongoing research initiatives into inflammaging as well as more recent research into COVID-19. Participant selection and grouping is depicted on Fig. 1. Participants ($$n = 5755$$) were invited to complete a COVID-19 Personal Experience (CoPE) Questionnaire which included questions about COVID-19 infection, related symptoms and fatigue over the previous three months [15]. CoPE was completed in multiple waves during the pandemic: April 2020, August 2020, November 2020, and April 2021. Participants also provided serum for COVID-19 antigen and antibody testing at multiple points during the pandemic, with primary collections of $$n = 506$$ in April-June 2020, $$n = 5165$$ in August 2020, $$n = 137$$ in November–December 2020, and $$n = 4291$$ in April–May 2021.Fig. 1Participant flow diagram. CoPE COVID-19 Personal Experience Questionnaire CFQ Chadler Fatigue Scale We followed guidance on interpretation of antibody test results from the Centres for Disease Control and Prevention (https://www.cdc.gov/coronavirus/2019-ncov/lab/resources/antibody-tests-guidelines.html) to assign natural COVID-19 infection status from the results of swab antigen tests; and enzyme-linked immunosorbent assays (ELISA) anti-Nucleocapsid (anti-N) and anti-Spike (anti-S) Roche antibody tests [16] performed at both King’s College London and 3rd-party laboratories as described previously [17]. Individuals with a positive antigen test at any point over the CoPE questionnaire administration period, who were positive for anti-S antibodies prior to self-reported COVID-19 vaccination date, or positive for anti-N antibodies at any point, were classified as COVID-19-positive cases. Individuals with negative antigen or anti-N antibody results, or negative anti-S antibody results before COVID-19 vaccination, were classified as COVID-19 negative, or controls. Those with negative anti-N antibody and negative antigen results but positive anti-S after COVID-19 vaccination were excluded from the analysis as anti-S antibodies are generated in response to vaccination. Individuals with no laboratory antigen or antibody test results were also excluded from the analysis. Within the CoPE questionnaire, the Chalder Fatigue Scale (CFQ) [18] was used to classify participants into those experiencing chronic fatigue and those who did not. CFQ comprises 11 questions concerning physical and mental aspects of fatigue. Reliability of CFQ has been shown in clinical and non-clinical settings [18, 19]. CFQ responses were extracted from the CoPE questionnaires administered in August and November 2020. Responses were coded as 0 (“Less than usual”, “No more than usual”) or 1 (“More than usual”, “Much more than usual”) followed by summing the scores for different questions and assigning a diagnosis of fatigue to those with summary score of 4 or more [19]. Volunteers reporting fatigue at both time points were diagnosed as having chronic fatigue because it lasted 3 months or longer. Those who reported fatigue at a single time point were removed from the study. Pre-pandemic IL-6 levels were ascertained using plasma specimens obtained in 1997–2018 (median 2009). Samples were assayed using Olink Target 96 Inflammation assay (https://www.olink.com/products-services/target/inflammation/). Where multiple plasma specimens were available, we selected the most recent, pre-pandemic specimen. IL-6 levels were expressed as normalized protein expression on the Olink arbitrary unit in log2 scale. ## Statistical analysis A generalized mixed-effects model with chronic fatigue as a dichotomous categorical response variable and IL-6 levels as a predictor was examined, adjusting for age, body mass index (BMI), and sex as fixed effects. Family structure and zygosity were considered as random factors. IL-6 levels were adjusted for age and BMI followed by transformation of the residuals to a normal distribution using qqnorm function in R statistical environment. The model was fitted for COVID-19-positive and COVID-19-negative participants separately. A set of sensitivity analyses was performed. The first investigated sample integrity over time and comprised only plasma samples collected two years before the pandemic; the second was analysis without adjustment of IL-6 levels for age and BMI levels. Finally, we repeated the analysis after excluding individuals with major inflammatory disease (rheumatoid arthritis, systemic lupus erythematosus, ulcerative colitis, and Crohn’s disease; $$n = 11$$). ## Prevalence of fatigue After selecting participants reporting the same fatigue status at both time points, the sample for analysis comprised total $$n = 1274$$ participants, of whom $$n = 282$$ were classified COVID-19 positive, and $$n = 162$$ were classified as having chronic fatigue (Table 1). None of the COVID-19-positive participants had been hospitalized, so their COVID-19 was considered relatively mild. The prevalence of long-term fatigue was $17.4\%$ among COVID-19-positive participants and $11.4\%$ in COVID-19-negative participants (Fisher’s exact $$p \leq 0.011$$). COVID-19-positive participants were on average two years younger than COVID-19-negative participants, and there was no difference in BMI observed (Table 1). Two ($1.1\%$) and nine ($0.7\%$) cases of major inflammatory disease were reported by those with and without chronic fatigue, respectively (Fisher’s exact test $$p \leq 0.641$$).Table 1Characteristics of the TwinsUK sampleGroupSample sizeNumber with chronic fatigue (%)Age (SD), yearsBMI (SD), kg/m2COVID-19 cases28249 (17.4)65.4 (10.2)26.5 (5.3)COVID-19 controls992113 (11.4)67.4 (9.6)26.2 (4.8)p value0.0110.00040.356p value provided for comparisons between COVID-19 cases and controls using Fisher’s exact test or Student’s t testSD standard deviation ## Structure of fatigue The qualitative nature of chronic fatigue was similar in COVID-19-positive and -negative participants, with no differences in prevalence of positive answers in the CFQ (Table 2).Table 2Comparison of Chalder Fatigue Scale responses in COVID-19 cases and controlsChalder fatigue scale questionCOVID-19 cases ($$n = 49$$)COVID-19 controls ($$n = 113$$)p valueDo you have problems with tiredness?0.68 ± 0.050.74 ± 0.030.533Do you need to rest more?0.70 ± 0.050.69 ± 0.030.999Do you feel sleepy or drowsy?0.60 ± 0.050.59 ± 0.030.272Do you have problems starting things?0.64 ± 0.050.70 ± 0.030.199Do you lack energy?0.82 ± 0.040.77 ± 0.030.904Do you have less strength in your muscles?0.60 ± 0.050.56 ± 0.030.542Do you feel weak?0.48 ± 0.050.49 ± 0.030.381Do you have difficulties concentrating?0.72 ± 0.050.65 ± 0.030.299Do you make slips of the tongue when speaking?0.63 ± 0.050.57 ± 0.030.902Do you find it more difficult to find the right word?0.70 ± 0.050.71 ± 0.030.896How is your memory?0.65 ± 0.050.61 ± 0.030.345Prevalence (± standard error) of positive answers among those who was classified as having long-term fatigue is provided; P values estimated using Fisher’s exact test ## IL-6 levels In the total sample, chronic fatigue was associated with elevated pre-pandemic plasma IL-6 levels, which persisted after adjusting cytokine levels for age, sex, BMI, and COVID-19 status; chronic fatigue was as also associated with higher levels of BMI (Fig. 2).Fig. 2Association between fatigue and pre-pandemic levels of IL-6 and current levels of BMI. First panel shows unadjusted IL-6 values, second panel shows values undusted for age, sex, BMI, and COVID-19 status (via residuals). IL-6 values or the residuals have been transformed to achieve normal distribution *Stratified analysis* established that pre-pandemic plasma IL-6 levels were elevated in participants with chronic fatigue in the COVID-19-negative group. The same relationship was not seen in the COVID-19-positive group (Table 3). We also calculated prevalence of fatigue in COVID-19-positive and COVID-19-negative participants stratified by high and low levels of pre-pandemic IL-6. We defined high and low IL-6 levels as values equal to or above $75\%$ percentile of IL-6 distribution and equal to or below $25\%$ percentile, respectively. The prevalence of fatigue was found to be significantly higher in high IL-6 level group compared to low IL-6 level group in COVID-negative participants ($17.5\%$ vs $7.6\%$, $$p \leq 0.001$$); however, no such differences were found in COVID-positive group ($19.2\%$ vs $19.0\%$, $$p \leq 0.999$$). Sensitivity analysis of participants having plasma collected no earlier than 2017 showed similarity with the main analysis: β = 0.653 ± 0.375, $$p \leq 0.0814$$; and β = − 0.760 ± 0.625, $$p \leq 0.224$$ for COVID-19-negative and COVID-19-positive participants, respectively). Sensitivity analysis without adjusting IL-6 levels for age and BMI levels at the time of plasma collection, produced almost identical results to the main analysis: β = 0.321 ± 0.115, $$p \leq 0.005$$; and β = − 0.061 ± 0.180, $$p \leq 0.774$$ for COVID-19-negative and COVID-19-positive participants, respectively. Sensitivity analysis with excluding cases of major inflammatory disease, produced almost identical results, too: β = 0.307 ± 0.108, $$p \leq 0.004$$; and β = -0.078 ± 0.166, $$p \leq 0.639$$, for COVID-19-negative and COVID-19-positive participants, respectively. Table 3Risk factors for chronic fatigue in TwinsUK participants by COVID-19 antibody statusGroupVariableEstimateStd. errorz valuep valueCOVID-19-positive(Intercept)− 1.6791.356− 1.2380.216IL-6− 0.0840.166− 0.5090.611Age− 0.0230.017− 1.3890.165Sex− 1.1751.051− 1.1180.263BMI0.0630.0302.1460.032COVID-19-negative(Intercept)− 1.4020.920− 1.5240.127IL-60.3040.1072.8490.004Age− 0.0200.011− 1.8410.066Sex− 0.1440.510− 0.2820.778BMI0.0250.0211.1700.242Modeling pre-pandemic plasma IL-6 and other factors on the risk of chronic fatigue. IL-6 levels were adjusted for age and BMI followed by transformation to a normal distribution using qqnorm function in R. Model was fitted using age and BMI at the time CoPE questionnaire have been administered. Further adjustment was made for family structure and zygosity as random factorsStatistically significant associations are highligted in bold ## BMI Higher BMI was associated with chronic fatigue in COVID-19-positive participants and in the whole sample (Table 3). To explore the relationship between chronic fatigue and BMI in COVID-19-positive participants, we examined groups by BMI (BMI < 18.5; $$n = 6$$), healthy weight (BMI > 18.5 and < 25; $$n = 115$$), overweight (BMI > 25 and < 30; $$n = 102$$), and obese (BMI ≥ 30; $$n = 59$$). There was a significant difference in prevalence of these groups in COVID-19 positive cases with and without chronic fatigue (χ2 = 8.3, df = 3, p value = 0.040; Fig. 3). This was largely driven by a lower prevalence of healthy weight and higher prevalence of obesity in COVID-19-positive participants with chronic fatigue. Fig. 3Prevalence of different BMI classes among COVID-19-positive participants with and without chronic fatigue. BMI groups have been defined as the following: underweight (BMI < 18.5; $$n = 6$$), normal weight (BMI > 18.5 and < 25; $$n = 115$$), overweight (BMI > 25 and < 30; $$n = 102$$), and obesity (BMI ≥ 30; $$n = 59$$). The difference in the prevalence of the BMI groups is statistically significant (χ2 = 8.3, df = 3, p value = 0.040) ## Discussion This is among the first studies to examine pre-infective inflammatory cytokine levels and subsequent long-COVID chronic fatigue in participants who experienced mild COVID-19 illness. We found elevated pre-pandemic IL-6 levels increased the risk of developing fatigue in adult volunteers who had never had COVID-19; however, pre-pandemic IL-6 levels did not predict fatigue in our COVID-19-positive group. This finding is in keeping with other post-viral and post-infective fatigue research and studies in a general population [20, 21]. We previously demonstrated that greater pro-inflammatory cytokine elevations in response to IFN-α treatment was associated with developing persistent fatigue [10]. In that study, we also observed a role for pre-treatment higher cytokine levels of IL-6 and lower IL-10 and the development chronic fatigue — which led to the current hypothesis. While our COVID-19-positive group were not hospitalized and therefore classified with ‘mild’ illness, it is likely that IL-6 and other pro-inflammatory cytokines reach high levels during COVID-19 infection [20], well in excess of levels associated with well-known risk factors such as elevated BMI [21]. Taken together, these and our findings suggest any elevation of circulating pro-inflammatory cytokines, be it a protracted, low-grade inflammatory dysregulation or an acute sickness response to infection will increase chronic fatigue risk, with higher cytokine levels associated with higher risk. Baseline immune status or cytokine levels of an apparently healthy individual may only predict chronic fatigue in the absence of pronounced cytokine elevation, such as an acute sickness response to viral or bacterial infection. Our hypothesis that chronic fatigue in community dwelling adults after SARS-CoV-2 infection was predicated on pre-existing pro-inflammatory immune state) was not supported by our results. We did however find a statistically significant association between higher BMI and long-COVID fatigue in COVID-19-positive participants (Table 3). This is consistent with a study demonstrating association between obesity and fatigue while controlling for other potential contributors including IL-6 levels [22]. Indeed, almost a third of circulating IL-6 is thought to be produced by adipocytes [23], in keeping with BMI being an important risk factor for chronic fatigue. Of interest, the relationship with BMI and chronic fatigue was not evident in COVID-19-negative participants, an association usually apparent in the population [24]. Allied to this, higher BMI and advanced age are well-established risk factors for severe COVID-19 [22]; and both are associated with greater systemic inflammation [23]. Our findings show that following mild COVID-19 chronic fatigue does not appear to be qualitatively different from other forms of chronic fatigue, at least according to the CFQ, which is a well-validated tool to discriminate between clinical and non-clinical conditions [19]. Whether this is true for more severe disease requiring hospitalization is unclear. The most pronounced limitation to this study is the focus on a single cytokine and we recognize that other inflammatory mediators such as TNF-α are likely to contribute to chronic fatigue also. In summary, our work sheds light on the role of IL-6 in general chronic fatigue, but it does not support a specific role for IL-6 levels in the development of chronic fatigue following mild COVID-19. ## References 1. Carfi A, Bernabei R, Landi F. **Persistent symptoms in patients after acute COVID-19**. *JAMA* (2020) **324** 603-605. DOI: 10.1001/jama.2020.12603 2. Sudre CH, Murray B, Varsavsky T, Graham MS, Penfold RS, Bowyer RC. **Attributes and predictors of long COVID**. *Nat Med* (2021) **27** 626-631. DOI: 10.1038/s41591-021-01292-y 3. Townsend L, Dyer AH, Jones K, Dunne J, Mooney A, Gaffney F. **Persistent fatigue following SARS-CoV-2 infection is common and independent of severity of initial infection**. *PLoS One* (2020) **15** e0240784. DOI: 10.1371/journal.pone.0240784 4. Grygiel-Gorniak B, Puszczewicz M. **Fatigue and interleukin-6 - a multi-faceted relationship**. *Reumatologia* (2015) **53** 207-212. DOI: 10.5114/reum.2015.53998 5. Kolak A, Kamińska M, Wysokińska E, Surdyka D, Kieszko D, Pakieła M. **The problem of fatigue in patients suffering from neoplastic disease**. *Contemp Oncol (Pozn)* (2017) **21** 131-135. PMID: 28947882 6. Castelnovo L, Tamburello A, Lurati A, Zaccara E, Marrazza MG, Olivetti M. **Anti-IL6 treatment of serious COVID-19 disease: a monocentric retrospective experience**. *Medicine (Baltimore)* (2021) **100** e23582. DOI: 10.1097/MD.0000000000023582 7. Abidi E, El Nekidy WS, Alefishat E, Rahman N, Petroianu GA, El-Lababidi R. **Tocilizumab and COVID-19: timing of administration and efficacy**. *Front Pharmacol* (2022) **13** 825749. DOI: 10.3389/fphar.2022.825749 8. Kappelmann N, Dantzer R, Khandaker GM. **Interleukin-6 as potential mediator of long-term neuropsychiatric symptoms of COVID-19**. *Psychoneuroendocrinology* (2021) **131** 105295. DOI: 10.1016/j.psyneuen.2021.105295 9. Milaneschi Y, Kappelmann N, Ye Z, Lamers F, Moser S, Jones PB. **Association of inflammation with depression and anxiety: evidence for symptom-specificity and potential causality from UK Biobank and NESDA cohorts**. *Mol Psychiatry* (2021) **26** 7393-7402. DOI: 10.1038/s41380-021-01188-w 10. Russell A, Hepgul N, Nikkheslat N, Borsini A, Zajkowska Z, Moll N. **Persistent fatigue induced by interferon-alpha: a novel, inflammation-based, proxy model of chronic fatigue syndrome**. *Psychoneuroendocrinology* (2019) **100** 276-285. DOI: 10.1016/j.psyneuen.2018.11.032 11. Franceschi C, Campisi J. **Chronic inflammation (inflammaging) and its potential contribution to age-associated diseases**. *J Gerontol A Biol Sci Med Sci* (2014) **69** S4-9. DOI: 10.1093/gerona/glu057 12. Bartlett DB, Firth CM, Phillips AC, Moss P, Baylis D, Syddall H. **The age-related increase in low-grade systemic inflammation (Inflammaging) is not driven by cytomegalovirus infection**. *Aging Cell* (2012) **11** 912-915. DOI: 10.1111/j.1474-9726.2012.00849.x 13. Andrew T, Hart DJ, Snieder H, de Lange M, Spector TD, MacGregor AJ. **Are twins and singletons comparable? A study of disease-related and lifestyle characteristics in adult women**. *Twin Res* (2001) **4** 464-477. DOI: 10.1375/twin.4.6.464 14. Verdi S, Abbasian G, Bowyer RCE, Lachance G, Yarand D, Christofidou P. **TwinsUK: the UK adult twin registry update**. *Twin Res Hum Genet* (2019) **22** 523-529. DOI: 10.1017/thg.2019.65 15. Suthahar A, Sharma P, Hart D, García MP, Horsfall R, Bowyer RCE. **TwinsUK COVID-19 personal experience questionnaire (CoPE): wave 1 data capture April-May 2020**. *Wellcome Open Res.* (2021) **6** 123. DOI: 10.12688/wellcomeopenres.16671.1 16. Muench P, Jochum S, Wenderoth V, Ofenloch-Haehnle B, Hombach M, Strobl M. **Development and validation of the Elecsys Anti-SARS-CoV-2 immunoassay as a highly specific tool for determining past exposure to SARS-CoV-2**. *J Clin Microbiol* (2020) **58** e01694-e1720. DOI: 10.1128/JCM.01694-20 17. Seow J, Graham C, Merrick B, Acors S, Pickering S, Steel KJA. **Longitudinal observation and decline of neutralizing antibody responses in the three months following SARS-CoV-2 infection in humans**. *Nat Microbiol* (2020) **5** 1598-1607. DOI: 10.1038/s41564-020-00813-8 18. Jackson C. **The Chalder fatigue scale (CFQ 11)**. *Occup Med (Lond)* (2015) **65** 86. DOI: 10.1093/occmed/kqu168 19. Cella M, Chalder T. **Measuring fatigue in clinical and community settings**. *J Psychosom Res* (2010) **69** 17-22. DOI: 10.1016/j.jpsychores.2009.10.007 20. Cho HJ, Kivimaki M, Bower JE, Irwin MR. **Association of C-reactive protein and interleukin-6 with new-onset fatigue in the Whitehall II prospective cohort study**. *Psychol Med* (2013) **43** 1773-1783. DOI: 10.1017/S0033291712002437 21. Hickie I, Davenport T, Wakefield D, Vollmer-Conna U, Cameron B, Vernon SD. **Post-infective and chronic fatigue syndromes precipitated by viral and non-viral pathogens: prospective cohort study**. *BMJ* (2006) **333** 575. DOI: 10.1136/bmj.38933.585764.AE 22. Lim W, Hong S, Nelesen R, Dimsdale JE. **The association of obesity, cytokine levels, and depressive symptoms with diverse measures of fatigue in healthy subjects**. *Arch Intern Med* (2005) **165** 910-915. DOI: 10.1001/archinte.165.8.910 23. Fried SK, Bunkin DA, Greenberg AS. **Omental and subcutaneous adipose tissues of obese subjects release interleukin-6: depot difference and regulation by glucocorticoid**. *J Clin Endocrinol Metab* (1998) **83** 847-850. PMID: 9506738 24. Collin SM, Nikolaus S, Heron J, Knoop H, White PD, Crawley E. **Chronic fatigue syndrome (CFS) symptom-based phenotypes in two clinical cohorts of adult patients in the UK and The Netherlands**. *J Psychosom Res* (2016) **81** 14-23. DOI: 10.1016/j.jpsychores.2015.12.006
--- title: Systemic oxidative stress associates with disease severity and outcome in patients with new-onset or worsening heart failure authors: - Marie-Sophie L. Y. de Koning - Johanna E. Emmens - Esteban Romero-Hernández - Arno R. Bourgonje - Solmaz Assa - Sylwia M. Figarska - John G. F. Cleland - Nilesh J. Samani - Leong L. Ng - Chim C. Lang - Marco Metra - Gerasimos S. Filippatos - Dirk J. van Veldhuisen - Stefan D. Anker - Kenneth Dickstein - Adriaan A. Voors - Erik Lipsic - Harry van Goor - Pim van der Harst journal: Clinical Research in Cardiology year: 2023 pmcid: PMC10062262 doi: 10.1007/s00392-023-02171-x license: CC BY 4.0 --- # Systemic oxidative stress associates with disease severity and outcome in patients with new-onset or worsening heart failure ## Abstract ### Background Oxidative stress may be a key pathophysiological mediator in the development and progression of heart failure (HF). The role of serum-free thiol concentrations, as a marker of systemic oxidative stress, in HF remains largely unknown. ### Objective The purpose of this study was to investigate associations between serum-free thiol concentrations and disease severity and clinical outcome in patients with new-onset or worsening HF. ### Methods Serum-free thiol concentrations were determined by colorimetric detection in 3802 patients from the BIOlogy Study to TAilored Treatment in Chronic Heart Failure (BIOSTAT-CHF). Associations between free thiol concentrations and clinical characteristics and outcomes, including all-cause mortality, cardiovascular mortality, and a composite of HF hospitalization and all-cause mortality during a 2-years follow-up, were reported. ### Results Lower serum-free thiol concentrations were associated with more advanced HF, as indicated by worse NYHA class, higher plasma NT-proBNP ($P \leq 0.001$ for both) and with higher rates of all-cause mortality (hazard ratio (HR) per standard deviation (SD) decrease in free thiols: 1.253, $95\%$ confidence interval (CI): 1.171–1.341, $P \leq 0.001$), cardiovascular mortality (HR per SD: 1.182, $95\%$ CI: 1.086–1.288, $P \leq 0.001$), and the composite outcome (HR per SD: 1.058, $95\%$ CI: 1.001–1.118, $$P \leq 0.046$$). ### Conclusions In patients with new-onset or worsening HF, a lower serum-free thiol concentration, indicative of higher oxidative stress, is associated with increased HF severity and poorer prognosis. Our results do not prove causality, but our findings may be used as rationale for future (mechanistic) studies on serum-free thiol modulation in heart failure. ### Graphical abstract Associations of serum-free thiol concentrations with heart failure severity and outcomes ### Supplementary Information The online version contains supplementary material available at 10.1007/s00392-023-02171-x. ## Introduction Oxidative stress has been identified as an important pathophysiological mediator in the development and progression of heart failure (HF) [1, 2]. Oxidative stress reflects an imbalance between the production of reactive oxygen species (ROS) and the antioxidant capacity [3]. Although ROS provide important beneficial physiological functions at lower concentrations, excess of ROS can cause DNA damage and harmful modifications of proteins. This can result in cellular dysfunction, including changes in cardiomyocyte excitation–contraction coupling, calcium handling and energy metabolism [1]. Excess of ROS can also exert pro-fibrotic effects, leading to extracellular matrix remodeling and eventually worsening of diastolic and systolic function [4–6]. Free thiols, organosulfur compounds with an –SH group, act as one of the most potent and versatile endogenous defense mechanisms against oxidative stress. Extracellular, i.e. circulating, thiols are the sum of and high- and low-molecular-weight thiols and are referred to as total free thiols. Free thiols mainly comprise of high molecular weight proteins with an –SH group attached, of which albumin is the most relevant example, whereas circulating low-molecular weight thiols such as cysteine or glutathione only account for < 3–$5\%$ [7]. By forming stable disulfide bonds through ROS scavenging, free thiols prevent ROS from inflicting lipid and protein oxidation and subsequent myocardial structural damage [7]. Depletion of the antioxidant-free thiol pool reflects greater oxidative stress, and has been linked to the severity of oxidative stress-associated diseases [8, 9]. Increasing free thiol levels (e.g., by N-acetylcysteine) improved cardiac function in animal models of HF and cardiac injury [10–13]. In addition, clinical trials in small numbers of patients with myocardial infarction or HF suggested that N-acetylcysteine may reduce oxidative stress [14–16]. Hence, targeting thiol levels may hold promise as a potential therapeutic strategy in patients with HF. Accordingly, we investigated the associations between serum-free thiol concentrations and clinical characteristics and outcomes in a large cohort of patients with new-onset or worsening HF including a broad range of left ventricular phenotypes. ## Study population We measured the concentration of free thiols in archived serum samples of the multinational, prospective, observational BIOSTAT-CHF study. Study design and data collection have been described in full elsewhere [17]. In brief, in the BIOSTAT-CHF index cohort, 2516 patients with new-onset or worsening signs and/or symptoms of HF from 11 European countries were included between 2010 and 2012. Participants were documented with a left ventricular ejection fraction (LVEF) of ≤ $40\%$ or plasma N-terminal pro-B-type natriuretic peptide (NT-proBNP) of > 2000 ng/L. In addition, participants were considered to be on suboptimal evidence-based treatment for HF before enrollment [18]. A comparable validation cohort of BIOSTAT-CHF included another 1738 patients from six centers in Scotland between 2010 and 2014, who had to have a previously documented admission for HF. No additional NT-proBNP criteria were used for patients with a LVEF > $40\%$ in the validation cohort, which resulted in a higher percentage of patients with a LVEF > $45\%$: $34\%$ in the validation cohort versus $7\%$ in the index cohort. The BIOSTAT-CHF study was conducted according to the Declaration of Helsinki, approved by the ethics committee of each center and all participants provided written informed consent prior to any study-related procedures. ## Detection of serum-free thiols Blood samples were drawn upon enrollment in the BIOSTAT-CHF cohort. Serum samples were stored at − 80 °C until free thiol measurement. The free thiol concentration was detected as previously described, with minor modifications [19, 20]. In short, after thawing, 75 μl serum samples were diluted 1:4 with a 0.1 M Tris buffer (pH 8.2) and then transferred to a microplate. The background absorption was measured, using a Sunrise microplate reader (Tecan Trading AG, Männedorf, Switzerland) at 412 nm, with a reference filter at 630 nm. Subsequently, 10 μl 3.8 mM 5,5′-Dithio-bis(2-nitrobenzoic acid) (DTNB, CAS-number 69–78–3, Sigma Aldrich Corporation, Saint Louis, MO, USA) in a 0.1 M phosphate buffer (pH 7.0) was added to the samples. Following 20 min of incubation at room temperature, absorption was measured again and subtracted from background absorption. The concentration of free thiols in the samples was determined by parallel measurement of an l-cysteine (CAS-number 52–90–4, Fluka Biochemika, Buchs, Switzerland) calibration standard in the concentration range of 15.6–1000 μM in 0.1 M Tris and 10 mM EDTA (pH 8.2). All measurements were performed in duplo, where the mean of the free thiol value of both measurements was used for analyses. Measurements with a coefficient of variation > $20\%$ were excluded from further analysis. ## Clinical outcome parameters The primary clinical outcome parameter of this study was a composite endpoint of all-cause mortality and HF-related hospitalizations at 2 years. Our secondary outcome was all-cause mortality at 2 years. As sensitivity analysis, associations with cardiovascular mortality were studied. The cause of death and hospitalization were determined by the individual site investigators. Clinical events were collected during the 9-months follow-up study visit (index cohort), standard clinical follow-up and by telephonic contacts every 6 months for at least 2 years or until the end of follow-up [2015]. ## Statistical analysis For this study, both BIOSTAT-CHF cohorts were analyzed together. Normally distributed variables were displayed as mean with standard deviation (SD), non-normally distributed variables as median with interquartile range [IQR], and categorical variables as numbers with percentages (%). Distribution of continuous data was visually inspected using normal probability (Q–Q) plots. Baseline characteristics were presented according to tertiles of serum-free thiol levels. Between-group differences were compared using one-way analysis of variance (ANOVA), the Kruskal–Wallis test or the chi-square test, as appropriate. Clinical characteristics with a P-value < 0.1 were selected from the baseline table to investigate their associations with the serum-free thiol concentration using univariable, age- and sex-adjusted and multivariable linear regression analyses. All variables with $P \leq 0.1$ in age- and sex-adjusted analyses were included in multivariable analysis and subjected to backward elimination. Variables with $P \leq 0.05$ were retained in the final multivariable regression model. Prior to linear regression, normal distribution of residuals was checked, as well as presence of outliers. All variables were standardized and non-normally distributed variables were log-transformed before entry into regression analysis. To assess associations with clinical outcomes at 2-years follow-up, follow-up time and clinical events were truncated at 730.5 days. Kaplan–Meier survival curves were drawn for tertiles of serum-free thiols. The log-rank test was used to test for differences in outcomes between the tertiles. Subsequently, Cox proportional hazards regression analyses were performed to investigate associations between the free thiol concentration and disease outcome. The proportionality of hazards assumption was checked for all models to confirm absence of violation. Cox regression analyses were adjusted in a stepwise manner by first adjusting for age and sex and subsequently for sex and the previously published BIOSTAT-CHF risk models [21]. Variables in BIOSTAT-CHF risk model to predict the composite endpoint included age, HF hospitalization in the year before inclusion, edema, NT-proBNP, systolic blood pressure, hemoglobin, high-density lipoprotein levels, serum sodium concentration, and the failure to prescribe a beta-blocker. When investigating the associations between free thiols and cardiovascular mortality, non-cardiovascular mortality was used as competing risk. For the primary and secondary endpoint, pre-specified subgroup analyses were performed, testing for interactions for age (≤ 70 vs > 70), sex, HF groups (HF with reduced ejection fraction (HFrEF) vs HF with mildly reduced ejection fraction (HFmrEF) vs HFpEF), ischemic etiology, NYHA class (I–II vs III–IV) and history of chronic kidney disease (CKD). In this study, a P-value of < 0.05 was considered statistically significant. All analyses were conducted with R version 3.5.2 (R Foundation for Statistical Computing, Vienna, Austria). ## Patient characteristics Serum-free thiol levels were measured in 3802 participants of the BIOSTAT-CHF cohort. Mean free thiol concentration was 336 (SD 92) µmol/L. Baseline characteristics of the study population are presented according to tertiles of free thiol levels in Table 1. Patients within the lowest tertile were older (75 vs 69 years old, $P \leq 0.001$), more often female ($36\%$ vs $23\%$, $P \leq 0.001$), had less frequent an ischemic etiology of HF (56 vs $64\%$, $P \leq 0.001$) and shorter duration of HF diagnosis (median 10 vs 15 months, $$P \leq 0.003$$), compared with patients within the highest tertile of free thiol levels. Patients within the lowest tertile experienced more signs and symptoms of HF, had more advanced NYHA classes and higher NT-proBNP levels ($P \leq 0.001$ for all). Baseline characteristics are also presented for both cohorts separately (Supplementary Tables 1 and 2). The distribution of characteristics across the free thiols tertiles within the individual cohorts was quite comparable to the distribution in the combined study population. Table 1Baseline characteristics of the study population according to tertiles of serum-free thiol concentrations1st tertilen = 12682nd tertilen = 12673rd tertilen = 1267P-valueSerum-free thiols (μmol/L)246 [204;273]Full range (38–298)339 [320;359]Full range (298–378)423 [399;459]Full range (379–799)DemographicsAge (years)75.2 [66.9;81.5]73.4 [64.1;80.4]68.7 [59.7;76.5] < 0.001Female sex450 ($36\%$)403 ($32\%$)293 ($23\%$) < 0.001BMI (kg/m2)27.1 [23.8;31.1]27.3 [24.1;31.6]27.8 [24.6;31.9]0.007HF type0.52 HFrEF753 ($66\%$)746 ($65\%$)747 ($66\%$) HFmrEF197 ($17\%$)207 ($18\%$)223 ($20\%$) HFpEF188 ($17\%$)187 ($16\%$)167 ($15\%$)Months since HF diagnosis10 [0;47]19 [1;59]15 [2;58]0.003Ischemic etiology632 ($56\%$)701 ($65\%$)673 ($64\%$) < 0.001Inpatient at enrollment1004 ($79\%$)789 ($62\%$)628 ($50\%$) < 0.001NYHA class < 0.001 I/II372 ($30\%$)489 ($39\%$)629 ($50\%$) III/IV868 ($70\%$)756 ($61\%$)623 ($50\%$)Systolic BP (mmHg)120 [108;135]124 [110;140]123 [110;140] < 0.001Diastolic BP (mmHg)70 [60;80]70 [63;80]72 [65;80] < 0.001Heart rate (bpm)75 [65;88]74 [64;87]74 [65;85]0.033LVEF (%)35 [25;43]35 [25;43]35 [25;43]0.97Signs and symptomsPeripheral edema790 ($71\%$)652 ($60\%$)528 ($50\%$) < 0.001Elevated JVP372 ($38\%$)302 ($30\%$)233 ($24\%$) < 0.001Hepatomegaly165 ($14\%$)106 ($9\%$)92 ($8\%$) < 0.001Pulmonary congestion746 ($61\%$)613 ($50\%$)466 ($38\%$) < 0.001Medical historyAnemia560 ($45\%$)465 ($38\%$)342 ($28\%$) < 0.001Atrial fibrillation618 ($49\%$)576 ($46\%$)508 ($40\%$) < 0.001Diabetes Mellitus404 ($32\%$)425 ($34\%$)404 ($32\%$)0.60COPD242 ($19\%$)233 ($19\%$)193 ($15\%$)0.025CKD561 ($44\%$)470 ($37\%$)319 ($26\%$) < 0.001Hypertension798 ($63\%$)747 ($59\%$)751 ($59\%$)0.08PAVD181 ($14\%$)194 ($16\%$)222 ($18\%$)0.07Stroke179 ($14\%$)188 ($15\%$)133 ($11\%$)0.002PCI217 ($17\%$)250 ($20\%$)287 ($23\%$)0.002CABG221 ($17\%$)214 ($17\%$)220 ($17\%$)0.93MedicinesLoop diuretics1259 ($99\%$)1256 ($99\%$)1256 ($99\%$)0.88Loop diuretic dose (mg furosemide equivalent)50 [40;120]40 [40;80]40 [40;80] < 0.001ACEi/ARB860 ($68\%$)880 ($70\%$)978 ($77\%$) < 0.001Beta-blocker985 ($78\%$)989 ($78\%$)1017 ($80\%$)0.23MRA574 ($45\%$)549 ($43\%$)542 ($43\%$)0.42Laboratory dataHemoglobin (g/dL)12.8 [11.4;14.1]13.2 [11.8;14.5]13.7 [12.5;14.9] < 0.001Leucocytes (109/L)7.8 [6.3:9.7]7.7 [6.3;9.4]7.5 [6.2;9.2]0.015Sodium (mmol/L)139 [136;141]139 [137;141]140 [138;141] < 0.001Potassium (mmol/L)4.2 [3.9;4.6]4.3 [4.0;4.6]4.3 [4.0;4.6]0.007Urea (mmol/L)11.8 [8.1;19.3]9.5 [7.0;14.1]8.4 [6.2;12.1] < 0.001Serum creatinine (μmol/L)111 [88;146]101 [82;128]93 [78;114] < 0.001eGFR (mL/min/1.73 m2)52 [37;60]59 [44;62]60 [54;69] < 0.001NT-proBNP (ng/L)3466 [1398;7861]2212 [944;4810]1208 [479;2936] < 0.001 NT-proBNP in sinus rhythm2823 [1039; 7584]1746 [601;4058]921 [339;2479] < 0.001 NT-proBNP in atrial fibrillation/flutter3953 [2022; 8127]2763 [1492;5043]1789 [933;3578] < 0.001Albumin (g/L)31 [26;36]36 [31;40]38 [33;42] < 0.001LDL-cholesterol (mmol/L)2.1 [1.6;2.7]2.1 [1.6;2.9]2.3 [1.7;3.0]0.003HDL-cholesterol (mmol/L)1.06 [0.83;1.34]1.09 [0.90;1.35]1.08 [0.86;1.33]0.19Glucose (mmol/L)6.4 [5.4;8.1]6.3 [5.4;8.3]6.1 [5.2;8.1]0.015Data shown as median [IQR] or n (%). Significant P-values are bold-printedACEi angiotensin-converting enzyme inhibitor, ARB angiotensin receptor blocker, BMI body mass index, BP blood pressure, CABG coronary artery bypass graft, COPD chronic obstructive pulmonary disease, CKD chronic kidney disease, eGFR estimated glomerular filtration rate, HDL high-density lipoprotein, HF heart failure, HFmrEF heart failure with mildly reduced ejection fraction, HFpEF heart failure with preserved ejection fraction, HFrEF heart failure with reduced ejection fraction, IQR interquartile range, JVP jugular venous pressure, LDL low-density lipoprotein, LVEF left ventricular ejection fraction, MRA mineralocorticoid receptor antagonist, NT-proBNP N-terminal pro-B-type natriuretic peptide, NYHA New York Heart Association, PAVD peripheral arterial vascular disease, PCI percutaneous coronary intervention Associations between free thiol concentration and age, sex, signs and symptoms of HF and NT-proBNP were also observed in regression analyses (Table 2). Moreover, lower free thiol levels were associated with lower systolic and diastolic blood pressure, enrollment in the BIOSTAT-CHF cohort as an inpatient (instead of at the outpatient clinic), and the absence of treatment with an angiotensin-converting enzyme inhibitor (ACEi) or angiotensin receptor blocker (ARB). Furthermore, (laboratory parameters of) other diseases, including anemia and chronic kidney disease were associated with lower levels of serum-free thiols. Lower albumin, higher age, and higher urea and NT-proBNP were the strongest determinants of lower free thiol concentration ($P \leq 0.001$ for all; Table 2).Table 2Clinical characteristics and laboratory values associated with serum-free thiolsn = Age- and sex-adjustedMultivariableaStd βSEP-valueStd βSEP-valueAge3802− 0.220.02 < 0.001− 0.120.02 < 0.001Female sex3802− 0.080.03 < 0.001− 0.080.02 < 0.001BMI37340.010.020.90Systolic BP37740.090.02 < 0.0010.040.020.012Diastolic BP37740.070.02 < 0.001Heart rate3763− 0.070.02 < 0.001HF characteristics Ischemic etiology32570.120.04 < 0.001 Years since HF diagnosis17220.050.020.026 Inpatient3802− 0.270.03 < 0.001Signs and symptoms NYHA class III/IVb3737− 0.150.03 < 0.001 Peripheral edema3252− 0.150.03 < 0.001− 0.080.03 < 0.001 Elevated JVP2933− 0.090.04 < 0.001 Hepatomegaly3640− 0.110.05 < 0.001 Pulmonary congestion3668− 0.170.03 < 0.001− 0.040.030.022Medical history Anemia3704− 0.130.03 < 0.001 Atrial fibrillation3788− 0.040.030.018 Hypertension37870.010.030.63 COPD3787− 0.020.040.23 History of renal disease3779− 0.130.03 < 0.001− 0.050.040.013 Stroke3786− 0.0070.050.65 PCI37860.060.04 < 0.0010.050.040.003 PAVD37610.060.04 < 0.0010.070.04 < 0.001 ACEi/ARB use at baseline38020.090.04 < 0.001Laboratory parameters Hemoglobin37040.080.02 < 0.001 Leukocytes3505− 0.060.02 < 0.001 C-reactive protein2093− 0.160.02 < 0.001 Glucose3144− 0.010.020.53 LDL-cholesterol20140.020.020.42 Sodium37500.090.02 < 0.001 Potassium37410.020.020.25 Albumin36910.350.01 < 0.0010.320.02 < 0.001 eGFR37900.180.02 < 0.0010.080.02 < 0.001 Creatinine3792− 0.200.02 < 0.001 Urea3523− 0.250.02 < 0.001− 0.110.02 < 0.001 NT-proBNP3654− 0.250.02 < 0.001− 0.090.02 < 0.001 NT-proBNP sinus1921− 0.230.02 < 0.001 NT-proBNP AF1169− 0.270.03 < 0.001Significant P-values are bold-printedan = 2783, Adjusted R2 = 0.276bCompared to NYHA I/II as reference groupACEi angiotensin-converting enzyme inhibitor, ARB angiotensin receptor blocker, BMI body mass index, BP blood pressure, COPD chronic obstructive pulmonary disease, eGFR estimated glomerular filtration rate, JVP jugular venous pressure, LDL low-density lipoprotein, NT‐proBNP N‐terminal pro‐B‐type natriuretic peptide, NYHA New York Heart Association, PAVD peripheral arterial vascular disease, PCI percutaneous coronary intervention ## Serum-free thiols and follow-up outcomes Median follow-up time of the combined study population was 545 [IQR 255–730] days. In the index cohort, median follow-up time was 553 [IQR 253–730] days, and for patients in the validation cohort, the median follow-up time was 517 [266–730] days. During 2 years of follow-up, 932 ($24.5\%$) patients died, of which 594 ($64\%$) due to a cardiovascular cause, and 908 ($23.9\%$) patients were hospitalized for HF. The composite endpoint of all-cause mortality and HF hospitalization occurred in 1475 ($38.8\%$) patients. Incidences of the composite outcome, as well as secondary outcome, differed significantly across tertiles (log-rank test: $P \leq 0.001$), and occurred most frequent in the lowest tertile of serum-free thiol levels (Fig. 1). In Cox regression analyses, lower levels of free thiols were associated with adverse disease outcome, also after adjustment for factors included in the BIOSTAT risk model [Hazard Ratio (HR) per SD decrease in free thiols: 1.058, $95\%$-confidence interval (CI): 1.001–1.118, $$P \leq 0.046$$ for the composite endpoint; Table 3]. Lower free thiol concentrations were also independently associated with higher rates of all-cause mortality [HR per SD decrease: 1.253, $95\%$ CI: 1.171–1.341, $P \leq 0.001$] and cardiovascular mortality [HR per SD decrease: 1.182, $95\%$ CI: 1.086–1.288, $P \leq 0.001$].Fig. 1Cumulative incidence curves for the composite endpoint of all-cause mortality or heart failure-related hospitalizations at 2 years (A) and all-cause mortality at 2 years (B)Table 3Hazard ratios for serum-free thiol concentrations in predicting clinical endpointsComposite endpointAll-cause mortalityCardiovascular mortalitycHR per SD thiol decrease($95\%$ CI)P-valueHR per SD thiol decrease($95\%$ CI)P-valueHR per SD thiol decrease($95\%$ CI)P-valueUnivariable1.351 (1.283–1.422) < 0.0011.466 (1.374–1.565) < 0.0011.410 (1.300–1.530) < 0.001Age- and sex-adjusted1.294 (1.227–1.365) < 0.0011.381 (1.291–1.477) < 0.0011.330 (1.225–1.450) < 0.001BIOSTAT-CHF risk model1.058a (1.001–1.118)0.0461.253b (1.171–1.341) < 0.0011.182b (1.086–1.288) < 0.001Significant P-values are bold-printedCI confidence interval, HF heart failure, HR hazard ratio, SD standard deviationaBIOSTAT-CHF risk model for composite endpoint (all-cause mortality & HF hospitalization): age, HF hospitalization in the year before inclusion, edema, N-terminal pro-B-type natriuretic peptide, systolic blood pressure, hemoglobin, high-density lipoprotein levels, serum sodium concentration and failure to prescribe a beta-blockerbBIOSTAT-CHF risk model for predicting mortality: age, blood urea nitrogen, NT-proBNP, hemoglobin and the use of a beta-blocker at time of inclusioncNon-cardiovascular mortality was used as competing risk ## Subgroup analyses Pre-defined subgroup analyses on the primary endpoint and on all-cause mortality were performed for age, sex, HF type, ischemic origin, NYHA class, and history of CKD. No significant subgroup interactions were observed (Fig. 2; Supplementary Table 3).Fig. 2Forest plot depicting Hazard ratio’s for the composite endpoint of all-cause mortality or heart failure-related hospitalization (upper panel) and all-cause mortality alone (lower panel) during a 2-year follow-up, per SD decrease in serum-free thiols, across pre-specified subgroups. All Cox proportional hazards models were adjusted for the corresponding BIOSTAT-CHF risk model. Abbreviations: CKD chronic kidney disease, HF heart failure, HFmrEF heart failure with mildly reduced ejection fraction, HFpEF heart failure with preserved ejection fraction, HFrEF heart failure with reduced ejection fraction, NYHA New York Heart Association, SD standard deviation ## Discussion This study demonstrates that lower serum concentrations of free thiols, reflecting increased oxidative stress, are associated with greater HF severity and worse outcome for patients with new-onset or worsening HF. Our data provide rationale for future mechanistic studies on the role of free thiols in HF, and for studies evaluating effects of free thiol modulation for modifying disease outcomes for patients with HF. Extracellular free thiols are critically involved in redox signaling, but also possess a strong antioxidant buffering capacity to a variety of reactive species [7]. Under conditions of oxidative stress, the circulating free thiol pool (reduced thiols with -SH group) scavenge ROS, leading to the oxidation of thiols and the formation of disulfide bonds. Therefore, measurement of the reduced, i.e. free, thiol pool is a simple, direct, and robust method to systemically assess the degree of oxidative stress [8]. Depletion of the free thiol pool has been observed and linked to disease severity in a variety of non-cardiovascular and cardiovascular diseases before, including anemia, diabetes mellitus, inflammatory bowel disease, kidney disease, and myocardial infarction [8, 9, 22–24], which was also investigated and confirmed for anemia and kidney disease in present study. In small exploratory studies including 120 and 101 patients with chronic HF, associations between serum-free thiol concentrations and increased HF severity [25, 26], and poorer clinical outcomes were suggested [26]. We substantiate previous work and extended upon this studies in several ways. Our sample size was almost 40 times larger reducing the risk of a type 1 error. Our study also generalized the link to HF by including patients with HFpEF, whereas the previous studies were limited to patients with a LVEF of < $45\%$. Furthermore, our patient population consisted of less-stable patients with new-onset or worsening HF on suboptimal pharmacological treatment, which were possibly more subjected to increased levels of oxidative stress. We observed robust and independent association between free thiol levels and disease outcomes, whereas the previous studies were underpowered to perform multivariable analyses on outcome. Our findings are consistent with the previous prognostic value of free thiols in a general population-based cohort [27, 28]. In the present study, we did not observe significant effect modifications between free thiols and disease outcomes by predefined subgroups. Lower free thiol levels were consistently associated with signs of systemic or pulmonary congestion and natriuretic peptide levels. Other HF characteristics that were associated with lower free thiol levels were non-ischemic etiology, shorter duration of HF, inpatient study enrollment, and the absence of treatment with an ACEi or ARB. Differences in oxidative stress between ischemic vs dilated cardiomyopathy have been previously attributed to enhanced ROS-induced mitochondrial instability in the latter [29]. The activity of the antioxidant thioredoxin system was also higher in dilated cardiomyopathy, but it has been postulated this might be an indirect reflection of excessive oxidative stress [29, 30]. As for the time from HF diagnosis, we speculate that free thiols might be lower in the early phase of HF due to enhancement of ROS by catecholamines [31], and higher in a later phase due to the effect of pharmacological treatment. Participants that were enrolled as inpatients (acute HF) had likely more congestion and oxidative stress, resulting in lower free thiol levels. The association between the use of an ACEi or ARB might be associated with antioxidant effects of this class of therapy [32, 33], or might be coincidental due to the BIOSTAT-CHF design, which required participants to be on suboptimal pharmacological treatment. We cannot exclude that free thiol concentrations were also influenced by other factors, for example the production of thiol-containing molecules by the liver [7]. ## Therapeutic potential Associations between oxidative stress markers with heart failure severity and outcome have been established before, for example for 8-OHdG, malondialdehyde and uric acid [34, 35]. However, compared with other oxidative stress markers, free thiols constitute the central hubs of inter-organ redox communication [36], whereas malondialdehyde is merely a damage marker representing oxidative stress-induced lipid peroxidation. Moreover, free thiols are amendable for therapeutic modulation, for example by N-acetylcysteine or glutathione administration [10, 37, 38]. Our findings support the notion that thiol supplementation may have therapeutic potential in HF if a causal role of free thiols in the pathophysiology of HF can be addressed. Prior experimental studies showed that administration of N-acetylcysteine resulted in reduced oxidative stress and improved cardiac function in models with HF or ischemic injury [10–13, 39]. Beneficial effects of such thiol-containing drugs were also observed in patients with COVID-19 [40]. Thiol modulating studies in patients with HF are scarce and predominantly small or restricted to nitrate tolerance [14, 16, 41]. It has been suggested that administration of thiol-targeted antioxidants should be reserved for individuals with profound thiol depletion and incautious thiol supplementation could potentially disturb physiological redox signaling processes [42]. Therefore, more mechanistic studies and careful evaluation of treatment effects in patients with lower free thiols, are warranted. In this respect, circulating free thiols may aid in patient stratification to restore the systemic and local redox balance following a redox precision medicine approach. ## Strengths and limitations Strengths of our study include the large sample size, the phenotypically well-characterized patient population, combined with a systematic follow-up. Limitations of our study include the geographic restrictions of our cohort recruiting mainly Caucasians since some studies suggest ethnic and geographic differences in oxidative stress-related gene expression and antioxidant status [43, 44]. We also did not have data on dietary intake or total serum protein levels to make further adjustment for serum-free thiol level [7, 36]. Moreover, participants were enrolled back in 2010–2012, and HF treatments have advanced since then. At last, our results do not prove causality or mechanisms of free thiol depletion. ## Conclusions In patients with new-onset or worsening HF lower serum-free thiol concentrations, indicative of increased oxidative stress, are associated with greater HF severity and a poorer prognosis. If future studies prove a causal role of free thiols in the progression of HF, it might be of interest to study whether free thiol modulation, especially in patients with poor redox status, might improve clinical outcomes. ## Supplementary Information Below is the link to the electronic supplementary material. Supplementary file1 (PDF 209 KB) ## References 1. van der Pol A, van Gilst WH, Voors AA, van der Meer P. **Treating oxidative stress in heart failure: past, present and future**. *Eur J Heart Fail* (2019) **21** 425-435. DOI: 10.1002/ejhf.1320 2. van’t Erve TJ, Kadiiska MB, London SJ, Mason RP. **Classifying oxidative stress by F(2)-isoprostane levels across human diseases: a meta-analysis**. *Redox Biol* (2017) **12** 582-599. DOI: 10.1016/j.redox.2017.03.024 3. Sies H. **Oxidative stress: a concept in redox biology and medicine**. *Redox Biol* (2015) **4** 180-183. DOI: 10.1016/j.redox.2015.01.002 4. Takimoto E, Kass DA. **Role of oxidative stress in cardiac hypertrophy and remodeling**. *Hypertension* (2007) **49** 241-248. DOI: 10.1161/01.HYP.0000254415.31362.a7 5. Hage C, Löfgren L, Michopoulos F, Nilsson R, Davidsson P, Kumar C. **Metabolomic profile in patients with heart failure with preserved ejection fraction versus patients with heart failure with reduced ejection fraction**. *J Card Fail* (2020) **26** 1050-1059. DOI: 10.1016/j.cardfail.2020.07.010 6. Münzel T, Gori T, Keaney JFJ, Maack C, Daiber A. **Pathophysiological role of oxidative stress in systolic and diastolic heart failure and its therapeutic implications**. *Eur Heart J* (2015) **36** 2555-2564. DOI: 10.1093/eurheartj/ehv305 7. Turell L, Radi R, Alvarez B. **The thiol pool in human plasma: the central contribution of albumin to redox processes**. *Free Radic Biol Med* (2013) **65** 244-253. DOI: 10.1016/j.freeradbiomed.2013.05.050 8. Banne AF, Amiri A, Pero RW. **Reduced level of serum thiols in patients with a diagnosis of active disease**. *J Anti Aging Med* (2003) **6** 327-334. DOI: 10.1089/109454503323028920 9. Bourgonje AR, Gabriëls RY, de Borst MH, Bulthuis MLC, Faber KN, van Goor H. **Serum free thiols are superior to fecal calprotectin in reflecting endoscopic disease activity in inflammatory bowel disease**. *Antioxidants* (2019) **8** 351. DOI: 10.3390/antiox8090351 10. Ferrari R, Ceconi C, Curello S, Cargnoni A, Alfieri O, Pardini A. **Oxygen free radicals and myocardial damage: protective role of thiol-containing agents**. *Am J Med* (1991) **91** 95S-105S. DOI: 10.1016/0002-9343(91)90291-5 11. Adamy C, Mulder P, Khouzami L, Andrieu-abadie N, Defer N, Candiani G. **Neutral sphingomyelinase inhibition participates to the benefits of**. *J Mol Cell Cardiol* (2007) **43** 344-353. DOI: 10.1016/j.yjmcc.2007.06.010 12. Bourraindeloup M, Adamy C, Candiani G, Cailleret M, Bourin M-C, Badoual T. *Circulation* (2004) **110** 2003-2009. DOI: 10.1161/01.CIR.0000143630.14515.7C 13. Dludla PV, Dias SC, Obonye N, Johnson R, Louw J, Nkambule BB. **A systematic review on the protective effect of**. *Am J Cardiovasc Drugs* (2018) **18** 283-298. DOI: 10.1007/s40256-018-0275-2 14. Mehra A, Shotan A, Ostrzega E, Hsueh W, Vasquez-Johnson J, Elkayam U. **Potentiation of isosorbide dinitrate effects with**. *Circulation* (1994) **89** 2595-2600. DOI: 10.1161/01.cir.89.6.2595 15. Pasupathy S, Tavella R, Grover S, Raman B, Procter NEK, Du YT. **Early use of**. *Circulation* (2017) **136** 894-903. DOI: 10.1161/CIRCULATIONAHA.117.027575 16. Camuglia AC, Maeder MT, Starr J, Farrington C, Kaye DM. **Impact of**. *Heart Lung Circ* (2013) **22** 256-259. DOI: 10.1016/j.hlc.2012.10.012 17. Voors AA, Anker SD, Cleland JG, Dickstein K, Filippatos G, van der Harst P. **A systems BIOlogy Study to TAilored Treatment in Chronic Heart Failure: rationale, design, and baseline characteristics of BIOSTAT-CHF**. *Eur J Heart Fail* (2016) **18** 716-726. DOI: 10.1002/ejhf.531 18. Ponikowski P, Voors AA, Anker SD, Bueno H, Cleland JGF, Coats AJS. **2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: The Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC)**. *Eur Heart J* (2016) **37** 2129-2200. DOI: 10.1093/eurheartj/ehw128 19. Ellman GL. **Tissue sulfhydryl groups**. *Arch Biochem Biophys* (1959) **82** 70-77. DOI: 10.1016/0003-9861(59)90090-6 20. Hu ML, Louie S, Cross CE, Motchnik P, Halliwell B. **Antioxidant protection against hypochlorous acid in human plasma**. *J Lab Clin Med* (1993) **121** 257-262. PMID: 8381845 21. Voors AA, Ouwerkerk W, Zannad F, van Veldhuisen DJ, Samani NJ, Ponikowski P. **Development and validation of multivariable models to predict mortality and hospitalization in patients with heart failure**. *Eur J Heart Fail* (2017) **19** 627-634. DOI: 10.1002/ejhf.785 22. Boekhoud L, Koeze J, van der Slikke EC, Bourgonje AR, Moser J, Zijlstra JG. **Acute kidney injury is associated with lowered plasma-free thiol levels**. *Antioxidants* (2020) **9** 1135. DOI: 10.3390/antiox9111135 23. Kundi H, Ates I, Kiziltunc E, Cetin M, Cicekcioglu H, Neselioglu S. **A novel oxidative stress marker in acute myocardial infarction; thiol/disulphide homeostasis**. *Am J Emerg Med* (2015) **33** 1567-1571. DOI: 10.1016/j.ajem.2015.06.016 24. Bourgonje AR, Abdulle AE, Bourgonje MF, Binnenmars SH, Gordijn SJ, Bulthuis M. **Serum free sulfhydryl status associates with new-onset chronic kidney disease in the general population**. *Redox Biol* (2021) **48** 102211. DOI: 10.1016/j.redox.2021.102211 25. Radovanovic S, Savic-Radojevic A, Pljesa-Ercegovac M, Djukic T, Suvakov S, Krotin M. **Markers of oxidative damage and antioxidant enzyme activities as predictors of morbidity and mortality in patients with chronic heart failure**. *J Card Fail* (2012) **18** 493-501. DOI: 10.1016/j.cardfail.2012.04.003 26. Koning AM, Meijers WC, Pasch A, Leuvenink HGD, Frenay A-RS, Dekker MM. **Serum free thiols in chronic heart failure**. *Pharmacol Res* (2016) **111** 452-458. DOI: 10.1016/j.phrs.2016.06.027 27. Abdulle AE, Bourgonje AR, Kieneker LM, Koning AM, la Bastide-van GS, Bulthuis MLC. **Serum free thiols predict cardiovascular events and all-cause mortality in the general population: a prospective cohort study**. *BMC Med* (2020) **18** 130. DOI: 10.1186/s12916-020-01587-w 28. Schöttker B, Brenner H, Jansen EHJM, Gardiner J, Peasey A, Kubínová R. **Evidence for the free radical/oxidative stress theory of ageing from the CHANCES consortium: a meta-analysis of individual participant data**. *BMC Med* (2015) **13** 300. DOI: 10.1186/s12916-015-0537-7 29. Neidhardt S, Garbade J, Emrich F, Klaeske K, Borger MA, Lehmann S. **Ischemic cardiomyopathy affects the thioredoxin system in the human myocardium**. *J Card Fail* (2019) **25** 204-212. DOI: 10.1016/j.cardfail.2019.01.017 30. Kishimoto C, Shioji K, Nakamura H, Nakayama Y, Yodoi J, Sasayama S. **Serum thioredoxin (TRX) levels in patients with heart failure**. *Jpn Circ J* (2001) **65** 491-494. DOI: 10.1253/jcj.65.491 31. Costa VM, Carvalho F, Bastos ML, Carvalho RA, Carvalho M, Remião F. **Contribution of catecholamine reactive intermediates and oxidative stress to the pathologic features of heart diseases**. *Curr Med Chem* (2011) **18** 2272-2314. DOI: 10.2174/092986711795656081 32. Münzel T, Gori T, Bruno RM, Taddei S. **Is oxidative stress a therapeutic target in cardiovascular disease?**. *Eur Heart J* (2010) **31** 2741-2748. DOI: 10.1093/eurheartj/ehq396 33. Williams HC, Griendling KK. **NADPH oxidase inhibitors: new antihypertensive agents?**. *J Cardiovasc Pharmacol* (2007) **50** 9-16. DOI: 10.1097/FJC.0b013e318063e820 34. Romuk E, Wojciechowska C, Jacheć W, Zemła-Woszek A, Momot A, Buczkowska M. **Malondialdehyde and uric acid as predictors of adverse outcome in patients with chronic heart failure**. *Oxid Med Cell Longev* (2019) **2019** 9246138. DOI: 10.1155/2019/9246138 35. Di Minno A, Turnu L, Porro B, Squellerio I, Cavalca V, Tremoli E. **8-Hydroxy-2-deoxyguanosine levels and heart failure: a systematic review and meta-analysis of the literature**. *Nutr Metab Cardiovasc Dis* (2017) **27** 201-208. DOI: 10.1016/j.numecd.2016.10.009 36. Cortese-Krott MM, Koning A, Kuhnle GGC, Nagy P, Bianco CL, Pasch A. **The reactive species interactome: evolutionary emergence, biological significance, and opportunities for redox metabolomics and personalized medicine**. *Antioxid Redox Signal* (2017) **27** 684-712. DOI: 10.1089/ars.2017.7083 37. Deneke SM. **Thiol-based antioxidants**. *Curr Top Cell Regul* (2000) **36** 151-180. DOI: 10.1016/s0070-2137(01)80007-8 38. Atkuri KR, Mantovani JJ, Herzenberg LA, Herzenberg LA. **N-Acetylcysteine–a safe antidote for cysteine/glutathione deficiency**. *Curr Opin Pharmacol* (2007) **7** 355-359. DOI: 10.1016/j.coph.2007.04.005 39. Parra-Flores P, Riquelme JA, Valenzuela-Bustamante P, Leiva-Navarrete S, Vivar R, Cayupi-Vivanco J. **The association of ascorbic acid, deferoxamine and**. *Antioxidants* (2019) **8** 614. DOI: 10.3390/antiox8120614 40. Cazzola M, Rogliani P, Salvi SS, Ora J, Matera MG. **Use of thiols in the treatment of COVID-19: current evidence**. *Lung* (2021) **199** 335-343. DOI: 10.1007/s00408-021-00465-3 41. Lang NN, Ahmad FA, Cleland JG, O’Connor CM, Teerlink JR, Voors AA. **Haemodynamic effects of the nitroxyl donor cimlanod (BMS-986231) in chronic heart failure: a randomized trial**. *Eur J Heart Fail* (2021) **23** 1147-1155. DOI: 10.1002/ejhf.2138 42. Andreadou I, Efentakis P, Frenis K, Daiber A, Schulz R. **Thiol-based redox-active proteins as cardioprotective therapeutic agents in cardiovascular diseases**. *Basic Res Cardiol* (2021) **116** 44. DOI: 10.1007/s00395-021-00885-5 43. Sayanthooran S, Magana-Arachchi DN, Gunerathne L, Abeysekera TDJ, Sooriyapathirana SS. **Upregulation of oxidative stress related genes in a chronic kidney disease attributed to specific geographical locations of Sri Lanka**. *Biomed Res Int* (2016) **2016** 7546265. DOI: 10.1155/2016/7546265 44. Gut A, Shiel N, Kay PM, Segal I, Braganza JM. **Heightened free radical activity in blacks with chronic pancreatitis at Johannesburg, South Africa**. *Clin Chim Acta* (1994) **230** 189-199. DOI: 10.1016/0009-8981(94)90271-2
--- title: Evaluation of ten (10) SARS-CoV-2 rapid serological tests in comparison with WANTAI SARS-CoV-2 ab ELISA in Burkina Faso, West Africa authors: - Henri Gautier Ouedraogo - Abdou Azaque Zoure - Tegwinde Rebeca Compaoré - Herve Ky - Sylvie Zida - Dezemon Zingué - Oumarou Ouedraogo - Serge Théophile Soubeiga - Tani Sagna - Charlemagne Dabiré - Dinanibè Kambiré - Dramane Zongo - Albert Théophane Yonli - Abdoul Rahamani Nikiema - Désiré Nezien - Gnintassa Cyrille Bansé - Brice Wilfried Bicaba - Sophie Perier - Charles Sawadogo - Zakariya Yabre - Lassana Sangare journal: Virology Journal year: 2023 pmcid: PMC10062271 doi: 10.1186/s12985-023-02011-4 license: CC BY 4.0 --- # Evaluation of ten (10) SARS-CoV-2 rapid serological tests in comparison with WANTAI SARS-CoV-2 ab ELISA in Burkina Faso, West Africa ## Abstract ### Background The aim of this study was to evaluate the performance of ten [10] SARS-CoV-2 serological rapid diagnostic tests in comparison with the WANTAI SARS-CoV-2 Ab ELISA test in a laboratory setting. ### Materials and methods Ten [10] SARS-CoV-2 serological rapid diagnostic tests (RDTs) for SARS-CoV-2 IgG/IgM were evaluated with two [2] groups of plasma tested positive for one and negative for the other with the WANTAI SARS-CoV-2 Ab ELISA. The diagnostic performance of the SARS-CoV-2 serological RDTs and their agreement with the reference test were calculated with their $95\%$ confidence intervals. ### Results The sensitivity of serological RDTs ranged from 27.39 to $61.67\%$ and the specificity from 93.33 to $100\%$ compared to WANTAI SARS-CoV-2 Ab ELISA test. Of all the tests, two tests (STANDARD Q COVID-19 IgM/IgG Combo SD BIOSENSOR and COVID-19 IgG/IgM Rapid Test (Zhejiang Orient Gene Biotech Co., Ltd)) had a sensitivity greater than $50\%$. In addition, all ten tests had specificity greater than or equal to $93.33\%$ each. The concordance between RDTs and WANTAI SARS-CoV-2 Ab ELISA test ranged from 0.25 to 0.61. ### Conclusion The SARS-CoV-2 serological RDTs evaluated show low and variable sensitivities compared to the WANTAI SARS-CoV-2 Ab ELISA test, with however a good specificity. These finding may have implications for the interpretation and comparison of COVID-19 seroprevalence studies depending on the type of test used. ## Introduction The recommended reference technique for the diagnosis of COVID-19 is “Reverse transcription polymerase chain reaction” (RT-PCR) test on respiratory samples [1, 2]. The diagnostic result by this technique is usually obtained within four hours. The high cost and time constraints associated with RT-PCR have led to the emergence of alternative diagnostic methods, including antigenic tests or serological tests for the diagnosis of SARS-CoV-2 [1]. These tests are generally based on the lateral flow immunochromatographic principle, which is simple to use and provides results in less than 30 min, or the automated enzyme-linked immunosorbent technique with a delay of approximately 1.5 min for results [1, 3]. Automated serological tests can be categorized according to the reading platforms used to detect SARS-CoV-2 antibodies [4]. They include Enzyme-Linked Immunosorbent Assay (ELISA) and Sandwich Enzyme Immunoassay with Final Fluorescence Detection (FEIA), as well as Chemiluminescence Immunoassay (CLIA), Chemiluminescent Microparticle Immunoassay (CMIA) and Electrochemiluminescence Immunoassay (ECLIA) [4]. These require specific laboratory equipment that are not available in resource-limited settings, often resulting in the use of rapid antibody diagnostic tests both in the laboratory and in seroprevalence studies (Zhao et al., 2020). In addition, evaluations’ results in the literature show, however, a great variability in the diagnostic performance of commercially available serological tests [5–11]. The vast majority of the evaluations performed have been carried out by comparison with RT-PCR [8, 9, 12–14]. One of the main limitations of RT-PCR is the risk of false negative and sometimes false positive results. [ 15, 16]. False-negative or false-positive results of RT-PCR tests may result in a decrease in the specificity and sensitivity, respectively, of the serological tests being evaluated. In addition to the risk of false-positive RT-PCR results [17–19], studies have shown that some patients infected with SARS-CoV-2 do not produce antibodies [20, 21]. The use of RT-PCR as a reference may therefore lead to an underestimation of the sensitivity of serological tests if such patients are included in the sample panel used. The use of a reliable serological test could help to eliminate these undetectable SARS-CoV-2 specific antibody producing patients from the evaluation sample panels. Thus, this study proposed to evaluate the performance of ten [10] immunochromatographic tests for the rapid detection of SARS-CoV-2 antibodies in comparison with the WANTAI SARS-CoV-2 Ab ELISA, one of the tests that has shown good performance through several independent evaluations in the literature [9, 10]. ## Study design This was an evaluation of the COVID-19 IgG/IgM rapid serological diagnostic tests at the Biomedical Research Laboratory (LaReBio), one of the COVID-19 diagnostic laboratories in Ouagadougou, Burkina Faso. ## Composition of the sample panel The rapid serological tests were evaluated using two [2] panels of human plasma previously tested for the presence or absence of SARS-CoV-2 antibodies with the WANTAI SARS-CoV-2 Ab ELISA kit on the “Elisys Uno” automated machine (Human, Germany). All plasma samples were collected between December 2020 and April 2021, before the introduction of vaccination against COVID-19 in Burkina Faso. Venous blood samples were collected using EDTA tubes and centrifuged at 3000 rpm for 10 min to separate the plasma. The plasma was then used to perform the serological tests. Blood samples were collected independently of the history of SARS-CoV-2 infection. ## Panels of positive and negative samples The positive panel consisted of 157 SARS-CoV-2 antibodies positive plasma with the WANTAI SARS-CoV-2 Ab ELISA. The negative panel consisted of 157 SARS-CoV-2 antibodies negative plasma confirmed by the WANTAI SARS-CoV-2 Ab ELISA test. ## Index tests (serological tests in evaluation) All SARS-CoV-2 serological rapid diagnostic tests (RDTs) evaluated were rapid lateral flow immunochromatographic tests for the qualitative detection of IgG/IgM antibodies to SARS-CoV-2 in either whole blood and/or plasma and serum [22]. They consist of a test membrane and a plastic cassette. The test cassette displays the letters C (control line), G (the IgG test line) and M (the IgM test line) on the right side of the reading window and the letter S (the sample well) above the sample well of the cassette. To use the test, the sample is applied first to the sample well S, then 2–3 drops of the buffer solution will be added. The sample and buffer mixture migrates along the test membrane to the reading window. On the nitrocellulose membrane inside the reading window, human anti-IgG and anti-IgM antibodies are present in the G-zone and M-zone respectively, and a goat anti-rabbit antibody is present in the C-zone. If the sample is positive for SARS-CoV-2 IgG, the G line will appear. If the sample is positive for SARS-CoV-2 IgM, the M line will appear. The validity of the test is indicated by the appearance of the C line regardless of the G and/or M result [23]. The ten index serological tests were: (T1) COVID-19 IgG/IgM Rapid Test: (Whole blood/Serum/Plasma) Sienna TM; (T2) COVID-19 BSS (IgG/IgM) BIOSYNEX; (T3) COVID-19 IgG/IgM cassette (whole blood/serum/plasma) ACCU-Tell, (T4) COVID-19 IgG/IgM Rapid Test (whole blood/serum/plasma) InnoScreen™; (T5) COVID-19 IgG/IgM Rapid Test Device (WB/S/P) Safecare Bio-Tech; (T6) COVID-19 IgG/IgM Rapid Test (Whole blood/Serum/Plasma); (T7) 2019-nCOV IgG/IgM Rapid test Device Hangzhou Realy Tech; (T8) COVID-19 IgG/IgM Rapid Test Cassette (Whole blood/Serum/Plasma) Zhejiang Orient Gene Biotech Co.,Ltd (T9) Standard Q COVID-19 IgM/IgG Combo SD Biosensor; (T10) Panbio COVID-19 IgG/IgM RAPID test device (fingerstick whole blood/venous whole blood/serum/plasma Abbott; The characteristics of these tests according to their manufacturers are shown in Table 1. Table 1Characteristics of the reference test (WANTAI SARS-CoV-2 Ab ELISA) and index tests (RDTs) according to the manufacturersCharacteristicsReference testIndex testsWANTAI SARS-CoV-2 Ab ELISACOVID-19 IgG/IgM Rapid Test:(Whole blood/Serum/Plasma)COVID-19 BSS (IgG/IgM)COVID-19 IgG/IgM CASSETTE(Whole blood/Serum/Plasma)COVID-19 IgG/IgM Rapid Test(Whole blood/Serum/Plasma)COVID-19 IgG/IgM RAPID TEST DEVICE(WB/S/P)COVID-19 IgG/IgM Rapid Test(Whole blood/Serum/Plasma)2019-nCOV IgG/IgM Rapid test DeviceCOVID-19 IgG/IgM RapidCassette test(Whole blood/Serum/Plasma)STANDARD QCOVID-19 IgM/IgG ComboPanbioCOVID-19 IgG/IgM RAPID TEST DEVICEManufacturer’s nameBeijing Wantai BiologicalSienna TMBiosynexACCU-TellInnoScreen™SafeCare BioTechMissingHangzhou Realy TechZhejiang OrientGene Biotech Co.,LtdSD BiosensorAbbottFormatLiquidCassetteCassetteCassetteCassetteCassetteCassetteCassetteCassetteCassetteCassettePrinciple of the testAntigen “sandwich” enzyme immunoassayImmunochromatographyImmunochromatographyImmunochromatographyImmunochromatographyImmunochromatographyImmunochromatographyImmunochromatographyImmunochromatographyImmunochromatographyImmunochromatographyAntibodies detectedTotal AbIgM and IgGIgM and IgGIgM and IgGIgM and IgGIgM and IgGIgM and IgGIgM and IgGIgM and IgGIgM and IgGIgM and IgGProduct codeWS-109610,222 A---ABT-IT-B352WCOV-23 M----K4602160GCCOV-402aQ-NCOV-01 C QC05020028AICO-T402PLot numberNCOA2021050120,050,504COV200400782,020,032,705SR202004013NC0200326032,020,025N01G16T2,004,1552020.06.01COV0042014Samples usedserum/ plasmaWhole blood/serum/ plasmaWhole blood/serum/ plasmaWhole blood/serum/ plasmaWhole blood/serum/ plasmaWhole blood/serum/ plasmaWhole blood/serum/ plasmaWhole blood/serum/ plasmaWhole blood/serum/ plasmaWhole blood/serum/ plasmaWhole blood/serum/ plasmaStorage (°C)+ 2–8°+ 2–30°+ 2–30°+ 2–30°+ 2–30°+ 2–30°+ 2–30°+ 2–30°+ 2–30°+ 2–30°+ 2–30°Reading/Interpretationplate reader, wavelength $\frac{450}{600}$ ~ 650 nmVisualvisualvisualvisualvisualvisualvisualvisualvisualvisualTime to obtain results (min)901010101510101021010Time to read results (max)1020202020151515151520Composition of the kitaccccccccccConsumables required but not suppliedbbbbbbbbbbbSensitivity according to the manufacturer (%)98.72(> 15 days from onsetof symptoms)91.76 (IgM) and 88.24 (IgG)100 (IgM) and 91.8 (IgG)91.8 (IgM) and 100 (IgG)93.7 (IgM) and 98.8 (IgG)Not providedNot provided92 (IgM) and 96 (IgG)87.9 (IgM) and 97.2 (IgG)94.51 combined sensitivity56.25 (IgM) and 95.83 (IgG)Specificity according to the manufacturer (%)98.6099.16 (IgM) and 99.46 (IgG)99.5 (IgM) and 99.2 (IgG)99.2 (IgM) and 99.5 (IgG)97.7 (IgM) and 98.7 (IgG)100 (IgM) and 100 (IgG)100 (IgM) and 100 (IgG)95.74Combined specificity94 (IgM) and 100 (IgG)Combined specificitya: Microwell plate, Cardboard plate cover, Negative calibrator, Positive calibrator, Horseradish peroxidase-conjugated, wash buffer, chrongen solution A, Chromogen solution B, Stop solution, Instruction for user (IFU); b.Freshly distilled or deionized water, disposable gloves and timer, appropriate waste containers for potentially contaminated materials, dispensing system and/or pipette, disposable pipette tips, absorbent tissue or clean towel, dry incubator or water bath, 37 ± 1 °C, plate reader, single wavelength 450 nm or dual wavelength $\frac{450}{600}$ ~ 650 nm, microwell aspiration/wash system;. c:Sampling tube, centrifuge for plasmas, micropipettes, stopwatch, lancets for capillary samples; d:IFU, cassettes, buffer, capillary tubes. ## Reference test: WANTAI SARS-CoV-2 Ab ELISA WANTAI SARS-CoV-2 Ab ELISA is an Enzyme-Linked Immunosorbent Assay (ELISA) intended for qualitative detection of total antibodies (including IgG and IgM) to SARS-CoV-2 in human serum or plasma [24]. It is a two-step incubation antigen “sandwich” enzyme immunoassay kit, which uses polystyrene microwell strips pre-coated with recombinant SARS-CoV-2 antigen. The antigen used in the assay is the receptor-binding domain of SARS-CoV-2 spike protein. Patient’s serum or plasma specimen is added, and during the first incubation, the specific SARS-CoV-2 antibodies will be captured inside the wells if present [24]. The microwells are then washed to remove unbound serum proteins. Second recombinant SARS-CoV-2 antigen conjugated to the enzyme Horseradish Peroxidase (HRP-Conjugate) is added, and during the second incubation, the conjugated antigen will bind to the captured antibody inside the wells. The microwells are then washed to remove unbound conjugate, and Chromogen solutions are added into the wells. In wells containing the antigen-antibody-antigen (HRP) “sandwich” immunocomplex, the colorless Chromogens are hydrolyzed by the bound HRP conjugate to a blue colored product. The blue color turns yellow after the reaction is stopped with sulfuric acid. The amount of color intensity can be measured and it is proportional to the amount of antibody captured inside the wells, and to the specimen respectively. Wells containing specimens negative for SARS-CoV-2 antibodies remain colorless. According to the manufacturer (Beijing Wantai Biological), clinical validation study of WANTAI SARS-CoV-2 Ab ELISA was observed that the detection rate of the test was closely related to the time of disease onset, the test showed higher positive detection rate in specimens from patients with long time post onset of first symptom. The test sensitivity was 55,$38\%$ for less than 7 days from symptoms; 84,$78\%$ between 8 and 14 day from symptoms and 98,$72\%$ for more than 15 days from symptoms [24]. In addition to the performance provided by WANTAI SARS-CoV-2 Ab ELISA compared to others [9, 10]. ## Panel plasma analysis The serological RDTs were evaluated using the WANTAI SARS-CoV-2 Ab ELISA positive and negative specimen (plasma). All tests were used according to the manufacturers’ specifications and the Good Laboratory Practices (GLP). Due to insufficient numbers of tests, the STANDARD Q COVID-19 IgM/IgG Combo and Panbio COVID-19 IgG/IgM RAPID test device were evaluated with only 60 positive and 60 negative samples, compared to 157 positive and 157 negative samples for the other eight RDTs. To avoid comparison of results between tests during laboratory analysis, each rapid test under evaluation was tested in one run with all samples in the panel before moving on to another test. The RDT result was considered positive if it detected IgG and/or IgM antibodies, and negative if no antibodies were detected. ## Statistical analysis Data were entered into Excel and then analyzed using OpenEpi software The results obtained with the serological RDT were compared with those of the ELISA, and the main performance characteristics of the RDT were determined. For this purpose, the results of each RDT were classified into 2 categories (positive or negative results). In relation to the known results of the serological ELISA (reference to the serological RDT), the RDT results were classified into true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN) on a double entry contingency table (Table 2). Test sensitivity (capacity to capture all true positives) was calculated according to the formula (TP)/(TP + FN), and diagnostic specificity (capacity to rull out all true negatives) was calculated according to the formula (TN)/(TN + FP). In addition to the two main characteristics (Sensitivity and Specificity) of the diagnostic performance of the test, other test-specific parameters such as positive predictive value (PPV, the probability that the plasma sample has the COVID-19 antibodies when restricted to those plasma who test positive) and negative predictive value (NPV, the probability that the plasma sample has not the COVID-19 antibodies when restricted to those plasma who test negative): PPV = TP/TP + FP and NPV = TN/TN + FN); the positive and negative likelihood ratios (LRP and LRN); and the Kappa Coefficient of agreement between RDT and ELISA. These characteristics were calculated with their $95\%$ confidence intervals. The results of these calculations were expressed as a percentage. The Kappa coefficient of agreement was interpreted according to the criteria of Landis and Koch [25] as follows: Kappa < 0, no agreement; 0 < kappa ≤ 0.2, slight agreement; 0.2 < kappa < 0.4, fair agreement; 0.4 < kappa ≤ 0.6,moderate agreement; 0.6 < kappa ≤ 0.8, substantial agreement; 0.8 < kappa ≤ 1, near perfect agreement. Table 2Cross tabulation of the index test results by the reference standard’s resultsIndex testsResultsWANTAI SARS-CoV-2 Ab ELISAPositive (n)Negative (n)TotalCOVID-19 IgG/IgM Rapid Test (Whole blood/Serum/Plasma) Sienna TMPositive 71 03 74 Negative 86 154 240 Total157157314COVID-19 BSS (IgG/IgM), BiosynexPositive760177Negative81156237Total157157314COVID-19 IgG/IgM cassette (whole blood/serum/plasma)ACCU-TellPositive640165Negative93156249Total157157314COVID-19 IgG/IgM Rapid Test (whole blood/serum/plasma)InnoScreen™Positive770481Negative80153233Total157157314COVID-19 IgG/IgM Rapid test device (WB/S/P)Safecare Bio-TechPositive620365Negative95154249Total157157314COVID-19 IgG/IgM Rapid Test (Whole blood/Serum/Plasma)Manufacturer name was missing (anonymous)Positive770178Negative80156236Total1571573142019-nCOV IgG/IgM Rapid test DeviceHangzhou Real TechPositive430346Negative114154268Total157157314 COVID-19 IgG/IgM Rapid Test Cassette (Whole blood/Serum/Plasma) Zhejiang Orient Gene Biotech Co.,Ltd Positive810485Negative76153229Total157157314STANDARD Q COVID-19 IgM/IgG Combo*SD BiosensorPositive370037Negative236083Total6060120Panbio COVID-19 IgG/IgM RAPID test device*AbbottPositive240428Negative365692Total6060120* STANDARD Q COVID-19 IgM/IgG Combo (SD BIOSENSOR) and Panbio COVID-19 IgG/IgM RAPID test device (Abbott) were evaluated with 120 samples of which 60 were positive and 60 were negative for WANTAI SARS-CoV-2 Ab ELISA. ## Test performances Tables 2 and 3 show the comparison results between the rapid tests and the reference test. Compared to the WANTAI SARS-CoV-2 Ab ELISA test, the results generally show that the serological RDTs have specificities ranging from 93.33 to $100\%$. However, all the RDTs evaluated had a sensitivity of less than $65\%$. The lowest sensitivity was $27.39\%$ (21.02–34.84) observed with the 2019-nCOV IgG/IgM Rapid test Device (HANGZHOU REALY TECH), and the highest was $61.67\%$ (49.02–72.91) obtained for the STANDARD Q COVID-19 IgM/IgG Combo SD Biosensor. For nine of the ten RDTs, the sensitivity was less than $50\%$ compared to the reference test. These are COVID-19 IgG/IgM Rapid Test, Sienna TM (T1); COVID-19 BSS (IgG/IgM) Biosynex (T2); COVID-19 IgG/IgM cassette (plasma) ACCU-Tell (T3); COVID-19 IgG/IgM Rapid Test, InnoScreen™ (T4); COVID-19 IgG/IgM Rapid test device, Safecare Bio-Tech (T5); COVID-19 IgG/IgM Rapid Test (T6); 2019-nCOV IgG/IgM Rapid test Device Hangzhou Realy Tech (T7), COVID-19 IgG/IgM Rapid Test Cassette, Zhejiang Orient Gene Biotech Co.,Ltd (T8) STANDARD Q COVID-19 IgM/IgG Combo SD Biosensor (T9); Panbio COVID-19 IgG/IgM RAPID test device, Abbott (T10). The negative predictive value ranged from $57.46\%$ (51.48–63.24) for the least sensitive test to $72.29\%$ (61.84–80.77) for the most sensitive. As for the positive predictive values (PPV), the lowest was $85.71\%$ (68.51–94.3) for the least specific test (Panbio™ COVID-19 IgG/IgM Rapid test device) to $100\%$ for the most specific (STANDARD Q COVID-19 IgM/IgG Combo SD Biosensor). Two tests had a kappa value of agreement with ELISA test between 0.2 and 0.4 (COVID-19 IgG/IgM RAPID TEST DEVICE (WB/S/P) Safecare Bio-Tech and 2019-nCOV IgG/IgM Rapid test Device Hangzhou Realy Tech). While the concordance of eight tests with WANTAI SARS-CoV-2 Ab ELISA test was between 0.41 and 0.6. The only test that recorded a Kappa coefficient value greater than 0.6 was the STANDARD Q COVID-19 IgM/IgG Combo SD Biosensor. ( Table 3). Table 3Estimates of diagnostic accuracy and their precision (such as $95\%$ confidence intervals)COVID-19 IgG/IgM RapidTestSienna TMCOVID-19IgG/IgM RapidTestBiosynexCOVID-19 IgG/IgMCassetteACCU-TellCOVID-19 IgG/IgMRapid Test InnoScreen™COVID-19 IgG/IgMRapid test Device WB/S/PSafecare Bio-TechParametersEstimate$95\%$CIEstimate$95\%$CIEstimateEstimateEstimate$95\%$CIEstimate$95\%$CISensitivity (%)45.2237.64–53.0348.4140.72–56.1739.4939.4949.0441.34–56.7939.4932.18–47.3Specificity (%)98.0994.53–99.3599.3696.48–99.8998.0998.0997.4593.63-99.098.0994.53–99.35 PPV (%) 95.9588.75–98.6198.793,0-99.7795.3895.3895.0687.98–98.0695.3887.29–98.42 NPV (%) 64.1757.92–69.9765.8259.57–71.5761.8561.8565.6759.36–71.4661.8555.68–67.66 PLR 23.6711.91–47.0376.010.42–55.4620.6720.6719.2511.49–32.2620.6710.24–41.69 NLR 0.560.54–0.570.520.51–0.530.620.620.520.51–0.530.620.60–0.63Kappa0.43310.34–0.520.480.38–0.570.370.370.460.37–0.560.370.29–0.46Accuracy (%)71.6666.43–76.3673.8968.76–78.4368.7968.7973.2568.09–77.8468.7963.46–73.66 COVID-19 IgG/IgM Rapid Test Whole blood/Serum/Plasma 2019-nCOV IgG/IgM Rapid test Device Hangzhou Realy Tech COVID-19 IgG/IgM Rapid Test (Zhejiang Orient Gene Biotech Co.-Ltd) STANDARD Q COVID-19 IgM/IgG Combo SD Biosensor Panbio COVID-19 IgG/IgM Rapid test Device Abbott ParametersEstimate$95\%$CIEstimate$95\%$CIEstimate$95\%$CIEstimate$95\%$CIEstimate$95\%$CISensitivity (%)49.0441.34–56.7927.3921.02–34.8451.5943.83–59.2861.6749.02–72.914028.57–52.63Specificity (%)99.3696.48–99.8998.0994.53–99.3597.4593.63-9910093.98–10093.3384.07–97.38 PPV (%) 98.7293.09–99.7793.4882.5- 97.7695.2988.52–98.1610090.59–10085.7168.51–94.3 NPV (%) 66.159.85–71.8457.4651.48–63.2466.8160.48–72.5972.2961.84–80.7760.8750.65–70.21 PLR 77.010.56–561.314.336.61–31.0920.2512.13–33.81Undefinedundefined’.6.03.25–11.07 NLR 0.510.50–0.520.740.73–0.750.500.48–0.510.380.35–0.410.640.61–0.68Kappa0.480.39–0.580.250.18–0.330.490.39–0.590.620.45–0.780.330.18–0.48Accuracy (%)74.269.09–78.7362.7457.27–67.974.5269.43–79.0380.8372.88–86.8866.6757.83–74.47Legend: Se: Sensitivity, Sp: Specificity, PPV: positive predictive value, NPV = negative predictive value, PLR: positive likelihood ratio, NLR: negative likelihood ratio. ## Discussion This study evaluated the performance of rapid serological tests (RDTs) for SARS-CoV-2 compared to the WANTAI SARS-CoV-2 Ab ELISA test as reference. It shows that the performance of serological RDTs ranged from 27.39 to $61.67\%$ for sensitivity, while specificity varied from 97.45 to $100\%$ depending on the brand of the test. The highest sensitivities in our study were obtained for COVID-19 IgG/IgM Rapid Test, Zhejiang Orient Gene Biotech Co., Ltd and STANDARD Q COVID-19 IgM/IgG Combo SD BIOSENSOR with $51.59\%$ and $61.67\%$ respectively. These two tests also ranked with the highest specificities. Of note, none of the tests evaluated had reached the sensitivity announced by the manufacturer. The literature reported that most rapid serological tests have lower sensitivity than ELISA tests [22, 26, 27]. The sensitivities of COVID-19 ELISA tests for IgG/IgM or IgG and IgM ranged from 75 to $93\%$ depending to the studies, while for rapid tests they ranged from 36 to $100\%$. The specificities reported in these studies were similar between ELISA serological tests (91.9–$100\%$) and rapid tests ($89\%$ and $100\%$) [27]. In a systematic review, Lisboa Bastos et al.,. reported lower combined sensitivities for serological RDTs ($66\%$, $95\%$CI: 49.3–79.3) than for ELISAs ($84.3\%$, $95\%$CI: 75.6–90.9) [26]. *In* general, the weak sensitivity of serological tests are more marked in asymptomatic subjects than in symptomatic subjects because the production of SARS-CoV-2 antibodies would be greater in symptomatic subjects than in asymptomatic ones [28]. Mercado et al. evaluated the clinical performance of nine serological RDTs compared to RT-PCR and found that their sensitivity was less than $40\%$ in asymptomatic patients [8]. In symptomatic subjects, however, the sensitivity of the tests ranged from 0,0 to $64.2\%$ for IgM and 11.11-$33.30\%$ for IgG during the first 8 days of symptoms, and from 37.50 to $93.75\%$ for IgM and 70.83-$93.75\%$ for IgG between 8ème and 11ème days [8]. Another study evaluating serological tests including RDTs also found that these had sensitivities ranging from 51.80 to $67.90\%$, and specificities ranging from 95.6 to $100.0\%$. [ 29]. Vásárhelyi B, Kristóf et al. obtained even lower sensitivities of $33.30\%$ and $35.48\%$ for the Ahui and Clungene tests respectively [7], comparable to the sensitivity of some of the RDTs evaluated in our study. In addition to the notion of symptoms, the performance of COVID-19 serological RDTs compared to ELISA tests may vary between brands/manufacturers. A study comparing the performance of COVID-19 serological RDTs and ELISA tests in the detection of SARS-CoV-2 antibodies in subjects who have been symptomatic for more than 14 days found high sensitivity for some RDTs (> $95\%$ for some RDTs (ACRO Biotech and VivaChek Laboratories), comparable to that of ELISA methods ($96\%$ for WANTAI SARS-CoV-2 Ab ELISA and Vircell® IgG), while other RDTs showed lower sensitivity ($66.7\%$ for Coris-Bioconcept) [9]. However, this study involved a very limited number of samples. Regarding RDTs specificities, except for the Panbio™ COVID-19 IgG/IgM (Abbott) (Sp: $93.33\%$), the evaluated tests, showed good specificity in the detection of SARS-CoV-2 antibodies (specificity ≥ $95\%$ compared to WANTAI SARS-CoV-2 Ab ELISA). The specificities reported in our study show that the RDTs evaluated have a high probability of detecting negative subjects, and providing few false-positive results. Several studies had already concluded that the specificities of the serological RDTs varied widely. Some studies have reported specificities close to $98\%$ ($96.7\%$ for WONDFO®) while others have reported specificities close to $50\%$ [11] while others report lower specificities ($72.85\%$ for Ahui and $85.02\%$ for Clungene) [7]. The high specificity of the RDTs evaluated in our study reinforce their positive predictive values (PPV). These positive predictive values, defined as the probability that the subject tested positive using the test is indeed positive for SARS-CoV-2 antibodies, ranged from 85.71 to $100\%$. The negative predictive value (NPV) ranged from 66.1 to $72.29\%$, representing the probability that subjects who tested negative with the index tests were negative for SARS-CoV-2 antibodies. Vásárhelyi B, Kristóf et al., in 2020 found PPVs of $7.28\%$ and $13.13\%$ for Ahui and Clungene respectively, [7]; even lower values than our study. Agreement between serological RDTs and the WANTAI SARS-CoV2 Ab ELISA was ‘fair’ for nine of the ten tests (kappa = 0.25 to 0.49), and ‘moderate’ for only one test, the STANDARD Q COVID-19 IgM/IgG Combo SD Biosensor (kappa = 0.61). The latter has the best overall value in the detection of SARS-CoV-2 antibodies with an estimated diagnostic accuracy of $80.83\%$. Our study has a number of limitations. The reference test used was the WANTAI SARS-CoV2 Ab ELISA for the qualitative detection of total antibodies to SARS-CoV-2 in human specimens. It does not allow separate assessment of the sensitivity and specificity of IgG and IgM of each of the ten index tests. Also, it is recognized that the detection rate of serological tests is closely related to the presence or absence of symptoms and the time of onset of symptoms, which our study did not report. Finally, most of the studies found in the literature on the evaluation of serological tests have used samples taken from symptomatic patients after a RT-PCR positivity of at least 7 to 21 days as reference. Despite these limitations, our study, which directly uses an ELISA test as a reference for rapid serological tests, is providing information to guide the choice and use between various types of serological tests in different contexts, such as in seroprevalence studies often performed in populations independently of the notions of history or delay of symptoms of COVID-19. ## Conclusion The COVID-19 serological RDTs evaluated in this study show variable, and low, sensitivities compared to the WANTAI COVID-19 Ab ELISA as reference. No tests meet the $95\%$ sensitivity criteria required for use in the serological diagnosis of COVID-19, regardless of history or time of onset of COVID-19 symptoms. On the other hand, the specificity of RDTs compared to WANTAI COVID-19 Ab ELISA remains relatively good. The results of this study should be interpreted with caution because serological tests generally have a better positive detection rate in specimens from symptomatic patients with a long period after the onset of symptoms. However, ours finding may have implications for the interpretation and comparison of COVID-19 seroprevalence studies depending on the type of test used. ## References 1. Benzigar MR, Bhattacharjee R, Baharfar M, Liu G. **Current methods for diagnosis of human coronaviruses: pros and cons**. *Anal Bioanal Chem* (2021.0) **413** 2311-30. DOI: 10.1007/s00216-020-03046-0 2. Teymouri M, Mollazadeh S, Mortazavi H, Naderi Ghale-Noie Z, Keyvani V, Aghababaei F. **Recent advances and challenges of RT-PCR tests for the diagnosis of COVID-19**. *Pathol Res Pract* (2021.0) **221** 153443. DOI: 10.1016/j.prp.2021.153443 3. Tantuoyir MM, Rezaei N. **Serological tests for COVID-19: potential opportunities**. *Cell Biol Int* (2021.0) **45** 740-8. DOI: 10.1002/cbin.11516 4. Shi AC, Ren P. **SARS-CoV-2 serology testing: Progress and challenges**. *J Immunol Methods* (2021.0) **494** 113060. DOI: 10.1016/j.jim.2021.113060 5. Wolf J, Kaiser T, Pehnke S, Nickel O, Lübbert C, Kalbitz S. **Differences of SARS-CoV-2 serological test performance between hospitalized and outpatient COVID-19 cases**. *Clin Chim Acta* (2020.0) **511** 352-9. DOI: 10.1016/j.cca.2020.10.035 6. Vengesai A, Midzi H, Kasambala M, Mutandadzi H, Mduluza-Jokonya TL, Rusakaniko S. **A systematic and meta-analysis review on the diagnostic accuracy of antibodies in the serological diagnosis of COVID-19**. *Syst Reviews* (2021.0) **10** 155. DOI: 10.1186/s13643-021-01689-3 7. Vásárhelyi B, Kristóf K, Ostorházi E, Szabó D, Prohászka Z, Merkely B. **The diagnostic value of rapid anti IgM and IgG detecting tests in the identification of patients with SARS CoV-2 virus infection**. *Orv Hetil* (2020.0) **161** 807-12. DOI: 10.1556/650.2020.31859 8. Mercado M, Malagón-Rojas J, Delgado G, Rubio VV, Muñoz Galindo L, Parra Barrera EL. **Evaluation of nine serological rapid tests for the detection of SARS-CoV-2**. *Rev Panam Salud Publica* (2020.0) **44** e149. DOI: 10.26633/RPSP.2020.149 9. Hanssen DAT, Slaats M, Mulder M, Savelkoul PHM, van Loo IHM. **Evaluation of 18 commercial serological assays for the detection of antibodies against SARS-CoV-2 in paired serum samples**. *Eur J Clin Microbiol Infect Dis* (2021.0) **40** 1695-703. DOI: 10.1007/s10096-021-04220-7 10. Harritshøj LH, Gybel-Brask M, Afzal S, Kamstrup PR, Jørgensen CS, Thomsen MK. **Comparison of 16 serological SARS-CoV-2 immunoassays in 16 Clinical Laboratories**. *J Clin Microbiol* (2021.0) **59** e02596-20. DOI: 10.1128/JCM.02596-20 11. 11.Breva SM, Clavero CM, Bertomeu FG, Picó-Plana E, Orús NS, Sánchez IP et al. Evaluation of five immunoassays and one lateral flow immunochromatography for anti-SARS-CoV-2 antibodies detection. Enfermedades Infecciosas Y Microbiologia Clinica. 10.1016/j.eimc.2020.12.002. 12. Gong F, Wei H, Li Q, Liu L, Li B. **Evaluation and comparison of serological methods for COVID-19 diagnosis**. *Front Mol Biosci* (2021.0) **8** 682405. DOI: 10.3389/fmolb.2021.682405 13. 13.Bond K, Nicholson S, Lim SM, Karapanagiotidis T, Williams E, Johnson D et al. Evaluation of Serological Tests for SARS-CoV-2: Implications for Serology Testing in a Low-Prevalence Setting.J Infect Dis. 2020:jiaa467. 14. Haguet H, Douxfils J, Eucher C, Elsen M, Cadrobbi J, Tré-Hardy M. **Clinical performance of the Panbio assay for the detection of SARS-CoV-2 IgM and IgG in COVID-19 patients**. *J Med Virol* (2021.0) **93** 3277-81. DOI: 10.1002/jmv.26884 15. 15.Sule WF, Oluwayelu DO. Real-time RT-PCR for COVID-19 diagnosis: challenges and prospects. The Pan African Medical Journal. 2020;35 Suppl 2. 16. Parmar H, Montovano M, Banada P, Pentakota SR, Shiau S, Ma Z. **RT-PCR negative COVID-19**. *BMC Infect Dis* (2022.0) **22** 149. DOI: 10.1186/s12879-022-07095-x 17. 17.Liu R, Han H, Liu F, Lv Z, Wu K, Liu Y et al. Positive rate of RT-PCR detection of SARS-CoV-2 infection in 4880 cases from one hospital in Wuhan, China, from Jan to Feb 2020. Clinica Chimica Acta; International Journal of Clinical Chemistry. 2020;505:172. 18. Healy B, Khan A, Metezai H, Blyth I, Asad H. **The impact of false positive COVID-19 results in an area of low prevalence**. *Clin Med* (2021.0) **21** e54. DOI: 10.7861/clinmed.2020-0839 19. Braunstein GD, Schwartz L, Hymel P, Fielding J. **False positive results with SARS-CoV-2 RT-PCR tests and how to evaluate a RT-PCR-Positive test for the possibility of a false positive result**. *J Occup Environ Med* (2021.0) **63** e159. DOI: 10.1097/JOM.0000000000002138 20. Pal R, Sachdeva N, Mukherjee S, Suri V, Zohmangaihi D, Ram S. **Impaired anti-SARS-CoV-2 antibody response in non-severe COVID-19 patients with diabetes mellitus: a preliminary report**. *Diabetes Metab Syndr* (2021.0) **15** 193-6. DOI: 10.1016/j.dsx.2020.12.035 21. 21.Goetz L, Yang J, Greene W, Zhu Y. A COVID-19 patient with repeatedly undetectable SARS-CoV-2 antibodies. The Journal of Applied Laboratory Medicine. 10.1093/jalm/jfaa137. 22. Deeks JJ, Dinnes J, Takwoingi Y, Davenport C, Spijker R, Taylor-Phillips S. **Antibody tests for identification of current and past infection with SARS-CoV-2**. *Cochrane Database Syst Rev* (2020.0) **6** CD013652. PMID: 32584464 23. 23.Paige D, Rapid. COVID Antibody Test | Antibody Tests - Assay Genie. 2021. https://www.assaygenie.com/rapid-covid19-antibody-detection-tests-principles-and-methods. Accessed 28 May 2022. 24. 24.Wantai. WANTAI SARS-CoV-2 Ab ELISA: ELISA for Total Antibody to SARS-CoV-2. 25. Landis JR, Koch GG. **The measurement of observer agreement for categorical data**. *Biometrics* (1977.0) **33** 159-74. DOI: 10.2307/2529310 26. Lisboa Bastos M, Tavaziva G, Abidi SK, Campbell JR, Haraoui L-P, Johnston JC. **Diagnostic accuracy of serological tests for covid-19: systematic review and meta-analysis**. *BMJ* (2020.0) **370** m2516. DOI: 10.1136/bmj.m2516 27. 27.HAS/France. Place des tests sérologiques rapides (TDR, TROD, auto- tests) dans la stratégie de prise en charge de la maladie COVID-19. France; 2020. 28. 28.Al-Jighefee HT, Yassine HM, Al-Nesf MA, Hssain AA, Taleb S, Mohamed AS et al. Evaluation of Antibody Response in Symptomatic and Asymptomatic COVID-19 Patients and Diagnostic Assessment of New IgM/IgG ELISA Kits. Pathogens. 2021;10. 29. 29.Bond K, Nicholson S, Lim SM, Karapanagiotidis T, Williams E, Johnson D et al. Evaluation of serological tests for SARS-CoV-2: Implications for serology testing in a low-prevalence setting. 2020:2020.05.31.20118273.
--- title: Prioritization of COVID-19 risk factors in July 2020 and February 2021 in the UK authors: - Sivateja Tangirala - Braden T. Tierney - Chirag J. Patel journal: Communications Medicine year: 2023 pmcid: PMC10062272 doi: 10.1038/s43856-023-00271-3 license: CC BY 4.0 --- # Prioritization of COVID-19 risk factors in July 2020 and February 2021 in the UK ## Abstract Tangirala et al. evaluate changes in the associations between various exposures and COVID-19 positivity and hospitalization across two non-consecutive waves of the pandemic in participants of the UK Biobank. They find that the strength of the association between certain risk factors, such as age and household-related factors, change over time. ### Background Risk for COVID-19 positivity and hospitalization due to diverse environmental and sociodemographic factors may change as the pandemic progresses. ### Methods We investigated the association of 360 exposures sampled before COVID-19 outcomes for participants in the UK Biobank, including 9268 and 38,837 non-overlapping participants, sampled at July 17, 2020 and February 2, 2021, respectively. The 360 exposures included clinical biomarkers (e.g., BMI), health indicators (e.g., doctor-diagnosed diabetes), and environmental/behavioral variables (e.g., air pollution) measured 10–14 years before the COVID-19 time periods. ### Results Here we show, for example, “participant having son and/or daughter in household” was associated with an increase in incidence from $20\%$ to $32\%$ (risk difference of $12\%$) between timepoints. Furthermore, we find age to be increasingly associated with COVID-19 positivity over time from Risk Ratio [RR] (per 10-year age increase) of 0.81 to 0.6 (hospitalization RR from 1.18 to 2.63, respectively). ### Conclusions Our data-driven approach demonstrates that time of pandemic plays a role in identifying risk factors associated with positivity and hospitalization. ## Plain language summary Social, demographic, and environmental factors have been shown to impact whether a person becomes infected following SARS-CoV-2 exposure. However, it is unclear whether the impact of different factors has changed as the pandemic has progressed. Here we analyze 360 factors and whether they are associated with the proportion of people being found to be infected with SARS-CoV-2 across two periods of time in the UK. Overall, we found that different risk factors were associated with testing positive for SARS-CoV-2 infection early in the pandemic compared to later in the pandemic. These results highlight that public health priorities should be adjusted as a consequence of changing risk and susceptibility to infection as the pandemic progresses. ## Introduction Observational studies of COVID-19 have implicated age, sex, and sociodemographic, clinical, and environmental inequalities1,2. Most of this literature, however, does not consider or contextualize association sizes among the vast array of potential risk factors or take advantage of the large number of variables available in current-day biobank-scale studies, potentially missing the fuller picture of COVID-19 susceptibility. Secondly, association sizes and their strength may change due to time of sampling of COVID-19. Time of sampling may be an important parameter as, for example, certain factors may confer greater or lesser risk for COVID-19 related outcomes (e.g., COVID-19 positive test, COVID-19 hospitalization) as time progresses with newer COVID-19 variants. In fact, some studies have analyzed certain subsets of risk factors over the course of the pandemic and found associations that change over time. Roso-Llorach et al. investigated age, sex, smoking status, socioeconomic status and comorbidity burden (using the Charlson comorbidity index3) in association with COVID-19 hospitalization and mortality-related outcomes (e.g., 30-day mortality [dying within the 30 days following admission due to COVID-19], transfer to intensive care unit) in Catalonia, Spain, during February 2020-21. Roso-Llorach et al. assessed the differences in mortality and clinical outcomes across four successive waves and found a notable increase in the proportion of socioeconomically-deprived patients being hospitalized due to COVID-19 after the first wave3. Here, to identify robust candidate observational risk factors, we perform a data-driven search for 360 correlates of COVID-19 positivity and complication across multiple time points in the pandemic (July 17, 2020 and February 2, 2021) inspired by “environment-wide association studies” (EWASs) in participants of the UK Biobank to identify how risk factors change during two different key time periods in the pandemic. Use of the EWASs can help to identify variables in large databases for prioritization of potential correlates between modifiable and non-modifiable behavior, environmental, and phenotypic factors associated with an outcome4,5, that may or not be investigated in a study that investigates a handful of variables at a time6–10. Second, we probe the variability of associations due to study design and model choice by performing the “vibration of effects” analysis11 for 13 of the top exposures identified from our analysis and compare this variation due to model selection with time of data collection. Overall, we found that different risk factors were associated with testing positive for SARS-CoV-2 infection early in the pandemic (e.g., frequency of shift work) compared to later (e.g., household-related factors such as presence of son and/or daughter in household) in the pandemic and age playing a more prominent role over time. ## Study population The UK Biobank cohort is a prospective cohort including over 500,000 participants of ages 40–69 during recruitment from 2006–201012. Differences between the UK Biobank cohort individuals and the general UK population were studied by Fry et al. in order to better understand sampling “uncertainty”13. Their study suggested that nonparticipants are more likely to be male, younger, and live in more socioeconomically deprived areas than UK Biobank participants13. Information regarding how the UK *Biobank data* is maintained and validated can be found at https://biobank.ndph.ox.ac.uk/~bbdatan/Data_cleaning_overall_doc_showcase_v1.pdf. We analyzed two non-overlapping subsets of the UK Biobank [UKB] cohort (total $$n = 502$$,628 participants) for which we had data pertaining to COVID-19 testing for tests administered until July 17, 2020 and tests administered between July 18, 2020 and February 2, 2021. COVID-19 testing in the UK was carried out in two major phases (Pillar I and Pillar II) during the time period we considered. The first phase (Pillar I) prioritized individuals with health complications and healthcare workers. We excluded participants whose ethnicity was not known, yielding samples of 9268 and 38,837 participants, respectively. The National Research Ethics Service Committee North West Multi-Centre Haydock has approved the UKB cohort research and written informed consent to participate in the study was provided by all participants14. Approval for the use of this data was approved by the UK Biobank (project ID: 22881). The Harvard internal review board (IRB) deemed the research as non-human subjects research (IRB: IRB16-2145). Formal consent was obtained by the UK Biobank (https://biobank.ctsu.ox.ac.uk/ukb/ukb/docs/Consent.pdf). ## COVID-19 outcomes In our investigation, we analyze two major COVID-19-related outcomes in UKB participants, COVID-19 test positivity, determined with microbiological (reverse transcriptase-polymerase chain reaction [RT-PCR]) testing14) and hospitalization due to COVID-19. We defined the outcome COVID-19 test positivity as the presence of at least one positive test result for a participant. ## Exposures prior to COVID-19 testing We investigated the association of 360 “exposures” that included (a) clinical and diagnostic biomarkers of chronic disease and infection, (b) “environmental” factors, and (c) self-reported, doctor-diagnosed health and disease indicators with COVID-19 positivity and hospitalization. We use data measured during baseline visits 10–14 years (2006–2010) before the COVID-19 time periods. The 63 real-valued biomarkers spanned five categories included adiposity and body characteristics (4 biomarkers, e.g., body mass index), blood count (23 biomarkers, e.g., white blood cell count), blood biochemistry (30 biomarkers, e.g., alkaline phosphatase), cardiovascular function (3 biomarkers, e.g., diastolic blood pressure), and lung function (3 biomarkers, e.g., forced vital capacity). Further, we use data measured during baseline visits (2006–2010). We performed rank-based inverse normal transformation (INT) to compare their associations across different models. We performed INT transformation using the RNOmni package (rankNorm function) with the offset parameter set to 0.5.15 Second, we investigated the association of 283 environmental factors in 14 categories (e.g., smoking, estimated nutrients consumed yesterday, infectious antigens) with COVID-19 positivity and hospitalization. We averaged the quantitative environmental factors (that fell under the estimated nutrients consumed yesterday [23 exposures] and infectious antigens [25 exposures] categories) over measurements from multiple instances or visits. For environmental factors that did not have many observations in subsequent instances, we used only the data from the baseline visit (first instance of measurement collected during 2006–2010) (e.g., environmental factors from the estimated nutrients yesterday category). We also performed INT-transformation of these factors (similar to the transformation of biomarkers) (as was also suggested by Millard et al.16). For categorical variables (which were also collected from multiple visits of a participant to the assessment center), we used data from the baseline visit (first instance of measurement collected during 2006–2010) as this contained the highest number of observations. Additionally, categorical variables with multiple levels were converted to sets of binary variables where each binary variable indicates whether a participant has a given value of this variable (as was suggested by Millard et al.)16. Ordinal categorical environmental factor variables were analyzed by treating such variables as continuous variables and real-valued quantitative environmental factor variables were scaled. Third, we considered 14 health and disease indicators in 6 categories (“overall health rating”, “diabetes diagnosed by doctor”, “cancer diagnosed by doctor”, “vascular/heart problems diagnosed by doctor”, and “blood clot, DVT, bronchitis, emphysema, asthma, rhinitis, eczema, allergy diagnosed by doctor”) with COVID-19 positivity and hospitalization. Moreover, we considered baseline demographic variables in our analysis including sex, age, age squared, assessment center, ethnicity, average total household income after tax, and 40 genetic principal components. Similarly, for these variables we used data from the baseline visit (first instance of measurement collected during 2006–2010). ## Data-driven association to identify risk factors associated with future COVID-19 positivity and hospitalization Niedzwiedz et al. report that Poisson regression may be preferred over logistic regression as odds ratios are often misinterpreted and Poisson regression allows for relative risks to be reported. As mentioned by highly cited COVID-19 papers (e.g.,2), using robust standard errors will help ensure “accurate estimation of p-values”. Zou shows error for estimated relative risk will be overestimated when Poisson regression is applied to binomial data. Therefore, Zou suggests robust standard errors may be an optimal solution to help overcome overestimation. We used Poisson regression (with log link) models with robust standard errors to associate each of the 360 factors and COVID-19 positivity (individually), while adjusting for sex, age, age squared, assessment center, ethnicity, average total household income after tax, and 40 genetic principal components (computed and provided by the UK Biobank). The model can be represented as log (π(xi)) = Exposure + Age + Age + Sex + Assessment Center + Income + Genetic Principal Component 1 + Genetic Principal Component 2 + … + Genetic Principal Component 40 + log(ti) where we assume that subject i has an underlying risk for a COVID-19 related outcome that is a function of xi, as π(xi). As Zou mentions, “since π(xi) must be positive, the logarithm link function is a natural choice for modeling π(xi)”17 and log(ti) is the offset of time in years between date of baseline visit and date of first positive COVID-19 test17. The approach of using robust standard errors involves correcting the original model-based standard errors using the variation of the difference between observed outcome values and predicted values from the model (or the residuals)17,18 Moreover, Mansournia et al. note the reason why this approach is also referred to as sandwich estimation18. Mansournia et al. mention that the terms corresponding to the variance based on the residuals is “sandwiched” in between the terms corresponding to the variance based on the model18. For details on the mathematical derivation of the approach, please see Zou et al.17.We report risk ratios and adjusted corresponding p-values for multiple comparisons using the false discovery rate (FDR) approach19. Also, we perform a sensitivity analysis running logistic regression and estimating odds ratios for COVID-19 positivity across both timepoints. Additionally, testing strategy during the time periods considered may confound the associations we observe. Given that there were two major phases of the testing strategy (Pillar I and Pillar II) where the first phase prioritized individuals with health complications and healthcare workers, it was unclear which tests performed on UK Biobank participants corresponded to Pillar II versus Pillar I; therefore, we ran a sensitivity analysis to adjust for criteria that were used to prioritize individuals to be tested—healthcare workers and people with health complications. Moreover, it has been shown by Williamson et al.20 that health complications most associated with COVID-19-related death earlier in the pandemic include obesity and diabetes. In order to account for testing strategy-related confounding effects, we additionally adjust for healthcare worker status, BMI (body mass index), diabetes, haematological malignancies (lymphoma, leukemia, multiple myeloma, myelofibrosis or myelodysplasia, and other haematological malignancy) and usage of immunosuppressants (see Supplementary Table 2 for medication codes used to identify immunosuppressants21) in addition to the baseline demographic covariates in the aforementioned analysis. Similarly, we investigated the association of all 360 risk factors with COVID-19 hospitalization. Also, we sought to quantify the difference in exposure associations with COVID-19 positivity between time points. We executed a Poisson regression (with log link) models for individual exposures (as described above) with an additional interaction term between each exposure and the time point variable (codified as a dummy variable to indicate the first [tests until July 17, 2020] or second time point [tests between July 18, 2020 and February 2, 2021]). We also report risk ratios for each interaction term and adjusted corresponding p-values for multiple comparisons using the false discovery rate (FDR) approach19. ## Probing the variation of associations due to model choice via “vibration of effects” Next, we executed a large sensitivity analysis to examine the fragility of associations due to model and covariate choice, in a large sensitivity analysis called the vibration of effects (VoE)11,22 for the top 12 exposures (ascertained by FDR values) identified from our analysis. Through our data-driven exposure-wide approach, we identified 12 FDR-significant exposures (FDR-corrected p-value in top 10 percentile) in the initial time period, including 1) “Apolipoprotein A”, 2) “Own accommodation outright”, 3) “Nitrogen oxides air pollution; 2010”, 4) “Current frequency of shift work”, 5) “Townsend deprivation index at recruitment”, “Body mass index (BMI)”, “HDL cholesterol”, “Urban (less sparse) home area population density”, “Qualifications (no education)”, “Son and/or daughter (including step-children) in household”,“Exposure to tobacco smoke outside home”, and “Alcohol intake frequency”. Given the computational complexity of running all possible models for all significant exposures, we selected the 12 exposures that included ones that were prominently featured (FDR in top 10 percentile) in our analysis) and mostly did not include multiple exposures from the same category (for example we did not include “nitrogen dioxide air pollution; 2010”). The set of varying adjustments in the models included the 12 exposures and the “baseline” variables that we kept in all models were gender, age, age squared, assessment center, ethnicity, and average total household income after tax. We performed the VoE analysis in the context of the 12 exposures as varying adjustments by running models with all possible combinations of adjustments from the set of 12 exposures. We ran a total of 8192 [212] models, while keeping the sample size ($$n = 2821$$) the same for all (as it has been suggested previously4)). We used Poisson regression models (with log link) to associate variables with COVID-19 positivity and extracted beta-estimates to compute risk ratios (RR). The heuristic that we used for computing VoE was as defined by Patel et al.4. ## Statistics and reproducibility All data processing and subsequent analyses were done using R Version 3.6.1 on O2 which is a high-performance computing cluster at Harvard Medical School that runs on Linux. We made our code accessible at (https://github.com/stejat98/UKB_COVID_XWAS) and on Zenodo (10.5281/zenodo.7542752)23. Summary statistics (including risk ratios, FDR-corrected p-values, sample sizes, etc.) can be found in Supplementary Data 1–10. ## Reporting summary Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. ## Baseline characteristics The baseline characteristics of the UK Biobank (UKB) cohort participants are reported in Supplementary Data 1. Out of 9268 individuals tested in the UKB cohort for COVID-19 until July 17, 2020, results from 1544 patients ($16.7\%$) were positive. Out of the 38,837 participants tested in the UKB cohort for COVID-19 between July 18, 2020 and February 2, 2021, results from 8539 patients ($22\%$) were positive. Of the 1544 patients that tested positive until July 17, 2020, 1011 ($65.5\%$) were hospitalized and 243 ($15.7\%$) died (Supplementary Data 2). Overall, age was significantly associated with COVID-19 positivity (Risk Ratio [RR] for a 10 year increase in age: 0.807, $95\%$ CI: [0.757, 0.86], FDR: 1.03 × 10−9), in addition to geographic proxy variables (assessment centers) such as Glasgow (7.91, [4.62,13.5], 1.78 × 10−12) among cases tested until July 17, 2020 [Table 1].Table 1Top baseline demographic associations for COVID-19 positivity for first time point (tests until $\frac{07}{17}$/2020).COVID-19 positivityCOVID-19 hospitalizationBaseline demographic variableRR ($95\%$ CI)p-valueFDRRR ($95\%$ CI)p-valueFDRGlasgow7.91 (4.62–13.5)4.75E−141.78E−121.13E−05 (1.58E−06 to 8.10E−05)7.92E−305.94E−28Age0.807 (0.757–0.860)4.11E−111.03E−091.09 (1.07–1.11)8.02E−241.50E−22Leeds3.05 (1.99–4.69)3.33E−076.24E−061.86 (1.59–2.17)4.78E−153.58E−14Liverpool2.70 (1.75–4.16)6.60E−069.90E−051.98 (1.70–2.31)2.90E−183.10E−17Sheffield2.63 (1.71–4.07)1.24E−051.55E−041.44 (1.21–1.70)2.46E−050.000108Gender (Male)1.21 (1.09–1.34)2.75E−042.95E−031.06 (1.04–1.09)1.00E−064.71E−06Average total household income before tax (<18,000 Euros)1.72 (1.26–2.33)5.51E−045.16E−031.02 (0.947–1.10)0.590.835Middlesborough2.06 (1.31–3.24)1.67E−031.39E−022.26 (1.94–2.62)2.83E−261.06E−24Croydon1.95 (1.25–3.04)3.13E−032.13E−021.34 (1.12–1.59)1.13E−034.71E−03Average total household income before tax (31,000 to 51,999 Euros)1.59 (1.17–2.15)3.05E−032.13E−021.02 (0.947–1.10)0.5850.835 Of the 8539 patients that tested positive until February 2, 2021, 2150 ($25.2\%$) were hospitalized and 169 ($1.98\%$) died (Supplementary Data 2). Similar to the results from the previous time point, age was significantly associated with COVID-19 positivity (Risk Ratio [RR] for a 10 year increase in age: 0.595, $95\%$ CI: [0.581, 0.610], FDR < 1 × 10−64), in addition to geographic proxy variables (assessment centers) such as Cardiff (6.37, [5.15, 7.88], 7.99 × 10−64) and Swansea (10.5, [8.03, 13.8], 6.33 × 10−64) among cases tested until February 2, 2021 [Supplementary Data 3]. ## Data-driven identification of risk factors associated with future COVID-19 positivity We systematically associated environmental factors, biomarkers, and health indicators with COVID-19 positivity for two different samples during two different timepoints: before and inclusive of July 17, 2020 and between July 18, 2020 and February 2, 2021 (Fig. 1). We identified 31 significant exposures (27 environmental factors and 4 biomarkers) (Fig. 2, Supplementary Data 4) and 36 significant exposures (34 environmental factors and 2 biomarkers and health indicators) (Fig. 3, Supplementary Data 5) that had FDRs in the top 10 percentile of all associations tested with thresholds of FDR-corrected p-value of less than 0.141 and FDR-corrected p-value of less than 1.94 × 10−4 respectively at each time (Supplementary Figs. 1, 2). It is also important to note that while we tested the same number of exposures [360] for both timepoints we had low sample sizes for 45 exposures that were all infectious antigens, leaving 315 exposures to report results for this first time point. As exposures associated with COVID-19 positivity varied between time points, we systematically quantified the difference in relative risk ratios of exposures between the two samples by testing for the interaction between each exposure variable and the time point variable and reported interaction term risk ratios. We identified 35 exposures with a FDR-corrected p-value in the top 10 percentile across all 360 exposures tested for the interaction effect with time point (Supplementary Data 6). The interquartile range of the interaction term Risk *Ratios is* 0.857 and 1.31. We also observed an overall negative trend between the difference in RRs and the RRs for the first timepoint ($\frac{07}{17}$/2020) in Fig. 4, thereby indicating an overall decrease in the association sizes between the two samples. Fig. 1Schematic overview of data-driven analysis of COVID-19 positivity risk factors across two timepoints. This schematic diagram depicts the analytic pipeline. [ 1] COVID-19 testing data was collected from two time periods (until $\frac{07}{17}$/2020 and between $\frac{07}{18}$/2020 and $\frac{02}{02}$/2021). [ 2] Data-driven association analysis was performed for each of the 360 exposures using Poisson regression (with log link). Associations were computed for each time period separately. Additionally, models were run to assess time-exposure interaction effects. The blue line on the scatterplot represents a linear regression line and the grey shading around it represents the $95\%$ confidence interval. Fig. 2Association size versus -log10(adjusted p-values) between 360 exposures and COVID-19 positivity for first time point (tests until $\frac{07}{17}$/2020).The risk ratios (RR) versus the negative log (base 10) of FDR-corrected p-values for 360 different exposures. The red color indicates FDR < 0.1 significant exposures and the blue color indicates FDR > 0.1 exposures. For the underlying dataset, $$n = 9268$.$Fig. 3Association size versus -log10(adjusted p-values) for COVID-19 positivity for second time point (tests between $\frac{07}{18}$/2020 and $\frac{02}{02}$/21).The risk ratios (RR) versus the negative log (base 10) of FDR-corrected p-values for 360 different exposures. The red color indicates FDR < 0.1 significant exposures and the blue color indicates FDR > 0.1 exposures. For the underlying dataset, $$n = 38$$,837.Fig. 4Change in relative risk between participants sampled in $\frac{2}{02}$/2021 and $\frac{7}{17}$/2020 vs. relative risk for participants sampled in July 2020 (x-axis).Scatterplot of associations with FDR < 0.1 significant interaction effects between risk ratios (RR) for first time point ($\frac{07}{17}$/2020) and change in factor of effect between time points. Exposures that significantly deviate from the overall negative linear trend are labelled. The blue line on the scatterplot represents a linear regression line and the grey shading around it represents the $95\%$ confidence interval. We begin by describing exposures found in each sample separately. For individuals sampled before July 17, 2020, the top significantly associated factor (in terms of FDR-significance) was “current frequency of shift work” (e.g.24) (RR [for a 1 standard deviation (SD) change in relative frequency of shift work]: 1.16, $95\%$ CI: [1.10, 1.23], FDR: 8.20 × 10−6, ΔAUC [Full Model AUC - AUC of baseline demographic covariates-only model]: 8.79 × 10−3 [0.679–0.67]) (Supplementary Table 1 for distribution of COVID-19 prevalence across shift work categories). For individuals who report they always conduct shift work, the prevalence of COVID-19 positivity was $28.6\%$ and for individuals who report that they never conduct shift work, the prevalence of COVID-19 positivity was almost half that reported always, $15.2\%$. However, and on the other hand, we did not find “current frequency of shift work” to be significantly associated with cases tested between July 18, 2020 to February 2, 2021 (RR [for a 1 standard deviation (SD) change in relative frequency of shift work]: 1.01, $95\%$ CI: [0.989, 1.03], FDR: 0.564). The interaction term RR was 0.891 (FDR: 6.80 × 10−4), which corresponded to a significant decrease by a factor of $10.9\%$ as time progressed between the two samples. We found that the “current frequency of shift work” is associated with positivity in the early time period. However, the association could be influenced by the type of worker, such as a healthcare worker. We tested the association of current frequency of shift work in the healthcare worker (HCW) sample and compared it to the association in non-HCW sample. We found the associations to be similar between non-HCW (RR: 1.11, $95\%$ CI: [1.04, 1.18], p-value: 2.63 × 10−3) and HCW-only (RR: 1.18, $95\%$ CI: [1.05, 1.32], p-value: 3.79 × 10−3) samples, indicating that being a healthcare worker is independent of the association between frequency of shift work and COVID-19 positivity. Other top factors include “nitrogen oxides air pollution measured in 2010” (RR: 1.14 [for a 1 SD change in nitrogen oxide air pollution], $95\%$ CI: [1.08, 1.21], FDR: 4.16 × 10−4, ΔAUC: −2.18 × 10−4 [0.648–0.649] and “nitrogen dioxide air pollution also measured in 2010” (RR [for a 1 SD change]: 1.15, $95\%$ CI: [1.08, 1.23], FDR: 6.88 × 10−4, ΔAUC: −1.29 × 10−3 [0.647–0.649]). For cases up to February 2, 2021, we found “nitrogen oxides air pollution measured in 2010” (RR: 1.10 [for a 1 SD change in nitrogen oxide air pollution], $95\%$ CI: [1.08, 1.13], FDR: 3.89 × 10−17) and “nitrogen dioxide air pollution measured in 2010” (RR: 1.13 [for a 1 SD change], $95\%$ CI: [1.1, 1.15], FDR: 9.11 × 10−23) to be significant. The interaction term RRs were 0.985 (FDR: 0.763; decrease by a factor of $1.5\%$ between timepoints) and 1.00 (FDR: 0.959; no change between timepoints), respectively. In total, we identified 11 exposures and 9 exposures to be significant in the air pollution category of exposures for cases leading up to July 17, 2020 and February 2, 2021, respectively. The top two significantly associated biomarkers with COVID-19 positivity were apolipoprotein A (RR [for a 1 SD change that is equivalent to 0.272 g/L]: 0.889, $95\%$ CI: [0.839, 0.942], FDR: 3.25 × 10−3, ΔAUC: −5.30 × 10−4 [0.646–0.646]) and HDL cholesterol (RR [for a 1 SD change that is equivalent to 0.382 mmol/L]: 0.900, $95\%$ CI: [0.849, 0.955], FDR: 1.11 × 10−2, ΔAUC: −1.08 × 10−3 [0.644–0.645]). HDL cholesterol (RR: 0.995, $95\%$ CI: [0.972, 1.02], FDR: 0.785, ΔAUC: −1.08 × 10−3 [0.644–0.645]) was also implicated in cases between July 18 and February 2, 2021, but apolipoprotein A (RR: 1.00, $95\%$ CI: [0.978, 1.02], FDR: 0.978) was not. The interaction term RRs for HDL cholesterol and apolipoprotein A were 1.11 (FDR: 3.99 × 10−3; significant increase by factor of $11\%$ between samples) and 1.12 (FDR: 3.01 × 10−3; significant increase by factor of $12\%$ between samples), respectively. The interquartile range (IQR) of the number of complete cases across all associations for individuals sampled before July 17,2020 is [4860, 7326]. We also plot the sample size versus risk ratio of COVID-19 positivity associations (Supplementary Fig. 3). For cases between July 18, 2020 and February 2, 2021, the top associations included “nitrogen dioxide air pollution measured in 2006” (RR: 1.17 [for a 1 SD change], $95\%$ CI: [1.14, 1.2], FDR: 2.23 × 10−26, ΔAUC: 6.71 × 10−3 [0.727–0.72]), “participant having son and/or daughter in household” (RR: 1.28, $95\%$ CI: [1.22, 1.34], FDR: 2.06 × 10−25, ΔAUC: 6.21 × 10−3 [0.726–0.72]), and “number of people in participant’s household” (RR: 1.12 [for a 1 SD change], $95\%$ CI: [1.09, 1.14], FDR: 1.41 × 10−20, ΔAUC: 5.78 × 10−3 [0.726–0.72]). For cases up to July 17, 2020, “participant having son and/or daughter in household” (RR: 1.19, $95\%$ CI: [1.06, 1.35], FDR: 0.06) and “nitrogen dioxide air pollution measured in 2006” (RR: 1.14 [for a 1 SD change], $95\%$ CI: [1.06, 1.23], FDR: 0.01) was significant but “number of people in participant’s household” (RR: 1.07 [for a 1 SD change], $95\%$ CI: [1.01, 1.13], FDR: 2.06 × 10−1) was not significantly associated with COVID-19 test positivity. The interaction RRs for “participant having son and/or daughter in household”, “nitrogen dioxide air pollution measured in 2006”, and “number of people in participant’s household” are 1.39 (FDR: 3.04 × 10−7; significant increase by factor of $39\%$ between timepoints), 1.02 (FDR: 0.659; non-significant increase by factor of $2\%$ between timepoints), and 1.14 (FDR: 9.18 × 10−6; significant increase by factor of $14\%$ between timepoints), respectively. In particular, “participant having son and/or daughter in household” and “number of people in participant’s household” are notable exceptions to the overall decreasing trend in the association sizes between the two samples. The interquartile range (IQR) of the number of complete cases across all associations for individuals sampled between July 18, 2020 and February 2, 2021 is [16,183, 31,521]. We also plot the sample size versus risk ratio of COVID-19 positivity associations (Supplementary Fig. 4). ## Assessing robustness of identified COVID-19 positivity associations to testing strategy Additionally, testing strategy (prioritization of health care workers and/or individuals with chronic disease) during the time periods considered may confound the associations we observe. In order to account for testing strategy-related confounding effects, we additionally adjust for healthcare worker status, BMI (body mass index), diabetes, haematological malignancies and usage of immune suppressants, in addition to the baseline demographic covariates in the aforementioned analysis. We report associations after adjusting for testing strategy-related confounding effects and contextualize these with the estimates not adjusted for testing strategy-related effects that we reported in the previous section. For individuals sampled before July 17, 2020, “current shift frequency”: (RR: 1.12, $95\%$ CI: [1.06, 1.19], FDR: 1.22 × 10−2) [testing strategy effects-unadjusted: RR: 1.16, $95\%$ CI: (1.10, 1.23), FDR: 8.20 × 10−6], “nitrogen oxides air pollution measured in 2010” (RR: 1.13, $95\%$ CI: [1.06, 1.19], FDR: 1.22 × 10−2) [testing strategy effects-unadjusted: RR: 1.14, $95\%$ CI: [1.08, 1.21], FDR: 4.16 × 10−4] and “nitrogen dioxide air pollution measured in 2010” (RR: 1.13, $95\%$ CI: [1.06, 1.20], FDR: 1.77 × 10−2) [testing strategy effects-unadjusted: RR: 1.15, $95\%$ CI: [1.08, 1.23], FDR: 6.88 × 10−4], remained among the top associations and were concordant in direction with associations reported prior to adjustment of the additional covariates. Similarly, for individuals sampled between July 18, 2020 and February 2, 2021, “participant having son and/or daughter in household” (RR: 1.28, $95\%$ CI: [1.22, 1.34], FDR: 7.92 × 10−25) [testing strategy effects-unadjusted: RR: 1.28, $95\%$ CI: (1.22, 1.34), FDR: 2.06 × 10−25] and “number of people in participant’s household” (RR: 1.11, $95\%$ CI: [1.09, 1.14], FDR: 9.81 × 10−20) [testing strategy effects-unadjusted: RR: 1.12, $95\%$ CI: (1.09, 1.14), FDR: 1.41 × 10−20] remained among the top associations and were concordant in direction with associations reported prior to adjustment of the additional covariates. ## Comparing model selection robustness across critical points in the pandemic in association strength and size We compared the variation due to time of sampling versus the variation due to covariate choice (Vibration of Effects [VoE]). Specifically, to test whether the associations differ due to model choice and compare them with the main findings of our study, we estimated the vibration of effects (VoE)11,22,25 for 12 variables that were initially identified as significant (FDR < 0.1) from our analysis (for cases up to July 17, 2020) at both timepoints (see Methods). For 7 out 12 FDR significant exposures, the time of sampling exhibited greater variation in RR versus model selection as detected by VoE. For example, apolipoprotein A, had a range of [0.572, 0.713] in cases up to July 17, 2020 and a range of [1.00, 1.05] in cases up to February 2, 2021. “ Son and/or daughter in household” had ranges of [1.07, 1.12] and [1.24, 1.26] in cases up to July 17, 2020 and February 2, 2021, respectively. Patel et al. and Tierney et al.11,25 have shown that empirically estimating the VoE makes it possible to detect significant differences in exposure associations can arise just due to choice of covariate (which can lead to multiple modes of associations otherwise referred to as the “multimodality of effects”11). To illustrate this, we plotted the vibration of effects for “current frequency of shift work”, “nitrogen oxides air pollution”, and BMI for cases up to July 17, 2020 (Supplementary Fig. 5) and February 2, 2021 (Supplementary Fig. 6). For example, “current frequency of shift work” did not display multimodality of effects as associations were consistent (had the same size) across all adjustment scenarios. Among participants tested up to July 17, 2020, all associations were in the positive direction and lying between 1.11 and 1.14 (Supplementary Fig. 5a). However, for participants tested between July 18, 2020 and February 2, 2021, “current frequency of shift work” were not consistent overall as the range of associations (1.00 and 1.01) included 1, thereby suggesting a null association (Supplementary Fig. 6a). Also risk ratios and -log10(p-values) shrank from the previous time point. The overall VoE for nitrogen oxides air pollution indicates positive association with COVID test positivity (RRR: 1.04, RP: 0.732). We identified four different modes. We visualized VoE by coloring the points based on the inclusion/exclusion of the 12 other significant exposures (providing 12 separate visualizations). For nitrogen oxide air pollution, we found that the four modes were indicative of the presence (or absence) of the Townsend deprivation index and Urban (less sparse) home area population density variables in the models (Supplementary Fig. 6b). The models containing Townsend deprivation index had smaller RR and smaller -log10(p-value) for nitrogen oxide air pollution. On the other hand, models containing Urban (less sparse) home area population density had smaller -log10(p-value) for nitrogen oxide air pollution. We also identified four modes in the associations between BMI and COVID-19 positivity (RRR: 1.01, RP: 0.925). The VoE plots (Supplementary Figs. 5c, 6c) indicated that the multimodality of BMI associations was driven by the presence (or absence) of apolipoprotein A and HDL cholesterol in the models. For instance, the -log10(p-value) for BMI risk ratio decreases with HDL cholesterol in the models. For participants tested between July 18, 2020 and February 2, 2021, we again found multiple modes that were indicative of the presence (or absence) of the Townsend deprivation index and Urban (less sparse) home area population density variables in the models associating nitrogen oxide air pollution (RRR: 1.02, RP: 3.06) with COVID-19 positivity. However, while the inclusion of the Townsend deprivation index (an area level/local geographic indicator of socioeconomic status) and Urban (less sparse) home area population density variable caused the risk ratios and -log10(p-values) to shrink, nitrogen oxide air pollution associations from all model combinations still attained significance. Similarly, despite the shrinkage in -log10(p-values) due to inclusion of apolipoprotein A and HDL cholesterol in the models, BMI associations from all possible model combinations (RRR: 1.00, RP: 2.95) still were significant. Overall, we note the tension between decreasing risk ratios and increasing -log10(p-values) for exposure associations with test positivity as the number of tests (and cases) also increase. ## Significant factors identified for COVID-19 hospitalization In addition, we systematically associated all factors with COVID-19 hospitalization (Supplementary Data 7) for cases up to July 17, 2020. We identified 28 of these factors to meet a threshold of FDR-corrected p-value in the top 10 percentile of associations tested (FDR-corrected p-value equivalent of 0.29 in top $10\%$). Given the high p-values observed across associations for this time point we also report the FDR-corrected p-value equivalent for the top $1\%$ of associations tested to be 0.0121. Top factors included alanine aminotransferase (RR [for a 1 SD change that is equivalent to 14.5 U/L]: 1.03, $95\%$ CI: [1.02, 1.04], FDR: 3.85 × 10−3, ΔAUC: 1.84 × 10−4 [0.732–0.732]) and glycated haemoglobin (HbA1c) (RR [for a 1 SD change that is equivalent to 8.29 mmol/mol]: 1.03, $95\%$ CI: [1.01, 1.04], FDR: 6.34 × 10−3, ΔAUC: 1.66 × 10−3 [0.731–0.729]). Similarly, for cases up to February 2, 2021, we systematically associated all factors with COVID-19 hospitalization (Supplementary Data 8). We identified 32 of those factors to meet a threshold of FDR-corrected p-value in the top 10 percentile of associations tested (FDR-corrected p-value threshold of 2.93 × 10−9). Top factors for COVID-19 hospitalization included overall health rating (RR: 1.06, $95\%$ CI: [1.05, 1.07], FDR: 2.67 × 10−37, ΔAUC: −3.33 × 10−3 [0.683–0.686]) and unable to work because of sickness or disability (RR: 1.19, $95\%$ CI: [1.15, 1.23], FDR: 4.32 × 10−23, ΔAUC: −5.27 × 10−3 [0.680–0.686]). ## Sensitivity analysis using logistic regression to estimate odds ratios for COVID-19 positivity during both timepoints Additionally, we used logistic regression to compute odds ratios (OR) for COVID-19 positivity across both timepoints as a sensitivity analysis and compare the OR of top findings with the RR computed from Poisson regression models (with robust standard errors). In brief, we did not have evidence to suggest that use of the Poisson biased the associations we identified. For individuals sampled before July 17, 2020, “current shift frequency”: (OR: 1.23, $95\%$ CI: [1.13, 1.32], FDR: 1.14 × 10−4) [Poisson regression model with robust errors: RR: 1.16, $95\%$ CI: (1.10, 1.23), FDR: 8.20 × 10−6], “nitrogen oxides air pollution measured in 2010” (OR: 1.19, $95\%$ CI: [1.10, 1.28], FDR: 1.04 × 10−3) [Poisson regression model with robust errors: RR: 1.14, $95\%$ CI: [1.08, 1.21], FDR: 4.16 × 10−4] and “nitrogen dioxide air pollution measured in 2010” (RR: 1.2, $95\%$ CI: [1.1, 1.29], FDR: 1.16 × 10−3) [Poisson regression model with robust errors: RR: 1.15, $95\%$ CI: [1.08, 1.23], FDR: 6.88 × 10−4], remained among the top associations and were concordant in direction with associations reported from Poisson regression model with robust errors (as we observed with our testing strategy effects sensitivity analysis). Similarly, for individuals sampled between July 18, 2020 and February 2, 2021, “participant having son and/or daughter in household” (RR: 1.44, $95\%$ CI: [1.35, 1.54], FDR: 1.73 × 10−6) [Poisson regression model with robust errors: RR: 1.28, $95\%$ CI: (1.22, 1.34), FDR: 2.06 × 10−25] and “number of people in participant’s household” (RR: 1.17, $95\%$ CI: [1.14, 1.21], FDR: 9.73 × 10−22) [Poisson regression model with robust errors: RR: 1.12, $95\%$ CI: (1.09, 1.14), FDR: 1.41 × 10−20] remained among the top associations and were concordant in direction with associations reported from Poisson regression model with robust errors (as we observed with our testing strategy effects sensitivity analysis). ## Discussion As is expected, the type, size, and number of risk factors changes as the pandemic progresses. The thought that risk factors may evolve throughout the pandemic has in fact been suggested by others. For example, Roso-Llorach et al. assessed the differences in mortality and clinical variables and found socioeconomic disparities to increase as the pandemic progressed3. However, identifying how risk factors change has been elusive despite the explosion in risk models for COVID-19. To mitigate these challenges, we tested a comprehensive list of all risk factors simultaneously, while accounting for multiple hypotheses in two time points and found 31 early in the pandemic, with occupation-related factors being prominently featured, and 36 later in the pandemic. The number and diversity of risk factors found increased as the pandemic progressed, reflecting the increasing number of individuals testing positive. We conclude that time of sampling has had an influence on association size and the number of variables identified with robust support. Given the sample sizes of participants for each time point (9268 and 38,837) and the number of significant exposures identified for each time point, more generally, we postulate that it may take a sample of 10,000 participants to see environmental sources of health disparity emerge from a convenience sample such as UK Biobank for as low as 10–$20\%$ increased/decreased risk. We also note an overall negative trend between the difference in RRs and the RRs for the first timepoint ($\frac{07}{17}$/2020), suggesting an overall decrease and “regression to the mean” in the association sizes between the two timepoints. Specifically, early in the pandemic, we have identified employment type—specifically surrounding the frequency of shift work—and re-identified air pollution, particularly nitrogen oxide and dioxide as risk factors most significantly associated with COVID-19 positivity and hospitalization, albeit with modest RR. Additionally, we found the association of “current frequency of shift work” to be robust to the healthcare worker (HCW) and non-HCW status sampled before July 17, 2020. Furthermore, while “current frequency of shift work” has a positive association with COVID-19 positivity, it has a negative association with COVID-19 hospitalization. We hypothesize that this opposite risk could potentially be indicative of socioeconomic barriers to hospital treatment, despite testing being made relatively accessible across socioeconomic strata. Moreover, we report negative associations (with hospitalization) of baseline demographic factors such as “Average total household income before tax (Less than 18,000 Euros)” [RR: 0.36, $95\%$ CI: (0.16, 0.83)] and “Average total household income before tax (31,000 to 51,999 Euros)” [RR: 0.36, $95\%$ CI: (0.15, 0.83)]. However, it is important to note that we found the association of “current frequency of shift work” to be negative after adjusting for the baseline demographic factors, including income. This suggests that individuals, who have jobs that involve a higher shift frequency are less likely to be admitted to the hospital early on in the pandemic. We suspect that participants who have a job with a higher frequency of shifts tend to not admit themselves to a hospital, potentially out of fear of job loss or other disciplinary proceedings.26 We also note that we do not find this factor to be significantly associated with COVID-19 positivity later in the pandemic (cases between July 18th 2020 and February 2nd 2021). It is also important to consider age, whose importance as a risk factor for COVID-19 outcomes/complications has been widely reported in the literature. Notably, we report that the RR for age (per 10 years) is less than 1 for COVID-19 positivity and is greater than 1 for COVID-19 hospitalization as we show in Table 1 and Supplementary Data 3. For example, for the first time point age had a RR of 0.807 for COVID-19 positivity and RR of 1.18 for COVID-19 hospitalization and for the second time point age had a RR of 0.595 and for COVID-19 positivity and a RR of 2.63 for COVID-19 hospitalization. These estimates are concordant in direction with age RR estimates for COVID-19 positivity and hospitalization reported elsewhere1. Later in the pandemic, we found household factors, such as “participant having son and/or daughter in household” and “number of people in participant’s household” associated with COVID-19 positivity and also found these associations to be robust to testing strategy criteria. Specifically, “participant having son and/or daughter in household” had a larger risk ratio than earlier in the pandemic (with an interaction term RR of 1.39 [FDR: 3.04 × 10−7; significant increase by factor of $39\%$ between timepoints]). Similarly, “number of people in participant’s household” had a larger risk ratio and smaller FDR-corrected p-value later in the pandemic than earlier in the pandemic (with an interaction RR of 1.14 [FDR: 9.18 × 10−6; significant increase by factor of $14\%$ between timepoints]). Both variables are notable exceptions to the overall negative trend between the difference in RRs and the RRs for the first timepoint ($\frac{07}{17}$/2020) in Fig. 3. Also, it is essential to consider that the majority of UKB participants are senior citizens of retired age (most participants were 60–69 years old)27 and younger individuals may have been going out more for work as the pandemic progressed. Thus, we suspect that having a son or daughter or a greater number of people in the household may have increased the chance of such younger individuals bringing the virus in from outside of the household. It is important to evaluate the clinical significance of household factor associations for COVID-19 positivity with respect to established risk factors in the literature (e.g., age) as the pandemic progressed. For example, “participants having a son and/or daughter in their household” accounted for an increase in incidence from $20\%$ to $32\%$ (incidence risk difference of $12\%$) between timepoints. On the other hand, for elderly participants (age >65) incidence decreased from $16\%$ to $13\%$ (difference of −$3\%$). It is also important to evaluate the relevance of geographic proxy variable risk factors such as assessment centers. Such geographic proxy factors may help serve as indicators of socioeconomic differences between groups. We report the proportion of participants in each family income category for each assessment center in Supplementary Data 9 and 10. For example, we identified during the second time period (tests between July 18, 2020 and February 2, 2021) the Swansea assessment center as the one that confers the greatest risk for testing positive (RR [$95\%$ CI]: 10.5, [8.03, 13.8], FDR: 6.33 × 10−64 vs. Oxford). Swansea has $27.9\%$ of its tested participants belonging to the lowest family income category (less than 18,000 Euros). In comparison, the reference location, Oxford, had $16.8\%$. We also report that many associations are influenced by modeling assumptions; however, time of sampling seems to exhibit a greater variation in associations versus model selection as detected by VoE. For example, while the association risk ratios for “current shift frequency” for the first timepoint (cases up to July 17th 2020) ranged between 1.11 and 1.14 (variation by a factor about $3\%$ due to presence or absence of factors in the models) in “current frequency of shift work”, the interaction term (between exposure and time point) RR was 0.891 (FDR: 6.80 × 10−4), which corresponded to a significant decrease by a factor of $10.9\%$ as time progressed between the two samples, thereby rendering the association null later in the pandemic. This seems to provide further evidence for the suggestion that COVID-19 observational associations are dependent on the time of pandemic. The COVID-19 Host Genetics Initiative conducted a genome-wide association study (GWAS) and a subsequent meta-analysis across cohorts (using study-specific summary statistics) to identify genetic risk factors associated with COVID-1928. Non-genetic risk factors with COVID-19 outcomes compare with those of genetic risk factors. Here, we focus on explicitly comparing genetic and nongenetic risk factor associations with COVID-19 hospitalization. For example, the COVID-19 Host Genetics Initiative reported an odds ratio (OR) for rs2271616’s association with COVID-19 hospitalization to be OR [$95\%$ CI] = 1.12 [1.06, 1.19]. We report here that nongenetic risk factors whose associations with COVID-19 hospitalization are comparable in magnitude with that of the OR of rs2271616. Other teams have executed analyses varying in consistency and breadth of candidate subsets of risk factors at a time in the UKB. For example, Niedzwiedz et al. specifically assessed the association of ethnicity with COVID-19 susceptibility and found certain minority ethnic groups to be associated with greater risk after adjusting for socioeconomic differences and behavioral risk factors or baseline health2. Hastie et al. examined only one biomarker, vitamin D, and analyzed its association with risk of COVID-19 positivity and ethnic differences in COVID-19 positivity and found no link29. However, others have shown conflicting results: an association between Vitamin D deficiency and hospitalization but no causal relationship between Vitamin D levels and COVID-19 severity30. In contrast, much can be learned when moving beyond isolated sets of candidate factors. For example, Chadeau-Hyam et al. examined subsets of factors across multiple categories (social, environmental, demographic factors) and pressure-tested a suite of sensitivity analyses1. Here, we examine a wider range of risk factors across categories across two timepoints and compare 63 laboratory biomarkers1. To the best of our knowledge, the association of the volume of “current frequency of shift work” (early in the pandemic) has not been reported; however, the finding is in agreement with job type31. Our findings regarding air pollution are also concordant with Travaglio et al. ’s findings32, though our efforts extended this result to demonstrate their lack of robustness in specific analytic model contexts via VoE. Our data-driven approach identified primarily sociodemographic-related factors such as “current frequency of shift work” associated with COVID-19 outcomes early in the pandemic. Age and household factors (e.g., as “participant having son and/or daughter in household”, “number of people in participant’s household”) played a more prominent role as the pandemic progressed. Previously reported associations (e.g., air pollution) are sensitive to both time of recruitment of patients at risk and time of COVID-19 test, in addition to modeling assumptions, such as adjustment variable selection. It is also important to note the limitations of our study. The UKB cohort is not a representative sample of the general UK population as many participants are less deprived socioeconomically, generally healthier overall, and are predominantly White Caucasian13,29. Others have shown that associations may also be affected by collider bias33. More specifically, our positivity outcome results may be biased by sampling and public health testing strategy. Additionally, there are unbalanced sample sizes corresponding to the two time periods, which might explain some of the variation of the exposure effects between the two time periods. Also, many exposures are unlikely to be available for all participants in the entire cohort. For example, the exposure category of infectious antigens (e.g., “1gG antigen for Herpes Simplex virus-1”) has the highest missingness rate of ~$98\%$. Additionally, we did not consider nonlinear associations. Also, we did not have the opportunity to consider confounders for each exposure or group of exposures as there is no consensus for what variables may be considered as ‘confounders’ for such associations. Lastly, our study considered only 14 disease and health conditions but comorbidity burden taken as a whole has been reported in the literature to play a significant role in risk for COVID-19 positivity (among other COVID-19 related outcomes)34. Moreover, cumulative comorbidity burden may mediate some of the age associations35–37. Future work should consider the correlated effects of comorbidity burden towards the stabilization (or lack thereof) of the laboratory parameters (blood biomarkers). Our results suggest that COVID-19 observational associations are dependent on the time of pandemic and public health priorities need to be nimble to changing risk as the pandemic progresses. ## Supplementary information Description of Additional Supplementary Data Files Supplementary Data 1 Supplementary Data 2 Supplementary Data 3 Supplementary Data 4 Supplementary Data 5 Supplementary Data 6 Supplementary Data 7 Supplementary Data 8 Supplementary Data 9 Supplementary Data 10 Supplementary Information Reporting Summary The online version contains supplementary material available at 10.1038/s43856-023-00271-3. ## Peer review information Communications Medicine thanks Jordi Piera-Jiménez and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. ## References 1. 1.Chadeau-Hyam, M. et al. Risk factors for positive and negative COVID-19 tests: a cautious and in-depth analysis of UK biobank data. Int. J. Epidemiol. 10.1093/ije/dyaa134 (2020). 2. Niedzwiedz CL. **Ethnic and socioeconomic differences in SARS-CoV-2 infection: prospective cohort study using UK Biobank**. *BMC Med.* (2020.0) **18** 160. DOI: 10.1186/s12916-020-01640-8 3. 3.Roso-Llorach, A. et al. Evolving mortality and clinical outcomes of hospitalized subjects during successive COVID-19 waves in Catalonia, Spain. Glob. Epidemiol.4, 100071 (2022). 4. Patel CJ. **The demographic and socioeconomic correlates of behavior and HIV infection status across sub-Saharan Africa**. *Commun. Med.* (2022.0) **2** 104. DOI: 10.1038/s43856-022-00170-z 5. Patel CJ, Bhattacharya J, Ioannidis JPA, Bendavid E. **Systematic identification of correlates of HIV infection: an X-wide association study**. *AIDS* (2018.0) **32** 933-943. DOI: 10.1097/QAD.0000000000001767 6. Patel CJ, Ioannidis JPA. **Studying the elusive environment in large scale**. *JAMA* (2014.0) **311** 2173-2174. DOI: 10.1001/jama.2014.4129 7. Ioannidis JPA, Loy EY, Poulton R, Chia KS. **Researching genetic versus nongenetic determinants of disease: a comparison and proposed unification**. *Sci. Transl. Med.* (2009.0) **1** 7ps8. DOI: 10.1126/scitranslmed.3000247 8. Patel CJ, Bhattacharya J, Butte AJ. **An Environment-Wide Association Study (EWAS) on type 2 diabetes mellitus**. *PLoS One* (2010.0) **5** e10746. DOI: 10.1371/journal.pone.0010746 9. McGinnis DP, Brownstein JS, Patel CJ. **Environment-Wide Association Study of Blood Pressure in the National Health and Nutrition Examination Survey (1999-2012)**. *Sci. Rep.* (2016.0) **6** 30373. DOI: 10.1038/srep30373 10. Patel CJ. **Systematic evaluation of environmental and behavioural factors associated with all-cause mortality in the United States national health and nutrition examination survey**. *Int. J. Epidemiol.* (2013.0) **42** 1795-1810. DOI: 10.1093/ije/dyt208 11. Patel CJ, Burford B, Ioannidis JPA. **Assessment of vibration of effects due to model specification can demonstrate the instability of observational associations**. *J. Clin. Epidemiol.* (2015.0) **68** 1046-1058. DOI: 10.1016/j.jclinepi.2015.05.029 12. Sudlow C. **UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age**. *PLoS Med.* (2015.0) **12** e1001779. DOI: 10.1371/journal.pmed.1001779 13. 13.Fry, A. et al. Comparison of Sociodemographic and Health-Related Characteristics of UK Biobank Participants With Those of the General Population. Am. J. Epidemiol.186, 1026–1034 (2017). 14. 14.Armstrong, J. et al. Dynamic linkage of COVID-19 test results between Public Health England’s Second Generation Surveillance System and UK Biobank. Microb. Genom.6. Preprint at 10.1099/mgen.0.000397 (2020). 15. 15.McCaw, Z. R., Lane, J. M., Saxena, R., Redline, S. & Lin, X. Operating characteristics of the rank-based inverse normal transformation for quantitative trait analysis in genome-wide association studies. Biometrics10.1111/biom.13214 (2019). 16. Millard LAC, Davies NM, Gaunt TR, Davey Smith G, Tilling K. **Software Application Profile: PHESANT: a tool for performing automated phenome scans in UK Biobank**. *Int. J. Epidemiol.* (2018.0) **47** 29-35. DOI: 10.1093/ije/dyx204 17. 17.Zou, G. A Modified Poisson Regression Approach to Prospective Studies with Binary Data. Am. J. Epidemiol.159, 702–706 (2004). 18. Mansournia MA, Nazemipour M, Naimi AI, Collins GS, Campbell MJ. **Reflection on modern methods: demystifying robust standard errors for epidemiologists**. *Int. J. Epidemiol.* (2021.0) **50** 346-351. DOI: 10.1093/ije/dyaa260 19. 19.Benjamini, Y. & Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J. R. Stat. Soc. Series B.57, 289–300 (1995). 20. Williamson EJ. **Factors associated with COVID-19-related death using OpenSAFELY**. *Nature* (2020.0) **584** 430-436. DOI: 10.1038/s41586-020-2521-4 21. 21.Wu, Y. et al. Genome-wide association study of medication-use and associated disease in the UK Biobank. Nat. Commun.10, 1891 (2019). 22. 22.Klau, S., Hoffmann, S., Patel, C. J., Ioannidis, J. P. A. & Boulesteix, A.-L. Examining the robustness of observational associations to model, measurement and sampling uncertainty with the vibration of effects framework. Int. J. Epidemiol.50, 266–278 (2021). 23. 23.stejat. stejat98/UKB_COVID_XWAS: v1.0.0. (Zenodo, 2023). 10.5281/ZENODO.7542752. 24. 24.Daghlas, I. et al. Selection into shift work is influenced by educational attainment and body mass index: a Mendelian randomization study in the UK Biobank. Int. J. Epidemiol.50, 1229–1240 (2021). 25. Tierney BT. **Leveraging vibration of effects analysis for robust discovery in observational biomedical data science**. *PLoS Biol.* (2021.0) **19** e3001398. DOI: 10.1371/journal.pbio.3001398 26. 26.Rogers, A. E., Hwang, W.-T., Scott, L. D., Aiken, L. H. & Dinges, D. F. The Working Hours Of Hospital Staff Nurses And Patient Safety. Health Affairs23, 202–212 (2004). 27. Tapela N. **Original research: Prevalence and determinants of hypertension control among almost 100 000 treated adults in the UK**. *Open Heart* (2021.0) **8** e001461. DOI: 10.1136/openhrt-2020-001461 28. 28.COVID-19 Host Genetics Initiative. Mapping the human genetic architecture of COVID-19. Nature10.1038/s41586-021-03767-x (2021). 29. Hastie CE. **Vitamin D concentrations and COVID-19 infection in UK Biobank**. *Diabetes Metab. Syndr.* (2020.0) **14** 561-565. DOI: 10.1016/j.dsx.2020.04.050 30. 30.Hernández, J. L. et al. Vitamin D Status in Hospitalized Patients with SARS-CoV-2 Infection. J. Clin. Endocrinol. Metab. 10.1210/clinem/dgaa733 (2020). 31. 31.Mutambudzi, M. et al. Occupation and risk of severe COVID-19: prospective cohort study of 120 075 UK Biobank participants. Occup. Environ. Med.78, 307–314 (2020). 32. 32.Travaglio, M. et al. Links between air pollution and COVID-19 in England. Environ. Pollut.268, 115859 (2021). 33. 33.Griffith, G. J. et al. Collider bias undermines our understanding of COVID-19 disease risk and severity. Nat. Commun.11, 5749 (2020). 34. Wynants L. **Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal**. *BMJ* (2020.0) **369** m1328. DOI: 10.1136/bmj.m1328 35. Monterde D. **Performance of Three Measures of Comorbidity in Predicting Critical COVID-19: A Retrospective Analysis of 4607 Hospitalized Patients**. *Risk Manag. Healthc. Policy* (2021.0) **14** 4729-4737. DOI: 10.2147/RMHP.S326132 36. Vela E. **Development and validation of a population-based risk stratification model for severe COVID-19 in the general population**. *Sci. Rep.* (2022.0) **12** 1-10. DOI: 10.1038/s41598-022-07138-y 37. Ho FK. **Is older age associated with COVID-19 mortality in the absence of other risk factors? General population cohort study of 470,034 participants**. *PLoS One* (2020.0) **15** e0241824. DOI: 10.1371/journal.pone.0241824 38. 38.Tangirala, S. COVID-19 Positivity results data for Figs. 1and 4. 10.6084/M9.FIGSHARE.21909726.V1 (2023). 39. 39.Tangirala, S. COVID-19 Positivity results data for Fig. 2. 10.6084/M9.FIGSHARE.21909408.V4 (2023). 40. 40.Tangirala, S. COVID-19 Positivity results data for Fig. 3. 10.6084/M9.FIGSHARE.21909711.V1 (2023).
--- title: 'Extent, Type and Reasons for Adaptation and Modification When Scaling-Up an Effective Physical Activity Program: Physical Activity 4 Everyone (PA4E1)' authors: - Matthew Mclaughlin - Elizabeth Campbell - Rachel Sutherland - Tom McKenzie - Lynda Davies - John Wiggers - Luke Wolfenden journal: Frontiers in Health Services year: 2021 pmcid: PMC10062321 doi: 10.3389/frhs.2021.719194 license: CC BY 4.0 --- # Extent, Type and Reasons for Adaptation and Modification When Scaling-Up an Effective Physical Activity Program: Physical Activity 4 Everyone (PA4E1) ## Abstract Background: Few studies have described the extent, type and reasons for making changes to a program prior to and during its delivery using a consistent taxonomy. Physical Activity 4 Everyone (PA4E1) is a secondary school physical activity program that was scaled-up for delivery to a greater number of schools. We aimed to describe the extent, type and reasons for changes to the PA4E1 program (the evidence-based physical activity practices, implementation support strategies and evaluation methods) made before its delivery at scale (adaptations) and during its delivery in a scale-up trial (modifications). Methods: The Framework for Reporting Adaptations and Modifications-Enhanced (FRAME) was used to describe adaptations (planned and made prior to the scale-up trial) and modifications (made during the conduct of the trial). A list of adaptations was generated from a comparison of the efficacy and scale-up trials via published PA4E1 protocols, trial registrations and information provided by trial investigators. Monthly trial team meetings tracked and coded modifications in “real-time” during the conduct of the scale-up trial. The extent, type and reasons for both adaptations and modifications were summarized descriptively. Results: In total, 20 adaptations and 20 modifications were identified, these were to physical activity practices ($$n = 8$$; $$n = 3$$), implementation support strategies ($$n = 6$$; $$n = 16$$) and evaluation methods ($$n = 6$$, $$n = 1$$), respectively. Few adaptations were “fidelity inconsistent” ($$n = 2$$), made “unsystematically” ($$n = 1$$) and proposed to have a “negative” impact on the effectiveness of the program ($$n = 1$$). Reasons for the adaptations varied. Of the 20 modifications, all were “fidelity consistent” and the majority were made “proactively” ($$n = 12$$), though most were “unsystematic” ($$n = 18$$). Fifteen of the modifications were thought to have a “positive” impact on program effectiveness. The main reason for modification was the “available resources” ($$n = 14$$) of the PA4E1 Implementation Team. Conclusions: Adaptations and modifications to public health programs are common. Modifications have the potential to impact the implementation and effectiveness of programs. Our findings underscore the importance of comprehensive reporting of the extent, type and reasons for modifications as part of process evaluations, as this data may be important to the interpretation of trial findings. Clinical Trial Registration: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=372870, Identifier ACTRN12617000681358. ## Introduction Physical activity has extensive benefits for health and society [1, 2]. One in four adults and four in five adolescents globally are insufficiently active to meet aerobic physical activity guidelines [3, 4]. While there are an abundance of evidence-based programs to address physical activity, many of these have been tested under optimal research conditions [5, 6], and few have successfully targeted adolescent physical activity [7, 8]. As such, many programs previously tested have utilized technical expertise, skills, resources and infrastructure that are not common in real-world operational environments where they are intended to be implemented [9, 10]. Further, research trials often recruit participant groups that differ markedly from those of the target population [11]. As physical activity programs examined in research trials are often unsuitable for replication in more real-world environments, they are frequently changed by end-users as part of efforts to make them more suitable for implementation and scale-up [9, 12]. These changes can take two forms – adaptations to a program prior to it being delivered, and modifications that occur during the delivery of the program. Adaptation [8, 13] has been defined as a process of thoughtful and deliberate alteration to the design or delivery of a program, with the goal of improving its fit or effectiveness in a given context [13]. Program adaptations can include both adaptations to the evidence-based practices and/or to the implementation support strategies provided to increase adoption of the practices in the setting (such as training for clinicians or teachers who will be delivering the program). Adaptations include those to core components of the program, cultural adaptations, mode of delivery adaptations, target audience adaptations and service setting adaptations [14]. Modification has been defined as encompassing any change to a program, whether deliberately and proactively, or in reaction to unanticipated challenges that arise in the context of its delivery [13]. Adaptations and modifications can also be made to evaluation methods. Systematic reviews demonstrate that program adaptations are ubiquitous as part of efforts to scale-up programs in practice. For example, a systematic review of physical activity programs [8] reported that $100\%$ of programs made adaptations to the program tested in an efficacy trial prior to undertaking a trial of its scale-up. The majority of adaptations focused on the “delivery mode” of programs [8, 15, 16], such as giving preference to online or telephone over face-to-face delivery modes, which are often undertaken to enable greater program reach [8]. Understanding program adaptations and modifications is important as they can have significant implications to the effectiveness of programs [9, 12, 17]. They have been attributed, in part, to a phenomenon labeled “voltage drop” whereby the effects of a program are reduced by 25–$50\%$ when they are implemented at scale in real world contexts [8, 16, 17]. However, they have also been hypothesized to improve the impact of programs. For example, improvements may be made by allowing tailoring of evidence-based programs and their implementation (i.e., the local culture, historical context, priorities and availability of funding, staffing and resources), strengthening key program components, reducing inequities by improving its cultural relevance, or reducing relative costs via delivery using less expensive modalities [12]. Understanding the nature of program adaptations and modifications is also important for the development of explanations about how they may impact program implementation and outcomes as part of trial process evaluations [18]. The Framework for Reporting Adaptations and Modifications-Enhanced (FRAME) [13, 19] was recently developed to support the consistent documentation and reporting of program adaptations and modifications. It provides a taxonomy of classifying adaptations and modifications [13] including what is adapted/modified, the nature of the adaptation/modification, who participated in the adaptation/modification decision, for whom/what is the adaptation/modification made and when it occurred. Despite the existence of FRAME and the need for consistent reporting, both adaptations made prior to program delivery and modifications made during the implementation are often poorly described in research reports [13, 15, 20, 21]. That is, individual trials seldom report adaptations for scale-up (prior to program delivery) using consistent taxonomies [13], instead trials rely on descriptions of adaptations that can't be compared between trials [13]. A systematic review of 42 evidence-based public health programs that reported adaptations to the evidence-based program practices [15] found that the most frequent types of adaptation were tailoring ($93\%$) or adding elements ($71\%$). Most commonly these adaptations were to content ($100\%$), context ($95\%$), cultural ($74\%$) and/or delivery ($62\%$). While the review provides useful insights into the frequency of adaptations to the evidence based program practices, it does not explicitly include adaptations to the implementation support strategies used or the evaluation methods [13, 22]. Also, the authors relied on published papers to retrospectively code adaptations to evidence-based program practices [15]. A limitation of relying on published papers is that sometimes the extent, types, context and reasons for adaptations and modifications may be unclear or absent completely from these documents, remaining instead with those people involved in the scale-up process [15]. Additionally, it is also unclear in many programs what modifications occur during delivery, and to the authors' knowledge, no physical activity studies have used a consistent taxonomy to report modifications during program delivery [23]. Importantly, prior studies have also not routinely reported who was responsible for program adaptations or modifications, why these were undertaken, and if they were considered to contribute, or detract, from the effects of the program. Such information could be used to help interpret trial findings in implementation-effectiveness studies [13, 22, 24]. In the absence of well-described adaptations prior to delivery and modifications during delivery, we present here a descriptive study of the adaptations and modifications made in the scale-up of an evidence based physical activity program targeting adolescents, Physical Activity 4 Everyone (PA4E1). PA4E1 is a secondary school physical activity program. After an efficacy trial, PA4E1 showed positive results (25–29), PA4E1 was adapted in preparation for scale-up [30]. The PA4E1 program includes both an evidence-based program (consisting of seven school physical activity practices) and seven implementation support strategies offered to help schools implement these physical activity practices (implementation support). The aims of the current paper are: ## Methods This research has been conducted and reported in accordance with the requirements of the Standards for Reporting Implementation Studies (StaRI) Statement (Additional File 1) and Template for Intervention Description and Replication (TIDieR) checklist (Additional File 2). ## Ethical Approval The efficacy and scale-up trials have been registered at ACTRN12612000382875 and ACTRN12617000681358, respectively. Ethical approvals were sought from Hunter New England Human Research Ethics Committee (Ref No. $\frac{11}{03}$/$\frac{16}{4.05}$), University of Newcastle (Ref No. H-2011-0210), NSW Department of Education and Communities (SERAP 2011111), Maitland Newcastle Catholic School Diocese, Broken Bay Catholic School Diocese, Lismore Catholic School Diocese, Armidale Catholic School Diocese, and the Aboriginal Health and Medical Research Council (AHMRC). ## Stages of Physical Activity 4 Everyone (PA4E1) An outline of the physical activity practices (evidence based program) for both the efficacy and scale-up trials are shown in Figure 1. The implementation support strategies offered to schools are outlined for both trials in Figure 2 (26, 27, 29–32). As is best practice in implementation science [33], we distinguish between program components, separating the evidence-based program practices (the physical activity practices) from the implementation support strategies, which are designed to assist schools to implement the physical activity practices. **Figure 1:** *Summary of the physical activity practices in the PA4E1 efficacy trial (25–27, 29) and the PA4E1 scale-up trial (30–32).* **Figure 2:** *Summary of the implementation support strategies offered to schools in the PA4E1 efficacy trial (25–27, 29) and the PA4E1 scale-up trial (30–32).* ## Efficacy Trial (2012–2014) The evaluation methods of the PA4E1 efficacy trial have been reported in a trial protocol [26]. Briefly, the PA4E1 efficacy trial was a 2-year (2012–2014) cluster randomized controlled trial involving 10 low-socioeconomic Australian secondary schools (five per group). Hunter New England Local Health District (HNELHD) led PA4E1 [34], supported by two other local health district delivery partners [26, 34] in a research-practice partnership with the University of Newcastle and New South Wales (NSW) Department of Education. PA4E1 had positive effects on students' device-measured moderate-to-vigorous physical activity and unhealthy weight gain (25–27, 29) and was deemed cost-effective [28]. The PA4E1 program consisted of seven physical activity practices (Figure 1) to support students to be more physically active (the evidence-based program) and six implementation support strategies designed to embed the physical activity practices within the school environment (Figure 2). ## Adaptation Process (2017) Adaptations were made in 2017 with the goal of scaling up the program (physical activity practices and implementation support strategies) employed in the efficacy trial and testing the effects again as part of a scale-up trial. The process has been reported in more detail elsewhere [30]. The scale-up adaptation process sought to retain the effects of the original program by retaining components deemed as core (physical activity practices and implementation support strategies) (25–27, 29) while enabling greater reach (scaling up to more schools). Briefly, to adapt PA4E1 for scale-up, we used a four-stage iterative scale-up process, based on a review of existing models and factors for scaling up public health programs [35] and a scoping review of frameworks for adapting public health programs [36]. Firstly, we identified barriers and enablers to the physical activity practices and implementation support strategies. Second, we mapped the identified barriers to the Theoretical Domains Framework [37] and the Behavior Change Wheel [38]. Thirdly, we prioritized the components of the program from the perspective of a health service requiring its delivery at scale, considering variables such as affordability, practicability and acceptability [39]. Finally, the PA4E1 Expert Advisory Group (comprised of senior health service staff, senior academics, NSW Education sector partners and the PA4E1 project staff) reviewed the prioritized program (physical activity practices and implementation strategies) and made the final judgement regarding the design and components of the resulting PA4E1 program [30]. ## Scale-Up Trial (2017–2019) The scale-up trial was a type III hybrid implementation-effectiveness trial [24]. Methods have been reported in both a trial protocol [30] and a process evaluation protocol [32]. The scale-up trial (2017–2019) tested the adapted PA4E1 program in a larger number of low-socioeconomic secondary schools ($$n = 49$$) across a larger geographic area (HNELHD leading three other local health districts, in a research-practice partnership with the University of Newcastle and NSW Department of Education) [34]. Program schools were offered seven implementation support strategies (incorporating 23 sub-strategies) to support their adoption of seven physical activity practices (30–32). The scale-up trial recruited 49 schools (24 program, 25 control) [31]. ## Defining Adaptation and Modification We operationally defined changes to PA4E1 (inclusive of the physical activity practices, implementation support strategies and the evaluation methods) temporally, as either adaptations which were planned and made prior to the scale-up trial or modifications which were made during the conduct of the trial. The methods are reported by aim. Aim one focuses on adaptations (prior to scale) and aim two focuses on modifications (during implementation of the program in the scale-up trial). ## Measures and Procedures The measures and procedures are reported by aim. Aim 1: To describe the extent, type and reasons for adaptations to PA4E1 that were made for scale-up to the physical activity practices, implementation support strategies and evaluation methods. We used the FRAME framework to code the adaptations to the PA4E1 program made for scale-up [13]. We applied the FRAME framework coding to the evidence-based practices (physical activity practices), the implementation support strategies [13] and the evaluation methods. The FRAME framework was used to provide a discrete set of codes for each category of adaptation (see Table 2 for a list of codes for each category). Additional coding categories (outlined below) were developed by the author team in line with methods from Rabin et al., [ 23] to report free-text descriptions of each adaptation. Table 2 shows the adaptation categories utilized and the response codes. Briefly, these are: The following categories were coded for each adaptation from the FRAME framework [6]: Certain coding categories were considered “not applicable” to the evaluation methods (as outlined in Table 2). Firstly, MM identified a list of adaptations between the efficacy and scale-up trial on physical activity practices, implementation support strategies and evaluation by triangulating data from a number of sources. The first source was published PA4E1 research papers: the trial protocol [26], 12 and 24-month outcome papers [27, 29] and cost-effectiveness paper for the efficacy trial [28]; and the trial protocol [30], process evaluation protocol [32] and 12-month practice outcome paper [31] for the scale-up trial. Secondly, we drew upon the trial registries for both trials. Finally, to provide important context and resolve discrepancies in recorded adaptations, we drew on the historical knowledge of three authors (LD, EC and RS) who were involved in both trials through meetings with the lead author (MM). To refine the initial codes, a consensus meeting was held between MM, TM, LD, RS, and EC. Following discussion, MM then finalized the coding for each of the adaptations. Finally, the final codes were agreed upon by email by MM, TM, LD, RS, and EC. Descriptive statistics for each adaptation category were calculated (e.g., the number and type of adaptations that were made). To synthesize the adaptations, we calculated descriptive statistics for each adaptation category (e.g., the number and type of adaptations that were made) overall, and for adaptations made to physical activity practices, to implementation support strategies and to evaluation. Aim 2: To describe the extent, type and reasons for modifications during the PA4E1 scale-up trial made to the physical activity practices, implementation support strategies and evaluation methods. We used the FRAME framework to describe and code modifications made during the delivery of the scaled-up PA4E1 program to the physical activity practices, the implementation support strategies and the evaluation methods. We used the method as outlined in the process evaluation protocol [32]. Throughout the 24-month program (2017–2019), Support Officers involved in the delivery of the program to schools and the PA4E1 Management Team involved in the day-to-day operations of the project continually tracked modifications to PA4E1 during the program in “real-time” by adding them to a Microsoft Excel spreadsheet [23]. A monthly meeting (up to 60 min) was held between the PA4E1 Management Team (including at least one Support Officer) to code the identified modifications onto the Stirman et al., [ 13] framework for modifications in the same spreadsheet [19]. MM subsequently updated the coding to reflect the additional categories included within the updated framework by Stirman et al., the FRAME framework [13, 19]. This method of ‘real-time ongoing coding’ has previously been found to be feasible to track modifications [23]. We used the same coding categories from Aim 1 for each modification (replacing the word adaptation for modification), as well as coding two additional categories. For the category of proposed impact on the program effectiveness at the time of modification (i.e., “positive”, “null” or “negative”), we coded this based on the predicted impact of the modification at the time of coding, rather than the actual or measured impact of the modification. At the end of the program, the final codes were collated by MM. The final codes were discussed and refined (by RS, EC, LD, TM) to reach consensus. Descriptive statistics for each modification category were calculated (e.g., the number and type of modifications that were made) overall and separately for physical activity practices, implementation support strategies and evaluation methods. ## Results Results are reported by adaptations (aim 1) and modifications (aim 2), respectively. ## Adaptations Aim 1 was to describe the extent, type and reasons for adaptations to PA4E1 that were made for scale-up to the physical activity practices, implementation support strategies and evaluation methods. Table 1 descriptively summarizes the main program adaptations from efficacy trial to scale-up trial for individual physical activity practices and implementation support strategies, including the codes “fidelity consistent or fidelity inconsistent”, “systematic or unsystematic” and “proposed positive, negative or null impact on the project” (as described in the methods). For a more expanded description of adaptations made for the scale-up trial and their coding, see Additional File 3. **Table 1** | Name of efficacy trial program component | Following adaptation, name of scale-up trial program component | Descriptive summary of main adaptations from efficacy trial to scale-up trial | Fidelity consistent?* | Systematic?* | Proposed positive (+), negative (-) or null (0) impact on the project | | --- | --- | --- | --- | --- | --- | | Practice 1: teaching strategies to maximize students' physical activity in health and physical education (PE) lessons | Practice 1: quality PE lessons | • Change of focus from focusing on “Active” (which is a single principle within “SAAFE” PE lesson guidelines) to all “SAAFE” principles PE Lessons (i.e., supportive, active, autonomous, fair and enjoyable) (40). • Though pedometers were made available to schools, this became less of a focus and was not a mandatory part of the practice. • The PE Department used documented principles or guidelines. • PE teacher should participate in peer observation of a practical PE lesson at least once per year. | X | ✓ | + | | Practice 2: development and monitoring of student physical activity plans within PE lessons | Practice 2: student PA plans | • As well as Grade 8's in year two, Grade 7's should also develop a physical activity plan in year two. • Goals were to be reviewed yearly, not termly. | ✓ | ✓ | + | | Practice 3: enhanced school sport program | Practice 3: enhanced school sport program | • Changed from “Program X” (41) to the “Resistance Training 4 Teens (RT4T)” program (42). • RT4T was offered as accredited training by the NSW Department of Education. | ✓ | ✓ | + | | Practice 4: development/ modification of school policies | Practice 5: school PA policy or procedure | • The policy must include the provision of at least 150 min of moderate to vigorous-intensity physical activity during school time for all students in Grade 7–10. | ✓ | ✓ | + | | Practice 5: physical activity programs during school breaks | Practice 4: recess and lunchtime PA | • Changed to 3 days per week (from two), ideally with at least one activity targeting girls specifically. • Schools were also asked to provide access to physical activity equipment to students at least 3 days per week. | ✓ | ✓ | + | | Practice 6: promotion of community physical activity providers (community links) | Practice 6: links with community PA providers | • Specification that schools form three links with community physical activity providers that go beyond the promotion of the provider. • It was desirable that at least one of the community links made were to promote free or low-cost options in the community. • Schools were asked to use multiple modes to promote (e.g., newsletter, parent app). • This replaced a 1-day community physical activity provider expo (as more feasible and sustained) | ✓ | ✓ | + | | Practice 7: parent engagement | Practice 7: communicating PA messages to all parents | • Schools were asked to use multiple modes to communicate the messages (e.g., newsletter, parent app). | ✓ | ✓ | + | | Strategy 1: in-school physical activity consultant | Strategy 2 and 3: embedded school staff: in-School Champion and External implementation support | • External physical activity consultant replaced by an in-School Champion (an existing PE teacher within the school) who was supported by a health promotion support officer employed by the respective local health district. • In-School Champions were funded $400 a fortnight. • Support Officer and in-School Champion maintained contact through face-to-face meetings, email and phone according to the schedule documented within the support strategy. • Support Officer was co-located in the same local health district with in-School Champions. | X | ✓ | + | | Strategy 2: establishing leadership and support | Strategy 1: executive and leadership support | • Less total committee members in the scale-up trial (i.e., no requirement for student, parent, Head PE teacher and community representative) and include both the in-School Champion and school executive. | ✓ | ✓ | + | | Strategy 3: teacher training | Strategy 4: teacher professional learning | • PE Teacher training via a website (six modules) rather than three face-to-face sessions. • NESA (New South Wales Education Standards Authority) accreditation attached to online training. • Specific training for writing a physical activity policy for in-school champions. • Three days training for in-school champion. | ✓ | ✓ | + | | Strategy 4: resources | Strategy 5: resources | • Paper-based resources were replaced by a website housing documentation and resources, except for printed posters outlining the SAAFE principles (for practice 1). • Less total equipment provided, five gymsticks and an equipment voucher were provided to schools rather than providing all the $6,000 equipment. • Promotional materials were not issued as part of the support strategy but instead for the completion of evaluation measures. | ✓ | ✓ | - | | Strategy 5: prompts | Strategy 6: provision of prompts and reminders | • Support officers reminded in-school champions to implement the program rather than the in-school consultant reminding teachers. • Automated prompts to in-School Champions and PE teachers delivered via the program website. | ✓ | ✓ | + | | Strategy 6: intervention implementation performance | Strategy 7: implementation performance monitoring and feedback | • Feedback was automated via the program website and was directly against the physical activity practice milestones (as practice implementation builds over two school years and is designed to be ongoing). • Feedback was automatically sent to (website registered) in-School Champions and Principals. • No direct observations were undertaken by the Support Officer. | ✓ | ✓ | 0 | ## Number of Adaptations A total of 20 adaptations were made to scale-up PA4E1. Eight adaptations were to the school physical activity practices, six to the implementation support strategies and the remaining adaptations were to the evaluation methods ($$n = 6$$). Table 2 summarizes the codes according to the FRAME framework for physical activity practices, implementation support strategies and evaluation methods, respectively [13]. Additional File 3 outlines each individual adaptation in detail and includes the full set of codes outlined in the methods. **Table 2** | Modification categories | Code | Physical activity practices n (%) | Implementation support strategies n (%) | Evaluation methods n (%) | Total (practices, strategies and evaluation) n (%)** | | --- | --- | --- | --- | --- | --- | | Program component? | Physical activity practices implementation support strategies evaluation | 8 (100) N/A N/A | N/A 6 (100) N/A | N/A N/A 6 (100) | 8 (40) 6 (30) 6 (30) | | Proposed impact on the project? | Positive Negative Null Not applicable | 8 (100) 0 (0) 0 (0) 0 (0) | 4 (66) 1 (17) 1 (17) 0 (0) | N/A N/A N/A 6 (100) | 12 (86) 1 (7) 1 (7) 6* | | Relationship to fidelity/core elements? | Fidelity consistent Fidelity inconsistent Not applicable | 7 (88) 1 (13) 0 (0) | 5 (83) 1 (17) 0 (0) | N/A N/A 6 (100) | 12 (86) 2 (14) 6 | | Were adaptations systematic or unsystematic? | Systematic Unsystematic | 7 (88) 1 (13) | 6 (100) 0 (0) | 6 (100) 0 (0) | 19 (95) 1 (5) | | Were adaptations proactive or reactive? | Proactive Reactive | 8 (100) 0 (0) | 6 (100) 0 (0) | 6 (100) 0 (0) | 20 (100) 0 (0) | | Who participated in the decision to modify?* | Program manager Treatment/Intervention team Other codes | 8 (100) 8 (100) 0 (0) | 6 (100) 6 (100) 0 (0) | 6 (100) 6 (100) 0 (0) | 20 (100) 20 (100) 0 (0) | | What was the goal?* | Improve fit with recipients Improve feasibility Improve effectiveness/outcomes Reduce cost Increase reach or engagement Increase satisfaction Other codes Not applicable | 4 (50) 0 (0) 3 (38) 0 (0) 1 (13) 1 (13) 0 (0) 0 (0) | 4 (67) 5 (83) 1 (17) 3 (50) 0 (0) 0 (0) 0 (0) 0 (0) | 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 6 (100) | 8 (57) 5 (36) 4 (29) 3 (21) 1 (7) 1 (7) 0 (0) 6 | | What is adapted?* | Content Implementation and scale-up activities Training and evaluation Contextual | 7 (88) 0 (0) 0 (0) 1 (13) | 6 (100) 6 (100) 0 (0) 0 (0) | 0 (0) 6 (100) 6 (100) 0 (0) | 13 (65) 12 (60) 6 (30) 1 (5) | | Contextual modifications are made to what? | Format Setting Personnel Population Not applicable | 1 (13) 0 (0) 0 (0) 0 (0) 7 (88) | 0 (0) 0 (0) 0 (0) 0 (0) 6 (100) | N/A N/A N/A N/A 6 (100) | 1 (100) 0 (0) 0 (0) 0 (0) 19 | | At what level of delivery were adaptations made? | Target intervention group (all schools) Cohort (group of schools sharing a characteristic) Clinic unit/level (individual schools) | 8 (100) 0 (0) 0 (0) | 6 (100) 0 (0) 0 (0) | 6 (100) 0 (0) 0 (0) | 20 (100) 0 (0) 0 (0) | | What is the nature of the content adaptation?* | Substituting Tailoring/tweaking/refining Lengthening/extending elements Adding elements Shortening/condensing Not applicable | 3 (38) 0 (0) 2 (25) 1 (13) 1 (13) 1 (13) | 4 (67) 0 (0) 0 (0) 1 (17) 2 (33) 0 (0) | 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 6 (100) | 8 (62) 3 (23) 2 (15) 2 (15) 3 (23) 7 | | Reasons–socio-political (i.e., broad context)* | None (no reason) Funding or resource availability/allocation Existing policies Societal/cultural norms Other codes Not applicable | 4 (50) 2 (25) 2 (25) 0 (0) 0 (0) 0 (0) | 5 (83) 1 (17) 0 (0) 0 (0) 0 (0) 0 (0) | 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 6 (100) | 9 (64) 3 (21) 1 (7) 1 (7) 0 (0) 6 | | Reasons –organization/setting (i.e., PA4E1 Implementation Team)* | None (no reason) Available resources (funds, staff, tech, space) Other codes Not applicable | 8 (100) 0 (0) 0 (0) 0 (0) | 0 (0) 6 (100) 0 (0) 0 (0) | 0 (0) 0 (0) 0 (0) 6 (100) | 8 (57) 6 (43) 0 (0) 6 | | Reasons – provider (i.e., Local Health District)* | None (no reason) Not applicable | 8 (100) 0 (0) | 6 (100) 0 (0) | 0 (0) 6 (100) | 14 (100) 6 | | Reasons – recipient (i.e., schools and in-School Champions)* | None (no reason) Cultural or religious norms Physical capacity Motivation and readiness Access to resources Other codes Not applicable | 4 (50) 0 (0) 2 (25) 1 (13) 2 (25) 0 (0) 0 (0) | 2 (33) 4 (66) 0 (0) 1 (17) 0 (0) 0 (0) 0 (0) | 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 6 (100) | 6 (43) 4 (29) 2 (14) 2 (14) 2 (14) 0 (0) 6 | The vast majority of adaptations were coded as “systematic” ($$n = 19$$), as they were made during the theory-informed iterative scale-up process. By definition, all adaptations ($$n = 20$$) were made “proactively” rather than in response to an unknown event or circumstance. Two adaptations were deemed “fidelity inconsistent”, as the core elements or functions had changed as a result of the adaptation [13]. Of the 14 adaptations to the practices and strategies, 12 were proposed to have a positive impact on program effectiveness. All adaptations involved both the “Program Manager” and “Treatment/Intervention Team” in the decision-making process, which included the PA4E1 Implementation Team (inclusive of the program manager, project staff and the expert advisory group) as described in the methods. ## Types of Adaptations The goals and types of adaptations varied. The most common goals were to “improve fit with recipients” ($$n = 8$$), “improve feasibility” ($$n = 5$$), “improve effectiveness/outcomes” ($$n = 4$$) and to “reduce cost” ($$n = 3$$). Most adaptations were to “content” ($$n = 13$$) and “implementation and scale-up activities” ($$n = 12$$). Fewer adaptations were to “training and evaluation” ($$n = 6$$) or “contextual” ($$n = 1$$). All adaptations ($$n = 20$$) were made at the “target intervention group” level, meaning that adaptations applied to all schools receiving the program, rather than certain schools or local health districts (e.g., “individual”, “cohort” or “individual practitioner” level). Content adaptations varied, including “substituting” ($$n = 8$$) “tailoring/tweaking/refining” ($$n = 3$$) and “shortening/condensing” ($$n = 3$$). ## Reasons for Adaptations The reasons for adaptations included the broad context of having “funding or resource availability/allocation” ($$n = 3$$) and also the PA4E1 Implementation Team having “available resources (funds, staff, technology, space)” ($$n = 6$$). School and in-School Champions (i.e., “recipients”) reasons included cultural or religious norms ($$n = 4$$), “physical capacity” ($$n = 2$$), “motivation and readiness” ($$n = 2$$) and “access to resources” ($$n = 2$$). ## Modifications Aim 2 was to describe the extent, type and reasons for modifications during the PA4E1 scale-up trial made to the physical activity practices, implementation support strategies and evaluation methods. Table 3 provides a brief description of each modification made to the physical activity practices and implementation support strategies during the scale-up trial delivery. **Table 3** | Modification number (term initiated) | Program component(s) and brief description of modification made during scale-up trial | Fidelity consistent?* | systematic?* | Proactive modification?* | Proposed positive (+), negative (–) or null (0) impact on the project? | | --- | --- | --- | --- | --- | --- | | 1 (1) | •Implementation strategy 2: for the entire duration of the program, funding for the in-school champions was increased from AUD$350 a fortnight to AUD$400. | ✓ | X | ✓ | + | | 2 (1) | •Implementation strategy 5: instead of simply providing the physical resources to schools, they were issued as 'incentives' upon completion of training, though all schools ended up receiving the resources as they all completed the necessary training. All schools ended up receiving the resources if they wanted them. | ✓ | X | ✓ | + | | 3 (1) | •Implementation strategy 3: due to staff turnover, for some schools, their support officer was not co-located within same local health district. | ✓ | X | X | – | | 4 (1) | •Implementation strategy 3: due to staff turnover, for some schools, their Support Officer not trained in physical education teaching. | ✓ | X | X | – | | 5 (2) | •Implementation strategy 6: prompting emails were supposed to be sent reminding users to complete professional development. However, these were not sent to PE Teachers or in-School Champions if they registered after the first term of the program. An error in the website coding. | ✓ | X | X | – | | 6 (4) | •Implementation strategy 5: additional resources were made available, these were a set of 30 pedometers made available to schools who wanted them. Not all schools wanted them. The PA4E1 had three sets available in total. | ✓ | X | ✓ | + | | 7 (4) | •Implementation strategy 3: enhanced school sport training delivered by support officers to a single school as department of education training dates had expired. In-school champions and PE teachers unable to receive accreditation for this ad-hoc training. | ✓ | X | X | + | | 8 (4) | •Implementation strategy 4: extra day of face-to-face training held halfway through the program (Term 6). While a second day of face-to-face training was outlined within the study protocol [Table 2 (30)]. School Champions were not made aware of this until Term 4 of the program. This was because the program team were unsure about available resources. | ✓ | X | ✓ | + | | 9 (5) | •Implementation strategy 5: Facebook group created by in-school champions to facilitate resource, discussion and knowledge exchange. | ✓ | X | ✓ | + | | 10 (5) | •Practice 2: physical activity plans to be completed by Grade 7 only in the second half of the program, not both Grade 7 and 8 [as originally described in the study protocol – see Table 2 (30)]. | ✓ | X | ✓ | – | | 11 (5) | •Implementation strategy 4: face-to-face training was repeated for schools unable to attend the centralized training held for all schools. This training was delivered locally to the schools at a location and time that suited the schools, to reduce travel times for the in-school champions. | ✓ | X | X | + | | 12 (6) | •Implementation strategy 5: additional physical resources for enhanced school sport training were sent to a single school who requested them from their support officer. | ✓ | X | X | + | | 13 (6) | •Practice 1: lesson observation forms could be submitted either through the website form or uploaded as a word document (new). | ✓ | X | ✓ | + | | 14 (7) | •Implementation strategy 7: all schools were sent incorrect termly survey feedback reports due to an error with the termly survey. A replacement report was sent with the correct | | | | | | 15 (7) | •Practice 6: termly survey definition of meeting practice 6, changed from mandatory to desirable to have a low or no-cost option community link. | ✓ | X | ✓ | + | | 16 (7) | •Implementation support strategy 1–7: extension of the whole implementation support program by one school term, extending the program from eight school terms to nine school terms. However, schools were not provided additional funds for release of the in-school champion (Implementation Strategy 2) | ✓ | ✓ | ✓ | + | | 17 (7) | •Implementation strategy 4: Extra day of face-to-face training held at the end of the program to support sustainability (Term 9). | ✓ | X | ✓ | + | | 18 (8) | •Implementation strategy 7: sustainability reports, similar to termly surveys and feedback reports, were designed to assist schools to plan strategies for sustaining the PA4E1 program in their school beyond the life of the research project. These were issued via email to be completed by in-School Champions in liaison with their school Principal. | ✓ | ✓ | ✓ | + | | 19 (8) | •Implementation strategy 1 and 7: all School Principals were offered a face-to-face meeting in Term 8 to explain their schools 24 month sustainability report. | ✓ | X | ✓ | + | ## Number of Modifications A total of 20 modifications were made during the delivery of the scale-up trial of PA4E1 from 2017–2019. Of these, 16 modifications were made to the implementation support strategies, three to the physical activity practices and one to the evaluation methods. All modifications were deemed “fidelity consistent” and most modifications were proposed to have a positive impact on the effectiveness of the program ($$n = 15$$). Table 4 summarizes the modification codes according to the FRAME framework [13]. Additional File 4 outlines each individual modification in detail and includes the full set of codes (as outlined in the methods). **Table 4** | Modification categories | Codes | Physical activity practicesn (%) | Implementation support strategiesn (%) | Evaluation methodsN (%) | Total (practices, strategies, whole program and evaluation)n (%)** | | --- | --- | --- | --- | --- | --- | | Program component?* | Implementation support strategies physical activity practices evaluation | N/A 3 (100) N/A | 16 (100) N/A N/A | N/A N/A 1 (100) | 16 (80) 3 (15) 1 (5) | | Proposed impact on the project? | Positive Negative Null Not applicable | 2 (67) 1 (33) 0 (0) 0 (0) | 13 (81) 3 (19) 0 (0) 0 (0) | 0 (0) 0 (0) 0 (0) 1 (100) | 15 (79) 4 (21) 0 (0) 1 | | Relationship to fidelity/core elements? | Fidelity consistent Fidelity inconsistent Not applicable | 3 (100) 0 (0) 0 (0) | 16 (100) 0 (0) 0 (0) | N/A N/A 1 (100) | 19 (100) 0 (0) 1 | | Were modifications systematic or unsystematic? | Systematic Unsystematic | 0 (0) 3 (100) | 2 (13) 14 (88) | 0 (0) 1 (100) | 2 (10) 18 (90) | | Were modifications proactive or reactive? | Proactive Reactive | 3 (100) 0 (0) | 9 (56) 7 (44) | 0 (0) 1 (100) | 12 (60) 8 (40) | | Who participated in the decision to modify?* | Program Manager Individual practitioners Treatment/Intervention Team Administrator Recipients None | 3 (100) 2 (67) 1 (33) 0 (0) 0 (0) 0 (0) | 13 (81) 8 (50) 2 (13) 2 (13) 1 (6) 1 (6) | 1 (100) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) | 17 (85) 10 (50) 3 (15) 2 (10) 1 (5) 1 (5) | | What was the goal?* | Improve fit with recipients Increase satisfaction Improve effectiveness/outcomes Increase reach or engagement Increase retention Improve feasibility None Not applicable | 2 (67) 0 (0) 0 (0) 0 (0) 0 (0) 1 (33) 0 (0) 0 (0) | 5 (31) 6 (38) 5 (31) 4 (25) 4 (25) 0 (0) 1 (6) 0 (0) | 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 1 (100) | 7 (37) 6 (32) 5 (26) 4 (21) 4 (21) 1 (5) 1 (5) 1 | | What is modified? | Content Training and evaluation Contextual Implementation and scale-up activities | 2 (67) 1 (33) 0 (0) 0 (0) | 12 (75) 4 (25) 0 (0) 0 (0) | 0 (0) 1 (100) 0 (0) 0 (0) | 14 (70) 6 (30) 0 (0) 0 (0) | | Context modifications are made to what? | Not applicable | 3 (100) | 16 (100) | 1 (100) | 20 | | At what level of delivery were modifications made? | Target intervention group (all schools) Cohort (group of schools sharing a characteristic) Clinic unit/level (individual schools) | 3 (100) 0 (0) 0 (0) | 11 (69) 3 (19) 2 (13) | 1 (100) 0 (0) 0 (0) | 15 (75) 3 (15) 2 (10) | | What is the nature of the content modification?* | Adding elements Tailoring/tweaking/refining Removing/skipping elements Lengthening/extending elements Substituting Reordering of intervention modules or segments Loosening structure Not applicable | 0 (0) 1 (33) 1 (33) 0 (0) 0 (0) 0 (0) 0 (0) 1 (33) | 5 (31) 2 (13) 1 (6) 1 (6) 1 (6) 1 (6) 1 (6) 4 (25) | 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 1 (100) | 5 (36) 3 (21) 2 (14) 1 (7) 1 (7) 1 (7) 1 (7) 6 | | Reasons – socio-political (i.e., broad context)* | None (no reason) Societal/cultural norms Historical context Not applicable | 3 (100) 0 (0) 0 (0) 0 (0) | 14 (88) 1 (6) 1 (6) 0 (0) | 0 (0) 0 (0) 0 (0) 1 (100) | 17 (89) 1 (5) 1 (5) 1 | | Reasons – organization/setting (i.e., PA4E1 implementation team)* | Available resources (funds, staff, tech, space) None (no reason) Social context Not applicable | 1 (33) 2 (67) 0 (0) 0 (0) | 13 (81) 3 (19) 1 (6) 0 (0) | 0 (0) 0 (0) 0 (0) 1 (100) | 14 (74) 5 (26) 1 (5) 1 | | Reasons – provider (i.e., Local health district)* | None (no reason) Not applicable | 3 (100) 0 (0) | 16 (100) 0 (0) | 0 (0) 1 (100) | 19 (100) 1 | | Reasons – recipient (i.e., Schools and in-school champions)* | None (no reason) Motivation and readiness Physical capacity Access to resources Cultural or religious norms Not applicable | 1 (33) 2 (67) 0 (0) 0 (0) 0 (0) 0 (0) | 8 (50) 5 (31) 3 (19) 2 (13) 1 (6) 0 (0) | 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 1 (100) | 9 (47) 7 (37) 3 (16) 2 (11) 1 (5) 1 | ## Types of Modifications The vast majority of modifications were “unsystematic” ($$n = 18$$), because most modifications did not make deliberate use of theory to make the modification. Two modifications made to support sustainability used theory and a specific process, and therefore were deemed systematic. Most modifications ($$n = 12$$) were made “proactively”, however eight modifications were made in response to an unknown event or circumstance and were therefore coded as “reactive”. Most modifications involved the “program manager” ($$n = 17$$). “ Individual practitioners” participated in half of all decisions ($$n = 10$$). Other groups and individuals participated in the decision making process less frequently, including the whole PA4E1 Implementation Team “treatment/intervention team” ($$n = 3$$), local health districts “administrator” ($$n = 2$$) and in-School Champion “recipients” ($$n = 1$$). The goals and types of modifications varied. The most common goals were to “improve fit with recipients” ($$n = 7$$), “increase satisfaction” ($$n = 6$$), “improve effectiveness/outcomes” ($$n = 5$$), “increase reach or engagement” ($$n = 4$$) and to “increase retention” ($$n = 4$$). All modifications were to either “content” ($$n = 14$$) or “training and evaluation” ($$n = 6$$). The nature of content modifications varied, including “adding elements” ($$n = 5$$), “tailoring/tweaking/refining” ($$n = 3$$) and “removing/skipping elements” ($$n = 2$$). Most modifications ($$n = 15$$) were made across all schools, in all local health districts, and were therefore coded the “target intervention group” level. The remaining modifications occurred at particular local health districts “cohort level” ($$n = 3$$) or individual schools “clinic/unit level” ($$n = 4$$). ## Reasons for Modifications Reasons for modifications were primarily related to the “available resources” (funds, staff, tech, space) ($$n = 14$$) of the PA4E1 Implementation Team, i.e., the “provider”. The second most common reason was schools and in-School Champions (i.e., recipient) “motivation and readiness” ($$n = 8$$). Further reasons for modification were few, but included “historical context” ($$n = 1$$) and the “social context” ($$n = 1$$) surrounding the “PA4E1 Implementation Team”. ## Principal Findings To the authors' knowledge, this is the first study to use a taxonomy to comprehensively report the number, types and reasons for both adaptations made to scale-up a program and also the modifications that occurred during the delivery of the scaled-up program. Our findings show that 20 adaptations were made to the PA4E1 program for scale-up [26, 27, 29], including eight adaptations to the physical activity practices, six to the implementation support strategies and six to the evaluation methods. Most adaptations were proposed to have a positive impact on the effectiveness of the program ($$n = 12$$). Additionally, 20 modifications were made during the delivery of the scaled-up program, of which 16 were proposed to have a positive impact on the effectiveness of the program. Most modifications were to the implementation support strategies ($$n = 16$$). Given that the use of adaptation and modifications data is being encouraged to explain the findings of scale-up trials [20], the findings of this study provide valuable data, together with detailed process evaluation data [32] to help to explain the findings of the scale-up trial [18, 32]. “Funding and resource availability” was a common reason adaptations and modifications were made. The occurrence of adaptations as part of a research trial, involving the original trial developers, and with a good understanding of the programs and its mechanism of effect may also explain the frequency with which they were fidelity consistent, and thought to have a beneficial impact. The findings underscore the importance of selecting programs that are congruent with the available resources to deliver them at scale, and in doing so, reduce the need for significant adaptations. The use of scalability assessment tools may assist policy makers and practitioners can assist with this process [43]. Both adaptations and modifications to the scale-up of PA4E1 were primarily made to “improve fit with recipients” (i.e., schools). Most adaptations were coded as “fidelity consistent” and “systematic” (i.e., informed by theory and used a process). Such findings are perhaps unsurprising, given they occurred in the context of a funded trial, involving the original trial developers with a good understanding of the program and its mechanism and who employed a considered process to informing adaptations [30]. It is also consistent with previous reviews of public health programs [15]. Adaptability is a key component of scalable programs, where optimal adaptations are those which are made to fit different contexts and environments while retaining fidelity consistency [12, 13, 21, 43]. Modifications, in contrast to adaptations, were found to be “unsystematic”, likely reflecting the rapid and reactive contexts in which these changes were made by school staff (in-School Champions) and practitioners delivering the program (Support Officers, Program Managers). Previous research into public health prevention programs has similarly found that modifications were unsystematic, suggesting they may be more likely to detract from intervention core functions, resulting in negative impacts on implementation-effectiveness outcomes [44, 45]. However, modifications to the program were all coded as “fidelity consistent”, and so retain the intended core functions of the program (outlined in Additional File 3). School staff may have, as a result of the training undertaken as part of the scale-up strategy, or via their existing tacit knowledge, have a good understanding of how the program may impact on the intended outcome and been mindful of this when undertaking modifications. Further research is warranted to explore and better explain such findings. Reviews of scaled-up physical activity [8] and obesity programs [16] that characterized the nature of adaptations made for scale-up, concluded that adaptations to the “mode of delivery” of programs were particularly prevalent [8, 14, 16]. Similarly, we found 13 content adaptations to scale-up PA4E1 and 14 content modifications during delivery of the scaled-up PA4E1.While changes to the delivery modes or content modifications may be perceived as fidelity consistent or improving the overall impacts of a program, for example, by increasing reach and the number of people who may benefit, they may also reduce the absolute effect size of a program [8, 46, 47]. That is, the “voltage drop” phenomena whereby the effect sizes of physical activity programs are reduced at scale, may be acceptable from a population perspective if the scaled-up program is capable of reaching and so benefiting (due to delivery mode adaptations) more people, at lower relative cost. Taking a population-level perspective is therefore important when weighing and assessing the potential impact of adaptations or modifications. ## Strengths and Limitations It has been recommended that researchers consider potential causal pathways of modifications, considering both the intended and unintended impacts of modifications on outcomes [20]. In line with our process evaluation protocol [32], we have comprehensively described the extent, type and reasons for adaptations and modifications to PA4E1. A strength of this study is the use of real-time tracking of modifications during delivery to record deviations from the planned protocols, which is expected during trial delivery but often not documented well. Indeed, we found the method to be feasible and informative within our study. We found coding using the FRAME to initially be quite difficult, despite the existence of a coding manual [48]. We would suggest to future researchers to consider annotating the FRAME framework coding for their own context [13, 48]. We also emphasize the importance of going beyond published papers to generate a list of adaptations. By also using the knowledge of those involved in both the efficacy and scale-up trials, we were able to code more accurately the reasons for adaptation. However, a limitation of our research is that we were not blinded to the outcomes of the implementation-effectiveness trial outcomes, which may have influenced our interpretation [18]. Additionally, although this study drew upon historical knowledge of those involved in delivering both the efficacy and scale-up trials, it is possible that some specific details were forgotten, given that the efficacy trial was completed in 2014 (7 years ago). Future studies reporting adaptations should therefore aim to do so prospectively [13, 18]. Finally, we used the FRAME to report adaptations and modifications to the physical activity practices, implementation support strategies and evaluation methods. Subsequent to our data analysis, the FRAME-IS (Implementation Strategy) was released which is designed for implementation support strategies and organized into modules. The use of both the FRAME [13] and FRAME-IS [22] may have improved our coding of adaptations and reduced the frequency of consensus meetings required. Additional support to use FRAME to code adaptations and modifications to evaluation methods may also be useful. ## Conclusions Adaptations and modifications to public health programs are common. Modifications have the potential to impact the implementation and effectiveness of programs. Our findings underscore the importance of comprehensive reporting of the extent, type and reasons for modifications as part of process evaluations, as this data may be important to the interpretation of trial findings. Making modifications that retain core components but better suit a particular context (program adaptability) is considered to be an important component of successfully scaled-up programs. However, it will be important for future programs to identify project management strategies to mitigate the occurrence of reactive operational modifications that are fidelity inconsistent. Describing the extent, type and reasons for adaptations and modifications made to public health programs provides valuable process evaluation data to help explain the findings of the program. For example, the data may be used to explain the expected reduction in effect size when they are scaled-up. The comprehensive and transparent description of adaptations and modifications will assist us to generate hypotheses relating to the trial process evaluation and implementation outcome data, which will be explored further. ## Data Availability Statement The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author/s. ## Ethics Statement The studies involving human participants were reviewed and approved by Hunter New England Area Human Research Ethics Committee. Written informed consent from the participants' legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements. ## Author Contributions MM: conceived the design of the study, led the data analysis, and led the development of the manuscript. MM, JW, EC, RS, and LW: developed the research questions, these were further refined by MM, EC, and LW. RS, EC, LW, and JW: obtained funding for the research. MM: led the ongoing data collection, with significant contributions from EC, RS, TM, and LD. MM, EC, RS, TM, and LD: were involved in the coding of modifications. All authors contributed to the interpretation of findings, provided critical comment on multiple versions of the manuscript, and approved the final manuscript. ## Funding This project is funded by the NSW Ministry of Health, Translational Research Grant Scheme. The NSW Ministry of Health has not had any role in the design of the study as outlined in this protocol and will not have a role in data collection, analysis of data, interpretation of data and dissemination of findings. This work was also supported by Cancer Council New South Wales. The project received infrastructure support from the Hunter Medical Research Institute (HMRI). RS is supported by a NHMRC TRIP Fellowship (APP1150661). LW is supported by a NHMRC Career Development Fellowship (APP1128348), Heart Foundation Future Leader Fellowship [101175] and a Hunter New England Clinical Research Fellowship. ## Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher's Note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary Material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/frhs.2021.719194/full#supplementary-material ## References 1. 1.Physical Activity Guidelines Advisory Committee. 2018 Physical Activity Guidelines Advisory Committee Scientific Report. U.S. Department of Health and Human Services (2018).. *2018 Physical Activity Guidelines Advisory Committee Scientific Report.* (2018) 2. **The Bangkok Declaration on Physical Activity for Global Health and Sustainable Development**. *Br J Sports Med* (2017) **51** 1389-91. DOI: 10.1136/bjsports-2017-098063 3. Guthold R, Stevens GA, Riley LM, Bull FC. **Global trends in insufficient physical activity among adolescents: a pooled analysis of 298 population-based surveys with 1.6 million participants**. *Lancet Child Adolesc Health* (2019) **4** 23-35. DOI: 10.1016/S2352-4642(19)30323-2 4. Guthold R, Stevens GA, Riley LM, Bull FC. **Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1.9 million participants**. *Lancet Glob Health.* (2018) **6** e1077-e86. DOI: 10.1016/S2214-109X(18)30357-7 5. Finch M, Jones J, Yoong S, Wiggers J, Wolfenden L. **Effectiveness of centre-based childcare interventions in increasing child physical activity: a systematic review and meta-analysis for policymakers and practitioners**. *Obes Rev.* (2016) **17** 412-28. DOI: 10.1111/obr.12392 6. Yoong SL, Wolfenden L, Clinton-McHarg T, Waters E, Pettman TL, Steele E. **Exploring the pragmatic and explanatory study design on outcomes of systematic reviews of public health interventions: a case study on obesity prevention trials**. *J Public Health.* (2014) **36** 170-6. DOI: 10.1093/pubmed/fdu006 7. Love R, Adams J, van Sluijs EMF. **Are school-based physical activity interventions effective and equitable? A meta-analysis of cluster randomized controlled trials with accelerometer-assessed activity**. *Obes Rev.* (2019) **20** 859-70. DOI: 10.1111/obr.12823 8. Lane C, McCrabb S, Nathan N, Naylor P-J, Bauman A, Milat A. **How effective are physical activity interventions when they are scaled-up: a systematic review**. *Int J Behav Nutr Phys Act.* (2021) **18** 16. DOI: 10.1186/s12966-021-01080-4 9. Milat AJ, King L, Bauman AE, Redman S. **The concept of scalability: increasing the scale and potential adoption of health promotion interventions into policy and practice**. *Health Promot Int.* (2013) **28** 285-98. DOI: 10.1093/heapro/dar097 10. Milat AJ, Bauman AE, Redman S, Curac N. **Public health research outputs from efficacy to dissemination: a bibliometric analysis**. *BMC Public Health.* (2011) **11** 934. DOI: 10.1186/1471-2458-11-934 11. Averitt AJ, Weng C, Ryan P, Perotte A. **Translating evidence into practice: eligibility criteria fail to eliminate clinically significant differences between real-world and study populations**. *NPJ Digit Med.* (2020) **3** 67. DOI: 10.1038/s41746-020-0277-8 12. Chambers DA, Glasgow RE, Stange KC. **The dynamic sustainability framework: addressing the paradox of sustainment amid ongoing change**. *Implement Sci.* (2013) **8** 117. DOI: 10.1186/1748-5908-8-117 13. Wiltsey Stirman S, Baumann AA, Miller CJ. **The FRAME: an expanded framework for reporting adaptations and modifications to evidence-based interventions**. *Implement Sci.* (2019) **14** 58. DOI: 10.1186/s13012-019-0898-y 14. Chambers DA, Norton WE. **The adaptome: advancing the science of intervention adaptation**. *Am J Prev Med.* (2016) **51** S124-31. DOI: 10.1016/j.amepre.2016.05.011 15. Escoffery C, Lebow-Skelley E, Haardoerfer R, Boing E, Udelson H, Wood R. **A systematic review of adaptations of evidence-based public health interventions globally**. *Implement Sci.* (2018) **13** 125. DOI: 10.1186/s13012-018-0815-9 16. McCrabb S, Lane C, Hall A, Milat A, Bauman A, Sutherland R. **Scaling-up evidence-based obesity interventions: a systematic review assessing intervention adaptations and effectiveness and quantifying the scale-up penalty**. *Obes Rev.* (2019) **20** 964-82. DOI: 10.1111/obr.12845 17. Movsisyan A, Arnold L, Evans R, Hallingberg B, Moore G, O'Cathain A. **Adapting evidence-informed complex population health interventions for new contexts: a systematic review of guidance**. *Implement Sci.* (2019) **14** 105. DOI: 10.1186/s13012-019-0956-5 18. Moore GF, Audrey S, Barker M, Bond L, Bonell C, Hardeman W. **Process evaluation of complex interventions: medical research council guidance**. *BMJ.* (2015) **350** h1258. DOI: 10.1136/bmj.h1258 19. Stirman SW, Miller CJ, Toder K, Calloway A. **Development of a framework and coding system for modifications and adaptations of evidence-based interventions**. *Implement Sci.* (2013) **8** 65. DOI: 10.1186/1748-5908-8-65 20. Kirk MA, Moore JE, Wiltsey Stirman S, Birken SA. **Towards a comprehensive model for understanding adaptations' impact: the model for adaptation design and impact (MADI)**. *Implement Sci.* (2020) **15** 56. DOI: 10.1186/s13012-020-01021-y 21. Yoong SL, Bolsewicz K, Grady A, Wyse R, Sutherland R, Hodder RK. **Adaptation of public health initiatives: expert views on current guidance and opportunities to advance their application and benefit**. *Health Educ Res.* (2020) **35** 243-57. DOI: 10.1093/her/cyaa014 22. Miller CJ, Barnett ML, Baumann AA, Gutner CA, Wiltsey-Stirman S. **The FRAME-IS: a framework for documenting modifications to implementation strategies in healthcare**. *Implement Sci.* (2021) **16** 36. DOI: 10.1186/s13012-021-01105-3 23. Rabin BA, McCreight M, Battaglia C, Ayele R, Burke RE, Hess PL. **Systematic, multimethod assessment of adaptations across four diverse health systems interventions**. *Front Public Health.* (2018) **6** 102. DOI: 10.3389/fpubh.2018.00102 24. Curran GM, Bauer M, Mittman B, Pyne JM, Stetler C. **Effectiveness-implementation hybrid designs: combining elements of clinical effectiveness and implementation research to enhance public health impact**. *Med Care.* (2012) **50** 217-26. DOI: 10.1097/MLR.0b013e3182408812 25. Hollis JL, Sutherland R, Campbell L, Morgan PJ, Lubans DR, Nathan N. **Effects of a “school-based” physical activity intervention on adiposity in adolescents from economically disadvantaged communities: secondary outcomes of the “Physical Activity 4 Everyone” RCT**. *Int J Obes.* (2016) **40** 1486-93. DOI: 10.1038/ijo.2016.107 26. Sutherland R, Campbell E, Lubans DR, Morgan PJ, Okely AD, Nathan N. **A cluster randomised trial of a school-based intervention to prevent decline in adolescent physical activity levels: study protocol for the “Physical Activity 4 Everyone” trial**. *BMC Public Health.* (2013) **13** 57. DOI: 10.1186/1471-2458-13-57 27. Sutherland R, Campbell L, Lubans D, Morgan P, Okely AD, Nathan N. **“Physical Activity 4 Everyone” school-based intervention to prevent decline in adolescent physical activity levels: 12 month (mid-intervention) report on a cluster randomised trial**. *Br J Sports Med.* (2015) **50** 488-95. DOI: 10.1136/bjsports-2014-094523 28. Sutherland R, Reeves P, Campbell E, Lubans DR, Morgan PJ, Nathan N. **Cost effectiveness of a multi-component school-based physical activity intervention targeting adolescents: the “Physical Activity 4 Everyone” cluster randomized trial**. *Int J Behav Nutr Phys Act.* (2016) **13** 94. DOI: 10.1186/s12966-016-0418-2 29. Sutherland RL, Campbell EM, Lubans DR, Morgan PJ, Nathan NK, Wolfenden L. **The Physical Activity 4 Everyone cluster randomized trial: 2-year outcomes of a school physical activity intervention among adolescents**. *Am J Prev Med.* (2016) **51** 195-205. DOI: 10.1016/j.amepre.2016.02.020 30. Sutherland R, Campbell E, Nathan N, Wolfenden L, Lubans DR, Morgan PJ. **A cluster randomised trial of an intervention to increase the implementation of physical activity practices in secondary schools: study protocol for scaling up the Physical Activity 4 Everyone (PA4E1) program**. *BMC Public Health.* (2019) **19** 883. DOI: 10.1186/s12889-019-6965-0 31. Sutherland R, Campbell E, McLaughlin M, Nathan N, Wolfenden L, Lubans DR. **Scale-up of the Physical Activity 4 Everyone (PA4E1) intervention in secondary schools: 12-month implementation outcomes from a cluster randomized controlled trial**. *Int J Behav Nutr Phys Act.* (2020) **17** 100. DOI: 10.1186/s12966-020-01000-y 32. McLaughlin M, Duff J, Sutherland R, Campbell E, Wolfenden L, Wiggers J. **Protocol for a mixed methods process evaluation of a hybrid implementation-effectiveness trial of a scaled-up whole-school physical activity program for adolescents: Physical Activity 4 Everyone (PA4E1)**. *Trials.* (2020) **21** 268. DOI: 10.1186/s13063-020-4187-5 33. Pinnock H, Barwick M, Carpenter CR, Eldridge S, Grandes G, Griffiths CJ. **Standards for reporting implementation studies (StaRI): explanation and elaboration document**. *BMJ Open.* (2017) **7** e013318. DOI: 10.1136/bmjopen-2016-013318 34. Wolfenden L, Yoong SL, Williams CM, Grimshaw J, Durrheim DN, Gillham K. **Embedding researchers in health service organizations improves research translation and health service performance: the Australian hunter New England population health example**. *J Clin Epidemiol.* (2017) **85** 3-11. DOI: 10.1016/j.jclinepi.2017.03.007 35. Milat AJ, Bauman A, Redman S. **Narrative review of models and success factors for scaling up public health interventions**. *Implement Sci.* (2015) **10** 113. DOI: 10.1186/s13012-015-0301-6 36. Escoffery C, Lebow-Skelley E, Udelson H, Boing EA, Wood R, Fernandez ME. **A scoping study of frameworks for adapting public health evidence-based interventions**. *Transl Behav Med.* (2018) **19** 1-10. DOI: 10.1093/tbm/ibx067 37. Cane J, O'Connor D, Michie S. **Validation of the theoretical domains framework for use in behaviour change and implementation research**. *Implement Sci.* (2012) **7** 37. DOI: 10.1186/1748-5908-7-37 38. Michie S, van Stralen MM, West R. **The behaviour change wheel: a new method for characterising and designing behaviour change interventions**. *Implement Sci.* (2011) **6** 42. DOI: 10.1186/1748-5908-6-42 39. Michie S, Atkins L, West R. *The Behaviour Change Wheel: A Guide to Designing Interventions* (2014) 40. Lubans DR, Lonsdale C, Cohen K, Eather N, Beauchamp MR, Morgan PJ. **Framework for the design and delivery of organized physical activity sessions for children and adolescents: rationale and description of the “SAAFE” teaching principles**. *Int J Behav Nutr Phys Act.* (2017) **14** 24. DOI: 10.1186/s12966-017-0479-x 41. Lubans DR, Morgan PJ, Callister R, Collins CE, Plotnikoff RC. **Exploring the mechanisms of physical activity and dietary behavior change in the program x intervention for adolescents**. *J Adolesc Health.* (2010) **47** 83-91. DOI: 10.1016/j.jadohealth.2009.12.015 42. Kennedy SG, Smith JJ, Morgan PJ, Peralta LR, Hilland TA, Eather N. **Implementing resistance training in secondary schools: a cluster randomized controlled trial**. *Med Sci Sports Exerc.* (2018) **50** 62-72. DOI: 10.1249/MSS.0000000000001410 43. Lee K, Milat A, Grunseit A, Conte K, Wolfenden L, Bauman A. **The intervention scalability assessment tool: a pilot study assessing five interventions for scalability**. *Public Health Res Pract.* (2020) **30** 3022011. DOI: 10.17061/phrp3022011 44. Carvalho ML, Honeycutt S, Escoffery C, Glanz K, Sabbs D, Kegler MC. **Balancing fidelity and adaptation: implementing evidence-based chronic disease prevention programs**. *J Public HealthManag Pract.* (2013) **19** 348-56. DOI: 10.1097/PHH.0b013e31826d80eb 45. Mackie TI, Ramella L, Schaefer AJ, Sridhar M, Carter AS, Eisenhower A. **Multi-method process maps: an interdisciplinary approach to investigate ad hoc modifications in protocol-driven interventions**. *J Clin Transl Sci.* (2020) **4** 260-9. DOI: 10.1017/cts.2020.14 46. Stewart AL, Gillis D, Grossman M, Castrillo M, Pruitt L, McLellan B. **Diffusing a research-based physical activity promotion program for seniors into diverse communities: CHAMPS III**. *Prev Chronic Dis.* (2006) **3** A51. PMID: 16539792 47. Hardy LL, Mihrshahi S, Gale J, Nguyen B, Baur LA, O'Hara BJ. **Translational research: are community-based child obesity treatment programs scalable?**. *BMC Public Health.* (2015) **15** 652. DOI: 10.1186/s12889-015-2031-8 48. 48.Stirman. FRAME Coding Manual. (2020). Available online at: http://med.stanford.edu/fastlab/research/adaptation.html. (accessed April 22, 2021).. (2020)
--- title: m6A reader IGF2BP1 accelerates apoptosis of high glucose-induced vascular endothelial cells in a m6A-HMGB1 dependent manner authors: - Anru Liang - Jianyu Liu - Yanlin Wei - Yuan Liao - Fangxiao Wu - Jiang Ruan - Junjun Li journal: PeerJ year: 2023 pmcid: PMC10062336 doi: 10.7717/peerj.14954 license: CC BY 4.0 --- # m6A reader IGF2BP1 accelerates apoptosis of high glucose-induced vascular endothelial cells in a m6A-HMGB1 dependent manner ## Abstract Emerging evidence indicates that N6-methyladenosine (m6A) plays a critical role in vascular biological characteristic. In diabetes mellitus pathophysiology, high glucose (HG)-induced vascular endothelial dysfunction is associated with diabetes vascular complications. Nevertheless, the underlying mechanism of high glucose (HG)-related m6A regulation on vascular endothelial cells is still unclear. Results indicated that m6A reader insulin-like growth factor 2 mRNA-binding protein 1 (IGF2BP1) was up-regulated in HG-treated human umbilical vascular endothelium cells (HUVECs) comparing to normal group. Functionally, results indicated that IGF2BP1 knockdown recovered the proliferation of HUVECs inhibited by HG-administration. Besides, IGF2BP1 knockdown reduced the apoptosis induced by HG-administration. Mechanistically, IGF2BP1 interacted with HMGB1 mRNA and stabilized its expression of m6A-modified RNA. Therefore, these findings provided compelling evidence demonstrating that m6A reader IGF2BP1 contributes to the proliferation and apoptosis of vascular endothelial cells in hyperglycaemia, serving as a target for development of diabetic angiopathy therapeutics. ## Introduction Diabetes mellitus (DM) is a multifactorial metabolic trait and chronic pathophysiological process (Cheok et al., 2020; Hayashi, Rakugi & Morishita, 2020). Multiple stimuli, including high glucose (HG), could result in endothelial dysfunctions (Vergès, 2020). In vascular homeostasis, vascular endothelium consists of endothelial cells and acts as the main barrier to maintain vascular permeability (Beazer et al., 2020; Cheng & Kishore, 2020). HG-induced vascular endothelial dysfunction contributes to multiple vascular metabolic disorders, including coronary artery disease, atherosclerosis, diabetic nephropathy and others (Wautier & Wautier, 2020). N6-methyladenosine (m6A) acts as the most prevalent type of methylations occurred on RNA, which has become a hotspot in the epigenetic research community (Lu et al., 2021; Su et al., 2021). The m6A is a methylation at N6 position of adenosine and enriched in this RRACH consensus sequence (R: A or G; A: m6A; and H: A, C, U) (Xu et al., 2021; Zhou et al., 2021). The biological functions of m6A modification were regulated by three core proteins: writers (methyltransferases), erasers (demethylases) and readers (m6A binding proteins). For the HG-induced vascular endothelium dysfunction, m6A plays critical roles. For instance, in oxidized low-density lipoprotein (ox-LDL)--induced human umbilical vascular endothelium cells (HUVECs), methyltransferase-like 3 (METTL3) knockdown inhibits the cellular tube formation, proliferation, migration and VEGF secretion and prevents in vivo embryos angiogenesis (Dong et al., 2021). Moreover, METTL14/METTL3 upregulates in ox-LDL treated HUVECs and the knockdown of METTL14/METTL3 increases bcl-2 expression level and viability of ox-LDL-incubated cells (Liu et al., 2022). Thus, these data suggest the essential role of m6A in vascular endothelium dysfunction. Here, our research aimed to address these questions by determining m6A-associated reader insulin-like growth factor 2 mRNA-binding protein 1 (IGF2BP1) expression patterns in HG-induced HUVECs. Consequently, IGF2BP1 emerged as highly expressed m6A reader in HG-induced HUVECs and the IGF2BP1 dysregulated expression dramatically modulated the proliferation and apoptosis. Interestingly, IGF2BP1 regulated the progression of HG-induced HUVECs by changing the stability of HMGB1 mRNA in an m6A-dependent manner. ## Cell culture and diabetes model treatment Human umbilical vascular endothelium cells (HUVEC) were purchased from ScienCell Research Laboratories (Carlsbad, CA, USA) and maintained in Endothelial Cell Medium (ECM, Carlsbad, CA, USA) added with $10\%$ fetal bovine serum (FBS; Gibco, Billings, MT, USA) and 5.6 mmol/L glucose. The diabetes model treatment was performed as previously described (Li et al., 2015; Zhao et al., 2022). For diabetes group (high glucose, HG), HUVECs were exposed to 30 mmol/L d-glucose. For control group (normal glucose, NG), HUVECs were cultured in culture medium with 5.6 mmol/L d-glucose and 24.4 mmol/L mannitol. ## Plasmids construction and cell transfections To construct silenced expression plasmids of IGF2BP1, the sequences of shRNA targeting IGF2BP1 and corresponding controls (sh-NC) were amplified respectively by purchased from GeneChem (Shanghai, China), and then cloned into the HUVECs following the manufacturer-recommended protocol. ## RNA extraction and qRT‑PCR Total RNA in cells was extracted in accordance with the manual provided with the TRIzol reagent (Thermo Fisher, Waltham, MA, USA). The extracted total RNA was treated with RNase-free DNase and its reverse transcription was performed in accordance with the manual provided ReverTra Ace qPCR RT Master Mix with gDNA Remover (Toyobo, Osaka, Japan). qPCR was performed in accordance with the manual provided by SYBR Green PCR kit (TaKaRa, Dalian, China) on Applied Biosystems 7300. After the reactions, the cycle threshold (CT) data were determined using fixed thresholds setting, and the mean CT value was determined from triplicate PCR. The primers were listed in Table S1. ## Western blot assay Total protein in HUVECs was extracted using RIPA buffer containing sodium chloride (NaCl, 150 mM), Tris-hydrochloride (HCl, 50 mM), 1 mM sodium fluoride, ethylenediaminetetraacetic acid (EDTA, 5 mM), $1\%$ Triton X-100, $1\%$ deoxycholate, 1 mM sodium vanadate and a protease inhibitor cocktail. The quality was quantified using BCA method (Thermo Fisher, Waltham, MA, USA). Subsequently, the protein was electrophoresed on sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) gels and transferred to polyvinylidene fluoride (PVDF) membrane (Millipore, Burlington, MA, USA). PVDF membranes were blocked with $5\%$ BSA for 1 h at room temperature and then incubated at 4 °C overnight with anti-IGF2BP1 (cat. D33A2, #8482, dilution of 1:1,000; Cell Signaling Technology, Danvers, MA, USA), anti-HMGB1 (cat. D3E5, #6893, dilution of 1:1,000; Cell Signaling Technology, Danvers, MA, USA), anti-β-Actin (cat. 8H10D10, #3700, dilution of 1:1,000; Cell Signaling Technology, Danvers, MA, USA). After incubation with the horseradish peroxidase-labeled goat anti-rabbit IgG secondary antibody (ab6721, cat. 1:2,000; Abcam, Cambridge, MA, USA), the PVDF membranes were visualized with enhanced chemiluminescence system kit (Millipore, Burlington, MA, USA) according to the manufacturer’s protocol. ## Cell counting kit‑8 (CCK8) assay Cell proliferation was determined by CCK-8 (Beyotime, Shanghai, China). HUVECs were differently treated (HG or NG or transfection) were cultured in a 96-well plate for 0, 24, 48, 72 h respectively and then incubated with CCK-8 kit. The proliferation was determined via the absorbance at 450 nm by microplate reader (Thermo Fisher Scientific, Waltham, MA, USA). ## Cellular apoptosis analysis The HUVEC cells were harvested and collected after indicated treatment. The, cells then were resuspended with pre-cold PBS (50 µl). After 30 min, the apoptotic cells were calculated by Annexin V-FITC Apoptosis Detection Kit (Beyotime, Shanghai, China) with flow cytometry. ## Ethynyl-2-deoxyuridine (EdU) incorporation assay EdU assay was performed to determine the proliferation of HUVECs. In brief, transfected HUVECs and corresponding RNA were incubated with EdU (100 μl of 50 μM) (Ribo Bio, Guangzhou, China) according to the Ribo Bio’s instructions per well at 37 °C for 2 h, respectively. The EdU incorporation rate was calculated as EdU-positive cells ratio to total Hoechst-positive cells (blue cells). The cells were counted using Image-Pro Plus (IPP) 6.0 software (Media Cybernetics, Rockville, MD, USA). ## m6A quantification assay The global m6A levels in mRNA were measured by EpiQuik m6A RNA Methylation Quantification Kit (Colorimetrically; Epigentek, Farmingdale, NY, USA) following the manufacturer’s protocol. Poly-A-purified RNA (200 ng) was used for each sample analysis. The m6A levels were colorimetrically quantified by reading each well absorbance at 450 nm wavelength, and calculated based on the standard curve. ## RNA immunoprecipitation RIP was performed to determine the interaction within IGF2BP1 and HMGB1 mRNA. HUVECs stably silencing IGF2BP1 and (sh-IGF2BP1) control cells (sh-NC) were lysed with radioimmunoprecipitation (RIP) lysis buffer (Magna RIP Kit; Millipore, Burlington, MA, USA) at 4 °C via disruptive sonication. Endogenous HMGB1 mRNA immunoprecipitations were performed using an anti-IGF2BP1 antibody (Abcam, Cambridge, MA, USA) overnight at 4 °C. The immunoprecipitated protein-RNA complex was subjected to quantitative real-time polymerase chain reaction (qRT-PCR) using primers and normalizing to input. ## m6A methylated RNA immunoprecipitation-PCR MeRIP-PCR was performed for the quantification of m6A-modified HMGB1 mRNA. Total RNA was isolated from HUVECs by Trizol, and anti-m6A antibody (cat. ABE572, 3 μg; Millipore, Burlington, MA, USA) or anti-IgG (Cell Signaling Technology, Danvers, MA, USA) was conjugated to protein A/G magnetic beads in IP buffer (140 mM NaCl, 20 mM Tris pH 7.5, 2 mM EDTA, $1\%$ NP-40). Total RNA (100 μg) was incubated with antibody in IP buffer supplemented with RNase inhibitor and protease inhibitor. After of the incubation, beads were eluted for further qRT-PCR assay. ## RNA stability The HMGB1 mRNA stability was detected by Actinomycin D administration. In brief, the actinomycin D (Act-D; Sigma-Aldrich, St. Louis, MO, USA, 5 μg/ml) was added to HUVEC cells. After harvesting of HUVECs, the RNA was isolated by TRIzol for qRT-PCR analysis. The half-life of HMGB1 mRNA was calculated normalizing to GAPDH data. ## Statistical analysis All the analysis was performed using GraphPad Prism V8.0 and SPSS V22.0. Data was calculated and displayed as Mean ± standard deviation (SD). The t-test and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\chi^2$\end{document}χ2-test were used to analyze the differences between different groups. P-value less than 0.05 was considered as statistical significance (**$p \leq 0.01$,*$p \leq 0.05$). All in vitro experiments were performed in triplicate and were repeated three times. ## IGF2BP1 was up-regulated in HG-induced HUVECs In present research, the cellular diabetes mellitus model was constructed in human umbilical vascular endothelium cells (HUVEC) with HG administration. Results indicated that the expression of IGF2BP1 mRNA was up-regulated in HUVECs with increasing dosage HG treatment (0, 10, 15, 20, 30 mM) (Fig. 1A). Then, with HG administration (30 mM), the expression of IGF2BP1 mRNA was up-regulated with time increasing (0, 12, 24, 48 h) (Fig. 1B). Moreover, the expression of IGF2BP1 protein was up-regulated as treatment time increasing (0, 12, 24, 48 h) (Fig. 1C). Furthermore, in HG-treated HUVECs, the m6A modification was significantly up-regulated (Fig. 1D). Overall, these findings suggested that IGF2BP1 expression was up-regulated in the HG-treated HUVECs. **Figure 1:** *IGF2BP1 was up-regulated in the HG-induced HUVECs.(A) RT-qPCR assay was performed to detect the IGF2BP1 mRNA in HUVECs treated by HG administration (0, 10, 15, 20, 30 mM). (B) RT-qPCR assay was performed to detect the IGF2BP1 mRNA in HUVECs treated by HG administration (0, 12, 24, 48 h). (C) Western blotting assay was performed to determine the IGF2BP1 protein level in HUVECs treated by HG administration (0, 12, 24, 48 h). (D) The m6A modification analysis detected the m6A level in HUVECs treated by HG administration (0, 12, 24, 48 h). *p < 0.05, **p < 0.01. All in vitro experiments were performed in triplicate and were repeated three times.* ## Knockdown of IGF2BP1 mitigated HG-induced apoptosis of HUVECs In HG-treated HUVECs, functional assays were performed to investigate the roles of IGF2BP1. The knockdown of IGF2BP1 was performed in HUVECs, and the efficient was examined by RT-PCR (Fig. 2A) and western blot (Fig. 2B). Cellular viability analysis found that HG administration reduced the cellular viability, and IGF2BP1 knockdown recovered the viability (Fig. 2C). For the proliferation of HUVECs, EdU assay indicated that HG administration reduced the cellular proliferation, and the IGF2BP1 knockdown facilitated the proliferation (Fig. 2D). Apoptosis analysis found that HG administration promoted the apoptosis of HUVECs, and IGF2BP1 knockdown reduced the apoptosis (Fig. 2E). Overall, these date suggested that knockdown of IGF2BP1 mitigated HG-induced apoptosis of HUVECs. **Figure 2:** *Knockdown of IGF2BP1 mitigated the HG-induced apoptosis of HUVECs.(A) RT-PCR and (B) western blotting analysis were respectively performed to detect the IGF2BP1 mRNA or protein levels in HG-induced HUVECs. (C) Cellular viability analysis by CCK-8 assays was performed for HUVECs’ viability. (D) EdU assay showed the cellular proliferation of HUVECs with HG administration upon IGF2BP1 knockdown or control. (E) Apoptosis analysis by flow cytometry revealed the apoptosis of HUVECs transfected with IGF2BP1 knockdown or control. *p < 0.05; **p < 0.01. All in vitro experiments were performed in triplicate and were repeated three times.* ## HMGB1 acted as the target of IGF2BP1 by m6A modified sites on HMGB1 mRNA To discover the potential downstream target of IGF2BP1, we took advantage of bioinformatics prediction online system (SRAMP, http://www.cuilab.cn/sramp) to analyze its binding targets. Results inspired that there was a significant m6A site on HMGB1 genome (Fig. 3A). Results indicated that HMGB1 mRNA expression was up-regulated with HG dosage increasing (0, 10, 15, 20, 30 mM) (Fig. 3B). Furthermore, with HG treatment (30 mM), HMGB1 mRNA expression was up-regulated along treatment time increasing (0, 12, 24, 48 h) (Fig. 3C). The m6A motif of IGF2BP1 on HMGB1 genome was GGAC (Fig. 3D). In the 3′-UTR of HMGB1 mRNA, blast analysis revealed the m6A modified site (Fig. 3E). Collectively, these data suggested that HMGB1 acted as the target of IGF2BP1 by m6A modified sites on HMGB1 mRNA. **Figure 3:** *HMGB1 acted as the target of IGF2BP1 by m6A modified sites on HMGB1 mRNA.(A) Bioinformatics prediction online system (SRAMP, http://www.cuilab.cn/sramp) was performed to analyze the binding targets of IGF2BP1. (B) RT-PCR analysis revealed the expression of HMGB1 mRNA in HUVECs with HG increasing dosage (0, 10, 15, 20, 30 mM). (C) RT-PCR analysis revealed the expression of HMGB1 mRNA in HUVECs with HG administration (30 mM) as the treatment time increasing (0, 12, 24, 48 h). (D) The m6A motif of IGF2BP1 on HMGB1 genome was GGAC. (E) BLAST analysis was performed to reveal the m6A modified site in the 3′-UTR of HMGB1 mRNA. *p < 0.05; **p < 0.01. All in vitro experiments were performed in triplicate and were repeated three times.* ## IGF2BP1 enhanced the stability of HMGB1 mRNA via m6A-dependent manner In the HG administration HUVECs, MeRIP-PCR was performed to detect the m6A modified enrichment on HMGB1 mRNA. Results suggested that the m6A enrichment of HMGB1 mRNA was elevated upon HG administration (Fig. 4A). Moreover, the interaction within HMGB1 mRNA and IGF2BP1 was identified using RIP-PCR, and results indicated that HMGB1 mRNA remarkably bound with IGF2BP1 in HUVECs (Fig. 4B). Furthermore, the precipitated HMGB1 mRNA enrichment was reduced in the IGF2BP1 knockdown (Fig. 4C). RNA stability assay indicated that IGF2BP1 knockdown decreased the HMGB1 mRNA remaining in Act D administration in HUVECs (Fig. 4D). Then, the HMGB1 protein level was decreased in IGF2BP1 knockdown in HUVECs (Fig. 4E). Collectively, these data suggested that IGF2BP1 enhanced the stability of HMGB1 mRNA via m6A-dependent manner. **Figure 4:** *IGF2BP1 enhanced the stability of HMGB1 mRNA via m6A-dependent manner.(A) MeRIP-PCR was performed to detect the m6A modified enrichment on HMGB1 mRNA using anti-m6A antibody in the HG administration HUVECs. (B) RIP-PCR assay was performed to detect the interaction within HMGB1 mRNA and IGF2BP1 using anti-IGF2BP1 antibody. (C) RIP-PCR assay was performed to detect the interaction within HMGB1 mRNA and IGF2BP1 in HUVECs transfected with IGF2BP1 shRNA (sh-IGF2BP1) and control (sh-NC). (D) RNA stability assay following qPCR was performed to detect the HMGB1 mRNA remaining in Act D administration in HUVECs transfected with IGF2BP1 shRNA (sh-IGF2BP1) and control (sh-NC). (E) Western blot assay was performed to identify the HMGB1 protein in HUVECs transfected with IGF2BP1 shRNA (sh-IGF2BP1) and control (sh-NC). *p < 0.05; **p < 0.01. All in vitro experiments were performed in triplicate and were repeated three times.* ## Discussion Endothelial cells apoptosis is one of the main biochemical characteristics of endothelial dysfunction, which is triggered by various stimulations, including high glucose, hypoxia, oxidized low density lipoproteins, oxidative stress and angiotensin II (Lin et al., 2022; Luchetti et al., 2017). In vascular endothelial cells, high glucose could accelerate the apoptosis and aggravate the abnormity (Barnes, Mesarwi & Sanchez-Azofra, 2022; Caporali et al., 2022). N6-methyladenosine (m6A), the most common RNA chemical modification on posttranscription, could participate in numerous pathophysiological processes (Dhawan et al., 2022; Ye et al., 2022). In the vasculopathy, more and more literatures have indicated the essential roles of m6A. For instance, m6A methyltransferase METTL14 plays major roles in TNF-α-induced endothelial cell inflammation through directly targeting m6A modification of important transcription factor FOXO1. METTL14 enhances FIXO1 translation through subsequent YTHDF1 recognition (Jian et al., 2020). Regarding to m6A methyltransferase METTL3, the silencing or overexpression of METTL3 altered the endothelial cell viability/proliferation/migration/tube formation through regulating Wnt signaling via the m6A modification of target genes (LRP6, DVL1) to enhance the translation of LRP6 and DVL1 in an YTHDF1-dependent manner (Yao et al., 2020). Collectively, these studies suggest that m6A-mediated modification play an important mechanism in HG-related Vascular pathology. Here, our work focused on the functions of m6A reader IGF2BP1 on the blood vessel endothelium. We found that IGF2BP1 levels increased upon HG administration. The knockdown of IGF2BP1mitigated the HG-induced apoptosis of HUVECs, besides, IGF2BP1 knockdown renewed the proliferation. Thus, based on our results, we concluded that IGF2BP1 could remarkably regulate the HG-induced vascular pathophysiology. Given that IGF2BP1 regulated the apoptosis and proliferation of HUVECs, we utilized this discovery to further explore the undergoing mechanism. Interestingly, we found that IGF2BP1 directly bound with the HMGB1 mRNA via m6A modification site. Moreover, IGF2BP1 enhanced the stability of HMGB1 mRNA to up-regulate its protein outcome. In the endothelial cell injury, HMGB1 has been reported to regulate the apoptosis (Zhang & Liu, 2021), inflammation (Foglio et al., 2022) and autophagy (Feng et al., 2022) of vascular endothelial cell. Thus, these data suggested the critical roles of HMGB1 in pathological changes of blood vessels. As the mechanism of the relationship between inflammatory response and atherosclerosis, m6A has become a novel focus in the clinical therapeutic strategy for diabetes mellitus. m6A-dependent post-transcription modification may be a target for diabetes mellitus therapy. Here, we utilized the bio-functional assays to investigate that whether IGF2BP1 and m6A can affect the phenotypic modulation of HUVECs through m6A modification. IGF2BP1 regulates the high glucose-induced vascular endothelial cells apoptosis via m6A/HMGB1 axis by m6A-dependent manner. For the limitation of this cell line, this study utilized HUVECs with HG-treatment to model diabetic endothelial dysfunctions. Limited by the experiment condition and epidemic, there are a lot of defects and insufficient for the assay data and design. Such as it is, this finding still provides a instructive insight for m6A and diabetic endothelial dysfunctions. Taken together, our data provide robust evidence that IGF2BP1 is an efficient regulator in HG-induced HUVECs. IGF2BP1 and m6A-dependent modification may be one of the primary pathogenesis of vascular pathology and hyperglycemia (Fig. 5). These findings strongly support an integral role for m6A in vessel homeostasis and accelerate high glucose-induced dysfunction of endothelial cells. **Figure 5:** *IGF2BP1/m6A/HMGB1 axis regulates high glucose-induced vascular endothelial cells apoptosis via m6A-dependent manner.* ## References 1. Barnes LA, Mesarwi OA, Sanchez-Azofra A. **The cardiovascular and metabolic effects of chronic hypoxia in animal models: a mini-review**. *Frontiers in Physiology* (2022) **13** 873522. DOI: 10.3389/fphys.2022.873522 2. Beazer JD, Patanapirunhakit P, Gill JMR, Graham D, Karlsson H, Ljunggren S, Mulder MT, Freeman DJ. **High-density lipoprotein’s vascular protective functions in metabolic and cardiovascular disease—could extracellular vesicles be at play?**. *Clinical Science* (2020) **134** 2977-2986. DOI: 10.1042/CS20200892 3. Caporali S, De Stefano A, Calabrese C, Giovannelli A, Pieri M, Savini I, Tesauro M, Bernardini S, Minieri M, Terrinoni A. **Anti-inflammatory and active biological properties of the plant-derived bioactive compounds luteolin and luteolin 7-glucoside**. *Nutrients* (2022) **14** 1155. DOI: 10.3390/nu14061155 4. Cheng Z, Kishore R. **Potential role of hydrogen sulfide in diabetes-impaired angiogenesis and ischemic tissue repair**. *Redox Biology* (2020) **37** 101704. DOI: 10.1016/j.redox.2020.101704 5. Cheok A, George TW, Rodriguez-Mateos A, Caton PW. **The effects of betalain-rich cacti (dragon fruit and cactus pear) on endothelial and vascular function: a systematic review of animal and human studies**. *Food & Function* (2020) **11** 6807-6817. DOI: 10.1039/D0FO00537A 6. Dhawan P, Vasishta S, Balakrishnan A, Joshi MB. **Mechanistic insights into glucose induced vascular epigenetic reprogramming in type 2 diabetes**. *Life Sciences* (2022) **298** 120490. DOI: 10.1016/j.lfs.2022.120490 7. Dong G, Yu J, Shan G, Su L, Yu N, Yang S. **N**. *Frontiers in Cell and Developmental Biology* (2021) **9** 731810. DOI: 10.3389/fcell.2021.731810 8. Feng L, Liang L, Zhang S, Yang J, Yue Y, Zhang X. **HMGB1 downregulation in retinal pigment epithelial cells protects against diabetic retinopathy through the autophagy-lysosome pathway**. *Autophagy* (2022) **18** 320-339. DOI: 10.1080/15548627.2021.1926655 9. Foglio E, Pellegrini L, Russo MA, Limana F. **HMGB1-mediated activation of the inflammatory-reparative response following myocardial infarction**. *Cells* (2022) **11** 216. DOI: 10.3390/cells11020216 10. Hayashi SI, Rakugi H, Morishita R. **Insight into the role of angiopoietins in ageing-associated diseases**. *Cells* (2020) **9** 2636. DOI: 10.3390/cells9122636 11. Jian D, Wang Y, Jian L, Tang H, Rao L, Chen K, Jia Z, Zhang W, Liu Y, Chen X, Shen X, Gao C, Wang S, Li M. **METTL14 aggravates endothelial inflammation and atherosclerosis by increasing FOXO1 N**. *Theranostics* (2020) **10** 8939-8956. DOI: 10.7150/thno.45178 12. Li S, Li Q, Yu W, Xiao Q. **High glucose and/or high insulin affects HIF-1 signaling by regulating AIP1 in human umbilical vein endothelial cells**. *Diabetes Research and Clinical Practice* (2015) **109** 48-56. DOI: 10.1016/j.diabres.2015.05.005 13. Lin X, Ouyang S, Zhi C, Li P, Tan X, Ma W, Yu J, Peng T, Chen X, Li L, Xie W. **Focus on ferroptosis, pyroptosis, apoptosis and autophagy of vascular endothelial cells to the strategic targets for the treatment of atherosclerosis**. *Archives of Biochemistry and Biophysics* (2022) **715** 109098. DOI: 10.1016/j.abb.2021.109098 14. Liu Y, Luo G, Tang Q, Song Y, Liu D, Wang H, Ma J. **Methyltransferase-like 14 silencing relieves the development of atherosclerosis via m**. *Bioengineered* (2022) **13** 11832-11843. DOI: 10.1080/21655979.2022.2031409 15. Lu S, Ding X, Wang Y, Hu X, Sun T, Wei M, Wang X, Wu H. **The relationship between the network of non-coding RNAs-molecular targets and N**. *Frontiers in Cell and Developmental Biology* (2021) **9** 772542. DOI: 10.3389/fcell.2021.772542 16. Luchetti F, Crinelli R, Cesarini E, Canonico B, Guidi L, Zerbinati C, Di Sario G, Zamai L, Magnani M, Papa S, Iuliano L. **Endothelial cells, endoplasmic reticulum stress and oxysterols**. *Redox Biology* (2017) **13** 581-587. DOI: 10.1016/j.redox.2017.07.014 17. Su Y, Maimaitiyiming Y, Wang L, Cheng X, Hsu CH. **Modulation of phase separation by RNA: a glimpse on N**. *Frontiers in Cell and Developmental Biology* (2021) **9** 786454. DOI: 10.3389/fcell.2021.786454 18. Vergès B. **Cardiovascular disease in type 1 diabetes: a review of epidemiological data and underlying mechanisms**. *Diabetes & Metabolism* (2020) **46** 442-449. DOI: 10.1016/j.diabet.2020.09.001 19. Wautier JL, Wautier MP. **Cellular and molecular aspects of blood cell-endothelium interactions in vascular disorders**. *International Journal of Molecular Sciences* (2020) **21** 5315. DOI: 10.3390/ijms21155315 20. Xu Y, Zhang M, Zhang Q, Yu X, Sun Z, He Y, Guo W. **Role of main RNA methylation in hepatocellular carcinoma: N**. *Frontiers in Cell and Developmental Biology* (2021) **9** 767668. DOI: 10.3389/fcell.2021.767668 21. Yao MD, Jiang Q, Ma Y, Liu C, Zhu CY, Sun YN, Shan K, Ge HM, Zhang QY, Zhang HY, Yao J, Li XM, Yan B. **Role of METTL3-dependent N**. *Molecular Therapy* (2020) **28** 2191-2202. DOI: 10.1016/j.ymthe.2020.07.022 22. Ye H, He Y, Zheng C, Wang F, Yang M, Lin J, Xu R, Zhang D. **Type 2 diabetes complicated with heart failure: research on therapeutic mechanism and potential drug development based on insulin signaling pathway**. *Frontiers in Pharmacology* (2022) **13** 816588. DOI: 10.3389/fphar.2022.816588 23. Zhang J, Liu L. **Anagliptin alleviates lipopolysaccharide-induced inflammation, apoptosis and endothelial dysfunction of lung microvascular endothelial cells**. *Experimental and Therapeutic Medicine* (2021) **22** 1472. DOI: 10.3892/etm.2021.10907 24. Zhao F, Fei W, Li Z, Yu H, Xi L. **Pigment epithelium-derived factor-loaded PEGylated nanoparticles as a new antiangiogenic therapy for neovascularization**. *Journal of Diabetes Research* (2022) **2022** 1193760. DOI: 10.1155/2022/1193760 25. Zhou M, Liu W, Zhang J, Sun N. **RNA m**. *Frontiers in Cell and Developmental Biology* (2021) **9** 794754. DOI: 10.3389/fcell.2021.794754
--- title: Store-operated Ca2+ entry regulatory factor alters murine metabolic state in an age-dependent manner via hypothalamic pathways authors: - Diana Gataulin - Yael Kuperman - Michael Tsoory - Inbal E Biton - Tomer Nataniel - Raz Palty - Izhar Karbat - Anna Meshcheriakova - Eitan Reuveny journal: PNAS Nexus year: 2023 pmcid: PMC10062355 doi: 10.1093/pnasnexus/pgad068 license: CC BY 4.0 --- # Store-operated Ca2+ entry regulatory factor alters murine metabolic state in an age-dependent manner via hypothalamic pathways ## Abstract Store-operated calcium entry (SOCE) is a vital process aimed at refilling cellular internal Ca2+ stores and a primary cellular signaling driver for transcription factors’ entry to the nucleus. SOCE-associated regulatory factor (SARAF)/TMEM66 is an endoplasmic reticulum (ER)-resident transmembrane protein that promotes SOCE inactivation and prevents Ca2+ overfilling of the cell. Here, we demonstrate that mice deficient in SARAF develop age-dependent sarcopenic obesity with decreased energy expenditure, lean mass, and locomotion without affecting food consumption. Moreover, SARAF ablation reduces hippocampal proliferation, modulates the activity of the hypothalamus–pituitary–adrenal (HPA) axis, and mediates changes in anxiety-related behaviors. Interestingly, selective SARAF ablation in the hypothalamus's paraventricular nucleus (PVN) neurons reduces old age-induced obesity and preserves locomotor activity, lean mass, and energy expenditure, suggesting a possible central control with a site-specific role for SARAF. At the cellular level, SARAF ablation in hepatocytes leads to elevated SOCE, elevated vasopressin-induced Ca2+ oscillations, and an increased mitochondrial spare respiratory capacity (SPC), thus providing insights into the cellular mechanisms that may affect the global phenotypes. These effects may be mediated via the liver X receptor (LXR) and IL-1 signaling metabolic regulators explicitly altered in SARAF ablated cells. In short, our work supports both central and peripheral roles of SARAF in regulating metabolic, behavioral, and cellular responses. ## Introduction Overweight and obesity affect almost 2 billion people worldwide [as calculated by the World Health Organization (WHO) in 2016] and are considered a twenty-first-century pandemic. Diet and exercise are the primary prevention and treatment strategies (https://www.who.int/health-topics/obesity#tab=tab_1). However, understanding the genetic factors involved in the central circuitry, metabolism, and adipose tissue homeostasis may improve the ability to treat obesity with precision [1, 2]. Metabolic homeostasis of the entire organism is a balanced feedback mechanism involving both the central nervous system (CNS) and periphery, where the hypothalamic paraventricular nucleus (PVN) plays a central role [3]; it, directly and indirectly, regulates critical hormones such as corticosterone (cortisol in humans), adrenaline, vasopressin, oxytocin, and thyrotropin-releasing hormone (TRH) [4], which control the body's metabolic state [5]. Many intracellular pathways respond to these signaling cues to affect metabolic homeostasis, where changes in intracellular Ca2+ levels are central (6–8). It is thus critical to have tight regulation of intracellular and intraorganelle Ca2+ levels. This task is a well-coordinated action involving many proteins that either pump Ca2+ out from the cytosol or into intracellular organelles such as mitochondria and the endoplasmic reticulum (ER) [8]. Store-operated calcium entry (SOCE) is one of several processes that participate in the cell's Ca2+ homeostasis [9]. In most cell types, it replenishes the Ca2+ cellular stores, like the ER and mitochondria. In some, it also plays a crucial role in transcription factors’ entry to the nucleus to activate gene transcription [10]. The activation of SOCE follows the release of Ca2+ from ER stores by inositol triphosphate (IP3), the product of the breakdown of phosphatidylinositol diphosphate (PIP2) by PLCβ or PLCγ activation via Gq/11-coupled G protein-linked receptors, or by receptor tyrosine kinases, respectively [11, 12]. SOCE activity depends on two proteins, a stromal interaction molecule (STIM), an ER transmembrane protein with a Ca2+-sensing domain at its ER luminal side, and a plasma membrane (PM)-resident Ca2+ channel (Orai) (13–17). SOCE is triggered by the depletion of Ca2+ from the ER lumen, oligomerization of STIM, and activation of Orai at PM–ER junctions, allowing the flow of Ca2+ ions into the cell to replenish the depleted stores. An additional layer of SOCE activity involves STIM and Orai interactions with the transient receptor potential channels (TRPCs) and nonselective cation channels [18, 19]. Calcium signaling involving the ER and mitochondria is compromised in obesity and metabolic diseases [6, 20]. SOCE is a crucial element in lipid metabolism and adiposity; specifically, it plays a role in the mobilization of fatty acids from lipid droplets, lipolysis, and mitochondrial fatty acid oxidation [21, 22]. SOCE is also necessary for glucose-stimulated pancreatic insulin secretion [23, 24]. Moreover, Ca2+ signaling and SOCE are involved in proper liver function, including bile secretion, proliferation, oscillatory response to hormones, cholesterol, and glucose metabolism [25, 26]. Alterations in liver Ca2+ homeostasis are associated with ER stress, inflammation, impaired insulin function, and abnormal glucose metabolism [27, 28]. Furthermore, SOCE activity was found to be impaired in the liver of obese murine models [29, 30]. SOCE was also implicated in myocytes’ function, specifically its importance in the proper operation of the sarcoplasmic reticulum (31–33), and its activity changes with age [34, 35]. SARAF is an ER-resident protein that was shown to associate with STIM and promote SOCE inactivation (36–39). SARAF plays a key role in shaping cytosolic Ca2+ signals and determining the content of the major intracellular Ca2+ stores, which is probably important in protecting the cell from Ca2+ overfilling. SARAF is localized to PIP2-rich PM junctions [40] and is activated by dimerization at its ER luminal end [41] when stores are full by a still unknown mechanism. Reduced SARAF levels increase intracellular SOCE and were recently found to be involved in several physiological and pathological conditions (42–44); for review, see [45]. Moreover, SARAF is an androgen-responsive marker for prostate cancer and mTOR-dependent cardiac growth [46, 47]. Here, we report on the consequences of knocking out SARAF globally and in a PVN-specific manner in mice. Knockout (KO) of SARAF introduces a new role for SARAF in a physiological context. SARAF-KO mice gain weight and lose lean mass at a later age. This weight gain is not the consequence of increased food intake but is dependent on PVN-associated circuits. At the cellular level, SARAF increases mitochondrial respiration in addition to its expected reduction of SOCE activity. This report may place SARAF as an important component in the pathophysiology of obesity. ## Generation of SARAF-KO mice To study the physiological role of SARAF in the whole animal context, we utilized the knockout-first allele gene trap KOMP repository to generate, first, LacZ-reporting animals followed by the generations of SARAF conditional knockout animals, termed SARAFfl/fl, as described in the experimental procedure section and graphical representation (Fig. 1A). SARAF, although ubiquitously expressed, is highly expressed in the immune and neuronal tissues [36]; we focused on its expression in the brain via LacZ staining and found it has a marked expression in the hippocampus and hypothalamus (specifically the PVN) and the amygdala (Fig. 1B). A whole-body knockout line was generated by crossing SARAFfl/fl mice with transgenic animals ubiquitously expressing the PGK promoter-driven Cre recombinase to generate PGK-Cre+:SARAFfl/fl, termed SARAF-KO. The knockout of SARAF was validated via Western blotting of the brain tissue and compared with their littermates SARAFfl/flPGK-Cre−, who were homozygous to the floxed SARAF, but did not express Cre recombinase, termed SARAF-WT (Figr. S1A). SARAF-KO mice were viable and bred normally following Mendelian distribution. **Fig. 1.:** *Generation of SARAF conditional mice, SARAF expression in the brain, and SARAFfl/fl PGK-Cre+ metabolic phenotype. A) Schematic representation of the SARAF knock-in cassette and after flipase exertion of the cassette leaving crucial exon 3 flanked by loxP sites. B) X-gal-stained coronal brain sections of KOMP cassette-inserted heterozygous mice, expressing β-gal at the sites of SARAF expression (scale bar: 1 mm) (see magnified images in Fig. S5). C) Changes in body weight of SARAF-WT (n = 13) and SARAF-KO (n = 11) over time. Inset, representative photos of SARAF-WT (right) and SARAF-KO (left) mice at 1 year old. D) Lean and fat mass percentage of SARAF-WT (n = 13), and SARAF-KO (n = 11) mice at 3 months and 1 year old. E and F) PhenoMaster calorimetry metabolic analysis of 3-month-old mice (SARAF-WT, n = 8; SARAF-KO, n = 10). E. Heat production over time. F) Dark-phase locomotion. G) Total food intake. H) Three-month-old mice glucose tolerance test (SARAF-WT, n = 7; SARAF-KO, n = 9).* ## SARAF-KO mice have impaired metabolic function and lipid accumulation Male SARAF-KO mice were compared with their WT littermates and displayed a significant increase in body weight. The weight differences were significantly higher from early adulthood (3 months), where the SARAF-KO mice weight was, on average, about $10\%$ more than that of SARAF-WT mice. The weight differences increased with age. At 1 year old, SARAF-KO mice weigh nearly $20\%$ more than the WT mice (Fig. 1C). Linear growth was not altered in the SARAF-KO mice (Supp.1B). Interestingly, the weight differences were also reflected in body composition alterations, including an increased percentage of fat mass and a lower percentage of lean mass in the SARAF-KO mice, suggesting a possible role for SARAF in mediating sarcopenic obesity-like symptoms (Fig. 1D). Indirect calorimetry assessment of SARAF-KO mice revealed a lower metabolic rate (as manifested in heat production) and reduced locomotion (measured during the active phase in the diurnal cycle) (Fig. 1E and F). However, the abovementioned changes were not associated with significant differences in food intake (Fig. S1C). Furthermore, food intake after 5 hours of fasting, in a refeed experiment, did not alter either the weight loss or the weight gain (Fig. S1D). Interestingly, fasting glucose levels and the response to glucose load (glucose tolerance test, GTT) were normal in the SARAF-KO mice (Fig. 1G). We then examined the adipose tissue distribution in the elderly SARAF-KO mice using computed tomography (CT) imaging to reveal that the excess fat was distributed throughout the body with a high tendency for abdominal accumulation (Fig. 2A–C). Hematoxylin and eosin (H&E)-stained white adipose tissue (WAT) droplet size analysis of inguinal and visceral adipose tissues (iWAT and vWAT, respectively) further revealed significant hypertrophy of fat cells. Moreover, we found that the fat accumulated both in the intracapsular brown adipose tissue (iBAT) and in the liver, resulting in iBAT whitening and hepatic steatosis (Fig. 2D–H). These histological phenotypes were seen in 3-month-old mice and an upsurge in 1-year-old mice, except for liver fat accumulation, which is not apparent yet in younger mice (Supp.1E-I). The BAT is an organ that contributes to systemic metabolic homeostasis and thermoregulation, and its size is associated with various pathologic conditions, including obesity [48, 49]. Interestingly, iBAT whitening can be prevented by inhibiting Ca2+ overload in the mitochondria [50]. Hepatic steatosis is accompanied by cellular Ca2+ imbalance and is a predisposition for a nonalcoholic fatty liver disease that might lead to liver cirrhosis and cancer [51]. The weight- and fat-associated changes in elderly SARAF-KO mice were also accompanied by subclinical hypothyroidism, manifested by normal serum levels of thyroxine and cholesterol and elevated TSH levels [52] (Fig. 2I–K). Subclinical elevation of TSH might influence resting metabolic rate and hint at early thyroid dysfunction [53]. The correlation between the old age increase in TSH and hepatic steatosis may raise the possibility of a thyroid–liver interaction [54]. **Fig. 2.:** *Characterization of SARAF-KO lipid deposition phenotype and voluntary training treatment. A) Micro-CT imaging and whole-body fat (right) distribution analysis of representative 1-year-old SARAF-WT and SARAF-KO mice. B) Abdominal fat representative images of CT images of abovementioned mice. C) Quantification of visceral fat deposition. D) Inguinal WAT, visceral WAT, liver, and iBAT tissue histology in 1-year-old SARAF-WT and SARAF-KO mice. Size bar: 100 μm. E and F) Inguinal and visceral WAT droplet size quantification. G and H) iBAT and liver pixels of fat droplets/area quantification. I) Serum analysis of cholesterol levels of 1-year-old mice. J) Serum analysis of thyroxine levels of 1-year-old mice. K) Serum analysis of TSH levels of 1-year-old mice.* ## SARAF ablation in SIM1 neurons improves metabolic function and hints toward hypothalamic metabolic feedback SARAF-regulated metabolic phenotypes discussed above could stem from several metabolic organs, including adipose-, muscle-, and brain-derived regulation. Because of the marked SARAF expression in the hypothalamus, we chose to focus on the role of hypothalamic SARAF in energy homeostasis. To this end, we crossed the SARAFfl/fl mice to SIM1 promoter-driven Cre recombinase-expressing mice (SIM1-Cre) [55] to induce SARAF deletion specifically in the hypothalamic PVN, termed SARAF-SIM1KO. Allele recombination was validated via site-specific punch-PCR of the PVN (Suppl. 2A). Unlike SARAF-KO, 3-month-old SARAF-SIM1KO mice had similar body weights and composition to their WT littermates. However, at 1 year old, these mice were found to have improved age-related metabolic phenotypes, inversely mirroring the whole-body SARAF-KO. At 1 year old, SARAF-SIM1KO mice had lower body weight, reduced fat percentage, and increased lean mass percentage compared to SARAF-SIM1WT littermates (Fig. 3A and B). Like the whole-body knocked-out mice, SARAF-SIM1KO mice did not have an altered response to glucose or differences in food consumption, either under basal or refeed-challenged conditions (Fig. 3C and Supp. 2B-C). Moreover, their heat production and locomotor activity were elevated with an opposite tendency to the SARAF-KO mice (Fig. 3D–F). Those phenotypes are apparent only at old age (over 1 year old) except for increased locomotion, which was already evident in 3-month-old mice (Fig. S2D–F). The increased locomotion in SARAF-SIM1KO mice is of interest since it is consistent from a young age to old age and thus may drive the phenotype by increasing lean mass and/or changing energy expenditure. Several studies have implicated hypothalamic PVN in the regulation of locomotion (56–58). Interestingly, when examining lipid accumulation in the SARAF-SIM1KO mice, we noticed that the BAT had reduced fat accumulation compared with the wild-type littermates. We did not witness differences in WAT droplet size or fat accumulation in the liver (Fig. 3G–K). When challenging these mice with a diet rich in fat and carbohydrates (Western diet) for 18 weeks, their total increases in body weight, composition, and calorimetry analysis were similar between the two groups (Fig. S3A–E). In Western diet-fed mice, lipid accumulation phenotype, glucose tolerance, and refeed responses were also unchanged (Fig. S3E–J), thus strengthening the assumption that SARAF-related metabolic phenotypes in SARAF-SIM1KO mice are not related to food intake-related mechanism. These results point toward coordinating the BAT and thermogenesis by the PVN via the Ca2+ homeostasis mechanism [5]. SARAF-SIM1KO mice did not exhibit altered anxiety-related behavioral phenotypes, suggesting that SARAF, although ubiquitously expressed, plays a site-specific role (Fig. S4G–I). **Fig. 3.:** *SARAF-SIM1KO mice metabolic-related phenotypes. A) Three-month and 1-year-old SARAF-SIM1WT (n = 16) and SARAF-SIM1KO (n = 9) mice body weight comparison. B) Lean and fat mass percentage of SARAF-SIM1WT and SARAF-SIM1KO 3-month and 1-year-old mice of the same mice as in A. C) Three-month-old glucose tolerance test (SARAF-SIM1WT, n = 11; SARAF-SIM1KO, n = 6). D–F) PhenoMaster calorimetry metabolic analysis of 1-year-old mice (SARAF-SIM1WT, n = 10; SARAF-SIM1KO, n = 6). D) Heat production over time and heat production per body weight (inset). E) Dark-phase locomotion. F) Total food intake. G) Inguinal WAT, visceral WAT, liver, and iBAT tissue histology in 1-year-old SARAF-SIM1WT and SARAF-SIM1KO mice. Size bar: 100 μm. H and I) Inguinal and visceral WAT droplet size quantification. J and K) iBAT and liver pixels of fat droplets/area quantification.* A recent study examined the involvement of SOCE in another subset of hypothalamic neurons, Agouti-related peptide (AgRP)-producing neurons [59]. In this study, Chen et al. demonstrated that with specific ablation of STIM1 from these neurons, the mice displayed reduced appetite and increased heat production, associated with increased oxygen consumption. The opposite phenotypes were displayed when a constitutively active STIM1 mutant was introduced via viral infection to these neurons. This study underlines the complex regulation of feeding behavior, especially in light of the differential dependence of fat accumulation on the type of food consumed. The picture is even more complex when considering our results with SARAF-KO (increased SOCE activity), which shows that weight gain is not associated with disturbed satiety (Suppl. 1C and 2B). AgRP neurons negatively regulate the PVH that contains both MC4R and prodynorphin-expressing neurons [60]. On the other hand, AgRP neurons are negatively regulated by kisspeptin neurons via metabotropic glutamate transmission, and deletion of STIM1 from these neurons protects mice from developing obesity and glucose intolerance with high-fat dieting [61, 62]. In the hypothalamic arcuate–median eminence region, SIM1-expressing neurons neither express AgRP nor MC4R but do express STIM1 and SARAF [63] (Fig. S7, see also https://singlecell.broadinstitute.org/single_cell). Conversely, neurons that do not express SIM1 co-express AgRP, MC4R SARAF, and STIM1 to different degrees. This differential expression profile, and the intricate neuronal circuit controlling energy metabolism, may provide a clue into the opposite effect of SARAF-KO vs. SARAF-SIM1KO sarcopenic-like obesity and energy expenditure-observed phenotypes. ## SARAF ablation decreases hippocampal proliferation and affects the stress response and HPA axis activation SARAF was highly expressed in the hippocampus (Fig. 1B) and specifically in a subset of doublecortin (DCX)-positive neuronal progenitors, indicative of proliferating neural stem cells (Fig. S4A). Since hippocampal proliferation has a marked impact on anxiety and metabolism via the HPA axis [64, 65], we sought to examine the impact of SARAF on hippocampal proliferation and its effect on the HPA axis. The altered function of this system may affect the metabolic phenotype and may account, in part, for the phenotypes reported above. EdU (5-ethynyl-2′-deoxyuridine) incorporation was used to examine hippocampal cell proliferation in the SARAF-WT and the SARAF-KO mice. EDU incorporation assay revealed a significant decrease in proliferation in the dorsal and the ventral hippocampus dentate gyrus in the SARAF-KO mice (Fig. 4A). This decrease was independently validated via immunostaining against proliferating cell nuclear antigen (PCNA) as the proliferation marker (Fig. S4B) and repeated in primary mouse embryonic fibroblast (MEF) cultures derived from SARAF-WT and SARAF-KO using staining for Ki67 expression (Fig. S4C). **Fig. 4.:** *Hippocampal neurons proliferation, anxiety-related behavioral phenotypes, and hepatocyte cellular phenotypes of SARAFfl/flPGK-Cre+ mice. A) EDU (5-ethynyl-2´-deoxyuridine) proliferation analysis of SARAF-WT and SARAF-KO mice at the dorsal and ventral hippocampal dentate gyrus proliferation quantification. B and C) Three-month-old SARAF-WT (n = 11) and SARAF-KO (n = 10) mice performance in anxiety-related tests: B) dark–light transfer. C) Frequency of exits. D) ASR test. Reaction time in three blocks of 120-db stimuli. E) Blood corticosterone levels following 15-min restrain stress (SARAF-WT, n = 8; SARAF-KO, n = 7). F) Fura-2 AM Ca2+ imaging trace of primary hepatocytes extracted from SARAF-WT and SARAF-KO mice and the quantification of SOCE levels (inset). G) Ca2 + oscillations in hepatocytes induced by vasopressin (1 nM) as measured by FURA-2 AM. Quantification of Ca2+ elevation and oscillation interval (inset). H) Representative experiment of mitochondrial respiration of SARAF-WT (n = 3) and SARAF-KO (n = 3) primary hepatocytes and their SRC. I) Heatmap representation of LXR/RXR hepatic (liver X receptor) pathway activation which was indicated by ingenuity platform in MEF RNA sequencing results.* Behaviorally, SARAF-KO mice exhibited decreased anxiety-like behavior as manifested in the dark–light transfer (DLT) by increased time spent in the lit compartment (Fig. 4B and C) and in the acoustic startle response (ASR) experiment, where they exhibited a longer reaction time in the first set of stimulus presentations (Fig. 4D). Counterintuitively, SARAF-KO knocked-out mice exhibited a mild increase in HPA axis activation, as suggested by a more robust immediate corticosterone response to restraint stress (Fig. 4E). This discrepancy may stem from differences in the timing of the assessment, 5–10 min the initiation of the exposure to stress in the DLT and ASR tests, as opposed to about 30 min in the CORT assessment. Interestingly, despite the reduced neurogenesis observed, long-term memory, assessed using the Morris water maze (Fig. S4F), and short-term memory, assessed using the Y-maze (Fig. S4D and E), were not altered. ## SARAF ablation leads to increased cellular SOCE, higher mitochondrial spare respiratory capacity, and altered gene expression patterns Cellular metabolic functions influence global metabolic phenotypes and behavior; specifically, mitochondrial respiration greatly influences global metabolic phenotypes [66]. Moreover, since the hypothalamus, directly via neuronal connections and indirectly via hormonal secretion, regulates metabolic organs [67, 68], we sought to examine whether the cellular metabolic functions were consistent with the global phenotypes we observed in the KO animals. More specifically, the liver is tightly regulated by PVN innervation and hormonal regulation [69, 70]. For this reason, we extracted primary hepatocytes from SARAF-KO mice and examined their Ca2+ signaling and SOCE by FURA-2 AM-based Ca2+ imaging. We induced SOCE by the transient sarco/endoplasmic reticulum Ca2+-ATPase (SERCA) inhibitor BHQ. SOCE activity was elevated in the knocked-out hepatocytes, confirming our previous in vitro experiments [36]. Hepatocyte SOCE was inhibited by the Orai inhibitors La3+ or 2-ABP, therefore hinting at the involvement of the classical SOCE machinery [71] (Fig. 4F). Ca2+ oscillations are a significant driver of cellular signaling, and they are mediated by SOCE and by mitochondrial Ca2+ uptake [72]. Vasopressin is a pituitary-secreted hormone that regulates several physiological functions, including behavior, thermoregulation, water absorption, liver function, and adipogenesis [73, 74]. When examining Ca2+ oscillations triggered by physiological levels of vasopressin (1 nM), we observed a marked increase in release amplitude and an increase in oscillation intervals in the SARAF-KO-derived hepatocytes (Fig. 4G). The latter strengthens the idea that SARAF may affect hypothalamic control over metabolic organs, including the liver. Next, we sought to examine cellular metabolic function by measuring mitochondrial respiration by directly examining the cellular oxygen consumption rate (OCR) [75]. This function is highly influenced by the cellular Ca2+ levels and influences global metabolic phenotypes [66, 76, 77]. Cellular mitochondrial respiration was markedly altered in SARAF knocked-out hepatocytes, having a significantly higher spare respiratory capacity (SRC) function (Fig. 4H). These findings suggest that SARAF, via the modulation of SOCE activity, has a great influence on metabolic organs at a cellular level, like the liver, with altered mitochondrial metabolism and SOCE. Since Ca2+, in addition to its direct regulatory action, can affect, at longer time scales, gene transcription, we set to examine gene transcription patterns by RNA sequencing embryonic fibroblast (MEF) cells derived from SARAF-WT and SARAF-KO animals. We analyzed the results for canonical pathway enrichment using QIAGEN's Ingenuity software and identified that LXR (liver X receptor)/RXR (retinoid X receptor) signaling pathway was significantly altered (Fig. 4I), hinting, again, at SARAF importance in proper liver function. The LXR/RXR signaling pathway genes that were altered include IL1α, IL33, LBP, and ApoD, all of which have indications for metabolic function [78, 79]. LXR/RXR and the related LXR-regulated IL-1-signaling pathways were previously shown to influence metabolism via the hypothalamus and directly influence adipose tissue and the liver (80–82). ## Conclusion This study demonstrates that SARAF is involved in the physiological regulation of age-dependent metabolic rate (as manifested by energy expenditure) and locomotion without affecting food consumption or clear blood glucose handling. Moreover, SARAF was involved in a complex metabolic phenotype that regulates lipid metabolism in the adipose tissue, the BAT, and the liver. These global phenotypes are accompanied by SARAF-mediated cellular metabolic functions, including hepatocyte's SOCE, vasopressin-evoked Ca2+ oscillations, and mitochondria's SRC. Examining SARAF's contribution to the CNS regulation functions indicated its involvement in PVN-regulated autonomic–sympathetic control of energy expenditure, locomotion, and BAT profile. Moreover, SARAF was found to regulate hippocampal proliferation while having some effects on the HPA axis and anxiety-related behavior. Cellular Ca2+ balance is a significant factor in fine-tuning metabolic function in different organs, including the liver, muscle, adipose tissues, and neurons; even slight changes in Ca2+ levels might affect their function [83, 84]. SARAF is expressed in the Sim-1-expressing neurons of the brain, the muscle, and the BAT. SOCE homeostasis, regulated by SARAF, is especially important in a subset of specific cells/tissues described in this study; it is suggested, however, that additional subtle and yet-to-be-discovered SARAF-related physiological functions are probably affected as well. Moreover, other proteins essential for maintaining Ca2+ homeostasis, like SERCA, whose activity is reduced with aging [85], may be essential in the absence of SARAF. In this study, the influence of SARAF on SRC was indicated in the hepatocytes’ OCR measurements, underlining the importance of maintaining appropriate Ca2+ levels for healthy cell function [86]. Interestingly, SRC is a known influencer of aging muscle function and a driver of sarcopenia [87]. Thus, SRC might drive SARAF's involvement in lean mass maintenance. In the liver, SARAF causes improved SRC, seemingly opposite to the previously described papers; however, an increase in SRC is possible in case of damaged oxidative phosphorylation as indicated in some reports [88]. Interestingly, running wheel data show that training increases in aged muscles [89]. Furthermore, SRC is also elevated in the hyperglycemic adipocytes, allowing for fast adaptation and recovery. SARAF ablation in the metabolic organ forces increased SRC levels as an adaptive response to less than favorable conditions [90]. SARAF's role in hippocampal neurons needs further refining; specifically, it is perplexing that SARAF's involvement in hippocampal proliferation did not alter learning and memory. Nevertheless, it has been previously discussed that significant cellular aberration should occur to influence a behavioral phenotype [91]. Given the importance of Ca2+ regulation for cellular physiology, compensatory mechanisms may exist to provide an additional layer of protection from aberrant Ca2+ steady-state levels. This can be mediated by factors associated with SOCE, pumps that clear cytosolic Ca2+, or an increase in the cell-buffering capacity (92–95). Our study shows for the first time, to the best of our knowledge, that SARAF is an essential contributor to the dysregulation of general and PVN-regulated metabolic states. Moreover, it is a novel model for age-dependent sarcopenic obesity, which is not dependent on feeding or accompanied by diabetes. Pharmacological targeting of the SARAF signaling pathway may provide a novel approach for treating sarcopenic obesity. ## Mice The SARAF conditional KO strain used for this research project was generated from KOMP ES cell line Saraftm1a(KOMP)Wtsi, RRID:MMRRC_061775-UCD; was obtained from the Mutant Mouse Resource and Research Center (MMRRC) at the University of California at Davis, an NIH-funded strain repository; and was donated to the MMRRC by The KOMP Repository (University of California, Davis), originating from Pieter de Jong, Kent Lloyd, William Skarnes, and Allan Bradley, Wellcome Sanger Institute. The ES cells were created by introducing a splice acceptor/reporter cassette containing a poly-A site into an endogenous intron upstream of a critical exon. The cassette includes the lacZ gene and the neomycin resistance gene surrounded by FLP sites and the critical (third) SARAF exon floxed by loxP sites (Fig. 1A). The following Jackson laboratory mice lines were used for crossing and generation of conditional mice lines: Gt(ROSA)26Sortm1(FLP1)Dym, Tg(Pgk1-cre)1Lni, B6.FVB(129X1) Tg(Sim1-cre)1Lowl/J. *All* general and spatially specific SARAF knocked-out mice were compared to their wild-type littermates. All the animal procedures were approved by the Weizmann Institute Institutional Animal Care and Use Committee (IACUC). ## Genotyping Genomic DNA was isolated from a tail biopsy using 25-mM NaOH and 0.2-mM disodium EDTA extraction buffer (PH12) incubated for 1 h at 95°C, the extract was neutralized using 40-mM Tris–HCl neutralization buffer (PH5), and PCR identified mouse genotypes. Two PCR primers were synthesized to detect the intact *Cre* gene; three PCR primers were synthesized to detect the KOMP, WT, and loxP inserted SARAF gene, and four PCR primers were used to detect WT, KO, and HET mice in the SARAFfl/flPGK-Cre line (see Table S1). The PCR conditions were 95°C denaturing, 60°C (for SARAF), 55°C (for Cre), 65°C (for SARAFfl/flPGKCre) annealing, and 72°C extensions for 35 cycles using Taq polymerase and a DNA Thermal Cycler from Bio-Rad. The PCR products were resolved by electrophoresis in $1\%$ agarose gel. ## Mouse embryonic fibroblasts E14.5 embryos were dissected, and their trunks were extracted into cold PBS. The trunks were finely minced using a razor until they were pipettable. Cells were suspended in 3-ml trypsin-EDTA $0.05\%$, at 37°C for 10 min. Cells were flushed through a 21-g (3 ml) syringe three times and double serum levels to block the trypsin activity. Next, cells were centrifuged at 1,800 rpm for 5 min, and the pellet was re-suspended in 8 ml of MEF media, containing $10\%$ fetal calf serum, $2\%$ Penstrep, $1\%$ L-glutamine, and $1\%$ sodium pyruvate. Cells from each embryo were plated on a 100-mm dish coated with $0.1\%$ gelatin. Cells were split 1:4 and frozen when they reached confluence. ## Primary hepatocyte Mouse hepatocytes were isolated from 8 to 12-week-old male mice. Mice were anesthetized using ketamine and xylazine; the liver was exposed, perfused in two steps, and washed, and a digestion step was done by Liberase research-grade (Roche Diagnostics). Cells were centrifuged in Percoll (GE Healthcare) gradient and seeded on collagen type 1 (Sigma)-coated plates with HBM basal medium + HCM single Quotes (Lonza) media containing $1\%$ FBS for 2.5–3 h, later washed with non-serum-containing media, and used for experiments for up to 24 h. ## Primary hippocampal cultures Cultures were prepared from E18-P0 mouse pups, and pups were decapitated, their brains removed, and the hippocampi dissected free and placed in chilled (4°C), oxygenated Leibovitz L15 medium (Gibco, Gaithersburg, MD, USA). The hippocampi were dissociated mechanically, and cells were plated in a 24-well plate, onto round coverslips coated with L-polylysine. Glia were plated 14 days beforehand and grown in $10\%$ FBS and glutamate. Cultures grew in a medium containing $5\%$ HS, $5\%$ FBS, and B27 in an incubator at 37° C with $5\%$ CO2. Cells were imaged 13–17 days after plating (Fig. S6). ## Behavioral assays All behavioral assays were performed during the dark period (8 AM–8 PM) in a reverse cycle room. Before each experiment, mice were habituated to the test room for 2 h. ## Morris water maze For the acquisition phase, mice were subjected to four trials per day with an interval of 15 min, for 7 consecutive days. In each trial, the mice were required to find a hidden platform located 1 cm below the water surface in a 120-cm-diameter circular pool. In the testing room, only distal visual–spatial cues for locating the hidden platform were available. The escape latency in each trial was recorded up to 90 s. Each mouse was allowed to remain on the platform for 15 s and then was then removed from the maze. If the mouse did not find the platform within the 90 s, it was manually placed on it for 15 s. Memory was assessed 24 h after the last trial. The escape platform was removed, mice were allowed to search for it for 1 min, and the time spent in the different quadrants of the pool was recorded using a VideoMot2 automated tracking system (TSE Systems). ## Y-maze The maze contains three arms at 120° from each other. The mouse underwent training and test on the same day. During training, one arm was closed. During the test, the mouse starts at the end of one arm and then chooses between the other two arms. The amount of time and duration spent in the closed arm is measured to demonstrate learning and memory. ## Dark–light transfer test The test consists of a polyvinyl chloride box divided into a dark black compartment (14 × 27 × 26 cm) and a white 1050-lx illuminated light compartment (30 × 27 × 26 cm) connected by a small passage. Mice were placed in the dark compartment to initiate a 5-min test session. The time spent in the light compartment and the number of entries to the light compartment were measured. ## Acoustic startle response Mice were placed in a small Plexiglas mesh cage on top of a vibration-sensitive platform in a sound-attenuated, ventilated chamber. A high-precision sensor integrated into the measuring platform detected the movement. Two high-frequency loudspeakers inside the chamber produced all the audio stimuli. The ASR session began with 5-min acclimatization to white background noise (65 dB) maintained throughout the session. Thirty-two startle stimuli (120 dB, 40-ms duration with a randomly varying ITI of 12–30 s) were presented. The reaction time and latency to peak startle amplitude were measured. ## Immunohistochemistry The mice were anesthetized and perfused with $1\%$ PFA in PBS. Brains were carefully removed and fixed overnight in $30\%$ sucrose and $1\%$ PFA in PBS. The following day 30-µm coronal slices were prepared using a sliding microtome and stored in PBS $0.01\%$ Na-Azid. For X-gal staining, the 30-µm slices were incubated in PBS containing $1\%$ glutaraldehyde for 4 min and washed three times in PBS. Slices were incubated overnight in X-gal staining solution containing 3-mM K3Fe(CN)6, 3-mM K4Fe(CN)6, 1.3-mM MgCl2, $0.02\%$ NPO4, $0.01\%$ NaDOC, and 1-mg/ml X-gal in PBS, filtered through a 0.45-µM filter. The next day slices were washed three times in PBS and mounted on slides. For immunohistochemistry, the following primary antibodies were used: rabbit antibeta-galactosidase (1:1,000; Cappel) and goat antidoublecortin (1:100; Santa Cruz). Slices/cells were first incubated for 1.5 h at room temperature in a blocking solution containing $20\%$ normal horse serum (NHS) and $0.3\%$ triton ×100 in PBS, followed by 48–72-h incubation at 4°C in a primary antibody solution containing $2\%$ NHS and $0.3\%$ triton ×100 in PBS. The slices were then washed with PBS and incubated with biotin-conjugated goat antirabbit (1:200; Jackson) in $2\%$ NHS-PBS for 1.5 h at room temperature. Finally, slices were incubated for 1 h at room temperature with secondary antibody FITS-conjugated donkey antigoat (1:200; Jackson) and Cy3-streptavidin (1:200; Jackson). Slices were then washed with PBS and incubated for 10 min with Hoechst (1:2,000), washed, and mounted for fluorescence imaging. Cells were fixed with $4\%$ PFA in PBS for 15 min, washed, and permeabilized using $0.2\%$ triton ×100 in PBS for 30 min. Cells were blocked for 1.5 h, with $5\%$ horse serum in PBS. The primary rabbit anti Ki67 (1:200, Abcam) antibody was incubated overnight. Secondary goat antirabbit cy3 (1:200, Jackson) antibody was incubated for 30 min. Cells were then washed with PBS and incubated for 10 min with Hoechst (1:2,000), washed, and mounted for fluorescence imaging. ## EdU-proliferating cell staining Mice were given 0.2-mg/ml EdU (CarboSynth) in drinking water for 2 weeks and then sacrificed. Brains were embedded in paraffin, and hippocampal slices were then used for immunostaining, using the Click-iT EdU imaging kit (Invitrogen). Staining was done according to manufacturer instructions. ## Rabbit polyclonal anti-SARAF antibody production Rabbit polyclonal anti-SARAF antibody production was performed with the help of the Weizmann Institute core facilities and antibody engineering unit. Two NZW SPF rabbits (New Zealand white rabbits) were subcutaneously injected with SARAF's luminal domain (aa 30–164) fused to a 6×Histidine tag [41]. The first injection was done with Freund's complete adjuvant (Difco 263810), and the second was given a week later with Freund's incomplete adjuvant [263910]. Three boosters with an interval of 14 days were given IP in PBS, and serum was collected after each boost. The serums were purified for mouse IgG subclasses by affinity chromatography on protein-A Sepharose beads CL-4B (SPA-Sepharose, Pharmacia, Sweden). The best serum of the two was selected (according to western blotting), and additional purification steps were conducted as follows: rabbit serum was filtered using a 0.2-mM filter (Nalgene) and loaded onto the affinity HisTrap-NHS column. The column was washed with 10-CV PBS, 15-CV washing buffer, and an additional 10-CV PBS. The first elution step was performed with a glycine buffer (0.1-M glycine titrated to pH 2.3 with HCl). The second elution step was then done using the 5 CV of tetra-ethyl-ammonium (TEA) pH 11.5 (titrated with NaOH). The second fraction, eluted under basic conditions, had superior selectivity and specificity compared to the total serum and was used for Western blot analysis. All columns used are commercially available as prepacked media from GE Healthcare. ## Western blot Adult mice spleens were placed in PBS and minced mechanically. The crude tissue debris were allowed to sediment by gravitation for 5 min, and the supernatant was collected and centrifuged at 2,800g for 5 min. The resulting pellet was homogenized in lysis buffer [50-mM Tris-Cl (pH 7.6), 150-mM NaCl, 0.5-mM EDTA, $1\%$ IGEPAL (CA-630, Sigma), and protease inhibitor cocktail (Roche)], incubated on ice for 30 min and centrifuged at 17,000g for 10 min. The supernatant was collected into a new tube containing sample buffer and incubated at 95°C for 5 min. The resulting protein extract (10 μg) was separated using a $12\%$ SDS–polyacrylamide gel electrophoresis (SDS-PAGE), transferred to nitrocellulose membranes, blocked, and treated overnight with a rabbit polyclonal anti-SARAF (1:10,000), in TBS-T (50-mM Tris, 150-mM NaCl, pH 7.4, $0.1\%$ Tween20) with $1\%$ BSA. After washing, the membranes were incubated with horseradish peroxidase-conjugated goat antirabbit IgG antibody (Jackson) in TBS-T with $1\%$ skim milk and analyzed using an enhanced chemiluminescence (ECL) detection system (Bio-Rad). For re-blotting with anti-GAPDH (1:10,000), the membranes were washed, incubated in stripping buffer (62.5-mM Tris-Cl pH 6.8, $2\%$ SDS, and 0.1-M 2-mercaptoethanol) at 50°C for 30 min, and then underwent the same blocking and antibody incubation protocol as above using antibodies for GAPDH. ## Corticosteroid blood measurements Corticosterone was measured 5 h after the dark cycle began using the DetectX Corticosterone CLIA kit (Arbor assays). Five-μl tail blood samples from mice were collected before (basal), immediately after 15 min of restraint stress, and 30 and 75 min from stress initiation. The restraint stress was induced using a cut 50-ml plastic conical tube. Plasma samples were immediately centrifuged and stored at −80° C until assays for hormone measurement were conducted. Blood was analyzed according to the manufacturer's instructions. ## Micro-CT Mice were anesthetized with isoflurane ($3\%$ for induction, 1–$2\%$ for maintenance) mixed with oxygen (1 l/min) and delivered through a nasal mask. Once anesthetized, the mice were placed in a head-holder to assure reproducible positioning inside the scanner. The set of mice was scanned using a micro-CT device TomoScope 30S Duo scanner (CT Imaging, Germany) equipped with two source-detector systems. The operation voltages of both tubes were 40 kV. The integration time of protocols was 90 ms (360 rotation) for 3-cm length, and axial images were obtained at an isotropic resolution of 80 μ. Due to the maximum length limit, to cover the entire mouse body, imaging was performed in two parts with the overlapping area, and then, all slices merged into one dataset representing the entire ROI. The radiation dose for each mouse was 2.2 Gy. Fat quantification analysis was performed using a CT analysis (Skyscan Bruker MicroCT) software (version 1.19). ## Calcium imaging Cells were plated onto 24-mm L-polylysine-coated cover glass 4–24 h before the experiment. Before the experiment, the cover glass was mounted on an imaging chamber and washed with a $\frac{0}{2}$-mM Ca2+ solution. Fura-2 AM loading of cells was performed for 30–45 min. Cytosolic Ca2+ levels were recorded from Fura-2 AM–loaded cells, excited at wavelengths of $\frac{340}{20}$ and $\frac{380}{20}$ nm, and imaged with $\frac{510}{80}$-nm filters. For all single-cell imaging experiments, traces are of averaged responses from 10 to 50 cells. Ringer's solution with or without CaCl2 or solutions with the following constituents: Ca2+-free solution contained HBSS-/-, 20-mM HEPES, 1-mM MgCl2, 0.5-mM EGTA, and 10-mM glucose calibrated to pH = 7. 2–5-mM Ca2+ solution contained the same except for the absence of EGTA and the addition of 2–5-mM CaCl2 [36, 41]. Hepatocytes were placed in Ca2+-free media, and 20-μM BHQ was used to empty the internal stores. SOCE was measured upon the introduction of 2-mM Ca2+ to the extracellular solution. SOCE in hippocampal neurons was measured in the presence of TTX (1 μM), APV (10 μM), NBQX (2 μM), and nifedipine (50 μM) in 1.8-mM Ca2+. ## Murine metabolic studies Indirect calorimetry, food, water intake, and locomotor activity were measured using the LabMaster system (TSE Systems, Bad Homburg, Germany). Data were collected after 48 h of adaptation from singly housed mice. Body composition was assessed using the Bruker minispec mq7.5 live mice analyzer. ## Running wheel Mice were singly housed in standard cages equipped with a running wheel for 4 weeks (Columbus Instruments). Distances were recorded every 15 min from a counter attached to the wheel. The wheel circumference (111.76 cm) was converted to kilometers. ## Cellular respiration Measurement of intact cellular respiration was performed using the Seahorse XF24 analyzer (Seahorse Bioscience Inc.) and the XF Cell Mito Stress Test Kit according to the manufacturer's instructions. Respiration was measured under basal conditions and in response to oligomycin (ATP synthase inhibitor; 0.5 μM) and the electron transport chain accelerator ionophore, FCCP (trifluorocarbonylcyanide phenylhydrazone; 1 μM), to measure the maximal OCR. Finally, respiration was stopped by adding the electron transport chain inhibitor Antimycin A (1 μM) [96]. ## Glucose tolerance test Mice were fasted for 5 h and subsequently given 2-g/kg glucose solution by i.p. injection. Blood glucose was determined at 0, 15, 30, 60, 90, and 120 min after the glucose challenge (FreeStyle Freedom Lite, Abbott). ## RNA sequencing Total RNA was extracted from the indicated cell cultures using the RNeasy kit (QIAGEN). Then, RNA integrity was evaluated on a Bioanalyzer (Agilent 2100 Bioanalyzer), requiring a minimal RNA integrity number (RIN) of 8.5. Libraries were prepared according to Illumina's instructions accompanying the TruSeq RNA Sample Preparation Kit v2 (cat # RS-122–2001). According to the manufacturer's instructions, sequencing was carried out on Illumina HiSeq 2500v4 SR60, 20 million reads per sample. Sequenced reads were mapped to the *Mus musculus* genome version GRCm38, using TopHat v2.0.10. Genes were identified using a.gtf obtained from Ensembl release 82. *Per* gene, reads were counted using HTSeq. Normalization of reading counts and P-values for differentially expressed genes were computed using DESeq2. ## Statistical and image analysis Images were analyzed and quantified using the Fiji/ImageJ software. P-values were calculated using a Student's t-test for statistical comparisons of mean values using the GraphPad Prism software. Differences were regarded as significant for $P \leq 0.05$ (*) and highly significant for $P \leq 0.01$ (**) and $P \leq 0.001$(***). All data were checked for normality and displayed as mean ±S.E.M. ## Supplementary Material Supplementary material is available at PNAS Nexus online. ## Funding The authors declare no funding. ## Author Contributions D.G. was the driver of the project from its inception. She performed all the experiments, analyzed the data, and wrote the manuscript. Y.K. provided support on the metabolism-related experiments. M.T. provided support on the behavioral-related experiments. I.B. provided support on the CT scanning-related experiments. T.N. and R.P. provided the Western blot. I.K. purified the antibodies. A.M. was involved in genotype characterization and characterized the antibody. E.R. supervised the project, provided support, and wrote the manuscript. ## Data Availability The data that support the findings of this study are available on request from the corresponding author, E.R. RNA seq data were deposited to GEO-GSE193354 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE193354. ## References 1. González-Muniesa P. **Obesity**. *Nat Rev Dis Primers* (2017) **3** 1-18 2. van der Klaauw AA, Farooqi IS. **The hunger genes: pathways to obesity**. *Cell* (2015) **161** 119-132. PMID: 25815990 3. Kim KS, Seeley RJ, Sandoval DA. **Signalling from the periphery to the brain that regulates energy homeostasis**. *Nat Rev Neurosci* (2018) **19** 185-196. PMID: 29467468 4. Herman JP. **Regulation of the hypothalamic–pituitary–adrenocortical stress response**. *Compr Physiol* (2016) **6** 603-621. PMID: 27065163 5. Qin C, Li J, Tang K. **The paraventricular nucleus of the hypothalamus: development, function, and human diseases**. *Endocrinology* (2018) **159** 3458-3472. PMID: 30052854 6. Berridge MJ. **The inositol trisphosphate/calcium signaling pathway in health and disease**. *Physiol Rev* (2016) **96** 1261-1296. PMID: 27512009 7. Clapham DE. **Calcium signaling**. *Cell* (2007) **131** 1047-1058. PMID: 18083096 8. Pozzan T, Rizzuto R, Volpe P, Meldolesi J. **Molecular and cellular physiology of intracellular calcium stores**. *Physiol Rev* (1994) **74** 595-636. PMID: 8036248 9. Prakriya M, Lewis RS. **Store-operated calcium channels**. *Physiol Rev* (2015) **95** 1383-1436. PMID: 26400989 10. Hogan PG, Lewis RS, Rao A. **Molecular basis of calcium signaling in lymphocytes: STIM and ORAI**. *Annu Rev Immunol* (2010) **28** 491-533. PMID: 20307213 11. Berridge MJ. **Inositol trisphosphate and calcium signalling**. *Nature* (1993) **361** 315-325. PMID: 8381210 12. Putney JW. **A model for receptor-regulated calcium entry**. *Cell Calcium* (1986) **7** 1-12. PMID: 2420465 13. Prakriya M. **Orai1 is an essential pore subunit of the CRAC channel**. *Nature* (2006) **443** 230-233. PMID: 16921383 14. Feske S. **A mutation in Orai1 causes immune deficiency by abrogating CRAC channel function**. *Nature* (2006) **441** 179-185. PMID: 16582901 15. Liou J. **STIM is a Ca**. *Curr Biol* (2005) **15** 1235-1241. PMID: 16005298 16. Roos J. **STIM1, an essential and conserved component of store-operated Ca**. *J Cell Biol* (2005) **169** 435-445. PMID: 15866891 17. Yeromin AV. **Molecular identification of the CRAC channel by altered ion selectivity in a mutant of Orai**. *Nature* (2006) **443** 226-229. PMID: 16921385 18. Ambudkar IS, de Souza LB, Ong HL. **TRPC1, Orai1, and STIM1 in SOCE: friends in tight spaces**. *Cell Calcium* (2017) **63** 33-39. PMID: 28089266 19. Liao Y. **Orai proteins interact with TRPC channels and confer responsiveness to store depletion**. *Proc Natl Acad Sci U S A* (2007) **104** 4682-4687. PMID: 17360584 20. Arruda AP, Hotamisligil GS. **Calcium homeostasis and organelle function in the pathogenesis of obesity and diabetes**. *Cell Metab* (2015) **22** 381-397. PMID: 26190652 21. Baumbach J. **A drosophila in vivo screen identifies store-operated calcium entry as a key regulator of adiposity**. *Cell Metab* (2014) **19** 331-343. PMID: 24506874 22. Cuk M, Patel B, Moore KJ, Cuervo AM, Feske S. **Store-operated Ca**. *Cell Metab* (2017) **25** 698-712. PMID: 28132808 23. Sabourin J. **Store-operated Ca**. *J Biol Chem* (2015) **290** 30530-30539. PMID: 26494622 24. Tamarina NA, Kuznetsov A, Rhodes CJ, Bindokas VP, Philipson LH. **Inositol (1,4,5)-trisphosphate dynamics and intracellular calcium oscillations in pancreatic β-cells**. *Diabetes* (2005) **54** 3073-3081. PMID: 16249428 25. Amaya MJ, Nathanson MH. **Calcium signaling in the liver**. *Compr Physiol* (2013) **3** 515-539. PMID: 23720295 26. Aromataris EC, Castro J, Rychkov GY, Barritt GJ. **Store-operated Ca2 + channels and stromal interaction molecule 1 (STIM1) are targets for the actions of bile acids on liver cells**. *Biochim Biophys Acta Mol Cell Res* (2008) **1783** 874-885 27. Arruda AP. **Chronic enrichment of hepatic endoplasmic reticulum– mitochondria contact leads to mitochondrial dysfunction in obesity**. *Nat Med* (2014) **20** 1427-1435. PMID: 25419710 28. Wilson CH. **Steatosis inhibits liver cell store-operated Ca**. *Biochem J* (2015) **466** 379-390. PMID: 25422863 29. Arruda AP. **Defective STIM-mediated store operated Ca**. *Elife* (2017) **6** e29968. PMID: 29243589 30. Park SW, Zhou Y, Lee J, Lee J, Ozcan U. **Sarco(endo)plasmic reticulum Ca**. *Proc Natl Acad Sci U S A* (2010) **107** 19320-19325. PMID: 20974941 31. Stiber J. **STIM1 signaling controls store operated calcium entry required for development and contractile function in skeletal muscle**. *Nat Cell Biol* (2008) **10** 688-697. PMID: 18488020 32. Wei-Lapierre L, Carrell EM, Boncompagni S, Protasi F, Dirksen RT. **Orai1-dependent calcium entry promotes skeletal muscle growth and limits fatigue**. *Nat Commun* (2013) **4** 2805. PMID: 24241282 33. Zhao G, Li T, Brochet DXP, Rosenberg PB, Lederer WJ. **STIM1 enhances SR Ca**. *Proc Natl Acad Sci U S A* (2015) **112** E4792-E4801. PMID: 26261328 34. Thornton AM. **Store-operated Ca**. *Aging* (2011) **3** 621-634. PMID: 21666285 35. Zhao X. **Compromised store-operated Ca**. *Aging Cell* (2008) **7** 561-568. PMID: 18505477 36. Palty R, Raveh A, Kaminsky I, Meller R, Reuveny E. **SARAF inactivates the store operated calcium entry machinery to prevent excess calcium refilling**. *Cell* (2012) **149** 425-438. PMID: 22464749 37. Jardín I. **Fine-tuning of store-operated calcium entry by fast and slow Ca**. *Biochim Biophys Acta Mol Cell Res* (2018) **1865** 463-469. PMID: 29223474 38. Jha A. **The STIM1 CTID domain determines access of SARAF to SOAR to regulate Orai1 channel function**. *J Cell Biol* (2013) **202** 71-79. PMID: 23816623 39. Zomot E, Achildiev Cohen H, Dagan I, Militsin R, Palty R. **Bidirectional regulation of calcium release-activated calcium (CRAC) channel by SARAF**. *Journal of Cell Biology* (2021) **220** 40. Cao X. **The ER/PM microdomain, PI(4,5)P₂ and the regulation of STIM1-Orai1 channel function**. *Cell Calcium* (2015) **58** 342-348. PMID: 25843208 41. Kimberlin CR. **SARAF luminal domain structure reveals a novel domain-swapped β-sandwich fold important for SOCE modulation**. *J Mol Biol* (2019) **431** 2869-2883. PMID: 31082439 42. Son A. **Ca**. *Gastroenterology* (2019) **157** 1660-1672.e2. PMID: 31493399 43. la Russa D, Frisina M, Secondo A, Bagetta G, Amantea D. **Modulation of cerebral store-operated calcium entry-regulatory factor (SARAF) and peripheral orai1 following focal cerebral ischemia and preconditioning in mice**. *Neuroscience* (2020) **441** 8-21. DOI: 10.1016/j.neuroscience.2020.06.014 44. Galeano-Otero I. **SARAF and Orai1 contribute to endothelial cell activation and angiogenesis**. *Front Cell Dev Biol* (2021) **9** 639952. PMID: 33748129 45. Dagan I, Palty R. **Regulation of store-operated Ca**. *Cells* (2021) **10** 1887. PMID: 34440656 46. Romanuik TL. **Novel biomarkers for prostate cancer including noncoding transcripts**. *Am J Pathol* (2009) **175** 2264-2276. PMID: 19893039 47. Sanlialp A. **Saraf-dependent activation of mTORC1 regulates cardiac growth**. *J Mol Cell Cardiol* (2020) **141** 30-42. PMID: 32173353 48. Enerbäck S. **Human brown adipose tissue**. *Cell Metab* (2010) **11** 248-252. PMID: 20374955 49. Shimizu I, Walsh K. **The whitening of brown fat and its implications for weight management in obesity**. *Curr Obes Rep* (2015) **4** 224-229. PMID: 26627217 50. Gao P. **Inhibition of mitochondrial calcium overload by SIRT3 prevents obesity-or age-related whitening of brown adipose tissue**. *Diabetes* (2020) **69** 165-180. PMID: 31712319 51. Ali ES, Rychkov GY, Barritt GJ. *Advances in experimental medicine and biology* (2017) 595-621 52. Fatourechi V. **Subclinical hypothyroidism: an update for primary care physicians**. *Mayo Clin Proc* (2009) **84** 65-71. PMID: 19121255 53. Tagliaferri M. **Subclinical hypothyroidism in obese patients: relation to resting energy expenditure, serum leptin, body composition, and lipid profile**. *Obes Res* (2001) **9** 196-201. PMID: 11323445 54. Huang Y-Y, Gusdon AM, Qu S. **Cross-talk between the thyroid and liver: a new target for nonalcoholic fatty liver disease treatment**. *World J Gastroenterol* (2013) **19** 8238-8246. PMID: 24363514 55. Balthasar N. **Divergence of melanocortin pathways in the control of food intake and energy expenditure**. *Cell* (2005) **123** 493-505. PMID: 16269339 56. Gladfelter WE, Brobeck JR. **Decreased spontaneous locomotor activity in the rat induced by hypothalamic lesions**. *Am J Physiol* (1962) **203** 811-817. PMID: 13948326 57. Mönnikes H, Heymann-Mönnikes I, Taché Y. **CRF in the paraventricular nucleus of the hypothalamus induces dose-related behavioral profile in rats**. *Brain Res* (1992) **574** 70-76. PMID: 1638411 58. McCormick CM, Ibrahim FN. **Locomotor activity to nicotine and Fos immunoreactivity in the paraventricular nucleus of the hypothalamus in adolescent socially-stressed rats**. *Pharmacol Biochem Behav* (2007) **86** 92-102. PMID: 17270257 59. Chen Z. **Deficiency of ER Ca**. *Cell Rep* (2021) **37** 109868. PMID: 34686338 60. Li MM. **The paraventricular hypothalamus regulates satiety and prevents obesity via two genetically distinct circuits**. *Neuron* (2019) **102** 653-667.e6. PMID: 30879785 61. Qiu J. **Deletion of stim1 in hypothalamic arcuate nucleus kiss1 neurons potentiates synchronous GCaMP activity and protects against diet-induced obesity**. *J Neurosci* (2021) **41** 9688. PMID: 34654752 62. Nestor CC. **Optogenetic stimulation of arcuate nucleus kiss1 neurons reveals a steroid-dependent glutamatergic input to POMC and AgRP neurons in male mice**. *Mol Endocrinol* (2016) **30** 630-644. PMID: 27093227 63. Campbell JN. **A molecular census of arcuate hypothalamus and median eminence cell types**. *Nat Neurosci* (2017) **20** 484-496. PMID: 28166221 64. Kanoski SE, Grill HJ. **Hippocampus contributions to food intake control: mnemonic, neuroanatomical, and endocrine mechanisms**. *Biol Psychiatry* (2017) **81** 748-756. PMID: 26555354 65. Snyder JS, Soumier A, Brewer M, Pickel J, Cameron HA. **Adult hippocampal neurogenesis buffers stress responses and depressive behaviour**. *Nature* (2011) **476** 458-461. PMID: 21814201 66. Picard M. **Mitochondrial functions modulate neuroendocrine, metabolic, inflammatory, and transcriptional responses to acute psychological stress**. *Proc Natl Acad Sci U S A* (2015) **112** E6614-E6623. PMID: 26627253 67. Gore AC. *Fundamental Neuroscience* (2013) 799-817 68. Roh E, Song DK, Kim M-S. **Emerging role of the brain in the homeostatic regulation of energy and glucose metabolism**. *Exp Mol Med* (2016) **48** 216 69. O’Hare JD, Zsombok A. **Brain-liver connections: role of the preautonomic PVN neurons**. *Am J Physiol-Endocrinol Metab* (2015) **310** E183-E189. PMID: 26646097 70. Uyama N, Geerts A, Reynaert H. **Neural connections between the hypothalamus and the liver**. *Anat Rec* (2004) **280A** 808-820 71. Bird GS, Putney JW. *Calcium entry channels in non-excitable cells* (2018) 311-324 72. Dolmetsch RE, Xu K, Lewis RS. **Calcium oscillations increase the efficiency and specificity of gene expression**. *Nature* (1998) **392** 933-936. PMID: 9582075 73. Jones BF, Boyles RR, Hwang S-Y, Bird GS, Putney JW. **Calcium influx mechanisms underlying calcium oscillations in rat hepatocytes**. *Hepatology* (2008) **48** 1273-1281. PMID: 18802964 74. Maus M. **Store-operated Ca**. *Cell Metab* (2017) **25** 698-712. PMID: 28132808 75. Traba J, Miozzo P, Akkaya B, Pierce SK, Akkaya M. **An optimized protocol to analyze glycolysis and mitochondrial respiration in lymphocytes**. *J Vis Exp.* (2016) **2016** 54918 76. Böhm A. **Increased mitochondrial respiration of adipocytes from metabolically unhealthy obese compared to healthy obese individuals**. *Sci Rep* (2020) **10** 12407. PMID: 32709986 77. Gherardi G, Monticelli H, Rizzuto R, Mammucari C. **The mitochondrial Ca**. *Front Physiol* (2020) **11** 554904. PMID: 33117189 78. Jakobsson T, Treuter E, Gustafsson J-A, Steffensen KR. **Liver X receptor biology and pharmacology: new pathways, challenges and opportunities**. *Trends Pharmacol Sci* (2012) **33** 394-404. PMID: 22541735 79. Miller AM. **Interleukin-33 induces protective effects in adipose tissue inflammation during obesity in mice**. *Circ Res* (2010) **107** 650-658. PMID: 20634488 80. Barbier L. **Interleukin-1 family cytokines: keystones in liver inflammatory diseases**. *Front Immunol* (2019) **10** 2014. PMID: 31507607 81. Ericsson A, Kovács KJ, Sawchenko PE. **A functional anatomical analysis of central pathways subserving the effects of interleukin-1 on stress-related neuroendocrine neurons**. *J Neurosci* (1994) **14** 897-913. PMID: 8301368 82. Kruse MS, Suarez LG, Coirini H. **Regulation of the expression of LXR in rat hypothalamic and hippocampal explants**. *Neurosci Lett* (2017) **639** 53-58. PMID: 28038938 83. Ong HL, Subedi KP, Son GY, Liu X, Ambudkar IS. **Tuning store-operated calcium entry to modulate Ca**. *Biochim Biophys Acta Mol Cell Res* (2019) **1866** 1037-1045. PMID: 30521873 84. Toescu EC, Verkhratsky A. **The importance of being subtle: small changes in calcium homeostasis control cognitive decline in normal aging**. *Aging Cell* (2007) **6** 267-273. PMID: 17517038 85. Lompré AM, Lambert F, Lakatta EG, Schwartz K. **Expression of sarcoplasmic reticulum Ca(2+)-ATPase and calsequestrin genes in rat heart during ontogenic development and aging**. *Circ Res* (1991) **69** 1380-1388. PMID: 1834363 86. Chemaly ER, Troncone L, Lebeche D. **SERCA control of cell death and survival graphical abstract**. *Cell Calcium* (2018) **69** 46-61. PMID: 28747251 87. Hiona A. **Mitochondrial DNA mutations induce mitochondrial dysfunction, apoptosis and sarcopenia in skeletal muscle of mitochondrial DNA mutator mice**. *PLoS One* (2010) **5** e11468. PMID: 20628647 88. Porter C. **Endocrine and metabolic dysfunction during aging and senescence mitochondrial respiratory capacity and coupling control decline with age in human skeletal muscle**. *Am J Physiol Endocrinol Metab* (2015) **309** 224-232 89. Waters DL, Brooks WM, Qualls CR, Baumgartner RN. **Skeletal muscle mitochondrial function and lean body mass in healthy exercising elderly**. *Mech Ageing Dev* (2003) **124** 301-309. PMID: 12663127 90. Keuper M. **Spare mitochondrial respiratory capacity permits human adipocytes to maintain ATP homeostasis under hypoglycemic conditions**. *FASEB J* (2014) **28** 761-770. PMID: 24200885 91. Dauer W, Przedborski S. **Parkinson's disease: mechanisms and models**. *Neuron* (2003) **39** 889-909. PMID: 12971891 92. Carreras-Sureda A. **ORMDL3 modulates store-operated calcium entry and lymphocyte activation**. *Hum Mol Genet* (2013) **22** 519-530. PMID: 23100328 93. Feng JM. **Golli protein negatively regulates store depletion-induced calcium influx in T cells**. *Immunity* (2006) **24** 717-727. PMID: 16782028 94. Karakus E. **The orphan solute carrier SLC10A7 is a novel negative regulator of intracellular calcium signaling**. *Sci Rep* (2020) **10** 7248. DOI: 10.1038/s41598-020-64006-3 95. Srivats S. **Sigma1 receptors inhibit store-operated Ca**. *J Cell Biol* (2016) **213** 65-79. PMID: 27069021 96. Ruggiero A. **Loss of forebrain MTCH2 decreases mitochondria motility and calcium handling and impairs hippocampal-dependent cognitive functions**. *Sci Rep* (2017) **7** 1-13. PMID: 28127051
--- title: 'The effect of integrating midwifery counseling with a spiritual content on improving the antenatal quality of life: A randomized controlled trials' authors: - Masoumeh MonfaredKashki - Azam Maleki - Kourosh Amini journal: Journal of Mother and Child year: 2022 pmcid: PMC10062411 doi: 10.34763/jmotherandchild.20222601.d-22-00003 license: CC BY 4.0 --- # The effect of integrating midwifery counseling with a spiritual content on improving the antenatal quality of life: A randomized controlled trials ## Abstract ### Background Poor antenatal Quality of Life (QoL) is associated with adverse outcomes. ### Objective This study was performed to examine the effect of integrating midwifery counseling with spiritual content on improving the antenatal quality of life. ### Method This randomized controlled trial was performed on 60 first-time pregnant women who were referred to two childbirth preparation centers in Zanjan city, Iran in 2019. The counseling was conducted in eight sessions. The QoL SF-36 questionnaire was completed before and two months after the intervention. Data were analyzed using the chi-square test, independent t-test, and paired-samples t-test. The level of significance was $p \leq 0.05.$ ### Results After intervention based on an independent t-test the total score of QoL was significantly greater in the intervention group compared to the control group ($$p \leq 0.001$$). After the intervention, the mean scores of four domains of QoL (Role-Physical, General Health, Vitality, Role-Emotional, and Mental Health) were significantly higher than the control group($$p \leq 0.001$$). While in terms of Physical Functioning, Bodily Pain and Social Functioning domains were not statistically significant ($p \leq 0.05$). ### Conclusion Integrating midwifery counseling with spiritual content had a positive impact on improving the psychological aspect of quality of life more than the physical and social aspects. It can be used by providers for planning antenatal care programs. ## Introduction Pregnancy is a physiological phenomenon in a women’s life. Physical and psychological changes during pregnancy can affect the social and physical performance, as well as the quality of life (QoL) of pregnant women [1]. The quality of life (QoL) reflects the subjective perceptions of the individual’s situation in life-based on the cultural and value system, given the individual’s goals, expectations, standards, and attitudes [2]. According to the World Health Organization (WHO), health-related QoL refers to the physical, psychological, social, and spiritual dimensions of individuals’ well-being [3]. Furthermore, the QoL of pregnant women could be affected by many factors such as gestational age, social and economic support, and complications before or during pregnancy [4]. On the other hand, poor pregnancy QoL is associated with adverse outcomes for example preterm labor pain, and pregnancy-related symptoms such as fatigue, and low back and pelvic pain[5]. Additionally, low QoL in pregnancy contributes to low QoL in the postnatal period [6]. Spirituality is known as an important component of health and well-being. Although the concepts of religion and spirituality are similar in some aspects and are often used interchangeably, they mean different meanings. Spirituality is a way of perceiving the sublime, understanding certain values and goals of life, and experiencing positive and satisfying behaviors and emotions in life through non-physical methods [7]. In this respect, spiritual care can be educated in nursing and midwifery to be able to provide spiritual care as part of holistic and person-centred care [8, 9]. Childbearing is one of the ideal conditions for enriching spirituality. Some people believe that the process of pregnancy and childbirth is a time to get closer to God and make life more meaningful [10]. Spirituality is defined as sensitivity or attachment to religious values, or to things of the spirit as opposed to material or worldly interests. Spiritual experience is a unique experience and includes understanding the meaning of life, positive life experiences, feeling happy, and life satisfaction [11]. In Iran, spiritual care has not been routinely included in antenatal care programs, while in recent years, valuable results from the implementation of interventions based on religion and spirituality in improving anxiety, depression, and coping with stress have been reported [12]. The use of spiritual counseling alone or in combination with cognitive-behavior therapy can improve QoL in women with a high-risk pregnancy, postpartum depression, and fear of labor pain [13, 14, 15]. However, there is a gap in the effectiveness of spiritual-based interventions in the culture and context of Iran on health-related QoL in women with the first pregnancy. Given the importance of spiritual care and the presence of limited studies in this field, this study aimed to examine the effect of integrating midwifery counseling with spiritual content on improving the antenatal quality of life. ## Study aim and design This parallel randomized controlled trial was performed to examine the effect of integrating midwifery counseling with spiritual content on improving the antenatal quality of life among first-time pregnant women. ## Setting The study was performed on 60 first-time pregnant women who were referred to two childbirth preparation centers in *Zanjan a* city in northwest Iran, in 2019. There are three childbirth preparation centers in Zanajn. One of the childbirth preparations centers is located in a hospital and covers most high-risk pregnancies, so sampling was done from only two centers that provide services in the urban health community center. ## Participants The study population included first-time pregnant women who were referred to two childbirth preparation centers in Zanjan. Inclusion criteria consisted of living in Zanjan city, gestational age of 20-24 weeks, willingness to participate in the study, obtaining scores ≤10 according to the Edinburgh Postnatal Depression Scale (EPDS) [16], scores of 19 to 37 based on the Cohen Perceived Stress Scale (PSS-14) [17] and having a normal pregnancy with a singleton fetus. Exclusion criteria before randomization were the presence of medical or obstetric complications, psychiatric disorders or use of psychiatric drugs, and no access to telephone for follow-up. There was no attrition in the study and after the interventions. ## Procedure Pregnant women who met the inclusion criteria and signed the informed consent form were allocated into two intervention and control groups using randomized a block size of four. To ensure the concealment of the sequence of enrolment, an opaque sealed envelope system was used [18]. Envelope preparation and random allocation sequencing were performed by a person not involved in the research process. In the present study, participants & researcher were not blinded only outcome assessors were blinded. The research process is shown in Figure 1. **Figure 1:** *Flow chart of the participant’s selection.* ## Intervention The counselling was conducted by a midwife (the first author) familiar with counselling approaches. The content of the sessions was developed under the supervision of a spiritual advisor by following the study of khodaKarmari et al.[19] and the method suggested by Richard and Bergin [20]. The intervention group received eight sessions of counseling in addition to routine care. The counseling was held in 8 sessions, as a group counseling (8-10 people) for 4 weeks (2 times per week for 45 minutes) in preparation classrooms. The counseling was conducted by a midwife (the first author) who that familiar with counseling approaches under the supervision of a clinical psychologist. Educational content was prepared using the Holy Quran and religious books (Hadis) and integrated with routine midwifery counseling. The main topic of counseling was reported in Table 1. **Table 1** | Sessions | Counseling content | Session homework | | --- | --- | --- | | First | The first session was to meet the participants and researcher, to explain the aim, the rules, and the brief full program. Providing pre-test. Talking about the concept of quality of life, self-concept in pregnancy, and checking misconceptions. Focus on human creation and discussing concerning the status of women in the continuity of creation | Practice looking at their life issues from other angles. Prepare a list of pregnancy stressors. Be aware of the stresses that accumulate all over your body and make you suffer. | | Second | Assessing attitudes and beliefs of the pregnant woman on spiritual issues, the role of god, and religion in her life. Talking about the spiritual aspects of pregnancy and childbearing. Listening to the physical and mental problems, worries, fears, ambivalence sense in early pregnancy and her actions in daily life. | Visualize unfavorable conditions and try to switch appropriate and positive reactions in your mind to inappropriate ones. | | Third | Discussing the positive effects of helping each other in our life. Finding the truth of their existence, not just addressing personal desires in pregnancy Focus on the concept of trust, resort, patience, kindness. Listening to positive statements of participants based on reading the holy book, and spiritual issues in overcoming or feeling calm in stressful situations. | Create a daily spiritual space of time or place at home. Reflect on what others are saying and pay attention to the root cause of others’ behavior or speech and try not to react too quickly. | | Fourth | Blessings of God and the role of it in reinterpreting the concept of pregnancy and overcoming the worrisome symptoms of pregnancy. Strengthening individuals’ inner hope and powers for coping with pregnancy and childbearing. Teaching relaxing muscles with deep breathing for getting rid of the stress. Repeat twice daily for 10 to 15 minutes | Book therapy / listening to Qur’an voices for 10 min. | | Fifth | Discussing the role of patience and trust in God in enduring the pain of childbirth and the spiritual reward of pregnancy, childbirth, and breastfeeding for the mother. Encouraging to express their feeling after/ during creating a daily spiritual space. Talking about the experience of participating in religious programs or doing spiritual issues. Discuss the effect of spiritual’s beliefs on eating habits on the fetus, taking care of oneself in pregnancy. | Listening to “Nature’s Music” the sound of birds, rivers, and waterfalls… | | Sixth | Encouraging to refer to people who create a positive atmosphere or with which she feels comfortable. Illustration and slowly moving tone using meditation relaxation technique along or with listening to relaxing music Take a realistic look at the issues and changes that have taken place in their pregnancies Do not be strict or easy-going. Allocate as much attention and time as needed to each issue | Book therapy / listening to Qur’an voices for 10 min. | | Seventh | Pay attention to the concepts of resentment, unforgiveness, guilt, and forgiving oneself and others. Discuss the strategy of prayer therapy to reduce some symptoms of pregnancy related to pregnancy and increase hope Express the pleasure and responsibility of being a mother from the point of view of the Quran” Divine Responsibility Reward” | Focus on motherhood and look at pregnancy in terms of your productive and fertile period Enjoy the hardships of pregnancy and childbirth happily. Face these hardships and endure it | | Eighth | Reviewing and summarizing the previous sessions’ topics | Relaxing muscles with deep breathing for getting rid of the stress. Repeat twice daily for 10 to 15 minutes | Each session was started with a focus on breathing exercises or the sacred name like “Allah”. Next, the counselor described the subject of the meeting and encouraged the mothers to express emotions, needs, concerns, and thoughts on pregnancy. At the same time, the counselor guided the participants to increase their knowledge to choose the appropriate remedy for emotional reactions during pregnancy and pay attention to spiritual aspects of life. Further advice was given as homework. At the end of each session, explanations and summaries were provided and the women discussed the topic. According to the guidelines of the Iranian Ministry of Health, routine childbirth preparation classes were held from the 20th week of gestation every two weeks until the 32nd week of gestation. The sessions focused on making the mothers familiar with the different stages of pregnancy from fertilization to delivery, personal hygiene, nutrition, mental and physical changes during pregnancy, pregnancy risks, childbirth planning, postpartum health, breastfeeding, and child care. However, no spiritual content was included. The control group only received routine care. ## Outcomes The main outcome of this study was to determine antenatal QoL of first-time pregnant women which were collected using the SF-36 as a standard questionnaire of QoL, which was completed by the participants before and two months after the last session. ## Demographic It included personal information about a woman’s age, education, occupation, and spouse’s occupational status. ## Health-related quality of life (HRQoL) -SF-36 SF-36 a multidimensional measure evaluating health-related quality of life. It is widely used in clinical research and is a reliable and valid measure of health-related QoL in different populations [21, 22]. It measures the perceptions of health-related QoL in eight domains of health status: physical functioning (10 items); physical role limitations (four items); bodily pain (two items); general health perceptions (five items); energy/vitality (four items); social functioning (two items); emotional role limitations (three items) and mental health (five items). Responses are scored on a 5-point scale, that is transformed into a score of 0–100 with higher scores indicating better functioning or well-being. The validity and reliability of The Persian version of the questionnaire have been assessed by Montazeri et al. [ 21]. ## Data analysis The statistical analysis was performed using the SPSS software version 16. Considering the $95\%$ confidence level (Z1-α = 1.96), the test power of $80\%$ (Z1-β = 0.85) and based on the QOL variable in Zamani’s study with the mean and standard deviation in the intervention group (M1=32.10 and S1=2.63), control group (M2=25.90 and S2=2.33), an attrition rate of $15\%$ the sample size of was calculated for 30 pregnant women in each group [15]. Descriptive statistics were employed to describe demographic data. The chi-square test was used to compare the demographic characteristics of the groups. The Kolmogorov-Smirnov test revealed that the scores of the QoL and its components had normal distributions. Therefore, to compare total scores and all domains between and within the groups in pre-and post-intervention, the independent t-test, and paired samples t-test were applied, respectively. The level of significance was $p \leq 0.05.$ ## Results Among 146 pregnant women evaluated by the researcher, sixty women met the eligibility criteria for the study ## Demographic characteristics The demographic data are shown in Table 2. Most of the participants were housewives and have academic-level education. There were no statistically significant differences between the two groups before the intervention in terms of demographic characteristics. The mean (SD) of gestational age in the counseling group and control group were 21.80 ± 1.27 and 21.60 ± 1.40 weeks, respectively. Also, term of the mean age of the participants and gestational age was not statistically significant between the two groups ($p \leq 0.05$) (Table 2) **Table 2** | Variable | Groups | Groups.1 | Groups.2 | Groups.3 | Unnamed: 5 | P-value | | --- | --- | --- | --- | --- | --- | --- | | Variable | Intervention | Intervention | Control Number (percent) | Control Number (percent) | | P-value | | Variable | Frequency | Percentage | Frequency | Percentage | | P-value | | Woman’s Education woman | GuidanceHigh schoolDiplomaAcademic | 211215 | 6.73.34050 | 211017 | 6.73.333.356.7 | 0.44 | | Woman’s Employment | EmployedHousewife | 1416 | 46.753.3 | 1020 | 33.366.7 | 0.43 | | Spouses’ employment | EmployedUnemployed | 1713 | 56.743.3 | 219 | 7030 | 0.42 | | Age (years) | Mean ± standard deviation | 25.80 ± 6.37 | 25.80 ± 6.37 | 24.30 ± 6.80 | 24.30 ± 6.80 | 0.38 | | Gestational age (week) | | 21.80 ± 1.27 | 21.80 ± 1.27 | 21.60 ± 1.40 | 21.60 ± 1.40 | 0.56 | The mean (SD) of PSS-14 in the intervention group was 23.57 ± 3.81 and in the control group was 23.09 ± 4.29. In pre-intervention based on an independent t-test, the total score of PSS-14 was not statistically significant between the two groups ($$p \leq 0.399$$). The mean (SD) of the Edinburgh Postnatal Depression Score (EPDS) in intervention and control groups was 8.43 ± 1.56 and 8.40 ± 1.68, respectively. In pre-intervention based on an independent t-test, the total score of EPDS was not statistically significant between the two groups ($$p \leq 0.092$$). All participants met the eligibility criteria for the study due to the scores of EPDS being lower than 10 and the scores of PSS-14 being between 19 to 37. ## Health-Related Quality of Life (HRQoL) Intervention group before counseling the mean score of overall QoL was 85.66 ± 5.44 which increased to 96.46 ± 4.44 and in the control group, it was 86.86 ± 3.36 before intervention that decreased to 85.76 ± 4.04 two months after the intervention. The observed differences between the two groups were statistically significant after intervention ($$p \leq 0.001$$). After intervention based on an independent t-test, the mean score of four domains of QoL (Physical Role Limitations, General Health, Vitality, Role-Emotional, and Mental Health) in the counseling group was significantly higher than the control group ($$p \leq 0.001$$). While in terms of Physical Functioning, Bodily Pain and Social Functioning domains were not statistically significant ($p \leq 0.05$). Comparing within-the group (before and after) scores of QoL and its domains in the control group showed no statistically significant differences ($p \leq 0.05$). Comparing within-the group (before and after) scores of QoL and the domains of “Physical Functioning, Physical Role Limitations, General Health, Vitality, Role-Emotional, and Mental Health” in the intervention group showed statistically significant improvements ($p \leq 0.05$). Only the scores of two domains of “Bodily Pain, Social Functioning” were not statistically significant ($p \leq 0.05$) (Table 3). **Table 3** | Unnamed: 0 | Unnamed: 1 | Intervention | Intervention.1 | Control | Control.1 | Unnamed: 6 | | --- | --- | --- | --- | --- | --- | --- | | SF-36 Domains | | Mean | SD | Mean | SD | P-value | | Physical functioning | Pretest | 26.75 | 8.43 | 30.25 | 5.14 | 0.05 | | | Post-test | 31.08 | 8.29 | 27.58 | 5.62 | 0.06 | | P-value | | Paired t-test = 0.0001 | Paired t-test = 0.0001 | Paired t-test = 0.07 | Paired t-test = 0.07 | | | Bodily pain | Pretest | 32.91 | 22.62 | 33.75 | 16.78 | 0.87 | | | Post-test | 33.75 | 25.24 | 38.33 | 20.74 | 0.44 | | P-value | | Paired t-test = 0.73 | Paired t-test = 0.73 | Paired t-test = 0.34 | Paired t-test = 0.34 | | | physical role limitations | Pretest | 6.25 | 5.91 | 5.20 | 5.46 | 0.48 | | | Post-test | 13.33 | 7.65 | 6.25 | 5.44 | 0.0001 | | P-value | | Paired t-test = 0.0001 | Paired t-test = 0.0001 | Paired t-test = 0.16 | Paired t-test = 0.16 | | | Emotional role functioning | Pretest | 16.66 | 8.47 | 16.66 | 8.47 | 1 | | | Post-test | 29.16 | 9.22 | 16.94 | 8.32 | 0.0001 | | P-value | | Paired t-test = 0.0001 | Paired t-test = 0.0001 | Paired t-test = 0.91 | Paired t-test = 0.91 | | | Social role functioning | Pretest | 35.41 | 12.74 | 37.08 | 8.97 | 0.56 | | | Post-test | 38.33 | 11.80 | 37.91 | 8.97 | 0.87 | | P-value | | Paired t-test = 0.18 | Paired t-test = 0.18 | Paired t-test = 0.72 | Paired t-test = 0.72 | | | Mental health | Pretest | 57.00 | 7.61 | 58.50 | 6.45 | 0.41 | | | Post-test | 62.33 | 8.78 | 57.16 | 6.78 | 0.01 | | P-value | | Paired t-test = 0.0001 | Paired t-test = 0.0001 | Paired t-test = 0.45 | Paired t-test = 0.45 | | | Vitality | Pretest | 49.79 | 8.44 | 49.16 | 8.64 | 0.77 | | | Post-test | 63.33 | 4.85 | 49.16 | 8.64 | 0.0001 | | P-value | | Paired t-test = 0.0001 | Paired t-test = 0.0001 | Paired t-test = 0.18 | Paired t-test = 0.18 | | | General health perceptions | Pretest | 35.16 | 7.59 | 33.83 | 5.20 | 0.43 | | | Post-test | 46.66 | 7.46 | 32.33 | 4.09 | 0.0001 | | P-value | | Paired t-test = 0.0001 | Paired t-test = 0.0001 | Paired t-test = 0.34 | Paired t-test = 0.34 | | | Total Quality of Life Score | Pretest | 85.66 | 5.44 | 86.86 | 3.36 | 0.31 | | | Post-test | 96.46 | 4.44 | 85.76 | 4.04 | 0.0001 | | P-value | | Paired t-test = 0.0001 | Paired t-test = 0.0001 | Paired t-test = 0.23 | Paired t-test = 0.23 | | ## Discussion The study was done to examine the effect of integrating midwifery counseling with spiritual content on improving the antenatal quality of life among first-time pregnant women. Our results showed that integrating midwifery counseling with spiritual content could be improved the overall QoL. However, three domains of SF-36 QoL (physical function, bodily pain, and social function) showed no improvements. The current study emphasized that integrating midwifery counseling with spiritual content improved the psychological aspects of QoL more than the physical and social aspects. Limited information is available on the effectiveness of spiritual-based education for improving the QoL of first-time healthy pregnant women. However, our results were consistent with some studies that were conducted on multiparous or high-risk pregnancy samples. In this regard, Moazedi et al 2018, showed that Islamic teaching-based religious-spiritual psychotherapy could be improved the quality of life of infertile women [13]. The content of the counseling in our study was similar to their study and include religious and spiritual instruction in increasing the acceptance of pregnancy and responsibility through attention to the spiritual reward of pregnancy in the presence of God, the increase of trust, reflection on human creation and the greatness of creation, blessings of God and the role of it in reinterpreting the concept of pregnancy and overcoming the worrisome symptoms of pregnancy. Strengthening individuals’ inner hope and powers for coping with pregnancy and childbearing. Zamani et al [2018] in a semi-experimental study with a pretest-posttest design showed that integrating cognitive-behavioral therapy with Islamic spirituality instructions had an effective impact on the quality of life of pregnant women [15]. Beigi et al. [ 2015] showed that the implementation of group spiritual therapy was effective in reducing anxiety and increasing the quality of life of women with gestational diabetes [23]. Also, similar efficacy has been reported in another study by Niaz Azari et al. in 2017 [14]. Constituency in results emphasized that the spiritual-based approach can be used to improve the quality of life of women in the antenatal and postpartum periods. According to the World Health Organization recommendation, ‘every woman has the right to the highest attainable standard of health, which includes the right to dignified, respectful health care throughout pregnancy and childbirth. The respect for pregnant women’s overall needs and their satisfaction leads to a holistic women-centered approach to care [24]. Various ideas that have been reported concerning the biological and psychological effects of spiritual experience on diseases have been emphasized in some studies. It can be claimed that some cognitive patterns, psychological characteristics, and behavioral patterns created by spirituality-oriented methods lead to strengthening health and improving the physiological function of the body and consequently increase the psychological resistance of the person in poor physical and social situations. Accordingly, spiritual practices and the religious aspect of spirituality lead to increased tolerance, patience, self-control, satisfaction, emotional control, optimism, self-efficacy (based on trust in God’s blessing), altruism, kindness, and love [25, 26]. Religion and spirituality can increase QoL by changing people’s attitudes, increasing their sense of responsibility towards themselves and others, promoting the search for meaning in life, and having a greater sense of happiness and self-esteem [27]. The effectiveness of the spiritual approach on improving QoL in the different populations [28, 29, 30] has been shown that spirituality is a universal element [31]. Belief in God creates a change in the perspective toward life [19]. The spiritual aspects of pregnancy and childbearing are often neglected in the literature. Integration of midwifery-led counseling with the spiritual approach for improving the quality of life of women is necessary. It can be concluded that spiritual counseling had a positive impact on improving the QoL of first-time pregnant women. The integration of spiritual counseling with the educational content of childbirth preparation can improve the psychological aspect of QoL of pregnant women more than the physical and social aspects. Therefore, it can be used for planning suitable interventions among pregnant women. ## Strengths of study All the principles of control trial studies were observed in this study and we don’t have a loss of following in participants. Data collection tools were standard and psychometric properties of the Persian form of the questionnaires have been evaluated based on Iranian culture. ## Limitations The sample size was small and the follow-up period was short. Also, samples were limited to the participants of childbirth preparation classes with moderate levels of perceived stress, which can affect the generalizability of findings. Also, the long duration of each session could be led to the exhaustion of mothers. However, the women were allowed to have rest and walk for a few minutes during the sessions. In the present study, the spiritual intelligence of the participants was not examined before the intervention and is considered a limitation. It is suggested that additional studies should be performed by measuring spiritual intelligence and perceived stress with the long follow-up period, and participation of their spouses in future studies. Furthermore, for better conclusions about the long-term effects of the spiritual-based intervention on antenatal QoL, studies with a mixed-method design are needed to be conducted in the future. ## Key points Poor antenatal Quality of Life (QoL) is associated with adverse outcomes. The integration of spiritual counseling with antenatal care can improve the QoL of pregnant women. ## References 1. Bjelica A, Cetkovic N, Trninic-Pjevic A, Mladenovic-Segedi L. **The phenomenon of pregnancy—A psychological view**. *Ginekologia polska* (2018.0) **89** 102-6. DOI: 10.5603/GP.a2018.0017 2. Cai T, Verze P, Bjerklund Johansen TE. **The Quality of Life Definition: Where Are We Going?**. *Uro* (2021.0) **1** 14-22. DOI: 10.3390/uro1010003 3. Post M. **Definitions of quality of life: what has happened and how to move on**. *Topics in spinal cord injury rehabilitation* (2014.0) **20** 167-80. DOI: 10.1310/sci2003-167 4. Lagadec N, Steinecker M, Kapassi A, Magnier AM, Chastang J, Robert S. **Factors influencing the quality of life of pregnant women: a systematic review**. *BMC Pregnancy Childbirth* (2018.0) **18** 1-14. PMID: 29291732 5. Lau Y. **The effect of maternal stress and health-related quality of life on birth outcomes among Macao Chinese pregnant women**. *The Journal of Perinatal & Neonatal Nursing* (2013.0) **27** 14-24. DOI: 10.1097/JPN.0b013e31824473b9 6. Fobelets M, Beeckman K, Buyl R, Daly D, Sinclair M, Healy P. **Mode of birth and postnatal health-related quality of life after one previous cesarean in three European countries**. *Birth: Issues in Perinatal Care* (2018.0) **45** 137-47. DOI: 10.1111/birt.12324 7. Delgado C. **A discussion of the concept of spirituality**. *Nursing science quarterly* (2005.0) **18** 157-62. DOI: 10.1177/0894318405274828 8. McSherry W, Ross L, Attard J, Van Leeuwen R, Giske T, Kleiven T. **Preparing undergraduate nurses and midwives for spiritual care: Some developments in European education over the last decade**. *Journal for the Study of Spirituality* (2020.0) **10** 55-71. DOI: 10.1080/20440243.2020.1726053 9. Lewinson LP, McSherry W, Kevern P. **Spirituality in pre-registration nurse education and practice: A review of the literature**. *Nurse education today* (2015.0) **35** 806-14. PMID: 25707759 10. Crowther S, Stephen A. **Association of psychosocial–spiritual experiences around childbirth and subsequent perinatal mental health outcomes: An integrated review**. *Journal of Reproductive and Infant Psychology* (2020.0) **38** 60-85. DOI: 10.1080/02646838.2019.1616680 11. Bożek A, Nowak PF, Blukacz M. **The relationship between spirituality, health-related behavior, and psychological wellbeing**. *Frontiers in Psychology* (2020.0) **11** 1997. DOI: 10.3389/fpsyg.2020.01997 12. Jabbari B, Mirghafourvand M, Sehhatie F. **The effect of holly Quran voice with and without translation on stress, anxiety and depression during pregnancy: a randomized controlled trial**. *Journal of Religion and Health* (2020.0) **59** 544-54. DOI: 10.1007/s10943-017-0417-x. 13. Moazedi K, Porzoor P, Pirani Z, Adl H, HJJoH Ahmadi. **The effectiveness of Islamic teaching based religious-spiritual psychotherapy on quality of life, in infertile women**. *Journal of Health* (2018.0) **9** 589-98 14. Niaz Azari M, Abdollahi M, Zabihi Hesari NK, Ashoori JJJoR. **Effect of spiritual group therapy on anxiety and quality of life among gestational diabetic females**. *Biannual of Mazandaran University of Medical Sciences* (2017.0) **5** 11-20 15. Zamani SN, Zarei E, Alizadeh KH, Naami AZJHMJ. **Effectiveness of Combination of Cognitive-Behavioral Therapy and Resilience Training Based on Islamic Spirituality and Cognitive Flexibility on Postpartum Depression, Fear of Labor Pain and Quality of Life**. *Hormozgan medical journal* (2019.0) **22**. DOI: 10.5812/hmj 16. Montazeri A, Torkan B, Omidvari S. **The Edinburgh Postnatal Depression Scale (EPDS): translation and validation study of the Iranian version**. *BMC psychiatry* (2007.0) **7** 1-6. PMID: 17214899 17. 17 http://www.psy.cmu.edu/~scohen/scales.html 18. Doig GS, FJJocc Simpson. **Randomization and allocation concealment: a practical guide for researchers**. *Journal of Critical Care* (2005.0) **20** 187-91. DOI: 10.1016/j.jcrc.2005.04.005 19. Khodakarami B, Golalizadeh Bibalan F, Soltani F, Soltanian A, Mohagheghi HJH, Ethics M. **Prognostic role of spiritual intelligence components in pregnant women’s depression, anxiety, and stress**. *Health Spiritual Med Ethics* (2016.0) **3** 16-23 20. Richards PS, Bergin AE. *A spiritual strategy for counseling and psychotherapy* (1997.0) 21. Montazeri A, Goshtasebi A, Vahdaninia M, BJQolr Gandek. **The Short Form Health Survey (SF-36): translation and validation study of the Iranian version**. *Qual Life Res* (2005.0) **14** 875-82. DOI: 10.1007/s11136-004-1014-5 22. Lins L, FMJSom Carvalho. **SF-36 total score as a single measure of health-related quality of life: Scoping review**. *SAGE Open Medicine* (2016.0) **4** 2050312116671725. DOI: 10.1177/2050312116671725 23. Beigi A, Habibi S, Rezaei Hesar H, Niasty R, Shams Ali Z, Ashoori J. **Effect of spiritual training on decreased anxiety and increased quality of life of women with gestational diabetesin the assement of nursing and modern care**. *Journal of Diabetes Nursing* (2016.0) **4** 19-29. DOI: 10.5603/GP.a2018.0017 24. Tunçalp Ӧ, Pena-Rosas JP, Lawrie T, Bucagu M, Oladapo OT, Portela A. **WHO recommendations on antenatal care for a positive pregnancy experience-going beyond survival**. *Bjog* (2017.0) **124** 860-2. PMID: 28190290 25. Abdollahpour S, Khosravi A. **Relationship between spiritual intelligence with happiness and fear of childbirth in Iranian pregnant women**. *Iran J Nurs Midwifery Res* (2018.0) **23** 45. DOI: 10.4103/ijnmr.IJNMR_39_16 26. Seybold KS, PCJCdips Hill. **The role of religion and spirituality in mental and physical health**. *Current Directions in Psychological* (2001.0) **10** 21-4. DOI: 10.1111/467-8721.00106 27. **Toward a unifying theoretical and practical perspective on well-being and psychosocial adjustment**. *Journal of Counseling Psychology* (2004.0) **51** 482. DOI: 10.1037/0022-167.51.4.482 28. Hamid N, Khodadost F. **Prediction of Life Quality and Daily Activities of Kidney Transplant Patients in Ahvaz According to Spiritual Intelligence, Health Control Source and Coping Strategies**. *Jundishapur Journal of Medical Sciences* (2017.0) **16** 411-24. DOI: 10.22118/JSMJ.2017.51056 29. Manshaee G, Haji Mohammad Kazemi S, Ghamarani AJA. **Prediction Model of Quality of Life Promotion on the Basis of Emotion Expression, Spiritual Intelligence and Pain acceptance in Female Patients with Fibromyalgia**. *Journal of Anesthesiology and Pain* (2018.0) **9** 60-73 30. **Spirituality, mental health, physical health, and health-related quality of life among women with HIV/ AIDS: integrating spirituality into mental health care**. *Issues in Mental Health Nursing* (2006.0) **27** 185-98. DOI: 10.1080/01612840500436958 31. Harrison M, Koenig HG, Hays JC, Eme-Akwari AG. **Pargament KIJIrop. The epidemiology of religious coping: A review of recent literature**. *International Review of Psychiatry* (2001.0) **13** 86-93. DOI: 10.1080/09540260124356
--- title: 'Online Information Related to Symptoms of Carpal Tunnel Syndrome: A Google Search Analysis' journal: Cureus year: 2023 pmcid: PMC10062431 doi: 10.7759/cureus.35586 license: CC BY 3.0 --- # Online Information Related to Symptoms of Carpal Tunnel Syndrome: A Google Search Analysis ## Abstract Introduction While *Google is* frequently used to access internet-based health resources, the quality of online health information remains variable. Our purpose was to assess suggested resources identified through Google search features for common symptoms related to carpal tunnel syndrome (CTS). Methods Two searches were performed. The first, labeled "symptom-related," included the terms "hand numbness," "hand tingling," and "hand falling asleep." The second, labeled "CTS-specific," included "carpal tunnel syndrome," "carpal tunnel surgery," and "carpal tunnel release." A novel feature of Google’s search engine is to display similar searches made by other users ("People Also Ask" snippet). For each search, the first 100 results snippets and the associated website links were recorded. A list of unique questions was compiled and classified into 1 of 3 categories using the Rothwell classification: fact, policy, or value. Questions were also classified based on the diagnoses suggested by the query. Website authorship was determined, and the corresponding links were categorized by two independent reviewers. Results The "symptom-related" searches yielded 175 unique questions and 130 unique website links, and the "CTS-specific" searches yielded a total of 243 questions and 179 unique links. For "symptom-related" searches, $65\%$ of questions suggested a diagnosis, with CTS being suggested as a diagnosis for only $3\%$ of questions. In contrast, CTS was suggested by $92\%$ of questions in "CTS-specific" searches. In both searches, nearly $75\%$ of questions were classified as "facts." Commercial websites were the most common in both searches. Conclusion Google searches for common symptoms of median nerve compression rarely yield information related to CTS. ## Introduction The landscape of how patients obtain information regarding medical conditions and treatments has changed rapidly in the 21st century [1,2]. Patient use of the internet to learn about medical conditions has become almost ubiquitous in recent years [2,3]. Approximately $75\%$ of adults in the United States use the internet as their first resource when seeking information for health-related conditions [3]. However, the reliability of online health information continues to be questioned. Within upper-extremity surgery, prior investigations have suggested that anywhere from $20\%$ to $50\%$ of sites with health information may be misleading and often reinforce common misconceptions [4-8]. Furthermore, patients often have difficulty finding helpful information, with reports that patients easily find the information they seek less than $50\%$ of the time [3]. Carpal tunnel syndrome (CTS) is the most common compressive neuropathy of the upper extremity, affecting one to three patients per 1,000 per year [9]. Costs associated with the care of CTS exceed two billion dollars annually [9]. The quality of online patient information regarding CTS is particularly concerning [4-6]. A novel feature of Google’s search engine is the "People Also Ask" snippet, where similar searches by other users are displayed. These serve to guide patients to related topics and provide a single website link with relevant information. Recent studies involving both shoulder and lower extremity arthroplasty examined these "frequently asked questions" and found that information came from a variety of sources and provided insights into what patients were viewing online [10,11]. The results of this Google search feature, as it pertains to CTS or symptoms of median nerve compression, remain unknown. The purpose of this investigation was to quantify and assess suggested resources identified through Google search features for common symptoms related to CTS as well as for direct searches regarding CTS and related surgery. In addition, we aimed to characterize the suggested resources provided by Google’s "People Also Ask" snippet. We hypothesized that suggested information found using symptom-related search terms would be related to CTS and would be predominantly produced by academic centers. ## Materials and methods Two groups of searches were performed. The first, labeled "symptom-related," included the terms "hand numbness," "hand tingling," and "hand falling asleep." The second, labeled "CTS-specific," included "carpal tunnel syndrome," "carpal tunnel surgery," and "carpal tunnel release." Each term was individually queried into a Google Web Search (www.google.com) that was clean-installed with the browser history removed to eliminate bias from Google’s personalized search algorithm. The first search was performed in September 2021, and the second search was performed in December 2022. For each search, the first 100 results of the "People Also Ask" snippet and the associated website links were recorded. A list of unique questions was compiled. Each question was categorized, according to Rothwell’s classification, into fact (is the given question true?) or policy (will a course of action solve a specific problem?) or value (is an idea, object, or event good or bad?) [ 12]. Questions populated by the "People Also Ask" snippet were classified based on any diagnosis suggested by the question, post hoc. These categories included cardiovascular, psychological, diabetes, nutrition, hydration, multiple sclerosis, nerve injury/damage, carpal tunnel syndrome, COVID-19, and none for the searches related to symptoms of carpal tunnel. Similar to previous studies [10,11], each website was also categorized by site authorship/ownership. These categories included commercial, academic, medical practice, single surgeon practice, government, and social media. The questions and the corresponding links were categorized by two independent reviewers. Interobserver reliability was calculated using Cohen’s kappa coefficient. Discrepancies were resolved via consensus discussion with the senior author, where the category was discussed amongst all parties and the final decision was made by consensus regarding the definitive category. When discussing agreement levels for Cohen’s kappa coefficient, the following guidelines were utilized: slight (0.01-0.20), fair (0.21-0.40), moderate (0.41-0.60), substantial (0.61-0.81), and near perfect (>0.81) [13,14]. ## Results The "symptom-related" searches yielded a total of 230 unique questions and 130 unique website links for review. Fifty-five ($24\%$) questions were deemed irrelevant and excluded, leaving 175 questions for evaluation. Irrelevant questions often related to common suggested diagnoses but did not allude to any of the queried symptoms. Cohen’s kappa coefficient was 0.954 for question classification and 1.000 for website classification. The "CTS-specific" searches yielded a total of 243 unique questions and 179 unique links for review. No questions were deemed irrelevant. Cohen’s kappa coefficient is 0.815 for question classification and 0.844 for website classification. For "symptom-related" searches, a diagnosis was suggested in $65\%$ ($\frac{120}{175}$) of questions (Figure 1). The most common suggested diagnosis was nutrition-based ($13\%$, $$n = 23$$), followed by psychological ($12\%$, $$n = 22$$) and cardiovascular ($11\%$, $$n = 20$$). CTS and nerve damage/injury were suggested in $3\%$ ($$n = 5$$) and $2\%$ ($$n = 3$$) of questions, respectively. **Figure 1:** *Common diagnoses as suggested through Google’s “People Also Ask” snippet for queries of “symptom-related” searches.* For "CTS-specific" searches, a diagnosis was suggested in $93\%$ ($\frac{227}{243}$) of questions (Figure 2). CTS was the most commonly suggested diagnosis, occurring in $92\%$ ($$n = 225$$) of the questions. Two questions ($0.82\%$) were related to nerve damage/injury. **Figure 2:** *Common diagnoses as suggested through Google’s “People Also Ask” snippet for queries of “CTS-specific” searches.* For "symptom-related" searches, $75\%$ ($$n = 131$$) of questions were classified as fact questions (Table 1). Value questions represented $20\%$ ($$n = 35$$) of search questions, and policy questions represented $5.1\%$ ($$n = 9$$). The most common website categories were commercial ($53.1\%$, $$n = 69$$), followed by academic ($22.3\%$, $$n = 29$$), and medical practice ($17.7\%$, $$n = 23$$) (Table 2). For "CTS-specific" searches, $72\%$ ($$n = 176$$) of questions were classified as fact questions. Value questions represented $14.4\%$ ($$n = 35$$) of search questions, and policy questions represented $13.17\%$ ($$n = 32$$). The most common website categories were commercial ($30.7\%$, $$n = 55$$), followed by medical practice ($27.9\%$, $$n = 50$$) and academic ($18.4\%$, $$n = 33$$). ## Discussion Contrary to our hypothesis, search terms representing the classic symptoms of CTS do not commonly yield resources pertaining to CTS. While nearly two-thirds of FAQs alluded to a diagnosis, less than $5\%$ were related to CTS. Other common causes of these symptoms within orthopedics, such as radiculopathy or other compressive peripheral neuropathies, were not represented at all. The more commonly recommended diagnoses from this study, such as nutritional deficiencies, dehydration, and diabetes, rarely present with hand numbness [15-17]. Most suggested questions were fact-based, which is in alignment with previous studies [10,11]. Unlike previous studies, policy questions were less common in the present study, likely representing a fundamental difference in searches seeking information on symptoms compared to those examining surgical procedures. For "symptom-related" searches, over half of all recommended questions were associated with a commercial link. Similarly, commercial websites were the most common category for "CTS-specific" searches. These findings are in contrast to previous studies on Google’s "People Also Ask" snippet [10,11]. Sudah et al. found that academic centers were the most common source of information, with commercial sources providing only $18\%$ of links [11]. Shen et al. similarly noted academic centers as the most common source of information, but commercial sites were the second most common source, accounting for $30\%$ of links [10]. However, previous studies investigating online resources for CTS found that commercial websites were the most common source of information over a 15-year time period [4,5]. While the authors reported a high rate of misleading information, they did not compare this to other website categories. Other studies have noted a decreased quality of commercial websites [18,19]. Although prioritized by search engines, patients should be aware of the limitations of health information from commercially sponsored websites. Given our search findings, it is reasonable to advise patients to use caution when utilizing Google’s "People Also Ask" tool, as it may not be helpful or reliable for patients seeking health information online related to common CTS symptoms or CTS itself. These data highlight the limitations of patient-driven searches for online health information. The issue remains that resources from academic centers and professional societies are not prioritized by Google’s search engine, as evidenced by two different search queries. Patients searching online highly value convenience when selecting websites to review [20]. Therefore, organizations such as the American Society for Surgery of the Hand and the American Academy of Orthopaedic Surgeons, with a vested interest in producing and maintaining high-quality educational resources, should work to optimize their online presence to better reach their patients. Several limitations must be considered when interpreting our results. First, while we selected our "symptom-related" search terms based on common symptoms of CTS, our terms may not be representative of all common search terms used by patients. For example, we used the term "hand numbness" but not "why are my hands numb." Also, while *Google is* the most common search engine [21], our results may not be generalizable to other search engines. Because Google’s search algorithm is continually updated and changing, our results may not be generalizable over time. Google’s search algorithm also relies on a user’s previous search history, which may lead to greater variation in search results. Lastly, the impact of the suggested FAQs and associated websites on patients’ understanding and decision-making remains unknown. ## Conclusions Google searches for common symptoms of median nerve compression rarely yield information related to CTS. Information produced by academic institutions and professional societies is infrequently accessed via these searches, and the suggested information has a strong commercial influence. Suggested FAQs by Google may not be a reliable source for health information. ## References 1. Trotter MI, Morgan DW. **Patients' use of the Internet for health related matters: a study of Internet usage in 2000 and 2006**. *Health Informatics J* (2008) **14** 175-181. PMID: 18775824 2. **The social life of health information. Pew research center’s internet & american life project**. (2022) 3. Finney Rutten LJ, Blake KD, Greenberg-Worisek AJ, Allen SV, Moser RP, Hesse BW. **Online health information seeking among us adults: measuring progress toward a healthy people 2020 objective**. *Public Health Rep* (2019) **134** 617-625. PMID: 31513756 4. Lutsky K, Bernstein J, Beredjiklian P. **Quality of information on the internet about carpal tunnel syndrome: an update**. *Orthopedics* (2013) **36** 0-41 5. Beredjiklian PK, Bozentka DJ, Steinberg DR, Bernstein J. **Evaluating the source and content of orthopaedic information on the Internet. The case of carpal tunnel syndrome**. *J Bone Joint Surg Am* (2000) **82** 1540-1543. PMID: 11097441 6. Goyal R, Mercado AE, Ring D, Crijns TJ. **Most youtube videos about carpal tunnel syndrome have the potential to reinforce misconceptions**. *Clin Orthop Relat Res* (2021) **479** 2296-2302. PMID: 33847604 7. Foster BK, Malarkey WM, Maurer TC, Barreto Rocha DF, Udoeyo IF, Grandizio LC. **Distal biceps tendon rupture videos on YouTube: an analysis of video content and quality**. *J Hand Surg Glob Online* (2022) **4** 3-7. PMID: 35415601 8. Foster BK, Malarkey WM, Mettler AW. **Shoulder and elbow arthroplasty videos on YouTube: an analysis of video content and quality**. *Semin Arthroplasty* (2022) **32** 211-217 9. Sevy JO, Varacallo M. **Carpal Tunnel Syndrome**. (2022) 10. Shen TS, Driscoll DA, Islam W, Bovonratwet P, Haas SB, Su EP. **Modern internet search analytics and total joint arthroplasty: what are patients asking and reading online?**. *J Arthroplasty* (2021) **36** 1224-1231. PMID: 33162279 11. Sudah SY, Pagani NR, Nasra MH, Moverman MA, Puzzitiello RN, Guss MS, Menendez ME. **What patients want to know about shoulder arthroplasty: a google search analysis**. *Semin Arthroplasty* (2022) **32** 162-168 12. Rothwell J. **Mixed Company: Communicating in Small Groups and Teams**. (2006) 13. Landis JR, Koch GG. **An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers**. *Biometrics* (1977) **33** 363-374. PMID: 884196 14. Lindenhovius A, Karanicolas PJ, Bhandari M, Ring D. **Radiographic arthrosis after elbow trauma: interobserver reliability**. *J Hand Surg Am* (2012) **37** 755-759. PMID: 22397846 15. Suter PM, Russell RM. **Vitamin and trace mineral deficiency and excess**. *Harrison's Principles of Internal Medicine* (2018) 16. Powers AC, Niswender KD, Evans-Molina C. **Diabetes mellitus: diagnosis, classification, and pathophysiology**. *Harrison's Principles of Internal Medicine* (2018) 17. Powers AC, Stafford JM, Rickels MR. **Diabetes mellitus: complications**. *Harrison's Principles of Internal Medicine* (2018) 18. Dy CJ, Taylor SA, Patel RM, McCarthy MM, Roberts TR, Daluiski A. **Does the quality, accuracy, and readability of information about lateral epicondylitis on the internet vary with the search term used?**. *Hand (N Y)* (2012) **7** 420-425. PMID: 24294163 19. Noback PC, Trofa DP, Dziesinski LK, Trupia EP, Galle S, Rosenwasser MP. **Kienböck Disease: quality, accuracy, and readability of online information**. *Hand (N Y)* (2020) **15** 563-572. PMID: 30556422 20. Wong DK, Cheung MK. **Online health information seeking and ehealth literacy among patients attending a primary care clinic in Hong Kong: a cross-sectional survey**. *J Med Internet Res* (2019) **21** 0 21. **Top 10 search engines in the world**. (2022)
--- title: 'Retro walking treadmill training reduces C—reactive protein levels in overweight and obese young adults: A randomized comparative study' authors: - Ajith Soman - Sunil Chandy - Khalid Alkhathami - Baranitharan Ramamoorthy - Bijad Alqahtani journal: Health Science Reports year: 2023 pmcid: PMC10062441 doi: 10.1002/hsr2.1169 license: CC BY 4.0 --- # Retro walking treadmill training reduces C—reactive protein levels in overweight and obese young adults: A randomized comparative study ## Abstract ### Background and Aims Retro walking has been shown to acquire a greater metabolic cost, placing a higher cardiopulmonary demand on the body, when compared with forward walking at a similar speed. The aim of this study was to compare the effect of retro walking with that of forward walking on C‐reactive protein (CRP) levels, body mass index (BMI) and blood pressure (BP) and to understand the influence of independent factors namely systolic blood pressure (SBP), diastolic blood pressure (DBP) and BMI on CRP in untrained overweight and obese young adults. ### Methods This was a randomised controlled trial whereby 106 participants underwent either retro walking ($$n = 53$$) or forward walking ($$n = 53$$) treadmill training four times a week for 12 weeks before and after which CRP, BMI, and BP levels were measured. Comparison of the measured values before and after intervention and between the groups was done and influence of BMI and BP on CRP levels was determined. ### Results Both groups recorded a significant decrease ($p \leq 0.001$) in CRP, BMI, and BP levels postintervention. The participants who underwent retro walking training showed a significantly ($p \leq 0.001$) higher decrease in all the outcomes as compared with the forward walking group. C‐reactive protein levels were seen to be influenced by BMI and DBP. ### Conclusion Retro‐walking training leads to greater decrease in CRP, BMI, and BP when compared with forward walking, and CRP levels are influenced by BMI and DBP. Retro walking treadmill training can be used preferentially to bring about reduction in cardiovascular risk factors. ## INTRODUCTION Obesity is an epidemic escalating across the globe. Obese individuals suffer from a higher fatality rate of $50\%$–$10\%$ from multiple causes, especially cardiovascular events. 1 Additionally, they suffer from a low‐grade chronic proinflammatory state, which is the factor that links endothelial dysfunction to insulin resistance and obesity. C‐reactive protein (CRP), an important inflammatory marker in serum, has been reported to be elevated in persons who are obese, and correlates with insulin resistance and endothelial dysfunction. 2 Obesity or an increase of fatty tissue causes chronic inflammation in the body, which in turn causes an increase in cytokine synthesis. 3 The level of CRP in blood at an acute stage is an early marker of inflammation or infection, and has been associated with central obesity. CRP is normally produced by the liver in response to IL‐6 (Interleukin‐6) in acute inflammatory conditions, and the normal concentration of this acute‐stage protein in the blood is less than 0.3 mg/dl. 4, 5, 6 A small amount of CRP is also generated by cells other than those in the liver; atherosclerotic plaques, lymphocytes, monocytes, and neurons, for instance. 7 Furthermore, CRP is most consistently associated with atherogenesis, especially by macrophages and smooth muscle cells, when compared with other inflammatory markers, and hence poses a greater cardiovascular risk. 7, 8 Many studies have demonstrated the interrelationship of CRP, BMI, and blood pressure (BP), though this relationship has not been conclusively proven. 9, 10 *It is* a widely accepted fact that obesity is not much amenable to medical treatment. Diet and exercise, systematically undertaken, is the only way to treat obesity, manifested as a high body mass index (BMI). Of the different modes of exercise, walking at a brisk pace is beneficial to the cardiovascular health and helps in maintaining a healthy body weight. 11, 12 Retro walking or backward walking performed on a treadmill has been proven to expend energy at a greater extent than forward walking. Retro walking also makes a higher metabolic demand on the body and helps in improving exercise capacity to a greater extent than forward walking. The discrepancy in metabolic cost among the two types of walking is postulated due to increased stride frequency, decreased stride length and also owing to the concentric contraction of the quadriceps muscle as opposed to eccentric contraction, resulting in increased energy expenditure after retro walking. 13, 14 These effects may indirectly have an effect on CRP and BMI, since inflammatory and obesity markers are linked to exercise capacity and energy expenditure. The feasibility of retro walking as a possible mode of exercise in robust adults has been analyzed in several studies. Terblanche et al., when studying the metabolic energy expenditure of backward running and walking, established that running and walking backward caused higher submaximal heart rates, blood lactate levels, and oxygen consumption responses, when compared with running forward at a similar velocity. 15 Likewise, Ordway inferred that retro walking can not only produce performance gains, but also favorably influence parameters like disease‐related disability and pain in persons with specific disease conditions. 16 The authors of the present study identified a need to compare the effectiveness of backward walking as opposed to forward walking in bringing about changes in inflammatory outcome measures, especially CRP, which is an important inflammatory marker with great influence on atherogenesis and hence on development of lifestyle diseases. Both forms of walking are exercise modes that are easy to administer, and are potentially able to modify risk factors for cardiovascular disease. CRP is likely to be influenced by a number of factors like BMI, SBP and DBP, and it is important to identify the independent variables which can influence CRP. Keeping this aim in mind, the present study was conducted with the primary objective of ascertaining which forward walking and retro walking treadmill training had greater effect on CRP level in the blood among obese and untrained young men. The secondary objective was to determine the independent variables which were liable to influence blood CRP levels. ## Subjects The study included male subjects aged between 20 and 25 years, whose BMI was equal to or above 25 kg/m2. We excluded persons who were undergoing drug therapy for any medical conditions, smokers, those currently participating in any habitual exercise training, those who had any metabolic, orthopedic, neurological or respiratory conditions, which contraindicated exercise training or any apparent medical condition that causes hepatic interference of CRP secretion. The demographic data of the study participants, which included age, clinical data including height, weight, BMI, history of past illness, and physical activity profile was recorded. A complete physical examination was carried out for the participants. Those not fulfilling inclusion criteria were withdrawn from the study. ## Study design This study was a single‐blinded comparative pre–post experimental study without control. Young male students pursuing medical and paramedical courses from Shaqra University, Kingdom of Saudi Arabia, who volunteered to participate in the study upon being solicited by advertisements placed on the University's notice boards, e‐mails, and posts in the social media students’ groups were included as participants. Approval to conduct the study was obtained from the ethics research committee of the university (ERC_SU_20210057). Reporting of results was done in accordance with the CONSORT guideline recommendations for reporting clinical trials. ## Recruitment Before initial assessment, participants received a data package with details of the study, including their rights as participants of a research project, and a printed informed consent form. In addition, the participants were explained about the procedure, and were encouraged to clarify any doubts about the same before signing the written consent form. Once the form was signed, the subjects were allotted into two groups using sequentially numbered sealed opaque envelopes and assessment and interventions were carried out. Before intervention, the participants were screened using the pre‐participation questionnaire recommended by the American Heart Association/American College of Sports Medicine. 17 BMI was calculated, BP was measured, and CRP levels were ascertained by laboratory testing. ## Intervention Before initializing the intervention, basic training was provided by a physical therapist to those who were unfamiliar with forward and backward training, until they could walk confidently on the treadmill without support. Participants in either group underwent 5 min of warm‐up and cool–down each before and after intervention which comprised free range of motion exercises of all joints, heel raise exercises, hamstring stretching, and soleus stretching. The participants allotted to the retro walking group participated in a backward treadmill training program under supervision for 4 days a week for 12 weeks. Each session included an exercise period, which started with duration of 15 min, and progressed to 30 min over the 12‐week training period. During the exercise period, the participants were made to walk backwards at a speed of 4 km/h (or 67 meters/min) with a $10\%$ gradient 18, 19 The participants in the forward walking training group underwent a supervised treadmill training program with duration, intensity, and frequency similar to that of the retro walking treadmill training program. ## Assessment and outcomes Outcomes of the exercise program viz. BMI, CRP, and BP levels were measured by assessors who were trained by the research team and blinded to the objectives of the study, to the intervention groups to which the participants belonged, and to the phase of measurement, that is, preintervention or postintervention. All measures were evaluated the day before commencement of the training program and the day after the end of the training period. Blood samples were obtained from the antecubital vein after an overnight 12‐ hour fast and immediately before commencement of the treadmill exercise. The post‐exercise blood samples were taken 24–72 h following the last exercise session. The fluorescence immunochromatographic method (Wiz biotech®) was used to measure the CRP levels from the blood samples. 20 Body height, body weight, BMI, hip circumference, waist circumference, waist–height ratio and waist–hip ratio were measured using standardised methods. 21, 22 The BP was measured from the right arm using the auscultatory method, with a sphygmomanometer with a cuff size appropriate for arm length. Four readings were taken with the participant seated, the arm supported on a cushion at chest level, resting a minute between each measurement. The mean of the last three readings was considered the final level of BP. 9 ## Sample size To expect an improvement in the CRP level of 1.94 ± 0.9 after exercise, on the basis of the study conducted by Arikawa et al., 22 with $95\%$ connfidence interval (CI) and $90\%$ power with an allowable error of $5\%$, a minimum of 49 individuals per group were to be recruited. ## STATISTICAL ANALYSIS The data were analysed using statistical software SPSS version 21 (SPSS, Inc.). Baseline data are represented as mean and standard deviation with $95\%$ CI. Group (Retro walking vs. Forward walking) × Time (Baseline vs. 12 Week); mixed model analysis of variance (ANOVA) was used to assess the effect and interactions between the groups. Assumptions for normality, homogeneity of variance, and sphericity of the covariance matrix, when assessed, showed no major deviations. Independent t test was used to analyse the between group characteristics at baseline and a $p \leq 0.05.$ was assumed to be significant. The postintervention data were clubbed together and multiple linear regression analyses were performed to find the influence of independent variables (BMI, DBP, and SBP) on the dependent variable (CRP). Assumptions for normality, multicollinearity, and homoscedasticity, when assessed, showed no major deviations. The strength of relationship between the dependent and independent variables was assessed using the beta (β) standardized coefficient. To investigate the mediation effect of DBP and SBP on CRP a simple mediation analysis was performed using PROCESS. ## RESULTS A total of 148 participants were assessed for eligibility, of which 21 did not fulfill inclusion criteria and were excluded and 6 participants refused to participate during the initial screening. Randomization into groups was carried out for 121 participants, of which 15 dropped out during the course of the intervention. The final data was recorded from 106 participants of which 53 were from the forward walking group and 53 from the retro walking group. Figure 1 presents a CONSORT flow chart. **Figure 1:** *CONSORT flowchart.* The mean age of participants was 21.32 ± 1.73 with a mean BMI of 32.70 ± 4.88. The baseline characteristics of the subjects are presented in Table 1. **Table 1** | Variable | Forward Walking (n = 53) | Retro Walking (n = 53) | p (95% CI) | | --- | --- | --- | --- | | Age | 21.32 ± 1.73 | 21.47 ± 1.54 | 0.63 (−0.47 to 0.78) | | BMI | 32.70 ± 4.88 | 32.12 ± 4.37 | 0.52 (−2.37 to 1.21) | | Waist height ratio | 0.58 ± 0.07 | 0.56 ± 0.06 | 0.06 (−0.05 to 0.0001) | | Waist hip ratio | 0.86 ± 0.07 | 0.83 ± 0.06 | 0.09 (−0.05 to 0.0003) | | DBP | 84.19 ± 3.26 | 83.11 ± 2.48 | 0.06 (−0.38 to 2.19) | | SBP | 129.37 ± 13.92 | 130.90 ± 4.82 | 0.44 (−2.45 to 5.58) | | CRP | 4.29 ± 3.33 | 3.46 ± 2.48 | 0.15 (−1.96 to 0.30) | ## Group and time interactions Both groups showed significant improvements in all the outcomes during the 12‐week intervention. The mixed model ANOVA results for (Group × Time) interaction was significant for BMI ($p \leq 0.001$, F 48.75), DBP ($p \leq 0.001$, F 26.25) SBP ($p \leq 0.001$, F 13.52), and CRP ($p \leq 0.001$, F 26.34) (Table 2). **Table 2** | Unnamed: 0 | Pretreatment | Pretreatment.1 | Posttreatment | Posttreatment.1 | p | p.1 | | --- | --- | --- | --- | --- | --- | --- | | Outcome | Forward walking | Retro walking | Forward walking | Retro walking | Time | Time × group | | BMI | 32.70 ± 4.88 | 32.12 ± 4.37 | 29.76 ± 4.70 | 27.23 ± 3.52 | <0.0001 | <0.0001 | | DBP | 84.19 ± 3.26 | 83.11 ± 2.48 | 82.45 ± 3.12 | 78.98 ± 1.55 | <0.0001 | <0.0001 | | SBP | 129.37 ± 13.92 | 130.90 ± 4.82 | 128.71 ± 5.32 | 123.40 ± 3.90 | <0.0001 | <0.0001 | | CRP | 4.29 ± 3.33 | 3.47 ± 2.48 | 3.67 ± 2.96 | 1.47 ± 1.02 | <0.0001 | <0.0001 | ## Multivariate relationships Table 3 illustrates the findings of multiple regression analysis: BMI (β: 0.432, $p \leq 0.0001$, $95\%$ CI: 0.156–0.337) and DBP (β: 0.317, $p \leq 0.0001$, $95\%$ CI: 0.078–0.441) were the significant predictors of CRP. **Table 3** | Unnamed: 0 | Unstandardized coefficient | Unstandardized coefficient.1 | Standardized coefficient | Unnamed: 4 | 95% CI | 95% CI.1 | Model summary | Model summary.1 | | --- | --- | --- | --- | --- | --- | --- | --- | --- | | | B | SE | β | p Value | Lower bound | Upper bound | R 2 | p Value | | Constant | −31.899 | 4.712 | | <0.0001 | −41.245 | −22.554 | 0.538 | <0.0001 | | BMI | 0.246 | 0.047 | 0.432 | <0.0001 | 0.156 | 0.337 | | | | DBP | 0.260 | 0.092 | 0.317 | 0.005 | 0.078 | 0.441 | | | | SBP | 0.051 | 0.053 | 0.112 | 0.338 | −0.054 | 0.157 | | | ## Mediation effect The results of mediation effect of DBP on BMI and CRP have been descripted in Figure 1. The total effect of BMI on CRP was (0.366, $p \leq 0.0001$, $95\%$ CI: 0.281– 0.451) with a direct effect of 0.259, $p \leq 0.0001$, $95\%$ CI: 0.173–0.346 and an indirect effect of 0.107, $p \leq 0.0001$, $95\%$ CI; 0.060–0.162 (Figure 2). **Figure 2:** *Mediating role of diastolic blood pressure in the relationship between body mass index and C reactive protein.* ## DISCUSSION The primary aim of our study was to compare the effects of retro walking and forward walking training on blood CRP level and BMI of untrained young men. The results showed that both parameters decreased significantly in both groups following treatment. In addition, systolic and diastolic BP were analyzed, which were both seen to be reduced after normal walking and retro walking. The effect of retro walking in reducing the level of CRP over the period of intervention, BMI, DBP, and SBP was greater than that of forward walking. The variables SBP, DBP, and BMI were seen to influence CRP levels. At a similar level of intensity, backward walking places higher demands on metabolic sensorimotor, cardiovascular, and perceptual responses than forward walking does. Also, backward walking poses a considerable challenge to standing dynamic balance, thus recruiting more neurons in the process. 18, 23 Hyun‐Gyu and co‐workers found that backward walking stimulated the lower limb muscles and resulted in higher energy consumption in the lower limbs. They also stated that backward walking stimulates the quadriceps muscles and other muscles which subsequently move the knee joint in a considerably more balanced manner as compared with forward walking. 24 Owing to the increased challenge to the different systems of the body, retro walking increases energy expenditure relative to forward walking. This increased energy expenditure causes a decrease in the level of adiposity in the body, thus leading to decreased body weight and consequently decreased BMI, as proven in the results of the present study. Similarly, several researchers have found that retro walking produces more energy expenditure than forward walking at similar speeds. 18, 23, 25 Arikawa et al. exclaimed that a 16‐week aerobic exercise regime in obese females significantly reduced levels of CRP. 22 The CRP is an inflammatory marker and it is well‐documented that obese individuals express a chronic inflammatory status and that accounts for elevated CRP levels in this category. Persons who suffer from obesity or hyperinsulinemia tend to produce a higher amount of CRP from adipocytes along with other inflammatory markers. 26 CRP levels have been seen to be higher in overweight and obese persons when compared with those who were not. 27, 28 In the present study as well as in some previous studies BMI also has been identified to be a strong predictor of elevated CRP levels. 29, 30 Exercise and physical activity has been known to reduce levels of CRP by increasing levels of adiponectin‐ a relatively novel anti‐inflammatory adipocytokine known to improve insulin sensitivity. Leptin is yet another polypeptide, which is closely associated with CRP levels and is decreased with physical activity and exercise. Exercise in general, can decrease levels of adipose tissue and leptin levels and increase adiponectin levels, ultimately leading to decreased CRP levels. 26, 31 Physical exercise has been seen to have an influence on the immune system in that it reduces the number of mononuclear cells in blood, which in turn produce proinflammatory cytokines like (IL1, IL‐6, IL‐8, and CRP). 32 Moderate exercise done regularly can decrease CRP and IL‐6 levels in obese persons. 33 Exercise has an anti‐inflammatory effect which can reduce systemic inflammation and CRP levels. In agreement to this, the present study also saw a decrease in CRP levels following both modes of walking. As an exercise mode, which places more demand on the cardiovascular and metabolic system than regular walking, it could be expected that retro walking would have a similar effect, but of more magnitude, on CRP levels. The present study is the first in our knowledge to evaluate the effect of a backward walking program on an inflammatory marker and cardiovascular risk factor such as the CRP in young obese and preobese individuals. Terblanche et al. have found that a backward walking program can increase levels of cardiovascular fitness and produce changes in body composition. 34 Similarly, a meta‐analysis demonstrated that physical training can be correlated to reduced CRP levels regardless of age or gender, and that greater improvements in CRP levels could be seen additionally when the BMI is reduced. 35 In contrast, Mouridsen et al. noted that there was a spike in high‐sensitivity CRP as an immediate response to exercise, however, the increase was moderate and not independently associated with coronary artery disease. 36 The multiple linear regression analysis revealed that BMI and DBP were the significant predictors of CRP. Studies have demonstrated CRP to be associated with obesity, increased waist circumference and systolic BP; these parameters can be used for identification and intervention in children and adolescents with high risk of atherosclerosis. 9 An expanding body of evidence indicates that inflammation has a major role to play in the development of high BP; elevated levels of CRP have been shown to be associated with the incidence of hypertension in middle‐aged adults. 37, 38 In the present study, most of those who had stage 1 hypertension were in the high‐risk CRP category (CRP > 3 mg/L), and conversely, none of those with high‐risk CRP were with normal BP. ( Figure 3) This would demonstrate the importance of CRP as a predictor of cardiovascular risk factors, including hypertension and ischemic heart disease. 39 The study of factors, which can modify CRP levels in the body would play an important role in prevention and management of cardiovascular risk factors. **Figure 3:** *Pre‐ and post‐exercise high risk (CRP > 3 mg/L) CRP values. BMI, body mass index; CRP, C reactive protein.* Walking is an activity, which is said to be a complex interaction, which envelopes sensory, physiological and mechanical input to produce results that are optimum. 40 An important advantage of retro walking over forward walking is that it places less stress on the joints of the lower limb, which is an important factor to be considered during exercise in obese persons. Also, more energy can be expended in a shorter period of time, which makes the exercise more time efficient. Walking backwards was a novel task for most of the participants, and during the initial stages of the training, many expressed apprehension to walk backwards, especially on the treadmill. In the between‐groups comparison, there was a significant difference in the outcomes between the forward‐walking and retro‐walking groups. The present study adds to the existing body of literature to provide evidence about the benefits of retro walking by documenting a reduction in CRP, which is not only an important inflammatory marker, but also a cardiovascular risk factor. Retro walking also places less stress on the weight‐bearing joints of the lower limb, and thus reduces the probability of injury during exercise in people who are overweight or obese. It can be surmised that retro walking is a viable replacement to forward walking in reducing cardiovascular risk factors. The strengths of the present study were the relatively high sample size and the documentation of CRP levels, which is a less used outcome measure in study of effect of retro walking, as an outcome measure for the treatment program in obese and pre‐obese young adults. The limitations include lack of a control group, which would monitor any fluctuations in CRP levels in the absence of activity, lack of monitoring the diet of participants, which could have had a major effect on CRP levels and lack of an objective measure such as oxygen consumption or VO2 for measuring the intensity of exercise. Also, the noninclusion of women in the study sample might result in lack of generalizability of the study results across genders. This study did not address the long‐term effects of the exercise, hence we could not prove if the exercise would have a lasting effect on the CRP level after the intervention period of 12 weeks. ## CONCLUSIONS The present study demonstrates that both retro walking and forward walking can help alleviate CRP level and obesity. However, the retro walking program has added benefits over a forward walking program of similar intensity in modifying these outcomes. BMI and DBP have the potential to influence CRP levels in the blood. These factors can be considered while designing an exercise program to modify cardiovascular risk factors in young individuals. Considering the advantages and practicability of use, retro walking can be a valuable addition to any exercise program which aims at addressing obesity and cardiovascular risk factors. ## AUTHOR CONTRIBUTIONS Ajith Soman: Conceptualization; data curation; formal analysis; funding acquisition; investigation; methodology; project administration; resources; software; supervision; validation; writing—original draft; writing—review and editing. Sunil Chandy: Conceptualization; formal analysis; funding acquisition; investigation; methodology; project administration; resources; validation; writing—review and editing. Khalid Alkhathami: Conceptualization; funding acquisition; investigation; methodology; resources; validation; writing—original draft; writing—review and editing. Baranitharan Ramamoorthy: Data curation; software; supervision; writing—review and editing. Bijad Alqahtani: Data curation; funding acquisition; methodology; resources; writing—original draft; writing—review and editing. ## CONFLICT OF INTEREST STATEMENT The authors declare no conflict of interest. ## ETHICAL STATEMENT The study was conducted according to the guidelines of the Declaration of Helsinki, approval to conduct the study was obtained from the ethics research committee of the university (ERC_SU_20210057). ## TRANSPARENCY STATEMENT The lead author Ajith Soman affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained. ## DATA AVAILABILITY STATEMENT The data that support the findings of this study are available from the corresponding author upon reasonable request. ## References 1. Jain A. **Treating obesity in individuals and populations**. *BMJ* (2005) **331** 1387-1390. PMID: 16339251 2. Rojano‐Rodríguez ME, Valenzuela‐Salazar C, Cárdenas‐Lailson LE, Romero Loera LS, Torres‐Olalde M, Moreno‐Portillo M. **Nivel de proteína C reactiva en pacientes con obesidad mórbida antes y después de cirugía bariátrica**. *Rev Gastroenterol Mex* (2014) **79** 90-95. PMID: 24878218 3. Teixeira BC, Lopes AL, Macedo RCO. **Inflammatory markers, endothelial function and cardiovascular risk**. *Jornal Vascular Brasileiro* (2014) **13** 108-115 4. Sproston NR, Ashworth JJ. **Role of C‐Reactive protein at sites of inflammation and infection**. *Front Immunol* (2018) **9** 754. PMID: 29706967 5. Nehring SM, Goyal A, Bansal P. **C reactive protein**. *StatPearls* (2021) **65** 237-244 6. Venugopal SK, Devaraj S, Jialal I. **Macrophage conditioned medium induces the expression of C‐Reactive protein in human aortic endothelial cells**. *Am J Pathol* (2005) **166** 1265-1271. PMID: 15793305 7. Pepys MB, Hirschfield GM. **C‐reactive protein: a critical update**. *J Clin Invest* (2003) **111** 1805-1812. PMID: 12813013 8. García‐Lorda P, Bulló M, Balanzà R, Salas‐Salvadó J. **C‐reactive protein, adiposity and cardiovascular risk factors in a Mediterranean population**. *Int J Obes* (2006) **30** 468-474 9. Noronha JAF, Medeiros CCM, Cardoso AS, Gonzaga NC, Ramos AT, Ramos ALC. **C‐reactive protein and its relation to high blood pressure in overweight or obese children and adolescents**. *Rev Paul Pediatr* (2013) **31** 331-337. PMID: 24142315 10. Lopez‐Jaramillo P, Herrera E, Garcia RG, Camacho PA, Castillo VR. **Inter‐relationships between body mass index, C‐reactive protein and blood pressure in a Hispanic pediatric population**. *Am J Hypertens* (2008) **21** 527-532. PMID: 18437144 11. Melam GR, Alhusaini AA, Buragadda S, Kaur T, Khan IA. **Impact of brisk walking and aerobics in overweight women**. *J Phys Ther Sci* (2016) **28** 293-297. PMID: 26957777 12. Fernández Menéndez A, Saubade M, Hans D, Millet GP, Malatesta D. **The determinants of the preferred walking speed in individuals with obesity**. *Obes Facts* (2019) **12** 543-553. PMID: 31505515 13. Kachanathu S, Alabdulwahab S, Negi N, Anand P, Hafeez A. **An analysis of physical performance between backward and forward walking training in young healthy individuals**. *Saudi J Sports Med* (2016) **16** 68 14. Myatt G, Baxter R, Dougherty R. **The cardiopulmonary cost of backward walking at selected speeds**. *J Orthop Sports Phys Ther* (1995) **21** 132-138. PMID: 7742838 15. Terblanche E, Cloete WA, du Plessis PAL, Sadie JN, Strauss A, Unger M. **The metabolic transition speed between backward walking and running**. *Eur J Appl Physiol* (2003) **90** 520-525. PMID: 12898265 16. Ordway JD, Laubach LL, Vanderburgh PM, Jackson KJ. **The effects of backwards running training on forward running economy in trained males**. *J Strength Cond Res* (2016) **30** 763-767. PMID: 26332781 17. Ferguson B. **ACSM's guidelines for exercise testing and prescription 9th ed. 2014**. *J Can Chiropr Assoc* (2014) **58** 328 18. Thomas KS, Hammond M, Magal M. **Graded forward and backward walking at a matched intensity on cardiorespiratory responses and postural control**. *Gait Posture* (2018) **65** 20-25. PMID: 30558931 19. Kachanathu SJ, Al‐Kheraif AA, Alenazi AM, Hafez AR, Nuhmani S, Algarni AD. **Forward and retro locomotion training on anthropometrical body compositions and aerobic performance**. *Physikalische Medizin Rehabilitationsmedizin Kurortmedizin* (2020) **30** 377-381. DOI: 10.1055/A-1210-2847 20. Cheng X, Pu X, Jun P. **Rapid and quantitative detection of C‐reactive protein using quantum dots and immunochromatographic test strips**. *Int J Nanomedicine* (2014) **9** 5619. PMID: 25506215 21. Nuttall FQ. **Body mass index: obesity, BMI, and health: a critical review**. *Nutr Today* (2015) **50** 117-128. PMID: 27340299 22. Arikawa AY, Thomas W, Schmitz KH, KURZER MS. **Sixteen weeks of exercise reduces c‐reactive protein levels in young women**. *Med Sci Sports Exerc* (2011) **43** 1002-1009. PMID: 21085036 23. Adesola A, Azeez O. **Comparison of cardio‐pulmonary responses to forward and backward walking and running**. *Afr J Biomed Res* (2013) **12** 95-100 24. Cha HG, Kim TH, Kim MK. **Therapeutic efficacy of walking backward and forward on a slope in normal adults**. *J Phys Ther Sci* (2016) **28** 1901-1903. PMID: 27390443 25. Hooper TL, Dunn DM, Props JE, Bruce BA, Sawyer SF, Daniel JA. **The effects of graded forward and backward walking on heart rate and oxygen consumption**. *J Orthop Sports Phys Ther* (2004) **34** 65-71. PMID: 15029939 26. Kasapis C, Thompson PD. **The effects of physical activity on serum C‐Reactive protein and inflammatory markers**. *JACC* (2005) **45** 1563-1569. PMID: 15893167 27. Lavanya K, Ramamoorthi K, Acharya RV. **Association between overweight, obesity in relation to serum Hs‐CRP levels in adults 20‐70 years**. *J Clin Diagn Res* (2017) **11** OC32-OC35. PMID: 28384913 28. Mirhoseini M, Daemi H, Masoom Babaiee M, Asadi‐Samani M, Mirhoseini L, Sedehi M. **Serum concentration of hs‐CRP in obese individuals with and without metabolic syndrome and its association with parameters of metabolic syndrome**. *J Renal Inj Prev* (2018) **7** 297-300 29. Pavela G, Kim Y, Salvy SJ. **Additive effects of obesity and loneliness on C‐reactive protein**. *PLoS One* (2018) **13**. DOI: 10.1371/JOURNAL.PONE.0206092 30. Timpson NJ, Nordestgaard BG, Harbord RM. **C‐reactive protein levels and body mass index: elucidating direction of causation through reciprocal mendelian randomization**. *Int J Obes* (2011) **35** 300-308 31. Baldari C, Rodríguez‐Rosell D, Marinho DA. **Inflammatory effects of high and moderate intensity Exercise‐A systematic review**. *Front Physiol* (2020) **1** 1550 32. Meneses‐Echávez JF, Correa‐Bautista JE, González‐Jiménez E. **The effect of exercise training on mediators of inflammation in breast cancer survivors: A systematic review with meta‐analysis**. *Cancer Epidemiol Biomarkers Prevent* (2016) **25** 1009-1017 33. Vella CA, Taylor K, Drummer D. **High‐intensity interval and moderate‐intensity continuous training elicit similar enjoyment and adherence levels in overweight and obese adults**. *Eur J Sport Sci* (2017) **17** 1203-1211. PMID: 28792851 34. Terblanche E, Page C, Kroff J, Venter RE. **The effect of backward locomotion training on the body composition and cardiorespiratory fitness of young women**. *Int J Sports Med* (2005) **26** 214-219. PMID: 15776337 35. Fedewa MV, Hathaway ED, Ward‐Ritacco CL. **Effect of exercise training on C reactive protein: a systematic review and meta‐analysis of randomised and non‐randomised controlled trials**. *Br J Sports Med* (2017) **51** 670-676. PMID: 27445361 36. Mouridsen MR, Nielsen OW, Carlsen CM. **High‐sensitivity C‐reactive protein and exercise‐induced changes in subjects suspected of coronary artery disease**. *J Inflamm Res* (2014) **7** 45-55. PMID: 24715762 37. Smith GD, Lawlor DA, Harbord R. **Association of C‐reactive protein with blood pressure and hypertension: life course confounding and mendelian randomization tests of causality**. *Arterioscler Thromb Vasc Biol* (2005) **25** 1051-1056. PMID: 15731495 38. Hage FG. (2014). DOI: 10.1038/jhh.2013.111 39. Soinio M, Marniemi J, Laakso M, Lehto S, Rönnemaa T. **High‐Sensitivity C‐Reactive protein and coronary heart disease mortality in patients with type 2 diabetes**. *Diabetes Care* (2006) **29** 329-333. PMID: 16443882 40. Viggiano D, Corona K, Cerciello S, Vasso M, Schiavone‐Panni A. **The kinematic control during the backward gait and knee proprioception: insights from lesions of the anterior cruciate ligament**. *J Hum Kinet* (2014) **41** 51-57. PMID: 25114731
--- title: The impact of dental metal restorations on the oral oxidative stress level authors: - Zlatina Tomova - Desislav Tomov - Angelina Vlahova journal: Journal of Clinical and Experimental Dentistry year: 2023 pmcid: PMC10062465 doi: 10.4317/jced.60175 license: CC BY 2.5 --- # The impact of dental metal restorations on the oral oxidative stress level ## Abstract ### Background Dental materials may influence the equilibrium between production and destruction of free radicals, thus creating conditions for developing of local or general oxidative stress. Metal ions, emitted from base dental alloys, may cause changes in cell structures and functions. Isoprostane concentration may indicate possible cell damage, caused by free radicals, and can be used for evaluation of the oxidative stress level. The aim of this study was to compare the level of 8-isoPGF2-alpha in saliva in patients with and without metal dental restorations. ### Material and Methods 35 patients were divided in two groups according to the presence or absence of metal dental objects. Non-stimulated and stimulated saliva samples were collected. The concentration of 8-isoPGF2-alpha was measured by liquid chromatography tandem mass spectrometry. For statistical analysis non-parametric Mann-Whitney test, Kruskal Wallis test, and Wilcoxon signed-rank test were applied. ### Results There was a significant difference in the concentration of 8-isoPGF2-alpha between the samples of non-stimulated and stimulated saliva. The concentration of 8-isoPGF2-alpha in non-stimulated saliva in patients with metal dental restorations was significantly higher than the one in the group of patients without metal objects. ### Conclusions The presence of metal dental restorations increases the concentration of 8-isoPGF2-alpha in non-stimulated saliva. Key words:Saliva, dental metal restorations, oxidative stress. ## Introduction Oral cavity is the place, in which different in nature foreign agents enter the body. These substances may influence the equilibrium between production and destruction of ROS (reactive oxygen species) and RNS (reactive nitrogen species), thus creating conditions for developing state of local or general oxidative stress. The connection between oxidative stress and pathological alterations in the body is bidirectional. Oxidative stress may be induced by some general diseases like diabetes, rheumatoid arthritis, chronic renal failure, Crohn’s disease etc, [1]. Some oral pathologies, like periodontal inflammatory diseases, may cause local oxidative stress, while others like leukoplakia, oral cancer, and recurrent aphthous stomatitis may increase the level of ROS in the blood plasma and the whole human body [2]. On the other side, oxidative stress contributes to various pathophysiological changes and is involved in multiple stages of carcinogenesis. In the oral cavity oxidative stress leads to progression of chronic inflammation, degradation of the extracellular matrix of the periodontium, and resorption of the bone [3]. There are numerous factors that may induce increased production of free radicals in the oral cavity [4]. Gingival and periodontal inflammatory diseases appear as a response to pathogenic oral microorganisms. The production of ROS in the inflammatory response plays a key role in the neutralization and elimination of bacteria. The increased amount of ROS, however, affects not only the pathological agents but also the tissues of the host [5]. High-fat and high-protein diet and the way food is prepared may also be a source of ROS entering the body, although it is worth mentioning that antioxidants are taken with food. Containing many pro-oxidative and carcinogenic substances, cigarette smoke is one of the most powerful oxidative stress inducers in the oral cavity. Alcohol consumption also affects the redox balance in the oral cavity [4]. Dental materials, like resin composites, different types of cements, root canal fillings and dental alloys, may disturb the local balance between formation and neutralization of free radicals [6]. Laser treatment of the soft and hard dental tissues is an important source of free radicals in the oral cavity. Laser photodynamic therapy is based on the increased production of ROS and their potential bactericidal action [7]. Recent study shows that bisphosphonate therapy in treatment of osteoporosis causes oxidative stress in oral fibroblasts leading to possible osteonecrosis of the jaws [8]. Foreign to the body materials with different chemical, mechanical and biological properties are used for dental treatment. They must provide longevity, esthetics, and safe use. Base dental alloys (nickel-chromium and cobalt-chromium) are used for production of removable and fixed prosthetic restorations. They are preferred because of the excellent mechanical properties that can withstand the forces during the mastication process, and because of the comparably low price. With the development and popularization of CAD/CAM technologies the production of metal-free restorations increases. However, metal ceramic prosthetic devices are still more common probably because of financial reasons. The most important feature of the dental alloy for its biocompatibility is the tendency to corrosion. Metal ions, emitted due to corrosion process, come in direct contact with the surrounding soft tissues and may enter the body through the gastrointestinal tract. They may cause allergic reactions, systemic and local toxicity, cancerogenic alterations, changes in cell structures and functions. It is suggested that even if the concentration of metal ions has no direct cytotoxic effect, it may have influence at molecular level and may contribute to alteration in the immune system response in patients with implants made of cobalt-chromium alloy [9]. Nickel and cobalt ions may react with hydrogen peroxide and via the Fenton reaction may generate hydroxyl radicals. Chromium and cobalt ions may undergo redox cycling, thus directly producing free radicals [10]. Saliva is a complex fluid, and although it consists of more than $95\%$ water, it also contains many organic and inorganic components. The average flow rate of non-stimulated saliva is about 0.3 mL/min with great individual variability and circadian rhythm. There is a significant difference in the flow rate and the composition of stimulated and non-stimulated saliva. Saliva provides non-invasive, painless, cost-effective, and fast sample collection and may be used as an alternative testing medium of blood and urine. Although it is not widely utilized biological fluid for metabolite analysis, the number of studies using it increases. Methods for analysing levels of different substances – cortisol, melatonin, creatinine, SARS-CoV-2 antibodies, etc., are already developed [11]. Formation of 8-isoPGF2-alpha is a result of interaction of free radicals with lipids, present in cell membranes. Isoprostane concentration may indicate possible cell damage. There are two big advantages of using isoprostanes as oxidative stress marker – presence in all fluids in the body and low reactivity. Furthermore, their local concentration may be used for evaluation and observation of the specific area of the body [12]. The aim of this study was to compare the salivary level of 8-isoPGF2-alpha as marker of oxidative stress in patients with and without metal restorations in the oral cavity. Materials and methods: 35 participants were divided in two groups according to the presence or absence of present metal restorations in the oral cavity – 17 of the volunteers had no metal objects in the mouth and 18 of them had metal dental restorations. All the patients included in the study have signed informed consent. All procedures performed in the studies were in accordance with the standards of the Institutional Committee of Scientific Ethics of Medical University of Plovdiv, Bulgaria (Decision № С – 03-$\frac{2}{10.04.2020}$) and with the Association Declaration of Helsinki from 1964. The participants had to meet the following criteria: non-smokers at the age between 18 and 65, without acute or chronic diseases. None of the patients reported symptoms of gastric disorders and no signs of acidic erosion on the hard dental tissues were found. Saliva samples were gathered in the dental office by spitting in low density polyethylene containers in the interval between 9.00 A.M. and 12.00 A.M. without exposure to any visual, taste or aromatic stimuli. Patients were instructed not to take any food or drinks except water before the dental visit. Non-stimulated and stimulated saliva samples were taken from the patients before any dental procedures were conducted. After rinsing the mouth with distilled water, the patients spat the saliva gathered at the bottom of the oral cavity for 15-20 minutes. After gathering 15 ml of non-stimulated saliva, 5 ml were placed in a centrifugal tube for detection of isoprostane 8-isoPGF2-alpha. Stimulated saliva samples were taken after placing $2\%$ citric acid over the tongue (100 µL each 30 seconds) for 5 minutes. The samples were immediately frozen at -20oC and later transferred to the Research Institute at Medical University of Plovdiv for storage at -70oC. For evaluation of oxidative stress level in the oral cavity concentration of 8-isoPGF2-alpha was measured by liquid chromatography tandem mass spectrometry (LC-MS/MS). An LC-MS/MS method was developed and validated for detection of 8-isoPGF2-alpha in saliva [13]. For statistical processing SPSS statistical package, version 19.0 was used. Non-parametric Mann-Whitney test, Kruskal Wallis Test, and Wilcoxon signed-rank test were applied. Level of significance was $p \leq 0.05.$ ## Results The descriptive analysis of concentration of 8-isoPGF2-alpha in the studied groups is given at Table 1. Table 1Concentration of 8-isoPGF2-alpha in non-stimulated (NS) and stimulated (SS) saliva in patients with and without metal dental restorations (ng/L). Results showed that there was a signiFigant difference in the concentration of 8-isoPGF2-alpha in the samples of non-stimulated and stimulated saliva – for the group withiout metal object $$p \leq 0.04$$ and for the group with metal restorations – $$p \leq 0.001.$$ Isoprostane concentration was higher in non-stimulated saliva. The concentration of 8-isoPGF2-alpha in non-stimulated saliva in patients with metal dental restorations was significantly higher than the one in the group of patients without metal objects in the oral cavity – $$p \leq 0.009$$ (Fig. 1). Comparing the stimulated saliva samples no significant differences were found in patients with and without metal restorations – $$p \leq 0.294$$ (Fig. 2). Figure 1Concentration of 8-isoPGF2-alpha in non-stimulated saliva (NS) in the groups with (YES) and without (NO) metal objects. Figure 2Concentration of 8-isoPGF2-alpha in stimulated saliva (SS) in the groups with (YES) and without (NO) metal objects. ## Discussion There are studies confirming significant daily variations in the concentration of some salivary oxidative stress markers [14]. This was the reason all saliva samples for our research were gathered in the interval between 9.00 AM and 12.00 AM. The results of our study suggest that the presence of metal restorations significantly increases the risk of developing oxidative stress in the oral cavity. According to the data gathered from the patients, all the metal prosthetic restorations had been in the mouth for more than 1 year, which means that the passivation process of the metal surfaces must have limited the corrosion process and the metal ion emission [15]. Despite that fact, the results lead us to the conclusion that even after passivation the metal objects still influence the oxidative stress level in the oral cavity, i.e., the area which is in direct contact with the metal parts. The results of our study correspond to the findings of Kovač et al., 2020, according to which CoCr alloys may induce increase in oxidative stress level [16]. Our study did not confirm the conclusions of McGinley et al., 2013, which found that dental CoCr alloys did not elicit adverse oxidative stress [17]. It may be suggested that not only the presence, but also the composition of the alloy define the effect of the metal restoration placed in the mouth. Non-stimulated saliva is a liquid that contains not only the saliva secreted from the salivary acini, but also products from the gingival fluid and from the biofilm covering all the surfaces in the oral cavity. Although stimulated saliva cannot be considered as analogue of blood plasma, it is clear from local affecting factors and may be used for analysis of the redox status of distant from the mouth areas. Zugla et al., 2019, found a correlation between levels of oxidative stress markers in saliva and plasma and concluded that saliva may be used as a medium for assessing the level of oxidative stress in the human body [18]. From the insignificant difference in the isoprostane concentration in stimulated saliva samples between the groups with and without metal restorations it may be suggested that the presence of metal restorations did not affect distant areas of the body, which were not in a direct contact with them. The limitation of this study was the small size of patients in the studied groups. The types of the alloys used for production of the metal prosthetic restorations of the patients could not be defined. Further investigations are needed to confirm the results. ## Conclusions Within the limitations of this study, it can be concluded that the presence of metal dental restorations increases the concentration of 8-isoPGF2-alpha in non-stimulated saliva and hides a risk of developing of oxidative stress in the oral cavity. ## References 1. Ighodaro OM. **Molecular pathways associated with oxidative stress in diabetes mellitus**. *Biomed Pharmacother* (2018) **108** 656-62. PMID: 30245465 2. Dursun E, Akalin FA, Genc T, Cinar N, Erel O, Yildiz BO. **Oxidative Stress and Periodontal Disease in Obesity**. *Medicine (Baltimore)* (2016) **95** e3136. PMID: 27015191 3. Tóthová L, Celec P. **Oxidative Stress and Antioxidants in the Diagnosis and Therapy of Periodontitis**. *Front Physiol* (2017) **8** 1055. PMID: 29311982 4. Avezov K, Reznick AZ, Aizenbud D. **Oxidative stress in the oral cavity: sources and pathological outcomes**. *Respir Physiol Neurobiol* (2015) **209** 91-4. PMID: 25461624 5. Sczepanik FSC, Grossi ML, Casati M, Goldberg M, Glogauer M, Fine N. **Periodontitis is an inflammatory disease of oxidative stress: We should treat it that way**. *Periodontol 2000* (2020) **84** 45-68. PMID: 32844417 6. Zieniewska I, Maciejczyk M, Zalewska A. **The Effect of Selected Dental Materials Used in Conservative Dentistry, Endodontics, Surgery, and Orthodontics as Well as during the Periodontal Treatment on the Redox Balance in the Oral Cavity**. *Int J Mol Sci* (2020) **21** 9684. PMID: 33353105 7. Kushibiki T, Hirasawa T, Okawa S, Ishihara M. **Blue laser irradiation generates intracellular reactive oxygen species in various types of cells**. *Photomed Laser Surg* (2013) **31** 95-104. PMID: 23390956 8. Taniguchi N, Osaki M, Onuma K, Ishikawa M, Ryoke K, Kodani I. **Bisphosphonate-induced reactive oxygen species inhibit proliferation and migration of oral fibroblasts: A pathogenesis of bisphosphonate-related osteonecrosis of the jaw**. *J Periodontol* (2020) **91** 947-55. PMID: 31863459 9. Akbar M, Brewer JM, Grant MH. **Effect of chromium and cobalt ions on primary human lymphocytes in vitro**. *J Immunotoxicol* (2011) **8** 140-9. PMID: 21446789 10. Jomova K, Valko M. **Advances in metal-induced oxidative stress and human disease**. *Toxicology* (2011) **283** 65-87. PMID: 21414382 11. MacMullan MA, Ibrayeva A, Trettner K, Deming L, Das S, Tran F. **ELISA detection of SARS-CoV-2 antibodies in saliva**. *Sci Rep* (2020) **10** 1-8. PMID: 31913322 12. Milne GL, Dai Q, Roberts LJ. **The isoprostanes - 25 years later**. *Biochim Biophys Acta - Mol Cell Biol Lipids* (2015) **1851** 433-45. PMID: 25449649 13. Tomova Z, Tomov D, Vlahova A, Chaova-Gizdakova V, Yoanidu L, Svinarov D. **Development and validation of an LC-MS/MS method for determination of 8-iso-prostaglandin f2 Alpha in human saliva**. *J Med Biochem* (2022) **41** 466-73. PMID: 36381076 14. Kamodyová N, Tóthová L, Celec P. **Salivary markers of oxidative stress and antioxidant status: influence of external factors**. *Dis Markers* (2013) **34** 313-21. PMID: 23478271 15. Tuna SH, Pekmez NÖ, Keyf F, Canli F. **The influence of the pure metal components of four different casting alloys on the electrochemical properties of the alloys**. *Dent Mater* (2009) **25** 1096-103. PMID: 19380161 16. Kovač V, Poljšak B, Primožič J, Jamnik P. **Are Metal Ions That Make up Orthodontic Alloys Cytotoxic, and Do They Induce Oxidative Stress in a Yeast Cell Model?**. *Int J Mol Sci* (2020) **21** 7993. PMID: 33121155 17. McGinley EL, Moran GP, Fleming GJP. **Biocompatibility effects of indirect exposure of base-metal dental casting alloys to a human-derived three-dimensional oral mucosal model**. *J Dent* (2013) **41** 1091-100. PMID: 23954576 18. Zygula A, Kosinski P, Wroczynski P, Makarewicz-Wujec M, Pietrzak B, Wielgos M. **Oxidative Stress Markers Differ in Two Placental Dysfunction Pathologies: Pregnancy-Induced Hypertension and Intrauterine Growth Restriction**. *Oxid Med Cell Longev* (2020) **2020** 1323891. PMID: 32685085
--- title: Single-Atom Ce-N4-C-(OH)2 Nanozyme-Catalyzed Cascade Reaction to Alleviate Hyperglycemia authors: - Guangchun Song - Jia Xu - Hong Zhong - Qi Zhang - Xin Wang - Yitong Lin - Scott P. Beckman - Yunbo Luo - Xiaoyun He - Jin-Cheng Li - Kunlun Huang - Nan Cheng journal: Research year: 2023 pmcid: PMC10062498 doi: 10.34133/research.0095 license: CC BY 4.0 --- # Single-Atom Ce-N4-C-(OH)2 Nanozyme-Catalyzed Cascade Reaction to Alleviate Hyperglycemia ## Abstract The enzyme-mimicking catalytic activity of single-atom nanozymes has been widely used in tumor treatment. However, research on alleviating metabolic diseases, such as hyperglycemia, has not been reported. Herein, we found that the single-atom Ce-N4-C-(OH)2 (SACe-N4-C-(OH)2) nanozyme promoted glucose absorption in lysosomes, resulting in increased reactive oxygen species production in HepG2 cells. Furthermore, the SACe-N4-C-(OH)2 nanozyme initiated a cascade reaction involving superoxide dismutase-, oxidase-, catalase-, and peroxidase-like activity to overcome the limitations associated with the substrate and produce •OH, thus improving glucose intolerance and insulin resistance by increasing the phosphorylation of protein kinase B and glycogen synthase kinase 3β, and the expression of glycogen synthase, promoting glycogen synthesis to improve glucose intolerance and insulin resistance in high-fat diet-induced hyperglycemic mice. Altogether, these results demonstrated that the novel nanozyme SACe-N4-C-(OH)2 alleviated the effects of hyperglycemia without evident toxicity, demonstrating its excellent clinical application potential. ## Introduction Single-atom nanozymes are nanoparticle catalysts with enzyme-mimicking properties [1–3] and have various advantages, including low cost, good stability, and high catalytic activity [4]. Many studies have shown that single-atom nanozymes are ideal nanozymes [5,6], owing to their geometric structures and maximum atomic utilization [7]. Single-atom nanozymes sites are distributed and have no obvious interaction [8,9], which greatly increases the atomic utilization rate and active center density [10,11]. Moreover, due to their similarities, single-atom nanozyme active sites possess the same catalytic characteristics as natural enzymes [12]. Several single-atom nanozymes have been applied in antisepsis [13], cancer [14], and tumor therapy [15–17] because they can generate reactive oxygen species (ROS) in the tumor environment by mimicking the activity of peroxidase (POD-like) and oxidase (OXD-like) [18,19]. However, an insufficient supply of substrate and H2O2 in vivo limits the application of single-atom nanozymes in other diseases, such as metabolic diseases. Metabolic diseases such as obesity and diabetes seriously threaten human health. Diabetes is a highly dangerous metabolic disease, characterized by high blood glucose levels [20,21]. At present, $95\%$ of patients with diabetes in China have type 2 diabetes, which is regarded as one of the largest global health crises facing the world [22,23]. Clinical hypoglycemic drugs mainly include metformin and sulfonylureas [24]. Although these drugs can effectively control the stability of blood glucose in the body, they may cause side effects such as liver damage, weight gain, hypoglycemia, pancreatic degeneration, and gastrointestinal discomfort [25]. Several studies have suggested an association between ROS and glucose metabolism [26–28]. In particular, a Fe3O4 nanozyme was reported to have potential efficacy in lowering blood glucose by exerting POD-like activity to locally produce •OH, activating adenosine 5'-monophosphate (AMP)-activated protein kinase (AMPK) to improve glucose tolerance and insulin sensitivity [22]. Therefore, this study selected a high-performance single-atom Ce-N4-C-(OH)2 (SACe-N4-C-(OH)2) nanozyme with tandem superoxide dismutase (SOD)-, OXD-, catalase (CAT)-, and POD-like activities in liver and muscle glucose-metabolizing tissues to overcome substrate limitations and self-sufficiently produce •OH, thus having a good therapeutic effect in alleviating hyperglycemia. ## The characterization of the SACe-N4-C-(OH)2 nanozyme Our previous studies [29,30] demonstrated that the SACe-N4-C-(OH)2 nanozyme had high oxygen reduction reaction (ORR) activity and good stability under both alkaline and acidic conditions. The SACe-N4-C-(OH)2 nanozyme is enriched with single Ce atoms coordinated by N doping and adsorbed hydroxyl species. In this study, we further characterized the structure and composition of the SACe-N4-C-(OH)2 nanozyme. Figure 1A shows the scanning electron microscopy image of the SACe-N4-C-(OH)2 nanozyme. A SACe-N4-C-(OH)2 nanozyme with a nanowire structure was observed. The energy-dispersive x-ray spectroscopy element mapping indicated the coexistence of C, N, O, and Ce and a uniform distribution in the SACe-N4-C-(OH)2 nanozyme (Fig. 1B). As shown in Fig. 1C, abundant isolated bright spots marked by red circles were observed, which could be attributed to single Ce atoms. The x-ray diffraction pattern of the SACe-N4-C-(OH)2 nanozyme showed 2 characteristic peaks of graphite at 25.2° and 42.5°, suggesting good crystallinity, which is the same as our previous study [29] (Fig. 1D). No crystalline Ce signal was observed, showing that Ce may be present in the SACe-N4-C-(OH)2 nanozyme as a single-atom species. Figure 1E shows x-ray absorption spectroscopy at the Ce LIII-edge of the SACe-N4-C-(OH)2 nanozymes and the reference sample of CeO2. The black line peak of the CeO2 reference is taller than that of the SACe-N4-C-(OH)2 nanozymes, demonstrating that the Ce oxidation state in the SACe-N4-C-(OH)2 nanozymes is lower than +4. A relatively weak peak at 3.46 Å was detected in the SACe-N4-C-(OH)2 nanozymes, which could have originated from a small quantity of nanosized Ce species [29] (Fig. 1F). **Fig. 1.:** *The characterization of the SACe-N4-C-(OH)2 nanozyme. (A) SEM. (B) The corresponding atomic EDS element mapping of the SACe-N4-C-(OH)2 nanozyme. (C) A high-magnification DF-STEM enlarged image of the SACe-N4-C-(OH)2 nanozyme. (D) The XRD of the SACe-N4-C-(OH)2 nanozyme. (E and F) The XAS of the SACe-N4-C-(OH)2 nanozymes. a.u., arbitrary units; SEM, scanning electron microscopy; EDS, energy-dispersive x-ray spectroscopy; DF-STEM density functional scanning transmission electron microscope; XAS, x-ray absorption spectroscopy; XRD, x-ray diffraction.* ## The catalytic mechanism of the SACe-N4-C-(OH)2 nanozyme The active center of the SACe-N4-C-(OH)2 nanozyme is shown in Fig. 2A. A unique single-atom active-site structure coordinated with N-doped, O-doped, and OH-doped carbon materials was observed. We detected ROS using an electron spin resonance instrument at different times and found that more ROS were detected at 5 min (Fig. 2B). **Fig. 2.:** *The enzyme-mimicking types and activity of the SACe-N4-C-(OH)2 nanozyme. (A) The active centers of the SACe-N4-C-(OH)2 nanozyme. (B) ESR. (C) The POD-like, SOD-like, CAT-like, and OXD-like catalytic mechanism of the SACe-N4-C-(OH)2 nanozyme. (D) The catalytic activity of the SACe-N4-C-(OH)2 nanozyme in vitro. (E to G) The SOD-like, CAT-like, and POD-like activity of the SACe-N4-C-(OH)2 nanozyme in the liver. (H) The free energy diagrams of 4-electron ORR for the Ce-N4-C-OH and Ce-N4-C-(OH)2 models. (I) The free energy diagrams of 2-electron ORR for the Ce-N4-C-OH and Ce-N4-C-(OH)2 models. (J) The free energy diagram of the H2O2 reduction process for the Ce-N4-C-OH model. ESR, electron spin resonance; POD-like, peroxidase-like; SOD-like, superoxide dismutase-like; OXD-like, oxidase-like; CAT-like, catalase-like; ORR, oxygen reduction reaction.* Based on the unique chemical structure of the SACe-N4-C-(OH)2 nanozyme, we found that it had a variety of enzyme activities, which was more obvious than other M-N-C nanozymes. More specifically, we speculated that the SACe-N4-C-(OH)2 nanozyme catalyzed SOD-, OXD-, CAT-, and POD-like cascade reactions to produce •OH (Fig. 2C). The POD- and OXD-like activities were higher than the SOD- and CAT-like in vitro (Fig. 2D), indicating that the POD- and OXD-like activities played a vital role in the catalytic process. At the same time, SOD-, OXD-, CAT-, and POD-like activities in the livers of mice were detected by enzyme assay kits; the results are shown in Fig. 2E to G. Clearly, SACe-N4-C-(OH)2 nanozyme treatment did not significantly affect changes in SOD- and CAT-like activity in mouse livers but significantly enhanced POD- and OXD-like activity. These results proved that the SACe-N4-C-(OH)2 nanozyme can increase the content of ROS in vivo via POD- and OXD-like activities. Density functional theory calculations were used to determine the source of the multienzyme-like activity of the SACe-N4-C-(OH)2 nanozyme. Our previous study [29] demonstrated that Ce-N4-C shows significantly strong binding interactions with oxygen-containing intermediates, leading to •OH coverage on the Ce active site owing to a large energy barrier for the reduction of •OH to H2O. The strongly bonded •OH species on the Ce active site acted as modifying ligands [31,32], weakening the binding of the intermediates on the catalyst surface. Here, we prepared an •OH species-modified Ce-N4-C model (denoted as Ce-N4-C-OH, Fig. S1), and the adsorption configurations of intermediates (•OOH, •O, and •OH) on the Ce-N4-C-OH model are shown in Fig. S2. The free energy diagram of the 4-electron ORR for the Ce-N4-C-OH model shows that the overpotential-determining step is the reduction of •OH to H2O, with a free energy of −0.34 eV (Fig. 2H). The low limiting potential (0.34 V) of the ORR for the Ce-N4-C-OH model indicates that the binding interactions between the intermediates and the catalyst surface are still strong, which might result in the partial coverage of •OH on the active site of the Ce-N4-C-OH catalyst. Therefore, we constructed an •OH species-modified Ce-N4-C-OH model (denoted as Ce-N4-C-(OH)2, Fig. S1). The adsorption configurations of intermediates (•OOH, •O, and •OH) on the Co-N4-C-(OH)2 model are shown in Fig. S2. The overpotential-determining step changes from the reduction of •OH to H2O for the Ce-N4-C-OH model to the formation of •OOH, with a free energy of −0.57 eV, for the Ce-N4-C-(OH)2 model. Indeed, when the adsorption of intermediates on the catalyst surface is stable, the rate-determining step is the reduction of •OH (e.g., Ce-N4-C-OH, Fig. 2H). At the same time, when the adsorption is weak, the reduction of O2 to •OOH becomes the rate-determining step (e.g., Ce-N4-C-(OH)2, Fig. 2H) [33]. The weak adsorption of •OOH on the active site of the Ce-N4-C-(OH)2 catalyst makes the reduction of •OOH to •O difficult due to poor O-O activation, leading to the selective formation of H2O2 through the 2-electron ORR process. Moreover, previous studies demonstrated that the 4-electron ORR process dominated when the adsorption-free energy of •OH (ΔG•OH) was close to 0.86 eV, while the 2-electron ORR process led to the competition when ΔG•OH was near 1.02 eV [34]. Our calculated ΔG•OH for the Ce-N4-C-(OH)2 catalyst was 1.30 eV (Table S1), suggesting that the 2-electron ORR process dominated over the 4-electron ORR process on the Ce-N4-C-(OH)2 catalyst. Figure 2I shows the free-energy diagrams of the 2-electron ORR for the Ce-N4-C-OH and Ce-N4-C-(OH)2 models. The adsorption configurations of the intermediate (•OOH) for the 2-electron ORR on the Ce-N4-C-OH and Ce-N4-C-(OH)2 models are shown in Fig. S3. When the H2O2 molecule was adsorbed on the active site of the Ce-N4-C-OH catalyst, it was first cleaved into 2 •OH species that were also adsorbed on the active site. One of the adsorbed •OH molecules subsequently dissociated and desorbed from the catalyst surface, generating active •OH and •OH adsorbed on the active site. The adsorbed •OH reacts with the protonated hydrogen atom under acidic conditions forming •H2O species adsorbed on the active site [35]. The catalyst surface returns to its initial state after desorption of the adsorbed •H2O species [36–38]. The adsorption configurations of the intermediates (•H2O2, 2•OH, and •OH) for H2O2 reduction in the Ce-N4-C-OH model are shown in Fig. S4. Figure 2J shows the free energy diagram for H2O2 reduction on the Ce-N4-C-OH catalyst. These reaction steps were downhill in the free energy for H2O2 reduction, implying facile reactions on the Ce-N4-C-OH catalyst. Therefore, active •OH radicals can be generated during H2O2 reduction. These results indicate that the SACe-N4-C-(OH)2 nanozyme exhibited excellent catalytic performance. ## The SACe-N4-C-(OH)2 nanozyme localizes in lysosomes by generating •OH to promote glucose uptake in HepG2 cells Considering the excellent catalytic performance of the SACe-N4-C-(OH)2 nanozyme, we hypothesized that it may be a potential therapeutic agent for alleviating the effects of hyperglycemia. First, we explored the cellular uptake of the SACe-N4-C-(OH)2 nanozyme connected with GFP in HepG2 cells for 4, 12, and 24 h. The results are shown in Fig. 3A. The fluorescence intensity increased with the treatment extension, indicating that the SACe-N4-C-(OH)2 nanozyme could enter the cells without toxic effects (Fig. S5). Furthermore, the SACe-N4-C-(OH)2 nanozyme was mainly distributed in acidic lysosomes and exhibited by POD- and OXD-like activities to produce a large amount of •OH (Fig. 3B to E). •OH could not be observed after NAC treatment. At the same time, HepG2 cells treated with the SACe-N4-C-(OH)2 nanozyme after 12 and 24 h showed significantly enhanced •OH generation ability. The effect was more significant after 24 h of treatment than after 12 h of treatment (Fig. 3D and E). **Fig. 3.:** *The metabolic responses in HepG2 cells. (A) The intake of the SACe-N4-C-(OH)2 nanozyme in HepG2 cells at different times. (B) Cell localization (green: SACe-N4-C-(OH)2 nanozyme-green fluorescent protein, red: Lyso-Tracker Red). (C) The qualitative detection of ROS by an atomic fluorescence microscope. (D) The quantitative detection of ROS by a flow cytometer after 12 h. (E) The quantitative detection of ROS by a flow cytometer after 24 h. (F) The qualitative uptake of fluorescent glucose by an atomic fluorescence microscope. (G) The quantitative uptake of fluorescent glucose by a flow cytometer. (H) Flow cytometry spectroscopy. ROS, reactive oxygen species.* Next, we explored the effect of the SACe-N4-C-(OH)2 nanozyme treatment on glucose uptake in HepG2 cells. The results are shown in Fig. 3F. The SACe-N4-C-(OH)2 nanozyme significantly stimulated the uptake of fluorescent glucose analogs (2-NBDG) in HepG2 cells, and the effect was equivalent to that of metformin at the same concentration. Quantitative analysis by flow cytometry is shown in Fig. 3G and H. These data suggested that the SACe-N4-C-(OH)2 nanozyme promoted glucose uptake by producing ROS. Furthermore, the in vitro experiment demonstrated that it mainly distributed in acid lysosomes of HepG2 cells to produce a large amount of •OH through cascade catalytic reaction. Thus, we further explored the effect and molecular mechanism of the SACe-N4-C-(OH)2 nanozyme in improving glucose metabolism in hyperglycemic mice. ## The SACe-N4-C-(OH)2 nanozyme is mainly distributed in liver and muscle tissues to alleviate glucose tolerance and insulin resistance in hyperglycemic mice To evaluate the effect of the SACe-N4-C-(OH)2 nanozyme on systemic glucose homeostasis, we conducted an oral glucose tolerance test and an insulin tolerance test in hyperglycemic mice fed a high-fat diet (HFD) (Fig. S6). The glucose (Fig. 4A and B) and insulin (Fig. 4C and D) tolerance decreased in the dimethyl sulfoxide (DMSO)-HFD group, and the impairment was ameliorated in the SACe-N4-C-(OH)2-HFD group. To sum up, these results showed that the SACe-N4-C-(OH)2 nanozyme alleviated glucose tolerance and insulin resistance in hyperglycemic mice. We also found that the SACe-N4-C-(OH)2 nanozyme had no obvious effect on the change in body weight of HFD mice (Fig. S7), indicating that the SACe-N4-C-(OH)2 nanozyme is advantageous for improving glucose metabolism. Therefore, we further explored the distribution of the SACe-N4-C-(OH)2 nanozyme using in vivo imaging technology to elucidate its mechanism of action. **Fig. 4.:** *The metabolic responses and distribution in hyperglycemic mice. (A) Glucose tolerance test. (B) Area under the curve of (A) and (C): Insulin tolerance test. (D)  Area under the curve of (C) and (E): The fluorescence of active imaging at different times. (F) The fluorescence quantification of active imaging at a different time. (G) The fluorescence distribution of tissues in mice at 24, 48, and 72 h. (H) The fluorescence quantification of tissues in mice at 24, 48, and 72 h. (I) The biological distribution of Ce in mice.* The highest fluorescence level was detected 24 h after injection, which was consistent with that observed in HepG2 cells (Fig. 4E and F). We also extracted organs at 24, 48, and 72 h after injection and found that among all organs analyzed, the liver had the highest fluorescence level in each period, followed by that in the ileum and colon (Fig. 4H and I). In addition, we evaluated the accumulation of the SACe-N4-C-(OH)2 nanozyme in each organ tissue after long-term administration by inductively coupled plasma–mass spectrometry (ICP-MS) and found that the SACe-N4-C-(OH)2 nanozyme was distributed in the liver and muscle tissues (Fig. 4J). Based on these data, we inferred that the SACe-N4-C-(OH)2 nanozyme alleviated glucose tolerance and insulin resistance mainly by targeting liver and muscle tissues. ## The SACe-N4-C-(OH)2 nanozyme catalyzed a cascade reaction to produce •OH, increasing the expression of p-Akt, p-GSK3β, and GS The SACe-N4-C-(OH)2 nanozyme exhibited SOD-, OXD-, CAT-, and POD-like activities to overcome the limitations associated with the substrate and produce •OH, generating local activation of the phosphorylation of protein kinase B (p-Akt), further promoting the phosphorylation of glycogen synthase kinase 3β (p-GSK3β) and the expression of glycogen synthase (GS), thus stimulating glycogen synthesis and improving systemic glucose homeostasis (Fig. 5A). **Fig. 5.:** *The mechanism and toxicology of the SACe-N4-C-(OH)2 nanozyme. (A) The metabolic mechanism in vivo. (B) Western blot analysis in liver and muscle tissues. (C) Quantification of Western blot results in the liver. (D) Quantification of Western blot results in muscle. (E) The glycogen synthesis in the liver. (F) The glycogen synthesis in muscle. (G) H&E staining of tissue sections from SACe-N4-C-(OH)2 nanozyme- or DMSO-treated C57BL/6J mice. Scale bar: 200 μm. DMSO, dimethyl sulfoxide; H&E, hematoxylin and eosin.* Previous studies demonstrated that endogenous and exogenous ROS stimulates glucose uptake through a mechanism involving the activation of Akt and/or AMPK [28]. Our results showed that the SACe-N4-C-(OH)2 nanozyme produced •OH in HepG2 cells (Fig. 3C to E) and liver tissue (Fig. 2E to G). We speculated that this effect was caused by treatment of the SACe-N4-C-(OH)2 nanozyme, but the role of endogenous POD will need to be determined by further experiments. Therefore, we determined whether the SACe-N4-C-(OH)2 nanozyme stimulated glucose uptake by activating Akt and/or AMPK. First, we found that the SACe-N4-C-(OH)2 nanozyme did not affect AMPK protein expression in liver and muscle tissues (Figs. S7 and S8). Interestingly, the protein expression of p-Akt was stronger in the SACe-N4-C-(OH)2-HFD group than in the DMSO-HFD group (Fig. 5B). Next, we further explored the expression of the downstream protein AKT. The AKT downstream enzyme GSK3β inhibits glycogen synthesis, promotes gluconeogenesis, and hinders insulin signal transduction. Activated AKT can deactivate GSK3β by phosphorylation (phosphorylation at Ser9), increasing the activity of GS, inhibiting gluconeogenesis, and increasing glycogen production [23,39]. In liver and muscle tissues, the phosphorylation of GSK3β and the expression of GS were significantly increased after treatment with the SACe-N4-C-(OH)2 nanozyme (Fig. 5B to D). In addition, the SACe-N4-C-(OH)2 nanozyme significantly enhanced glycogen synthesis in the liver and muscle tissues, enhancing glucose uptake in the blood and lowering blood glucose levels (Fig. 5E and F). ## The toxicology of the SACe-N4-C-(OH)2 nanozyme in vivo According to the weight ratio results (Fig. S9), the SACe-N4-C-(OH)2 nanozyme did not affect weight changes in individual organ tissues. Hematoxylin and eosin (H&E) staining showed no obvious pathological damage in any important organ tissue (Fig. 5G and Fig. S10). Blood chemistry analysis showed no damage to liver biochemistry and function or kidney function (Fig. S11). These results indicate that the toxicity of the SACe-N4-C-(OH)2 nanozyme was low or undetectable in hyperglycemic mice. Together, these results suggest that SACe-N4-C-(OH)2 nanozymes have great potential to alleviate hyperglycemia. ## Discussion The excellent enzyme-like catalytic performance of the SACe-N4-C-(OH)2 nanozyme has been studied and applied in previous in vitro experiments [35]. Herein, for the first time, the SACe-N4-C-(OH)2 nanozyme was applied to alleviate hyperglycemia. Our study revealed that the SACe-N4-C-(OH)2 nanozyme was mainly distributed in lysosomes to generate •OH and enhance glucose uptake in HepG2 cells. This effect was similar to that of metformin. We also discovered that the SACe-N4-C-(OH)2 nanozyme catalyzed a cascade reaction with SOD-, OXD-, CAT-, and POD-like activities to produce •OH in HepG2 cells, demonstrating that the SACe-N4-C-(OH)2 nanozyme markedly activated the phosphorylation of AKT and promoted the expression of pGSK3β and GS. Furthermore, promoting glycogen synthesis increases glucose intake and lowers blood glucose levels, thus alleviating glucose tolerance and insulin resistance caused by hyperglycemia. However, these cascade reactions need to be proved from multiple enzyme activities of the SACe-N4-C-(OH)2 nanozyme by successively eliminating various enzyme activities in mice. Therefore, the specific experiments need to be designed and proved in the future. Overall, our study is the first to demonstrate the role of the SACe-N4-C-(OH)2 nanozyme in alleviating hyperglycemia and elucidate its mechanism, which may lead to future clinical trials using the SACe-N4-C-(OH)2 nanozyme to alleviate hyperglycemia. ## Chemical reagents and materials Dulbecco’s Modified Eagle Medium (DMEM) sugar-free was purchased from Beijing Solarbio Technology Co., Ltd. Metformin was purchased from Sigma-Aldrich (Shanghai) Trading Co., Ltd. The ROS detection kit (DCFH-DA), the nuclear staining kit (dihydrochloride, 4,6-diamino-2-phenyl indole), the lysosomal red fluorescent probe (Lyso-Tracker Red), the POD detection kit, the CAT detection kit, the SOD detection kit, the OXD detection kit, the cell viability detection kit, and the quantitative protein kit were purchased from Shanghai Biyuntian Biotechnology Co., Ltd. The reactive oxygen inhibitor was purchased from Aladdin Reagent Shanghai Co., Ltd. ## The preparation of the SACe-N4-C-(OH)2 nanozyme The SACe-N4-C-(OH)2 nanozyme was prepared based on our previous work [29]. The preparation of the SACe-N4-C-(OH)2 nanozyme solution was as follows: The obtained SACe-N4-C-(OH)2 nanozyme was dissolved in a $\frac{1}{1}$,000 DMSO solution, and ultrasonic treatment was performed in the cell crusher until it was evenly dispersed for later use. ## The validation of the activity of the SACe-N4-C-(OH)2 nanozyme Our previous study showed that the SACe-N4-C-(OH)2 nanozyme exhibited excellent POD-like activity [35]. In addition, the activities of CAT-, SOD-, and OXD-like proteins were verified using detection kits. The binding of the SACe-N4-C-(OH)2 nanozyme and green fluorescent protein was performed as follows: 1. System adjustment. We added 1.0 ml of the SACe-N4-C-(OH)2 nanozyme solution into a 1.5-ml centrifuge tube and adjusted pH to 8.2 to 8.5 with 0.02 M of K2CO3 solution. 2. Addition of green fluorescent protein. Then, 5 to 10 μl of green protein (1 mg/ml) was added to the solution (1.0 ml) and shaken at room temperature for 30 min on a multipurpose rotating shaker. 3. Bovine serum albumin (BSA) closure. We added 110 μl of $10\%$ BSA to the above solution, which was shaken at room temperature for 30 min on a multipurpose rotating shaker to seal the area not covered by antibodies on the surface of the particles. 4. Cleaning and purification. The solution was centrifuged at 11,160 × g for 20 min, and the supernatant was removed. Subsequently, $1\%$ BSA solution was added and mixed evenly, and the centrifugal cleaning operation was repeated 3 times. 5. Probe resolution. The precipitate was re-dissolved in 100 μl of complex solution ($2\%$ BSA+$3\%$ sucrose) to obtain a black SACe-N4-C-(OH)2 nanozyme green fluorescent protein (SACe-N4-C-(OH)2-GFP) complex. ## The experiment of mice The animal study was approved by the Animal Ethics Committee of China Agricultural University (approval number: KY 1700025). Animal experiments were performed in the Specific Pathogen-Free Animal Room of the Beijing Agricultural Products Quality Supervision, Inspection, and Testing Center of the Ministry of Agriculture. Six-week-old male C57BL/6J mice were purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd. After an adaptation period of 1 week, mice were divided into 2 groups fed a chow diet (Research Diet, D12450B) and HFD (Research Diet, D12492) for 20 weeks to establish a diet-induced hyperglycemia model [40]. Mice fed a chow diet were randomly divided into 2 groups: a low-fat diet DMSO group (DMSO-Chow) and a low-fat diet SACe-N4-C-(OH)2 nanozyme group (SACe-N4-C-(OH)2-Chow). According to the fasting glucose level, mice fed an HFD were randomly divided into 2 groups: the HFD DMSO group (DMSO-HFD) and the HFD SACe-N4-C-(OH)2 nanozyme group (SACe-N4-C-(OH)2-HFD). The DMSO-Chow and DMSO-HFD group mice were given $1\%$ DMSO by intraperitoneal injection, and the SACe-N4-C-(OH)2-Chow and SACe-N4-C-(OH)2-HFD group mice were administered 10 mg/kg SACe-N4-C-(OH)2 nanozyme solution (dissolved in $1\%$ DMSO) by intraperitoneal injection for 4 weeks. During treatment, the DMSO-Chow and SACe-N4-C-(OH)2-Chow groups were fed a chow diet, and each group contained 6 mice. The hyperglycemia model mice, such as the DMSO-HFD and SACe-N4-C-(OH)2-HFD groups, were fed an HFD, and each group contained 3 mice. ## The SACe-N4-C-(OH)2 nanozyme entered HepG2 cells over time For imaging observation by atomic fluorescence microscopy, HepG2 cells (4 × 105 cells/ml) were inoculated in 6-well petri dishes at 37 °C and $5\%$ CO2 for 24 h, and then treated with SACe-N4-C-(OH)2-GPF for 4, 12, and 24 h, respectively. ## Co-location of the SACe-N4-C-(OH)2 nanozyme HepG2 cells were inoculated in a confocal culture dish at 37 °C and $5\%$ CO2 for 24 h, then treated with the SACe-N4-C-(OH)2 nanozyme for 24 h, and washed 3 times with phosphate-buffered saline (PBS). Then, the Lyso-Tracker Red raw solution was dissolved in DMEM (volume ratio: 1:13.33). Lyso-Tracker Red solution (300 μl) was added to each petri dish to stain the cell membrane for 60 min and then washed 3 times with PBS. Next, cells were fixed with 300 μl of $4\%$ paraformaldehyde, treated at room temperature for 20 min, and washed 3 times with PBS. Finally, 500 μl of PBS was added for confocal imaging. ## The effect of the SACe-N4-C-(OH)2 nanozyme on ROS in HepG2 cells HepG2 cells (4 × 105 cells/ml) were inoculated in a 6-well petri dish and incubated for 24 h. The SACe-N4-C-(OH)2 nanozyme and SACe-N4-C-(OH)2 nanozyme + reactive oxygen inhibitor (NAC, 5 mmol/L) were dissolved in DMEM. After 24 h of treatment, the cells were treated with 20 μmol/L DCFH-DA for 45 min to detect intracellular ROS levels. Then, cells were washed 3 times with PBS. Imaging was performed using a TCS SP8 confocal microscope. ROS were quantified using flow cytometry. ## The effect of the SACe-N4-C-(OH)2 nanozyme on glucose uptake in HepG2 cells HepG2 cells (4 × 105 cell/ml) were inoculated in 6-well petri dishes for 24 h and treated with the SACe-N4-C-(OH)2 nanozyme, metformin (200 mg/kg), and their mixture for 24 h. The cells were washed 3 times with PBS, and different concentrations of fluorescent glucose analogs (2-NDBG, 0, 100, and 200 μmol/L) were dissolved in sugar-free DMEM. The cells were treated for 30 min and then washed 3 times with PBS. A confocal laser microscope was used for imaging, and the fluorescence content was measured using a 96-well plate fluorescence spectrophotometer at 540 nm for quantification. ## The assay of glucose tolerance test C57B/6L mice were tested at week 23 using a glucose concentration of 2.0 g/kg body weight. The specific operation steps are as follows: 1. Mice were fasted for 12 h, 1 day before the experiment, but normal drinking water conditions were ensured. 2. On the second day, the mice were weighed and labeled, and approximately 1 mm was cut off at the end of the tail with sterilized scissors, and a drop of blood was gently squeezed out along the tail to dry it with paper towels. The second drop of blood was squeezed out. A glucose meter was used to measure the fasting blood glucose of the mice, recorded as the 0-min blood glucose value. 3. The corresponding volume of glucose solution was injected into the abdominal cavity of the mice according to the recorded body weight, which was recorded as 0 s. 4. The corresponding blood glucose values at 15, 30, 60, 90, and 120 min were measured, and the physiological status of the mice was observed at any time during the entire process. 5. After the experiment, the mouse tail was wiped gently with cotton alcohol, and food and water were provided. ## The assay of insulin resistance test C57B/6L mice were tested for insulin resistance at week 23 using an insulin concentration of 0.75 U/kg body weight. Glucose tolerance and insulin tolerance were tested 1 day later. The specific operation steps were as follows: 1. Mice were fasted for 4 h, but normal drinking water conditions were maintained. 2. After weighing and marking the mice, sterilized scissors were used to cut off approximately 1 mm from the end of the tail of the mice, gently squeeze out a drop of blood along the tail that was dried with paper towels, and then squeeze out the second drop of blood. A glucose meter was used to measure the fasting blood glucose of the mice, recorded as the 0-min blood glucose value. 3. The corresponding volume of insulin solution was injected into the abdominal cavity of the mice according to the recorded body weight, which was recorded at 0 s. 4. The corresponding blood glucose values at 15, 30, 60, and 90 min were measured, and the physiological status of the mice was observed during the entire process. 5. After the experiment, the mouse tail was wiped gently with cotton alcohol, and food and water were provided. ## ICP-MS Ten mouse organs—liver, muscle, brain, heart, spleen, kidney, epididymis (EP), pancreas, lung, and heart—were collected to study the biological distribution of the SACe-N4-C-(OH)2 nanozyme. The samples of each group were mixed with digestive solution (HNO3+HCl+HClO4, volume ratio: 3:1:2) and heated to 100 °C, and the Ce content in tissues and organs was measured by ICP-MS. ## In vivo imaging Nine mice were intraperitoneally injected with SAC-N-C-GFP, and a Lumina II imaging system was used for imaging at 0, 0.5, 1, 2, 4, 6, 8, 18, 24, 48, and 72 h after injection. The mice were sacrificed at 24, 48, and 72 h after imaging [41], and the inguinal fat, back brown fat, epididymal fat, subcutaneous fat, heart, liver, spleen, lung, kidney, pancreas, brain, muscle, testis, ileum, and colon were collected for imaging and quantitative calculation [42]. ## Blood panel analysis and blood biochemistry The collected blood was cleared for hematological index testing. Alanine aminotransferase, aspartate aminotransferase, uric acid, alkaline phosphatase, creatinine, blood urea nitrogen, cholesterol, triglyceride, high-density lipoprotein, and low-density lipoprotein levels were measured by blood biochemistry [43]. ## Weight ratio After fasting for 12 h, blood was collected, and the mice were sacrificed. The following organs were dissected, separated, and weighed: heart, liver, spleen, lung, kidney, brain, subcutaneous fat, EP, and testicles; organ coefficients were calculated. Part of the organs was fixed in $4\%$ paraformaldehyde and another part was frozen at –80 °C. ## H&E staining The mouse heart, liver, spleen, lungs, kidneys, subcutaneous fat, the pancreas, testis, EP, ileum, and colon viscera were fixed in $4\%$ paraformaldehyde solution, dehydrated, paraffin embedded, sectioned, and stained with H&E, and then during imaging, they were histopathologically observed under a light microscope. ## Data Availability The data that support the findings of this study are available from the corresponding author upon reasonable request. ## References 1. Xu B, Li S, Zheng L, Liu Y, Han A, Zhang J, Huang Z, Xie H, Fan K, Gao L. **A bioinspired five-coordinated single-atom iron nanozyme for tumor catalytic therapy**. *Adv Mater* (2022.0) **34** 2107088 2. Li X, Ding S, Lyu Z, Tieu P, Wang M, Feng Z, Pan X, Zhou Y, Niu X, Du D. **Single-atomic iron doped carbon dots with both photoluminescence and oxidase-like activity**. *Small* (2022.0) **18** 2203001 3. Ding S, Lyu Z, Fang L, Li T, Zhu W, Li S, Li X, Li J-C, Du D, Lin Y. **Single-atomic site catalyst with heme enzymes-like active sites for electrochemical sensing of hydrogen peroxide**. *Small* (2021.0) **17** 2100664 4. Cheng N, Li J-C, Liu D, Lin Y, Du D. **Single-atom nanozyme based on nanoengineered Fe–N–C catalyst with superior peroxidase-like activity for ultrasensitive bioassays**. *Small* (2019.0) **15** 1901485 5. Pei J, Zhao R, Mu X, Wang J, Liu C, Zhang X-D. **Single-atom nanozymes for biological application**. *Biomater Sci* (2020.0) **8** 6428-6441. PMID: 33141122 6. Lin S, Wei H. **Design of high performance nanozymes: A single-atom strategy**. *Sci China Life Sci* (2019.0) **62** 710-712. PMID: 30941648 7. Huo M, Wang L, Wang Y, Chen Y, Shi J. **Nanocatalytic tumor therapy by single-atom catalysts**. *ACS Nano* (2019.0) **13** 2643-2653. PMID: 30753056 8. Zhijun L, Dehua W, Yuen W, Yadong L. **Recent advances in the precise control of isolated single-site catalysts by chemical methods**. *Natl Sci Rev* (2018.0) **5** 673-689 9. Jiao L, Yan H, Wu Y, Gu PW, Zhu PC, Du DD, Lin PY. **When nanozymes meet single-atom catalysis**. *Angew Chem* (2020.0) **132** 2565-2576 10. Cheng N, Stambula S, Wang D, Banis MN, Liu J, Riese A, Xiao B, Li R, Sham TK, Liu LM. **Platinum single-atom and cluster catalysis of the hydrogen evolution reaction**. *Nat Commun* (2016.0) **7**. PMID: 27901129 11. Jing L, Jiao M, Lu L, Barkholtz HM, Li Y, Ying W, Jiang L, Wu Z, Liu D-j, Lin Z. **High performance platinum single atom electrocatalyst for oxygen reduction reaction**. *Nat Commun* (2017.0) **8**. PMID: 28737170 12. Huang L, Chen J, Gan L, Wang J, Dong S. **Single-atom nanozymes**. *Sci Adv.* (2019.0) **5** 5490 13. Xu B, Wang H, Wang W, Gao L, Li S, Pan X, Wang H, Yang H, Meng X, Wu Q. **A single-atom nanozyme for wound disinfection applications**. *Angew Chem Int Ed* (2019.0) **58** 4911-4916 14. Gong N, Ma X, Ye X, Zhou Q, Chen X, Tan X, Yao S, Huo S, Zhang T, Chen S. **Carbon-dot-supported atomically dispersed gold as a mitochondrial oxidative stress amplifier for cancer treatment**. *Nat Nanotechnol* (2019.0) **14** 379-387. PMID: 30778211 15. Cao F, Zhang L, You Y, Zheng L, Ren J, Qu X. **An enzyme-mimicking single-atom catalyst as an efficient multiple reactive oxygen and nitrogen species scavenger for sepsis management**. *Angew Chem* (2020.0) **132** 5108-5115 16. Chang M, Hou Z, Wang M, Yang C, Wang R, Li F, Liu D, Peng T, Li C, Lin J. **Single-atom Pd nanozyme for ferroptosis-boosted mild-temperature photothermal therapy**. *Angew Chem Int Ed* (2021.0) **60** 12971-12979 17. Wang D, Wu H, Phua SZF, Yang G, Qi Lim W, Gu L, Qian C, Wang H, Guo Z, Chen H. **Self-assembled single-atom nanozyme for enhanced photodynamic therapy treatment of tumor**. *Nat Commun* (2020.0) **11**. PMID: 31953423 18. Wu J, Wang X, Wang Q, Lou Z, Li S, Zhu Y, Qin L, Wei H. **Nanomaterials with enzyme-like characteristics (nanozymes): Next-generation artificial enzymes (II)**. *Chem Soc Rev* (2019.0) **48** 1004-1076. PMID: 30534770 19. Kim J, Cho HR, Jeon H, Kim D, Song C, Lee N, Choi SH, Hyeon T. **Continuous O**. *J Am Chem Soc* (2017.0) **139** 10992-10995. PMID: 28737393 20. Banerjee M, Vats P. **Reactive metabolites and antioxidant gene polymorphisms in type 2 diabetes mellitus**. *Redox Biol* (2014.0) **2** 170-177. PMID: 25460725 21. Brem H, Tomic-Canic M. **Cellular and molecular basis of wound healing in diabetes**. *J Clin Invest* (2007.0) **117** 1219-1222. PMID: 17476353 22. Zhou Y, Liu C, Yu Y, Yin M, Sun J, Huang J, Chen N, Wang H, Fan C, Song H. **An organelle-specific nanozyme for diabetes care in genetically or diet-induced models**. *Adv Mater* (2020.0) **32** 2003708 23. Zhao J-G, Wang H-Y, Wei Z-G, Zhang Y-Q. **Therapeutic effects of ethanolic extract from the green cocoon shell of silkworm**. *Toxicol Res (Camb)* (2019.0) **8** 407-420. PMID: 31160974 24. Scheen AJ. **Drug interactions of clinical importance with antihyperglycaemic agents: An update**. *Drug Saf* (2005.0) **28** 601-631 25. Fayyaz S, Ahmed D, Khalid S, Khan SN, Shah MR, Choudhary MI. **Synthesis of vildagliptin conjugated metal nanoparticles for type II diabetes control: Targeting the DPP-IV enzyme**. *New J Chem* (2020.0) **44** 20853-20860 26. Fan X, Wei X, Hu H, Zhang B, Yang D, Du H, Zhu R, Sun X, Oh Y, Gu N. **Effects of oral administration of polystyrene nanoplastics on plasma glucose metabolism in mice**. *Chemosphere* (2022.0) **288** 132607. PMID: 34678341 27. Lee MJ, Lee SJ, Yun SJ, Jang JY, Kang H, Kim K, Choi I-H, Park S. **Silver nanoparticles affect glucose metabolism in hepatoma cells through production of reactive oxygen species**. *Int J Nanomedicine* (2016.0) **11** 55-68. PMID: 26730190 28. Liemburg-Apers DC, Willems PHGM, Koopman WJH, Grefte S. **Interactions between mitochondrial reactive oxygen species and cellular glucose metabolism**. *Arch Toxicol* (2015.0) **89** 1209-1226. PMID: 26047665 29. Li J-C, Qin X, Xiao F, Liang C, Xu M, Meng Y, Sarnello E, Fang L, Li T, Ding S. **Highly dispersive cerium atoms on carbon nanowires as oxygen reduction reaction electrocatalysts for Zn–air batteries**. *Nano Lett* **21** 4508-4515. PMID: 33998804 30. Song G, Zhang J, Huang H, Wang X, He X, Luo Y, Li JC, Huang K, Cheng N. **Single-atom Ce-N-C nanozyme bioactive paper with a 3D-printed platform for rapid detection of organophosphorus and carbamate pesticide residues**. *Food Chem* (2022.0) **387** 132896. PMID: 35421648 31. Chao Z, Can X, Xiaokang L, Man Q, Zhijun L, Tongwei Y, Jing W, Yunteng Q, XiaoQian W, Fangyao Z. **Unraveling the enzyme-like activity of heterogeneous single atom catalyst**. *Chem Commun (Camb)* (2019.0) **55** 2285-2288. PMID: 30694288 32. Holby EF, Taylor CD. **Activity of N-coordinated multi-metal-atom active site structures for Pt-free oxygen reduction reaction catalysis: Role of *OH ligands**. *Sci Rep* (2015.0) **5** 9286. PMID: 25788358 33. Kulkarni A, Siahrostami S, Patel A, Nørskov JK. **Understanding catalytic activity trends in the oxygen reduction reaction**. *Chem Rev* (2018.0) **118** 2302-2312. PMID: 29405702 34. Zhang J, Yang H, Liu B. **Coordination engineering of single-atom catalysts for the oxygen reduction reaction: A review**. *Adv Energy Mater* (2020.0) **11** 2002473 35. Jiao L, Wu J, Zhong H, Zhang Y, Xu W, Wu Y, Chen Y, Yan H, Zhang Q, Gu W. **Densely isolated FeN**. *ACS Catal* (2020.0) **10** 6422-6429 36. Ruijuan Y, Si S, Jiang Y, Wei L, Junying W, Xiaoyu M, Qifeng L, Wenting H, Shaofang Z, Haile L. **Nanozyme-based bandage with single-atom catalysis for brain trauma**. *ACS Nano* (2019.0) **13** 11552-11560. PMID: 31553878 37. Herget K, Hubach P, Pusch S, Deglmann P, Götz H, Gorelik TE, Gural'skiy IA, Pfitzner F, Link T, Schenk S. **Haloperoxidase mimicry by CeO**. *Adv Mater* (2017.0) **29** 1603823 38. Lu X, Gao S, Lin H, Shi J. **Single-atom catalysts for nanocatalytic tumor therapy**. *Small* (2021.0) **17** 2004467 39. Dong X, Zhao SX, Yin XL, Wang HY, Wei ZG, Zhang YQ. **Silk sericin has significantly hypoglycaemic effect in type 2 diabetic mice via anti-oxidation and anti-inflammation**. *Int J Biol Macromol* (2020.0) **150** 1061-1071. PMID: 31743716 40. Liu L, Tang D, Zhao H, Xin X, Aisa HA. **Hypoglycemic effect of the polyphenols rich extract from rose rugosa Thunb on high fat diet and STZ induced diabetic rats**. *J Ethnopharmacol* (2017.0) **200** 174-181. PMID: 28213107 41. Xu X, Zeng Z, Chen J, Huang B, Guan Z, Huang Y, Huang Z, Zhao C. **Tumor-targeted supramolecular catalytic nanoreactor for synergistic chemo/chemodynamic therapy via oxidative stress amplification and cascaded Fenton reaction**. *Chem Eng J* (2020.0) **390** 1246281 42. Wang Z, Ju Y, Ali Z, Yin H, Sheng F, Lin J, Wang B, Hou Y. **Near-infrared light and tumor microenvironment dual responsive size-switchable nanocapsules for multimodal tumor theranostics**. *Nat Commun* (2019.0) **10** Article 4418. PMID: 31562357 43. Jesus NZT, Silva Júnior IF, Lima JCS, Colodel EM, Martins DTO. **Hippocratic screening and subchronic oral toxicity assessments of the methanol extract of**. *Rev Bras* (2012.0) **22** 1308-1314